The effects of public spending composition on firm productivity.
Kneller, Richard ; Misch, Florian
I. INTRODUCTION
It has long been understood within theories of economic growth and
development that changes to fiscal policy, including changes in the
composition of public spending, affect aggregate outcomes such as the
rate of economic growth (Barro 1990; Devarajan, Swaroop, Zou 1996).
Increasingly, cross-country empirical evidence has been found to support
these model predictions. Adam and Bevan (2005), Lopez and Miller (2007),
and Hong and Ahmed (2009) all find, for example, that greater productive
expenditures, usually defined as including spending on transport,
communication, education, and health, have significant positive growth
effects (Gemmell, Misch, and Moreno-Dodson 2012, providing a recent
survey).
The consistency of these findings suggests that they are robust.
But because they are generated using macro-data, they are still subject
to criticism of Schwellnus and Arnold (2008) that they hide variation in
their effects across firms and leave unclear the mechanism through which
they are effective. Complementary evidence at the micro-level is
relatively uncommon and focused on a relatively narrow set of questions,
however. As a consequence, the literature has concentrated on the
effects of changes to transport infrastructure, see for example, Datta
(2012), Shirley and Winston (2004), Reinikka and Svensson (2002), and
Arnold, Mattoo, and Narciso (2008), or the investment climate more
generally, see for example, Bastos and Nasir (2004) and Dollar,
Hallward-Driemeier, and Mengistae (2005). (1)
In this paper we contribute to our understanding of the effects of
fiscal policy by studying the effect of changes to the mix of public
spending on the productivity of South African firms. For this task we
exploit the richness of the fiscal data for South Africa, which include
detailed types of health, education, and transport expenditures at the
province level. In this regard we build most closely on the work of
Bekes and Murakozy (2005) and Gabe (2003), who find somewhat mixed
evidence for the effects of fiscal policy on firms. Bekes and Murakozy
(2005) find that in Hungary public investment by the central government
had positive and significant effects on firm productivity, but that the
effect of public investment by municipalities was negative. Gabe (2003)
uses expenditure and revenue data to explain the growth of U.S. firms
(measured as the change of employment), but finds no significant effect
from either.
Alongside our interest in the effects of the mix of government
spending we differ from the existing literature in our ability to
disentangle this from revenue raising policies. At the provincial level
in South Africa discretionary fiscal policy exists for the mix of
expenditures. All broad-based taxes are identical across provinces, and
borrowing at the subnational level is limited. (2) As a result, the
level of public spending can be viewed as exogenous to the individual
province as it is dependent on grants from the central government. This
is important in light of findings from macro-growth regressions which
show that the implicit assumptions about how fiscal changes are financed
have a strong effect on the relationship with growth (Kneller, Bleaney,
and Gemmell 1999).
To preview our results, we find that reallocating public resources
can affect the productivity of some firms in the short to medium run
(our data do not allow us to describe longer term impacts), where we use
the capital-labor ratio to differentiate different types of firms. In
this regard our findings support the argument of Schwellnus and Arnold
(2008) that the effects of fiscal policy differ across firms, and, in
addition, demonstrate that one of the transmission mechanisms through
which aggregate growth changes occur is by changes to the productivity
of firms. In our most parsimonious specifications we find that
increasing expenditures on education, health, and transport as a share
of total expenditures has a robust, positive, and significant effect on
the productivity of firms with the lowest capital-labor ratios (the
bottom quartile). (3) For those firms that use capital to labor with a
greater intensity the effects are less frequently significant, while for
those firms with the highest capital-labor ratios we find no effect.
We test the robustness of our findings to the inclusion of a wide
set of province-industry and time dummies that might plausibly capture
the effects of any omitted variables. For example, the productivity of
firms and the choice of province-level fiscal expenditures might be
affected by time-invariant province-industry specific factors such as
geography or climate. Or public expenditures might be targeted at
particular industries in particular provinces because they have lower or
higher productivity than elsewhere. Or it could occur that unobserved
province-specific shocks affect the productivity of firms within a
province and, through the automatic stabilizer mechanism, may generate a
change to the mix of expenditures. We continue to find evidence
throughout this part of the paper that firms with different capitallabor
ratios are affected differently by changes to the expenditure mix. We
cautiously describe this evidence as indicating rising (short-run)
productivity of those firms with relatively low capitallabor ratios, and
as consistent at least with a causal interpretation. The disadvantage of
such an approach is that we cannot identify the overall magnitude of the
effect of fiscal policy as any direct effects are captured by the
province and industry dummies we include.
We also explore whether other firm characteristics matter for
changes to the expenditure mix, by using information on the export
status of the firm and their size. We find no evidence that these firm
characteristics help to describe differences in the effects of fiscal
policy across firms. The large number of categories of fiscal
expenditure included within the South Africa data raises the possibility
that there may be other changes to the expenditure mix that affect the
productivity of firms other than those with low capital-labor ratios. We
explore this in detail and find limited evidence for such differences in
our data, although the size of the effects does differ quite noticeably.
The exception is for changes to education expenditure, funded by a
decrease in health and transport spending, where we find no difference
in the productivity effects across firms.
The paper is organized as follows. Section II presents the data and
descriptive statistics. Section III develops the modeling framework.
Section IV discusses the results, and Section V presents several
robustness checks of the results. Section VI concludes.
II. DATA
A. Firm-Level Data
The information on firms that we use comes from the World
Bank's Enterprise Surveys. These data are rich in detail on firm
characteristics, and are designed to be representative of the population
of (manufacturing) firms. They contain, however, at least in comparison
to other firm-level datasets, a small number of observations and a
limited panel dimension.
We use data from two rounds of the World Bank Enterprise Surveys in
South Africa in 2002 and 2006, providing a total possible sample of
1,113 observations, covering both manufacturing and service sector
firms. The survey contains questions that ask for information from
earlier years, such that while most control variables are only available
for 2002 and 2006, information on firm output and most inputs is
available for 4 years (2000, 2001, 2002, and 2006). The panel is
unbalanced with an average number of years per firm of approximately
1.95. We recognize that an implication of the limited time dimension of
the data is that we are likely to identify productive effects from
public spending that are relatively instantaneous and miss those that
take longer to affect firm decisions. We are careful to recognize this
point in the interpretation of our results for fiscal policy. Finally,
we corrected the data for obvious keypunch errors, deleted observations
with negative inputs or outputs, and one observation with
idiosyncratically high sales volatility.
The firms surveyed are located in four out of nine South African
provinces and include Gauteng, Western Cape, Kwazulu-Natal, and Eastern
Cape (in descending order by the number of firms located in each
province that are included in the surveys). Within each province
considered, the majority of the firms are located in the biggest city
(Johannesburg, Cape Town, Durban, and Port Elizabeth). (4) As most firms
are located far away from other provinces, it seems unlikely that they
benefit from spending from other provincial governments thereby
minimizing problems of spillovers across provinces.
From the survey we use total firm sales as a proxy of firm output,
the net book value of equipment and machines as a proxy of private
capital, the number of employees and the cost of materials and
intermediate goods. To account for the effects of inflation we deflate
output using a sector-specific producer price index and the inputs using
an economy-wide producer price index. We also collect from the
Enterprise Surveys information on firm ownership, from which we create a
dummy for foreign-owned firms, whether they export and whether they have
experienced losses due to crime. (5) Tables 1 and 2 contain details
about the firm-level variables and descriptive statistics.
B. Public Spending Data
Using information on the location of each firm it is possible to
merge the World Bank Enterprise Survey data with provincial spending
data provided by the South African Treasury and province-level control
variables which were constructed from various official sources. This
dataset includes public spending disaggregated at the subsectoral level
and is available for all provinces for the fiscal years from 2000/2001
to 2005/2006 and province-level indicators of the quality of education,
road infrastructure, levels of crime, and the GDP of the main city (see
Table 1). (6) The public spending data contain information on both a
broad set of functional expenditure categories, such as education and
health, as well as different subcategories of these functional
expenditures. For example, as shown in Table A2 in the Appendix, within
the education expenditure category, spending on ordinary school
education and further education and training as well as adult basic
training, early child development and subsidies for independent schools
are separately catalogued. Again these are available for each province
and fiscal year.
As described in the introduction, the objective of this paper is to
explore whether changes to the mix of public spending can affect the
productivity of at least some firms. Those effects will depend both on
the particular category of spending that is changed and which other
expenditure categories are assumed to decrease to compensate for this
increase in order to leave total spending unchanged. The estimated
coefficient will therefore reflect both the effects of the expenditure
category that is increased and the effects of the expenditure categories
that are decreased to compensate for the increase. It cannot be assumed,
for example, that the compensating categories have no effect on
productivity. A coefficient of zero is consistent with both an
interpretation that neither expenditure categories affect productivity,
and that the effects of both are of an equal size and therefore
offsetting each other. That the expenditure data for South Africa are
available at such a highly disaggregated level further stresses the need
to consider this point carefully.
Our approach to this issue is to aggregate various expenditure
categories to create a ratio variable in a way that we hope maximizes
the possibility of finding an effect on productivity for some firms. We
do so by including in the denominator total expenditure including those
expenditure categories that when reduced, are less likely to affect
productivity. In the numerator we generally include education, health,
and transport spending, but choose to remove various subcategories
within these areas (see Table A2 for more details), under a view that
these are unlikely to impact the productivity of firms. When total
expenditure is held constant, an increase in this ratio implies an
increase in the expenditure categories included in the numerator offset
by those expenditure categories only included in the denominator.
As these choices are, while informed by previous empirical
evidence, subjective, we do not follow the previous literature in
calling this the productive to unproductive expenditure ratio. Instead,
we prefer a label that better reflects the three main functional
expenditure categories in the numerator, education, health, and
transport. We label this the EHT expenditure mix as shorthand and
express it as a ratio to total public spending within a province in each
time period. We describe non-EHT spending as "other
expenditure" in the text. This expenditure is assumed to offset an
increase in EHT expenditure in the empirical specifications.
We also note that these three spending categories capture flows
into the stock of human capital and transport infrastructure within a
province. The effects of the stock of human capital and transport
infrastructure are themselves captured by the province-time effects that
are included in the regression. From our focus on flow expenditures we
anticipate that we likely capture productivity effects that occur
quickly, within 1-2 years, and our results must be interpreted with that
understanding in mind. We discuss the categorization of public spending
in greater detail in the Appendix and consider the robustness of our
findings to which specific items of government expenditure we consider
in the numerator of our expenditure variable in Section V.
[FIGURE 1 OMITTED]
Given that public spending may vary with business cycle
fluctuations and any effects on productivity become apparent only after
some lag, we follow the macroeconomic literature and average public
spending over time (in our case across 2 years). Specifically, we
combine the firm-level information for 2002 with the average of the
fiscal data for the 2000/2001 and 2001/2002 fiscal years, and combine
the average of fiscal data for the 2004/2005 and 2005/2006 fiscal years
with the firm data from 2006. The implication of this is that while our
firm data are additionally available for 2000 and 2001, the public
spending data are not. We trade this loss of information for reducing
possible co-movement of the business cycle with firm-level productivity
and the composition of government expenditure and against considering
longer lags in the effects of public spending. Depending on the
specification, we still use the 2000 and 2001 firm data for our
estimation of firm production functions.
Table 3 provides descriptive statistics, and Figure 1 displays EHT
spending and its subcomponents as shares of total provincial expenditure
by province and year. Even with a narrow definition of types of
education, health, and infrastructure spending, they account for 55% of
total province spending by the government. As the table makes clear, the
variation in public spending categories comes primarily from variation
between provinces rather than within provinces across time. The standard
deviation between provinces is around three times that within provinces.
Cross-time changes in the expenditure mix are evident though. The share
of EHT spending increased in all provinces between 2002 and 2006 (where
the 2002 and 2006 values are in fact both averages over two fiscal years
as explained above), and the increases in Eastern Cape and KwaZulu-Natal
were particularly large. The table also shows that a large part of EHT
spending is on education, which is around twice as large as those for
health and over seven times those on transport and capital expenditure.
Figure 1 implies that the shares of EHT spending increased in all four
provinces over the period considered, but the relative increase varied
and ranges from around 17% in Western Cape to around 25% in Eastern
Cape.
III. MODELING FRAMEWORK
A. Private Production
As is typical in the literature we assume that output, [Y.sub.it],
of firm i in year t, is produced using private capital ([K.sub.it]),
labor ([L.sub.it]), and materials ([M.sub.it]). Into this framework we
incorporate a composite public input that represents the level of public
services and public capital and that enhances firm productivity,
[G.sub.pt], which varies across time and provinces, where p denotes the
province. As production technology, we use a fairly general type of
constant elasticity of substitution production function originally
proposed by David and van de Klundert (1965) which allows for the
effects of [G.sub.pt] to be not Hicks-neutral:
(1) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(2) [G.sub.pt] = [T.sub.pt][[phi].sub.pt][C.sub.pt]
where [T.sub.pt] denotes total public spending in a given province
in year r, [[phi].sub.pt] denotes the share of total public spending on
[G.sub.pt] (i.e., that is devoted to productive categories, i.e., EHT
categories as defined in the previous sections), and [C.sub.pt]
represents other province-specific factors that relate to the efficiency
of public spending. [[epsilon].sub.Ki], [[epsilon].sub.Li], and
[[PHI].sub.i] are firm-specific technology parameters contrary to
[[beta].sub.1,2,3], A, [[PHI].sub.K,L], and o which are also technology
parameters but common to all firms. Including [G.sub.pt] in the
production function captures for example the idea that private vehicles
can be used more productively when the quality of the road network
improves due to lower maintenance requirements, or that labor
productivity is affected by health-related expenditures. (7)
B. Hypotheses
The inclusion of [G.sub.pt] in the production function implies that
public spending affects firm-level productivity, which Barro (1990)
refers to as the productive effects of public spending. Here, we are
interested in the effects of changes in the composition of public
spending which from Equations (1) and (2) can be written as
From Equation (3), we derive two key hypotheses with respect to the
nature of the productive effects of public spending. First, increasing
the share of public resources allocated to [G.sub.pt], [[phi].sub.pt],
affects private sector output via its effect on productivity (Hypothesis
1).
Second, these effects are heterogenous across firms as [partial
derivative][Y.sub.it]/[partial derivative][[phi].sub.pt], is a function
of [[epsilon].sub.Ki], [[epsilon].sub.Li], and [[PHI].sub.i] among other
factors (Hypothesis 2). A priori, there is no reason to believe that
these parameters are identical across all firms, and indeed, there is a
host of reasons of why this assumption is likely to hold true. For
example, the location of each firm determines access to public services
and thereby the impact of [G.sub.pt] on firm productivity, or some firms
may benefit from some types of expenditure more than others.
This simple framework does not make any predictions with respect to
which type of firms under this categorization benefits more or less from
EHT spending. However, it seems at least likely that [[epsilon].sub.Ki]
and [[epsilon].sub.Li], which are firm-specific determinants of the
capital intensity, and are correlated in specific ways for a given type
of G. If [[epsilon].sub.Ki]/[[epsilon].sub.Li] dictates that a
particular firm is relatively capital intensive and if G primarily
affects the productivity of labor, then we anticipate that [[PHI].sub.i]
is small. In such a case we would anticipate that firms that are
relatively labor intensive in their production technology are more
likely to benefit from spending on health, education, and public
transport. Following this, we initially use the capital-labor ratio of
the firm to identify differences in the effects of policy. We detail how
we measure the capital-labor ratio more fully in the next section of the
paper.
Econometric Specifications. In the empirical work we approximate
Equation (1) using the following Translog function to test Hypothesis 1:
(4)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
All variables are in logs (which is denoted by variables in lower
case), j denotes industry, and [[phi].sub.pt] is the share of EHT
spending in total expenditure. When testing for the effects of EHT
spending, we hold total spending constant in the analysis by including
[T.sub.pt]. In this regard, we follow a tradition established in the
macro literature by Devarajan, Swaroop, and Zou (1996) in estimating the
growth effects of changes in the public spending mix. We anticipate that
the sign on the estimated coefficient for this variable can be positive
or negative, depending on whether the negative effects of taxation
outweigh the positive effects of total public spending (Barro 1990).
An important concern when testing the hypothesis whether the
composition of public expenditures affects firm productivity (Hypothesis
1) is that we are capturing the effect of some other omitted variable
that is correlated with the expenditure mix and the error term from the
regression. This form of endogeneity bias might be caused by
time-varying changes to the preferences of regional governments toward
private enterprise. For example, regions could adopt a strategy of
openness toward international trade and foreign direct investment (FDI)
in order to encourage growth and investment and compensate the
(perceived) negative effects of this by voters to the security of their
employment by increased welfare payments (Rodrik 1998). Alternatively,
expenditures might be targeted at particular provinces because there is
some province-specific factor, such as its geography, that raises (or
lowers) the productivity of all firms located there.
To control for observable firm and province variables that may
affect the relationship between public spending and productivity, we
include a series of control variables denoted by [C.sub.it] and
[C.sub.pt], respectively. We include differences in the access to
foreign technology between firms, which are measured by whether they are
domestic or foreign owned, their export status variables, and size. To
control for the social environment in which firms operate, we add to the
regression an indicator of whether the firm has been a victim of crime.
We capture province-level characteristics that matter for productivity
by including an indicator of the province-level crime rate (the murder
rate), the level of public road infrastructure as the length of the road
network in relation to the surface of each province (road density), the
percentage of learners who passed grade 12 (grade), and to control for
possible agglomeration and congestion effects, the GDP of the main city
of the province where the firm is located (city_GDP). We also control
for more difficult to observe factors using various dummy variables. In
all of the regressions we include industry-year effects ([D.sub.jt]) to
capture differences in productivity shocks across industries.
In later regressions in Section IV we test the robustness of these
findings to the inclusion of province-year effects ([D.sub.pt]) and
province-industry effects ([D.sub.jp]). We use these to control for
policy changes other than those to the expenditure mix that occur within
a province during the sample period and time-invariant factors, such as
geography or the general policy environment which changes only slowly
over time, but which may affect the productivity of all firms within an
industry and province. A consequence of the inclusion of the
province-year effects within the equation is that we can no longer
identify the direct effect of changes in the expenditure mix, captured
by [[beta].sub.11] in Equation (4), and the level of government
spending, captured by [[beta].sub.10]. By contrast, the relative effect,
that is, differences in the effect of spending on different firms can
still be identified. We therefore turn to Hypothesis 2.
It has been discussed above that the relationship between changes
to the expenditure mix and the productivity of different South African
firms is likely to be dependent on the choice over which expenditure
changes are examined. It is also likely to be dependent upon the way in
which we identify those different firms. Our initial approach to this
issue and to test Hypothesis 2 is to use differences in the intensity
with which firms use two main inputs, capital and labor, within the
production function, the capital-labor ratio. That is not to deny the
possibility that other characteristics may also be important, perhaps
most obviously differences in firm size. Again, we test for the
robustness of the results to this assumption.
To remove the effects of province or industry-level factors that
might affect the chosen mix between capital and labor we express this as
a ratio to the annual province-industry mean. Specifically, we group
firms according to their capital intensity relative to the annual
province-industry mean, that is, whether their relative capital
intensity is low, lower medium, higher medium, or high based on the
quartiles of the distribution of capital intensities across all firms in
all provinces and years. We use quartiles to capture the possibility of
nonlinearities in the effects of public spending. Equation (4) then
becomes (5)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
and low, lmed, hmed, and high represent dummy variables for the
firms with relative capital intensities,
([K.sub.it]/[L.sub.it])/([K.sub.pjt]/[L.sub.pjt]), below the 25th,
between the 25th and the 50th, between the 50th and 75th percentiles,
and above the 75th percentile, respectively. We then interact these
dummies with the share of EHT spending in total expenditure Given that
capital and labor (in logs) are already included in various ways in the
translog production function we do not include the capital intensity as
an additional indicator in the regressions. While the relative effects
of public spending can still be identified in Equation (5), their
interpretation is made problematic by the omission of the direct effect.
We return to this issue and whether we can infer the direct effect of
changes to public spending from our results below.
IV. RESULTS
In regression (1) in Table 4 we display the results from the
estimation of Equation (4) to test Hypothesis 1. In this regression we
hold total expenditure constant and capture the effects of increasing
EHT expenditure as a share of total expenditure on firm sales.
Starting with the control variables we find that the production
function performs sensibly and the estimated elasticities (calculated at
the means of the other right-hand side variables) are within the
expected range. The elasticity with respect to physical capital and
labor in regression (1) are 0.075 and 0.229 respectively and there are
mildly increasing returns across all of the private inputs for the firms
in our sample. Of the firm and province-level variables only the crime
variable and the city_GDP variables are statistically significant at
conventional levels, with the latter having a surprising negative effect
on firm productivity. This may indicate that there are congestion
effects in large cities which lower productivity.
Turning next to the fiscal variables, the estimated coefficient for
total expenditure is negative and statistically significant. Using the
Barro (1990) model to interpret this result would imply that the level
of government expenditure is beyond the optimal point in South Africa,
such that the negative effects of taxation on growth outweigh any
positive effects that expenditures might have. For the main variable of
interest, the share of EHT spending, we find that this has a positive
relationship with firm sales (with significance at the 10% level),
suggesting that changes to the expenditure mix are associated with
rising productivity on average.
Regression (2) in Table 4 refers to our baseline estimation of
Hypothesis 2 and captures alongside the effect of changes to total
government spending the possibility that they differ across firms with
different capital-labor ratios. Note that the capital-labor ratio of the
firm is measured relative to the mean in each individual industry,
province, and time period. In all regressions we continue to control for
shocks to industries using a full set of industry-time dummies and use
province-industry clustered standard errors to control for intra-class
correlation. (8)
For the effects of EHT expenditure, we find evidence of an
interesting difference in its effect across firms. The coefficients for
this variable can be interpreted as the effect on firm-level
productivity of an increase in the EHT share compensated by a pro-rata
decrease in other types of public spending, leaving total province
expenditures constant. The results indicate that such a change to fiscal
policy would increase productivity for firms with all but the highest
capital-labor ratios with significance at the 10% level; for firms with
the highest capital-labor ratios, we find no effect at least over the
short
run. As already noted, this could imply either that there is no
effect from changes to the expenditure mix for these firms, or that the
compensating items have an effect that offsets the effect of increased
EHT spending for these firms.
In regression (3) we account for the effect of time-varying
province as well as time-invariant province-industry characteristics
that have been omitted from regression (2). The former might include
province-specific components of the business cycle and policy variables
not directly related to fiscal policy, while the latter allow for
province characteristics such as geography that might affect the
productivity of firms within an industry compared to those in the same
industry and a different province.
The province-time dummies are of course perfectly collinear with
the government expenditure variables, such that the total expenditure
variable must be omitted. This also necessitates a change in the way
that we include these dummies, and we omit the high capital-labor ratio
group, such that we now test for differences in the effects of fiscal
policy relative to the reference category.
The results from regression (3) now indicate that half of South
African firms are affected by changes to the expenditure mix (with
significance at the 5% and 10% level, respectively). As in regression
(2) we find that the productivity of those firms with the low and lower
medium capital-labor ratios is affected by changes to the expenditure
mix. A strict interpretation of the results from this regression would
be that firms which use less (low and lower medium) capital to labor in
production are affected differently compared with firms having other
capital-labor ratios from increasing the share of spending on EHT within
total fiscal expenditures over the short run. If, as implied by the
results from regression (2), the productivity of those firms with the
highest capital-labor ratios is unaffected by these changes to the
expenditure mix, then this interpretation might be further strengthened
to say that the productivity of low capital-labor ratio firms is
increased. We continue to make this assumption throughout the rest of
the paper.
To evaluate the magnitude of these effects further, from Table 3 we
calculate that the average increase in the share of EHT expenditures
within South African provinces over time (relative to the mean) was
equal to 4% (the mean is 0.552 and the within province standard
deviation 0.022). Multiplying this number with the coefficient estimate
suggests that for firms with a low capital-labor ratio productivity
would change by 0.88% compared with high capital-labor ratio firms. The
coefficient estimate of the share of EHT spending is large (for
instance, compared to the coefficient on private capital). However, in
practice, the productivity outcomes are not that large when the actual
variation in the fiscal data is considered.
As discussed above, a natural extension of the above analysis is to
explore whether there are alternative changes to the expenditure mix
that would stimulate the productivity of high capital-labor firms. We
consider this question in more detail in Table 6, using the remaining
regressions in Section V to explore the robustness of the initial
findings.
As already highlighted an important concern is whether our results
capture the effect of unobservable province-industry time-varying
factors that affect both the productivity of firms within a province and
the mix of fiscal expenditures. In the absence of suitable variables to
instrument for the expenditure mix within a province, we continue with
the practice started in regression (3) of adding further control
variables. This allows us to at least judge how important omitted
variables are likely to be for the results that we find. In regression
(3) we included a full set of province-year and province-industry
dummies. In regression (4) we develop this further and control for the
possibility that there are shocks that occur at the
province-industry-time level that affects productivity and the
expenditure variables. The effects of the changes to the expenditure mix
are therefore identified in this regression from differences in the
effect of policy across firms within the same province, industry, and
year.
Despite the demanding nature of this identification strategy we
find that our results are left unchanged. We continue to find evidence
that those firms that have a low or a medium-low ratio of capital to
labor relative to other firms in their industry in that province are
affected by shifting the expenditure mix compared to high K/L firms. We
also find that the magnitude of these effects is very similar to those
from regression (3), suggesting that omitted province-industry-time
effects were not an important explanation for the results in the earlier
regressions.
V. ROBUSTNESS OF THE RESULTS
A. Endogeneity of Private Inputs and the Clustering of the Standard
Errors
Given the lack of a counterpart in the empirical literature, we
believe that it is important to establish the robustness of our findings
to a number of different methodologies. Our first robustness check
addresses potential concerns about the endogeneity of the coefficients
on private inputs including private capital. It may be argued that the
parameter values on the private inputs in the production function are
poorly identified as our regressions do not exploit all of the
firm-level data available to us--so far they use data for 2002 and 2006
only.
In order to exploit the full 4 years of firm data, we proceed by
estimating Equation (5) in two steps. In the first step we estimate
Equation (5) including all 4 years of available data. In this step, we
include firm fixed effects and province-year effects for 2002 and 2006,
to avoid any bias caused by omitting the remaining variables (including
the fiscal variables) from this regression. (9) In the second step, we
impose the coefficients on the private input variables estimated in step
1 and then reestimate the model including the firm and fiscal variables
that were omitted from the first stage. We hope from this exercise to
improve the precision with which we estimate the coefficients on the
input variables.
In regression (5) of Table 4, we report the results from the
two-stage estimation where the first stage is estimated using firm fixed
effects. Again the results are robust to this point. (10) In the
two-stage estimation results we continue to find that the coefficients
on the fiscal variables are statistically significant and that the
estimated elasticities are largely unchanged. Changing the mix of
province-level expenditures toward EHT spending categories while holding
the total budget constant is associated with changes in firm-level
output for firms with low capital intensity. (11)
Another concern relates to the clustering of the standard errors.
In regression (1), Table 5, we therefore use the wild cluster bootstrap
estimator proposed by Cameron, Gelbach, and Miller (2008) to further
control for intra-class correlation at the province and industry level.
In this specification, we are only able to include province-year
effects. The coefficient on the share of EHT expenditure for firms with
low capital intensity remains significant and robust, although their
magnitude decreases.
B. Categorization of Firms and Unobserved Firm-Level
Characteristics
Thus far we have used differences in the relative factor intensity
of capital and labor of firms to identify the effects of changes to the
public expenditure mix on productivity performance. The decision to
express these capital-labor ratios relative to the mean value in each
industry, province, and year, along with the province-time and
province-industry dummies that we include, ensures that our results
cannot be explained by differences in the characteristics of particular
industries, or because province-specific differences in relative input
prices lead to different factor intensities across provinces. Along
similar lines, our results cannot reflect the decision by an
entrepreneur to open a firm producing a particular type of product in a
particular province because the expenditure mix in that province favors
a production technology of that type. Such effects will instead be
reflected in the mean value of the capital-labor ratio.
The possibility that other firm characteristics might explain our
results, or may also be important, remains however. For example, if
larger firms tend to be on average more capital intensive than smaller
firms, then it might be the relative size of firm, rather than
capital-labor intensity, which is important. Alternatively, it is now
well established in the international economics literature that
exporters are larger and more productive than firms that serve the
domestic market only and they may therefore respond to changes in the
expenditure mix differently.
In specification (2) in Table 5 we report the results when
including interactions between the export status of the firms and the
share of EHT expenditure alongside the versions based on the
capital-labor ratio. The results show that the export status has no
significant effect for the relationship with the change in the spending
mix that we examine. (12)
In regressions (3) and (4) in Table 5 we explore whether the size
of the firm affects the relationship with changes to the mix of public
spending, which we measure by the amount of labor, relative to the
province-industry mean, and the size of the capital stock, measured
relative to the province-industry mean (we retain the labeling of low,
lmed, and hmed).
We do not find that the share of EHT expenditures matters for any
of these types of firms. Firms that are small, or medium sized, when
measured by either the amount of labor or capital they possess, are not
significantly affected by an increase in EHT spending compensated by a
decrease in other types of spending. We conclude from this exercise that
the capital-labor ratio successfully captures which firms are affected
by the changes to fiscal policy that we examine.
As an additional exercise, we test whether our results are driven
by the particular way in which we group firms based on their relative
capital-labor intensity. In specification (5), instead of using
quartiles, we use quintiles of the distribution of capital-labor across
firms. In specification (5) we continue to find that the effects of the
share of EHT spending are significant for the bottom groups only. (13)
Another concern is that we insufficiently control for unobserved
firm-level characteristics. While we control for firm fixed effects in
the first step of the two-stage specifications, in specification (1) of
Table 6, we also add firm fixed effects. Although we only have 2 years
of data which is the reason why we normally do not use firm fixed
effects at this stage in the remaining specifications, our results
remain robust.
Finally, there may be a concern about the classification of firms
as our panel is unbalanced.
If capital intensity is correlated with productivity, then firm
exit rates can be assumed to be highest in the low capital-intensity
group and lowest in the high capital-intensity group. This would imply
that the panel is not unbalanced for idiosyncratic reasons which may
bias the results. In our sample, we define attrition as firms that are
included in the sample in the 2002 wave but not in the 2006 wave. (14)
Attrition rates do indeed differ between the groups, and they are
highest in the low capital-labor group. However, there does not appear
to be a systematic pattern, as they are by far lowest in the medium
capital-intensity group. The difference in attrition rates between the
low capital-intensity group and the omitted capital-intensity group
(which may have the highest productivity according to this reasoning) is
about 6 percentage points and therefore small. Table 7 provides details.
(15)
C. Definition of Public Expenditure and Exogeneity of Its
Composition
The richness of the public spending data for South Africa throws up
an interesting question, namely whether there are alternative changes to
the mix of public spending that affect the productivity of firms, and
whether those firms with low and medium-low capital-labor ratios are
affected by these changes. As explained above, given our definition of
the expenditure mix this will depend on the choice of fiscal
expenditures that are assumed to increase as well as which ones are
assumed to decrease to compensate for this. We use this section of the
paper to explore issues relating to our categorization of public
spending.
We begin with some discussion of the denominator of the EHT
variable, which includes the remaining province-level public spending.
Although we take averages of public spending over 2 years, it is
conceivable that the denominator of the spending share variable co-moves
with the business cycle because it includes transfers or other
expenditures that exhibit pro-cyclical behavior. While correlations
between regional growth rates and the public spending shares we
construct indicate that this is unlikely (the correlation coefficient
between the annual share of EHT spending and provincial growth is below
0.25 and statistically not different from zero), specification (2) of
Table 6 considers this more formally.
In regression (2) of Table 6 we express EHT expenditure as a share
of total expenditure on education, health, and transport sectors (i.e.,
the denominator also includes for instance administrative spending
within these categories, but no spending outside these categories). The
results from this regression suggest that reallocating resources within
total education, health, and transport spending also affects the
productivity of firms according to their capital-labor ratio. Again we
find no productivity response for firms with a capital-labor ratio above
this.
In specification (3), we develop this idea further and exclude both
spending on emergency care and on public works from the denominator. A
possibility exists that these expenditure categories have a different
sensitivity to cyclical fluctuations compared with the remaining
expenditure items. For political reasons, emergency care spending may be
safeguarded from cuts and remain fairly stable over the cycle, while in
contrast, public works are subject to long planning and execution
cycles. Specification (3) now suggests that only the productivity of
those firms with the lowest capital-labor ratios would be affected by
increasing this spending ratio.
Also of interest is whether our results are driven by the
particular way in which we define EHT expenditures. Thus far we have
included subcategories of education, health, and public infrastructure
and transportation that may be expected to be productive over the short
run. When we include all subcategories of health, education as well as
public infrastructure and transportation, where this now includes
expenditures on administration, in specification (4) of Table 6, the
coefficient of the share of EHT expenditure is again statistically
significant for firms with low- and medium-low capital-labor ratios.
In specification (5), we take the alternative approach and are more
selective in the subcategories of education, health, as well as public
infrastructure and transportation spending we include. In this
regression we include in the numerator expenditure on early childhood
development, district health services and spending on public
transportation only. The coefficients for medium-low and low K/L are
much reduced in size, but remain positive and significant in these
regressions. It is also noticeable that the estimated coefficients are
much smaller than those found up until this point, suggesting that while
significant the relative productivity changes they cause across firms
are comparatively small compared with other changes to the expenditure
mix.
As a final exercise we include education, health and transport as
separate categories and express them as a ratio to total EHT
expenditure. These are reported in regressions (1), (2), and (3) in
Table 8. Interestingly we now find a significant productivity effect
only for health and transport spending for low- and medium-low
capitallabor ratio firms. As already noted, this does not necessarily
imply that there are no productivity effects from changes to education
expenditure as they may be offset by the other compensating changes that
occur in order to leave total expenditure constant. It does at least
indicate that low K/L firms are not always affected by changes to the
expenditure mix in a way that is different from firms with higher K/L
ratios.
VI. CONCLUSIONS
This paper examines whether changes in the composition of public
spending affect firm productivity and whether these effects depend on
firm characteristics. We show that an increase in education, health, and
transport spending compensated by a pro-rata decrease in other types of
expenditure so as to leave total expenditure constant matters for firm
productivity, and that there is evidence that its effects vary across
firms depending on their capital intensities. We also show that those
effects vary with the exact change to the expenditure mix that occurs,
that differences are not found across all firms for all changes to the
expenditure mix, and that there are differences in the size of any
effects. We find, however, that firm characteristics other than the
capital-labor ratio play no important role. We also find indicative
evidence that low K/L firms benefit from changes to the expenditure mix
as their productivity rises in contrast to more capital intensive firms.
We conclude from the evidence we present that governments are able
to affect firm productivity by reallocating public spending. Given that
productivity at the firm level is likely to be fundamental for long-run
aggregate economic growth, changes to the expenditure mix may be less
expensive than raising total public spending and therefore raising
additional revenues. This is of current relevance given the large budget
deficits due to the recent economic crisis in many countries. Second, if
governments attempt to raise firm productivity via the reallocation of
public resources, our results indicate that it is important that they
take into account the characteristics of firms. While this issue needs
to be further explored in future research, our results indicate that
these effects depend on the technology of firms that in turn drive their
capital intensities.
We leave several possible extensions for future work. The
robustness of the results could be further tested through the use of
additional estimators and empirical methods. Our identification strategy
addresses endogeneity in a manner that is similar to many other papers
using macro- and micro-level data, but concerns over the direction of
causation therefore still remain. There are also other aspects of the
dataset that could be exploited further. For instance, it would be
possible to compare the effects of aggregate EHT spending when offset by
different elements of the government budget, and it would be possible to
explore the role of additional firm characteristics for the effects of
public spending.
One constraint of our data is certainly the availability of firm
information across fairly short time periods so that we are only able to
capture any effects on productivity that occur over the short run. This
implies that we are unable to rule out that these short-run productivity
gains come at the expense of long-run productivity gains.
ABBREVIATIONS
EHT: Education, Health, and Transport
FDI: Foreign Direct Investment
OLS: Ordinary Least Squares
doi: 10.1111/ecin.12092
Online Early publication May 26, 2014
APPENDIX
THE SYSTEM OF FISCAL DECENTRALIZATION IN SOUTH AFRICA
Since the end of the Apartheid era, South Africa has undergone
wide-ranging fiscal reforms, and a system of transparent,
constitutionally compliant intergovernmental fiscal relations has been
created. Government now comprises three spheres: national, provincial,
and local. The fiscal system departs from conventional prescripts of
fiscal federalism however because there is a mismatch between
expenditure and revenue powers at each of these different levels of
government (Ajam and Aron 2007).
Public expenditure policy is decentralized in a range of important
areas. Provincial governments are largely responsible for spending on
provincial roads, education (except higher education), health services,
public transportation, social welfare services, housing, and
agriculture. For these functions, the level of public spending by the
national government is very low, and the national government is mainly
responsible for setting minimum norms and standards and for monitoring
the overall implementation by provincial governments. It also collects
data on provincial public spending (Momoniat 2002). The expenditure that
the national government undertakes can be expected to leave firm
productivity unaffected over the medium run, or it finances public goods
such as national roads or higher education and research. In these cases,
significant country-wide spillovers imply that there is no or little
variation between the provinces. By contrast, provincial governments
provide goods and services that are unlikely to entail significant
spillovers across provinces.
At the same time, the revenue side of government in South Africa is
fairly centralized: provincial governments collect very little revenue,
and the income raised within the province typically amounts to less than
5% of the provincial budget (Ajam and Aron 2007). In the period that we
consider, provinces have neither imposed nor collected broad base taxes,
and the revenue collected came from various licences (notably motor
vehicle licences), sales of goods, services and capital assets, and
various small base taxes (e.g., taxes on gambling and horse racing). In
addition, while in principle, provincial governments are allowed to
borrow to finance capital expenditure, in practice borrowing is quite
limited. Provincial governments are therefore highly dependent on
transfers from the national government. They receive conditional grants
which they have to earmark for prespecified purposes, such as health,
infrastructure, housing, and social development, and they receive
non-earmarked grants (which are referred to as "equitable share
grants") (Ajam and Aron 2007). The level of the latter that a given
province receives depends on range of social and economic indicators.
INDUSTRY-LEVEL DESCRIPTIVE STATISTICS
This Appendix reports the number of firms in the sample by
industry, province, and year. In some industries and provinces, there
are a few firms (Table A1). Our results remain robust when we exclude
the industries and provinces with few observations (we do not report
these results).
CATEGORIZATION OF PUBLIC EXPENDITURE
In the empirical specifications, we assume that expenditure within
education, health, as well as public infrastructure and transportation
(EHT) is increased and that this increase is offset using expenditure
outside these areas and other expenditure on specific items within these
areas. The underlying assumption is that EHT expenditure affects
productivity to a larger extent than the offsetting expenditure over a
period of 1-2 years.
Within spending on public infrastructure and transportation
referred to as "transport expenditure," we assume that
spending on public transport, traffic management, and road
infrastructure which includes spending on road maintenance can be
expected to deliver fairly quickly tangible benefits for firms. For
instance, improved traffic lights may cut travel time, filling potholes
lowers the cost for repairs, and new bus lines help the work force to
reach their workplace more quickly. Spending on public health may also
rapidly improve labor productivity, if for instance it results in
increased availability of drugs against common diseases, or if public
awareness to prevent accidents or certain types of diseases increases.
We therefore consider spending on district and provincial health
services as productive. Here, we expect that public spending may ensure
that the work force remains fit for work.
Even spending on education may have almost immediate effects on
productivity: for instance, as a result of education spending on early
childhood development, labor productivity of the parents may improve
fairly quickly. In addition, improved education of students shortly
prior to graduation or spending on short courses for adults may affect
labor productivity over the medium run because this type of education is
rather short and provides parts of the workforce with skills which are
directly relevant for their jobs. We therefore expect that spending on
further education and training as well as adult basic education and
training as well as spending on public and private schools in general
potentially affects private productivity over the medium run.
However, other subcategories are likely to hardly affect
productivity of firms over the medium run, or not at all. For all three
areas, we exclude spending on administration as there are only indirect
effects, at best. For the same reason, within the health sector, we
exclude health care support services and health facilities management.
We also exclude spending on health sciences and training as any
potential productive effects only materialize over longer time horizons,
and we exclude central hospital services which are likely to treat many
patients who are not part of the work force, at least temporarily. In
addition, we exclude emergency medical services that include emergency
and planned patient transport; here the link to firm-level productivity
is also less clear. With respect to education spending, apart from
administration, we exclude spending on auxiliary and associated services
and on public special school education where the links with private
sector productivity are also less direct.
Finally, within the public infrastructure and transportation
category, there are a number of areas which are probably less or not
relevant at all for firm productivity. This includes public works which
affects productivity at best over longer horizons or not at all (e.g.,
spending on public works in agriculture which we are unable to separate
from spending on public works in say education) and spending on programs
within communities which can be expected to have social rather than
productive benefits.
Table A2 provides an overview of how we categorize public spending.
Obviously, we recognize that one could also make alternative choices
about which subcategories are considered as productive within the
health, education, and public infrastructure and transportation
categories. We therefore consider the robustness of our results to these
choices in Section V.
TABLE A1
Distribution of Firms by Industry across Provinces
Number of Percentage of
Observations Observations
industry (2002-2006) in KN
Mining and quarrying 3 0.0
Sale, maintenance, and repair of 2 0.0
vehicles
Manufacture of food and beverages 154 5.2
Manufacture of tobacco products 1 0.0
Manufacture of textiles 21 19.0
Manufacture of wearing apparel 115 14.8
Manufacture of luggage and footwear 20 15.0
Manufacture of wood 49 16.3
Manufacture of paper 26 19.2
Publishing, printing, and 47 14.9
reproduction
Manufacture of coke and refined 3 0.0
petroleum
Manufacture of chemicals 122 8.2
Manufacture of rubber and plastics 47 8.5
Manufacture of nonmetallic products 25 12.0
Manufacture of basic metals 20 0.0
Manufacture of metal products 149 15.4
Manufacture of machinery 64 4.7
Manufacture of office machinery 2 0.0
Manufacture of electrical machinery 65 6.2
Manufacture of motor vehicles 24 12.5
Manufacture of furniture 150 6.7
Recycling 1 0.0
Wholesale and retail trade 2 0.0
Hotels and restaurants 1 0.0
All, 2002-2006 1113 10.1
Percentage of Percentage of
Observations Observations
industry in GT in WC
Mining and quarrying 33.3 66.7
Sale, maintenance, and repair of 50.0 50.0
vehicles
Manufacture of food and beverages 68.2 18.2
Manufacture of tobacco products 100.0 0.0
Manufacture of textiles 38.1 33.3
Manufacture of wearing apparel 57.4 19.1
Manufacture of luggage and footwear 70.0 5.0
Manufacture of wood 61.2 20.4
Manufacture of paper 65.4 11.5
Publishing, printing, and 46.8 38.3
reproduction
Manufacture of coke and refined 100.0 0.0
petroleum
Manufacture of chemicals 69.7 14.8
Manufacture of rubber and plastics 68.1 19.1
Manufacture of nonmetallic products 68.0 20.0
Manufacture of basic metals 100.0 0.0
Manufacture of metal products 69.8 11.4
Manufacture of machinery 84.4 10.9
Manufacture of office machinery 100.0 0.0
Manufacture of electrical machinery 84.6 4.6
Manufacture of motor vehicles 62.5 4.2
Manufacture of furniture 65.3 22.0
Recycling 0.0 100.0
Wholesale and retail trade 100.0 0.0
Hotels and restaurants 100.0 0.0
All, 2002-2006 67.7 16.7
Notes: Eastern Cape is omitted and contains the remaining number
of firms. KN (Kwazulu-Natal), GT (Gauteng), and WC (Western Cape)
TABLE A2 Fiscal Variables Provided by the South African Treasury
Variable Description (All in Logs)
Total expenditure Total provincial expenditure / GDP
EHT expenditure Health expenditure, education expenditure,
Education expenditure transport and capital expenditure
Public ordinary school education, independent
school subsidies, further education and
training, adult basic training, early
childhood development
District health services, provincial hospital
services
Road infrastructure, public transport,
traffic management
Mainly agriculture,
social development, housing, sport,
recreation, arts and culture,
administration, education (only public
special school education, auxiliary and
associated services) health (emergency
medical services, central hospital
services, health sciences and training,
health care support services, health
facilities management) public
infrastructure and transport (public
works, community-based program)
Health expenditure Transport and capital exp. Other expenditure
(offsetting categories)
REFERENCES
Adam, C., and D. Bevan. "Fiscal Deficits and Growth in
Developing Countries." Journal of Public Economics, 89(4), 2005,
571-97.
Ajam, T., and J. Aron. "Fiscal Renaissance in a Democratic
South Africa." Journal of African Economies, 16(5), 2007, 745-81.
Arnold, J., A. Mattoo, and G. Narciso. "Services Inputs and
Firm Productivity in Sub-Saharan Africa: Evidence from Firm-Level
Data." Journal of African Economies, 17(4), 2008, 578-99.
Baltagi, B. H., and N. Pinnoi. "Public Capital Stock and State
Productivity Growth: Further Evidence from an Error Components Model.
" Empirical Economics, 20(2), 1995, 351-59.
Barro, R. "Government Spending in a Simple Model of Economic
Growth. " Journal of Political Economy, 98(5), 1990, 103-25.
Bastos, F., and J. Nasir. "Productivity and the Investment
Climate: What Matters Most?" World Bank Policy Research Paper,
3335, 2004.
Bekes, G., and B. Murakozy. "Firm Behaviour and Public
Infrastructure: The Case of Hungary." KTI/1E Discussion Papers,
(4), 2005.
Cameron, A., J. Gelbach, and D. Miller. "Bootstrap-Based
Improvements for Inference with Clustered Errors." Review of
Economics and Statistics, 90(3), 2008, 414-27. '
Datta, S. "The Impact of Improved Highways on Indian
Firms." Journal of Development Economics, 99(1), 2012, 46-57.
David, P., and T. Van de Klundert. "Biased Efficiency Growth
and Capital-Labor Substitution in the U.S., 1899-1960." American
Economic Review, 55(3), 1965, 357-94.
Devarajan, S., V. Swaroop, and H.-F. Zou. "The Composition of
Public Expenditure and Economic Growth." Journal of Monetary
Economics, 37(2), 1996, 313-44.
Dollar, D., M. Hallward-Driemeier, and T. Mengistae.
"Investment Climate and Firm Performance in Developing
Economies." Economic Development and Cultural Change, 54(1), 2005,
1-31.
Evans, P., and G. Karras. "Are Government Activities
Productive? Evidence from a Panel of US States." Review of
Economics and Statistics, 1994, 1-11.
Gabe, T. "Local Fiscal Policy and Establishment Growth."
Journal of Regional Analysis and Policy, 33(1), 2003, 57-80.
Garcia-Mila, T., and T. J. McGuire. "The Contribution of
Publicly Provided Inputs to States' Economies." Regional
Science and Urban Economics, 22(2), 1992, 229-41.
Gemmell, N., F. Misch, and B. Moreno-Dodson. "Public Spending
and Long-Run Growth in Practice: Concepts, Tools, and Evidence," in
Is Fiscal Policy the Answer?
A Developing Country Perspective, edited by B. Moreno-Dodson.
Washington, DC: World Bank, 2012, 69-108.
Hong, H.. and S. Ahmed. "Government Spending on Public Goods:
Evidence on Growth and Poverty." Economic & Political Weekly,
44(31), 2009. 103-08.
Kneller, R., M. Bleaney, and N. Gemmell. "Fiscal Policy and
Growth: Evidence from OECD Countries." Journal of Public Economics,
74(2), 1999, 171-90.
Levinsohn. J., and A. Petrin. "Estimating Production Functions
Using Inputs to Control for Unobservables." Review of Economic
Studies, 70(2). 2003. 317-41.
Ligthart, J. E., and R. M. M. Suarez. "The Productivity of
Public Capital: A Meta-Analysis," in Infrastructure Productivity
Evaluation, edited by W. Manshanden and W. Jonkhoff. SpringerBriefs in
Economics, Vol. 1. New York: Springer, 2011, 5-32.
Lopez, R., and S. Miller. "The Structure of Public
Expenditure: A Robust Predictor of Economic Development?"
Unpublished Manuscript, 2007.
Mera, K. "Regional Production Functions and Social Overhead
Capital: An Analysis of the Japanese Case." Regional and Urban
Economics, 3(2), 1973, 157-85.
Momoniat, I. "Fiscal Decentralisation in South Africa: A
Practitioner's Perspective," in Managing Fiscal
Decentralization, edited by E. Ahmad, and V. Tanzi. New York: Routledge,
2002, 350-74.
Reinikka, R., and J. Svensson. "Coping with Poor Public
Capital." Journal of Development Economics, 69(1), 2002,51-70.
Quist, R., C. Certan. and J. Dendura. "Republic of South
Africa. Public Expenditure and Financial Accountability. Public
Financial Management Performance Assessment Report." Report
commissioned by the European Commission Delegation South Africa to
ECORYS, 2008.
Rodrik, D. "The Debate over Globalization: How to Move Forward
by Looking Backward," in Launching New Global Trade Talks: An
Action Agenda, edited by J. J. Schott. Institute for International
Economics, Special Report 12, Washington, DC, 1998, 25-38.
Romp, W., and J. deHaan. "Public Capital and Economic Growth:
A Critical Survey." Perspektiven der Wirtschaftspolitik, 8(S1),
2007, 6-52.
Schwellnus, C., and J. Arnold. "Do Corporate Taxes Reduce
Productivity and Investment at the Firm Level? Crosscountry Evidence
from the Amadeus Dataset." OECD Economics Department Working Paper,
No. 641, 2008.
Shirley, C., and C. Winston. "Firm Inventory Behavior and the
Returns from Highway Infrastructure Investments." Journal of Urban
Economics, 55(2), 2004, 398-415.
Straub. S. "Infrastructure and Growth in Developing Countries:
Recent Advances and Research Challenges." World Bank Policy
Research Paper, No. 4460, 2008.
(1.) Our paper is also related to a large literature that dates
however at least back to Mera (1973) which examines the effects of the
stock of public capital (broadly defined) on private sector output at a
more aggregate level; see Romp and de Haan (2007), Straub (2008), and
Ligthart and Suarez (2011) for surveys of the literature. Several papers
of this literature also exploit variation at the subnational level
including Mera (1973), Garcia-Mila and McGuire (1992), Baltagi and
Pinnoi (1995), and Evans and Karras (1994). In addition, as Ligthart and
Suarez (2011) note, many studies estimate a production function with
public capital as in input. In this paper, we consider the flow of
public spending as an explanatory variable and use firm-level data.
(2.) We describe the details of the system of fiscal
decentralization in the Appendix.
(3.) To correct for province-specific industry factors that cause
the average capital-labor ratio to vary systematically across provinces
and industries, the capital-labor ratio of the firm is measured relative
to the mean in each individual industry and province.
(4.) In the Appendix, we describe the distribution of firms across
industries and provinces in greater detail.
(5.) A reliable variable for the age of the firm is not available
and cannot be included. Since it would be time-invariant, firm fixed
effects capture the effects of firm age.
(6.) Evidence suggests that the fiscal data for South Africa are of
a high quality. Ajam and Aron (2007) and Quist et al. (2008) find that
the level of transparency in South Africa's budget processes is
high, while South Africa ranks among the top five developing countries
in the Open Budget Survey on budget transparency and accountability.
(7.) We recognize that there may be less direct mechanisms through
which public spending affects firm productivity. For instance, total
factor productivity may depend on capacity utilization, inventory levels
and supplier relationships. Shirley and Winston (2004) develop a
theoretical argument along these lines.
(8.) The number of provinces is too small to cluster at the
provincial level only. To further control for intra-class correlation,
we use the wild cluster bootstrap indicator as proposed by Cameron,
Gelbach, and Miller (2008) which we discuss further below.
(9.) Given that the location of firms in 2000 and 2001 is unknown,
we cannot include province-year effects in these regressions. As a
robustness check, we also ran regressions with no province-year effects
and with province-year effects for all years.
(10.) The standard errors of the output elasticities with respect
to the private inputs come from the first stage.
(11.) Our results are also robust to the use of the Levinsohn and
Petrin (2003) estimator.
(12.) We do not rule out the possibility that this type of
interaction helps to identify an effect for a change in spending we have
not considered.
(13.) Our results also remain robust to using terciles of the
distribution of the relative capital intensities (not shown).
(14.) By construction of the firm-level data, there is no attrition
prior to 2002 as the panel dimension for 2000 and 2001 is constructed
from the 2002 questionnaire only. In addition, we do not observe
attrition after 2006.
(15.) When we only include firms with observations in 2002 and 2006
in specification (1) of Table 6, the coefficients remain robust which is
not surprising. However, this further suggests that differences in
attrition rates do not affect our results.
RICHARD KNELLER and FLORIAN MISCH *
* Generous financial support from Forfas is gratefully
acknowledged. We thank the participants at a seminar in Dublin organized
by Forfas for valuable comments. We are also grateful to Neil Rankin,
the National Treasury, Republic of South Africa, and Kenneth Creamer who
all facilitated the access and the use of the data used in this paper.
Kneller: School of Economics, University of Nottingham, Nottingham
NG7 2RD, UK. Phone +44 (0) 115 95 14734, Fax +44 (0) 115 95 14159,
E-mail Richard.Kneller@nottingham.ac.uk
Misch: Centre for European Economic Research, 68161 Mannheim,
Germany. Phone +49 (0) 621 1235-394, Fax +49 (0) 621 1235-223, E-mail
misch@zew.de
TABLE 1
Firm Variables and Provincial Variables
Variable Description Years
Sales (y) Total sales per firm (in logs) 2000, 2001, 2002,2006
Capital (k) Net book value of machinery, 2000, 2001, 2002, 2006
vehicles, and equipment
(in logs)
Labor (l) Total workers (in logs) 2000, 2001, 2002, 2006
Materials Total cost of raw materials and 2000, 2001, 2002, 2006
(m) intermediate goods (in logs)
Exporter Dummy (1 if firm sells goods in 2002, 2006
other countries)
Crime Dummy (1 if firm suffers losses 2002, 2006
due to theft, robbery,
vandalism, or arson)
Foreign Dummy (1 if foreign ownership 2002, 2006
>10%)
Large Dummy (1 if labor >50) 2002, 2006
Murder Murder rate in province (ratio 2001-2006
per 100,000 inhabitants,
in logs)
Road Length of road/surface in 2000-2006
density province (in logs)
Grade Learners who passed grade 12 2000-2006
in province (percentage,
in logs)
City_GDP GDP of the main city of 2000-2006
province (in logs)
TABLE 2
Firm Variables-Descriptive Statistics
Standard
Variable Mean Deviation Minimum Maximum
Sales 11.825 2.273 4.038 19.531
Capital 10.058 2.087 2.641 16.832
Labor 4.025 1.626 0 9.928
Materials 11.054 2.471 1.948 19.442
Exporter 0.092 0.289 0 1
Crime 0.463 0.499 0 1
Foreign 0.507 0.5 0 1
Large 0.671 0.47 0 1
Note: The variable definitions can be found in Table 1.
TABLE 3
EHT Public Spending (Not in Logs)
Standard
Variable (as a Share Deviation
of Total Expenditure) Mean (Overall)
EHT expenditure 0.552 0.070
Of which education expenditure 0.339 0.032
Of which health expenditure 0.174 0.024
Of which transport and capital expenditure 0.047 0.010
Standard Standard
Variable (as a Share Deviation Deviation
of Total Expenditure) (Between) (Within)
EHT expenditure 0.068 0.022
Of which education expenditure 0.032 0.008
Of which health expenditure 0.023 0.006
Of which transport and capital expenditure 0.009 0.004
TABLE 4
Results
(1) (2) (3)
Variables Sales Sales Sales
Capital 0.0754 *** 0.0563 *** 0.0481 ***
(0.0117) (0.0167) (0.0191)
Labor 0.229 *** 0.245 *** 0.258 ***
(0.0245) (0.0282) (0.0300)
Materials 0.721 *** 0.721 *** 0.719 ***
(0.0186) (0.0183) (0.0180)
Foreign 0.0392 0.0393 0.0305
(0.0268) (0.0268) (0.0268)
Large 0.0179 0.0235 0.0310
(0.0356) (0.0367) (0.0377)
Exporter 0.0259 0.0266 0.0198
(0.0249) (0.0249) (0.0242)
Crime -0.0268 * -0.0244 -0.0200
(0.0156) (0.0156) (0.0154)
Grade 0.470 0.548
(0.493) (0.470)
City GDP -0.346 *** -0.340 ***
(0.123) (0.127)
Murder -0.191 -0.240
(0.216) (0.209)
Road density 0.000182 -0.00129
(0.00706) (0.00665)
Total exp. -0.652 *** -0.622 **
(0.227) (0.236)
EHT exp. low([Kit/Lit]/ 1.481 * 0.221 **
[Kjpt/Ljpt]) (0.813) (0.0939)
EHT. exp. lmed. ([Kit/Lit]/ 1.425 * 0.125 *
[Kjpt/Ljpt]) (0.820) (0.0651)
EHT. exp. hmed.([Kit/Lit]/ 1.392 * 0.0745
[Ljpt/Ljpt]) (0.814) (0.0607)
EHT. exp. high([Kit/Lit]/ 1.325
[Ljpt/Ljpt]) (0.824)
EHT exp. 1.494 *
0.788)
Constant 10.38 *** 10.59 *** 3.040 ***
(3.233) (3.254) (0.404)
Observations 1113 1113 1113
R2 0.973 0.973 0.975
Province-year FE No No Yes
Industry-year FE Yes Yes Yes
Province-Ind. FE No No Yes
Prov.-ind.-year FE No No No
(4) (5)
Variables Sales Sales
Capital 0.0420 ** 0.0311
(0.0338) (0.0421)
Labor 0.264 *** 0.228 ***
(0.0202) (0.0626)
Materials 0.719 *** 0.745 ***
(0.0202) (0.0241)
Foreign 0.0264 0.0347
(0.0278) (0.0279)
Large 0.0356 0.0842 ***
(0.0396) (0.0306)
Exporter 0.0197 0.0253
(0.0248) (0.0280)
Crime -0.0151 -0.0273 *
(0.0161) (0.0153)
Grade
City GDP
Murder
Road density
Total exp.
EHT exp. low([Kit/Lit]/ 0.256 ** 0.260 ***
[Kjpt/Ljpt]) (0.109) (0.0734)
EHT. exp. lmed. ([Kit/Lit]/ 0.149 * 0.124 ***
[Kjpt/Ljpt]) (0.0755) (0.0455)
EHT. exp. hmed.([Kit/Lit]/ 0.0752 0.0446
[Ljpt/Ljpt]) (0.0647) (0.0639)
EHT. exp. high([Kit/Lit]/
[Ljpt/Ljpt])
EHT exp.
Constant 2.840 *** 4.094 ***
(0.409) (0.0427)
Observations 1113 1113
R2 0.975
Province-year FE No Yes
Industry-year FE No Yes
Province-Ind. FE No Yes
Prov.-ind.-year FE Yes No
Notes: ind.-prov. clustered standard errors in parentheses.
(1)-(4): ordinary least squares (OLS) estimation based on 2002
and 2006.
(5): Two-step estimation; first-step firm FE with private inputs
based on 2000, 2001, 2002, and 2006.
*** p<0.01; ** p<0.05; * p< 0.1.
TABLE 5
Robustness I
(1) (2) (3)
Variables Sales Sales Sales
Capital 0.0585 0.0482 *** 0.0755 ***
(0.0180) (0.0120)
Labor 0.253 0.258 *** 0.244 ***
(0.0190) (0.0282)
Materials 0.714 0.719 *** 0.720 ***
(0.0301) (0.0198)
Foreign 0.0509 *** 0.0308 0.0280
(0.0269) (0.0276)
Large -0.00309 0.0311 0.0277
(0.0376) (0.0330)
Exporter 0.0392 0.0192
(0.0241)
Crime -0.0270 *** -0.0199 -0.0207
(0.0154) (0.0152)
EHT exp. low 0.149 *** 0.220 ** -0.0845
(0.0939) (0.126)
EHT exp. lmed. 0.0939 *** 0.124 * 0.00714
(0.0652) (0.0791)
EHT exp. hmed. 0.0722 0.0742 0.0196
(0.0606) (0.0576)
EHT exp. high
EHT exp. [exporter] -0.0253
(0.0325)
Constant 2.859 *** 3.034 *** 2.016 ***
(0.773) (0.404) (0.446)
Observations 1,113 1,113 1,113
[R.sup.2] 0.970 0.975 0.975
Province-year FE Yes Yes Yes
Industry-year FE No Yes Yes
Province-Ind. FE No Yes Yes
Prov.-Ind.-year FE No No No
(4) (5)
Variables Sales Sales
Capital 0.0576 *** 0.0474 ***
(0.0194) (0.0180)
Labor 0.232 *** 0.259 ***
(0.0251) (0.0191)
Materials 0.718 *** 0.719 ***
(0.0195) (0.0303)
Foreign 0.0295 0.0302
(0.0270) (0.0273)
Large 0.0238 0.0313
(0.0369) (0.0376)
Exporter 0.0202 0.0197
(0.0248) (0.0243)
Crime -0.0194 -0.0201
(0.0149) (0.0155)
EHT exp. low 0.198 0.227 **
(0.138) (0.0970)
EHT exp. lmed. 0.141 0.130 *
(0.105) (0.0659)
EHT exp. hmed. 0.0531 0.0789
(0.0698) (0.0544)
EHT exp. high 0.0141
(0.0758)
EHT exp. [exporter]
Constant 3.174 *** 3.041 ***
(0.423) (0.403)
Observations 1,113 1,113
[R.sup.2] 0.975 0.975
Province-year FE Yes Yes
Industry-year FE Yes Yes
Province-Ind. FE Yes Yes
Prov.-Ind.-year FE No
Notes: ind.-prov. clustered standard errors in parentheses. OLS
estimates based on 2002 and 2006.
(1) Wild cluster bootstrap estimator with clustering at prov. and
ind. level.
(2) Share of EHT. exp. interacted with export status.
(3) Relative labor use interacted with share of EHT exp.
(4) Relative capital use interacted with share of EHT exp.
(5) Relative capital-intensity categories based on 20th
percentiles intervals.
*** p<0.01; ** p<0.05; * p<0.1.
TABLE 6
Robustness II
Variables (1) Sales (2) Sales (3) Sales
Capital -0.00379 0.0510 *** 0.0509 ***
(0.0306) (0.0296) (0.0304)
Labor 0.448 *** 0.255 *** 0.254 ***
(0.0876) (0.0181) (0.0183)
Materials 0.697 *** 0.719 *** 0.720 ***
(0.0303) (0.0190) (0.0192)
Foreign -0.0355 0.0306 0.0322
(0.102) (0.0268) (0.0268)
Large -0.0354 0.0306 0.0328
(0.151) (0.0379) (0.0387)
Exporter -0.0515 0.0197 0.0192
(0.0841) (0.0244) (0.0245)
Crime 0.0223 -0.0205 -0.0197
(0.0412) (0.0154) (0.0156)
EHT exp. lowfiKit/ 0.450 *** 0.308 * 0.369 *
Litl/fKipt/Liptl) (0.105) (0.155) (0.202)
EHT exp. lmed.([Kit/ 0.303 *** 0.175 0.223
Lit]/[Kjpt/Ljpt]) (0.0788) (0.107) (0.141)
EHT exp. hmed.t[Kit/ -0.0150 0.109 0.123
Lit]/[Ljpt/Ljpt]) (0.128) (0.0991) (0.126)
Constant 4.440 *** 3.021 *** 3.010 ***
(0.950) (0.403) (0.383)
Observations 1,113 1,113 1,095
[R.sup.2] 0.966 0.975 0.975
Number of eec_panelid 981
Province-year FE Yes Yes Yes
Industry-year FE Yes Yes Yes
Province-Ind. FE Yes Yes Yes
Firm FE Yes No No
Variables (4) Sales (5) Sales
Capital 0.0494 *** 0.0484 ***
(0.0192) (0.0185)
Labor 0.258 *** 0.258 ***
(0.0171) (0.0191)
Materials 0.719 *** 0.719 ***
(0.0301) (0.0302)
Foreign 0.0300 0.0309
(0.0268) (0.0269)
Large 0.0301 0.0290
(0.0375) (0.0377)
Exporter 0.0199 0.0201
(0.0242) (0.0242)
Crime -0.0195 -0.0203
(0.0154) (0.0154)
EHT exp. lowfiKit/ 0.587 *** 0.0541 **
Litl/fKipt/Liptl) (0.219) (0.0244)
EHT exp. lmed.([Kit/ 0.326 ** 0.0304 *
Lit]/[Kjpt/Ljpt]) (0.159) (0.0166)
EHT exp. hmed.t[Kit/ 0.179 0.0211
Lit]/[Ljpt/Ljpt]) (0.148) (0.0160)
Constant 3.048 *** 3.062 ***
(0.405) (0.402)
Observations 1,113 1,113
[R.sup.2] 0.975 0.975
Number of eec_panelid
Province-year FE Yes Yes
Industry-year FE Yes Yes
Province-Ind. FE Yes Yes
Firm FE No No
Notes: ind.-prov. clustered standard errors in parentheses. OLS
estimates based on 2002 and 2006.
(1) Firm fixed effects added.
(2) EHT exp. as a ratio of all exp. in these categories.
(3) Same as (2) except that emergency and public works are
excluded from denominator.
(4) Broad definition of EHT expenditure used.
(5) Narrow definition of EHT expenditure used.
*** p < 0.01; ** p < 0.05; * p < 0.1.
TABLE 7
Attrition Rates
Percentage of Firms
in 2002 That Are Still
Capital Intensity in Sample 2006
Low 24.6
Lower medium 34.8
Upper medium 28.9
High 30.2
TABLE 8
Robustness III
Variables (1) (2) (3)
Sales Sales Sales
Capital 0.0581 *** 0.0519 *** 0.0487 ***
(0.0183) (0.0183) (0.0304)
Labor 0.249 *** 0.255 *** 0.258 ***
(0.0287) (0.0191) (0.0191)
Materials 0.719 *** 0.719 *** 0.719 ***
(0.0192) (0.0300) (0.0182)
Foreign 0.0309 0.0309 0.0310
(0.0270) (0.0270) (0.0270)
Large 0.0253 0.0279 0.0280
(0.0379) (0.0376) (0.0375)
Exporter 0.0205 0.0202 0.0206
(0.0244) (0.0243) (0.0243)
Crime -0.0205 -0.0204 -0.0204
(0.0153) (0.0154) (0.0154)
EHT exp. low([Kit/ 0.182 0.1000 ** 0.0530 **
Lit]/[Kjpt/Ljpt]) (0.113) (0.0482) (0.0237)
EHT exp. lmed.([Kit/Lit]/ 0.0904 0.0542 * 0.0284 *
[Kjpt/Ljpt]) (0.0747) (0.0319) (0.0159)
EHT exp. hmed.([Kit/Lit]/ 0.100 0.0406 0.0204
[Ljpt/Ljpt]> (0.0794) (0.0323) (0.0157)
Constant 3.030 *** 3.062 *** 3.076 ***
(0.399) (0.404) (0.400)
Observations 1,113 1,113 1,113
[R.sup.2] 0.975 0.975 0.975
Province-year FE Yes Yes Yes
Industry-year FE Yes Yes Yes
Province-Ind. FE Yes Yes Yes
Notes: Ind.-prov. clustered standard errors in parentheses. OLS
estimates based on 2002 and 2006.
(1) EHT spending only includes education exp. categories.
(2) EHT spending only includes health exp. categories.
(3) EHT spending only includes transport exp. categories.
*** p<0.01; ** p<0.05; * p<0.1.
