Entry barriers, competition, and technology adoption.
Fang, Lei
I. INTRODUCTION
One of the challenging questions in economics is why income per worker differs so much across countries. Development accounting exercises typically find that differences in total factor productivity (TFP) are a crucial factor that accounts for a sizeable part of the income differences. (1) This suggests that we need to understand the reasons for why TFP differs across countries.
In this article, I study the quantitative effects of entry barriers on TFP. This is motivated by the empirical literature that has found a negative correlation between entry barriers and productivity. (2) My contribution is to build a fully articulated general-equilibrium model which 1 use to quantify the effect of entry barriers on TFP. I assume that the frontier technology is exogenously given. I show that higher entry barriers reduce TFP because they lead to the adoption of less productive technologies that are inside the frontier of available technologies. A key novelty of my work is that I directly measure the entry barriers using data on the regulatory cost of setting up a new business. Instead, the literature on entry barriers and technology adoption calibrates the entry barriers. Given that the regulatory cost of setting up a new business is just one form of entry barriers, my findings provide a lower bound on the effects of entry barriers on TFP, which is nonetheless found to be sizeable.
My model has the following key features. There are two final-goods sectors: agriculture and manufacturing, which are both competitive. There is an intermediate-goods sector with a continuum of industries that produce intermediate goods used in manufacturing. In each industry, there is monopoly power and a costly technology adoption decision. Although the crucial decisions about technology adoption are made in the intermediate-goods sector, I also include an agricultural sector in my model. The reason for doing this is that the majority of people in poor countries work in agriculture. As a result, the intermediate-goods sector in which technology adoption of the kind analyzed here plays a role that employs only a minority of workers in poor countries. Ignoring this feature of reality by leaving the agricultural sector out of the model would exaggerate my quantitative finding by construction, which would be undesirable.
Each intermediate-good industry has an incumbent firm that faces the threat that a potential entrant may steal its market. There is a fixed cost of entry. Both incumbent and potential entrant pay the same cost of adopting a technology from a set of exogenously given available technologies. Given a level of the entry cost, entering is profitable only if the productivity of the incumbent's technology is sufficiently low. I establish that in the equilibrium of my model the incumbent will prevent entry by choosing a technology that is sufficiently productive. I also establish that the productivity of the chosen technology is inversely related to the entry cost because higher entry costs make entry less profitable. My model is closely related to Schumpeterian models of endogenous growth; see for example Aghion and Howitt (2005) and Aghion et al. (2009). The key difference between these models and my model is that Schumpeterian models study how innovation moves the technology frontier ahead, whereas I study what determines the adoption of a technology from a given set of available technologies. Moreover, in Schumpeterian models there is no fixed cost of entry and an important source of innovation happens when the potential entrant actually enters and replaces the incumbent. In contrast, in my model there is fixed cost of entry and the mere threat of entry ensures that the incumbent chooses a sufficiently productive technology even though the potential entrant stays out of the market.
I restrict my model by calibrating the benchmark version to the U.S. economy. I then create a model version that reflects key features of the poorest 30% of countries in the world. Specifically, this model version has lower TFP in agriculture and higher entry barriers. I calibrate the entry barriers to the regulatory cost of setting up a new business as reported by the World Bank. I assess the effects of entry barriers by reducing the entry cost from the level of poor countries to the level of the United States. I find that this leads to a 12% increase in aggregate TFP and a 27% increase in the nonagricultural TFP of the poor countries.
There are several other papers about the channels through which cross-country differences in entry barriers may generate cross-country differences in TFP. Most closely related are Herrendorf and Teixeira (2011) and Parente and Prescott (1999). These authors study how entry barriers affect the adoption of technology by coalitions of labor market insiders, whereas I study how entry barriers affect the adoption of technology by firms. My approach has the advantage that I can bring to bear direct evidence on the entry cost that firms actually face. In contrast, the other papers use an indirect approach of calibrating the entry barriers which uses a very broad notion of entry barriers. As a result, Herrendorf and Teixeira (2011) find that the effects of entry barriers account for most of the TFP differences between United States and poor countries, whereas I find that the effects of the direct measure of entry costs on TFP is much smaller, but still sizable.
My article is also related to the work of Barseghyan (2008), Barseghyan and DiCecio (2011), and Moscoso Boedo and Mukoyama (2012), who argue that higher entry barriers reduce TFP because they lead to the misallocation of resources. In contrast, I argue that higher entry barriers reduce TFP because they lead to the adoption of less productive technologies. Both channels turn out to be quantitatively important, which is consistent with the independent evidence by Bloom, Draca, and Van Reenen (2015), Foster, Haltiwanger, and Krizan (2001), and Pavcnik (2002). Thus, my work nicely complements their work.
The rest of the article is organized as follows. Section II lays out the economic environment. Section III characterizes the equilibrium. Section IV assesses the quantitative effects of entry barriers on TFP. Section V concludes.
II. ENVIRONMENT
In this section, 1 develop the environment of my model by describing preferences, endowments, and technologies. There are two final goods: an agricultural good and a manufacturing good. The agricultural good is consumed. The manufacturing good is consumed and used as an intermediate input in agricultural production. Agricultural output is produced from labor, land, and the manufacturing good. The manufacturing good is produced from a continuum of intermediate inputs that are each produced from labor. The TFP of producing intermediate goods is endogenous.
A. Preferences and Endowments
There is a representative household. Preferences are described by the utility function:
u ([c.sub.a], [c.sub.m]) = [[[alpha] [([c.sub.a] -[[c.sub.a].bar]).sup.[sigma]] + (1 - [alpha]) [c.sup.[sigma].sub.m]].sup.1/[sigma]]
[alpha] [member of] (0,1) is a relative weight. [c.sub.a] is the consumption of the agricultural good, and [c.sub.m] is the consumption of the manufacturing good. [[c.sub.a].bar] > 0 is a subsistence level of agricultural consumption. [sigma] controls the elasticity of substitution between [c.sub.a] and [c.sub.m].
The household is endowed with one unit of time that yields no utility. The household is also endowed with one unit of land.
B. Technologies
The agricultural good is produced according to the following production function:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
[A.sub.a] is the agricultural TFP. [h.sub.a], L, and [z.sub.a] are the labor, land, and the manufacturing good used in the production, respectively. The production technology exhibits constant returns to scale, that is, [[theta].sub.h] + [[theta].sub.l] + [[theta].sub.z] = 1. I assume that land is used as an input only in agriculture. Hence, the land used in agriculture will equal the total available land, so I denote it by an upper-case letter and I drop the index a.
There is a continuum of measure one of intermediate goods [x.sub.j], j [member of][0,1], Each intermediate good can be produced with a linear technology:
[x.sub.j] = [A.sub.j][h.sub.j],
where [A.sub.j] is the TFP (and the labor productivity) and [h.sub.j] is the labor input. [A.sub.j] belongs to interval [[A.bar], [A.sup.f]] where [A.sup.f] denotes the technology frontier. Using [A.bar] is free. Using an [A.sub.j] better than [A.bar] requires to pay a cost of [phi]f([A.sub.j]), which is measured in the units of the manufacturing good. The function f is strictly increasing and convex, and it is the same for all intermediate goods.
The manufacturing good [y.sub.m] is produced by combining the intermediate goods via the following constant elasticity of substitution aggregator:
(1) [y.sub.m] [([[integral].sup.1.sub.0] [x.sup.[epsilon]-1/[epsilon].sub.j] [d.sub.j]).sup.[espilon]/ ([epsilon]-1)],
[epsilon] is the elasticity of substitution between intermediates and [x.sub.j] is the input of intermediate j. Following Parente and Prescott (1999) and Herrendorf and Teixeira (2011), I assume that 0 < [epsilon] < 1, that is, the intermediate goods are complements in the production of final manufacturing output.
III. EQUILIBRIUM
This section characterizes the equilibrium of the model economy. Because of the symmetry of the model environment, 1 focus on the symmetric equilibrium in which the equilibrium outcomes are symmetric with respect to intermediates. The final-goods sectors are competitive. The intermediate-goods sector is not competitive, but features incumbents that have monopoly power and potential entrants. The heart of my model is the entry game that incumbents play with potential entrants.
A. Household Sector
The household maximizes his utility subject to the budget constraint:
(2) [p.sub.a] [c.sub.a] + [c.sub.m] = w + r + [PI],
where [p.sub.a] is the price of the agricultural good, w is the wage rate, r is the land-rental rate, and n is the total profit from the intermediate-goods sector. I choose the manufacturing good as the numeraire. The necessary condition for the optimality of the household's choices is that marginal rate of substitution between the agricultural good and the manufacturing good equals the relative price:
(3) [alpha][c.sup.1-[sigma].sub.m]/(1-[alpha])[([c.sub.a] - [[c.sub.a].bar]).sup.1-[sigma]] = [p.sub.a].
B. Final-Goods Sectors
In each final good sector there is a representative firm that behaves competitively.
Taking prices as given, the agricultural firm solves the following problem:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The solution to this problem is characterized by equating the marginal products of inputs to the prices:
(4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(6) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The manufacturing firm solves the following problem:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
[p.sub.j] denotes the price of intermediate good j. The solution to this problem gives the demand for each intermediate j:
(7) [x.sub.j] = B[p.sup.-[epsilon].sub.j] ,
where B =[([[integral].sup.1.sub.0] [x.sup.([epsilon]-1)/[epsilon].sub.j][d.sub.j]).sup.[epsilon]/([epsilon]-1)]
C. Intermediate-Goods Sector
The intermediate-goods sector is not competitive. Instead, in each intermediate-good industry, there is an incumbent, a potential entrant, and a competitive fringe. The competitive fringe can operate the inferior technology [A.bar] without paying a cost. The competitive fringe may be thought of as capturing informal firms or home production. The incumbent and the potential entrant choose their production technologies subject to facing the same technology adoption cost. (3) To enter, the potential entrant must pay a fixed entry cost, k > 0. Note that I assume that k is non-negative, which excludes subsidies.
The incumbent, the potential entrant, and the competitive fringe play a three-stage entry game. Since each industry is infinitesimally small, all players take the demand function for industry output as given, thinking that the players in all other industries are making their equilibrium technology choices. (4) In the first stage of the entry game, the incumbent chooses which technology to adopt. In the second stage, the potential entrant decides whether to enter and, if so, with what level of technology. In the third stage, there are two possibilities: if the potential entrant enters, then the incumbent, the potential entrant, and the competitive fringe play a Bertrand game; if the potential entrant does not enter, then the incumbent and the competitive fringe play a Bertrand game. I assume that if multiple players post the same price, then the incumbent captures the entire market.
Before I go on to analyze the entry game, it may be useful to look ahead at the role that the potential entrant will play here. To this end, it is crucial to realize that both the incumbent and the potential entrant have access to the same technologies and pay the same cost of adopting a particular technology. As the potential entrant has to pay the entry cost in addition, the incumbent has an advantage over the potential entrant. Specifically, by adopting the technology that equates the potential entrant's profit with the entry cost, the incumbent will always be able to make positive profits. In order to beat the incumbent in the Bertand game, the potential entrant then has to choose a more productive technology. Given that with the previous technology he made zero profits, choosing a more productive technology would result in losses. Hence, there won't be entry in equilibrium. The threat of entry is nonetheless crucial for the incumbent's technology adoption decision, because the intermediate goods are complements, and so the demand for each of them is inelastic. Hence, the incumbent will choose the least productive technology (and thereby achieve the highest price) at which entry is deterred. The entry costs will affect this choice because at higher entry costs the incumbent can choose a lower productivity without triggering entry. This logic is the reason for why the entry game generates an inverse equilibrium relationship between the entry costs and productivity.
I will now formalize these arguments. I proceed in the reverse order of the game. As I focus on symmetric equilibrium, I will drop j that has so far indexed a particular intermediate good.
Stage 3: In this stage, the incumbent's technology [A.sup.i] and the potential entrant's entry decision have already been determined. Because the demand for intermediate goods is price inelastic, Bertrand competition implies that the player with the best technology will capture the entire market and charge a price equal to the marginal cost of the player with the second-best technology. Hence, if there is no entry, the incumbent adopts a technology no worse than [A.bar] and charges a limit price of p = w/[A.bar]. If, instead, the potential entrant has entered, the potential entrant must be the firm with the better technology. (5) Hence, conditional upon entry, the potential entrant captures the entire market and charges a limit price that is the incumbent's marginal cost w/[A.sup.i]. As [A.sup.i] [greater than or equal to] [A.bar], the equilibrium price cannot exceed w/[A.bar]. In other words, the competitive fringe sets an upper limit for the equilibrium price.
Stage 2: The potential entrant makes its entry decision. Conditional upon entry, the potential entrant maximizes profit taking [A.sup.i] determined in the first stage as given. Let [[pi].sup.e]([A.sup.e],[A.sup.i]) be the profit of the potential entrant when he adopts [A.sup.e] given the incumbent's technology choice of [A.sup.i]. The entrant's problem is then given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The solution to this problem implies the potential entrant's technology as a function of [A.sup.i], which I denote by [A.sup.e]([A.sup.i]). The entry decision depends critically on the incumbent's technology choice. Specifically, if [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) > k, the potential entrant will enter, and if [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) [less than or equal to] k, the potential entrant will not enter. Note that [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) is decreasing in [A.sup.i]. This follows because demand is inelastic here ([epsilon] < 1), so profits are increasing in the price but the price is decreasing in [A.sup.i].
Stage 1: Because the potential entrant's profit [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) is determined in Stage 2 as a function of [A.sup.i], the incumbent can alter [A.sup.i] to influence the potential entrant's entry decision. In particular, to be able to attain a positive profit, the incumbent must adopt an [A.sup.i] that deters entry, that is, [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) - k [less than or equal to] 0. Let [[pi].sup.i]([A.sup.i]) be the incumbent's profit when [A.sup.i] deters entry. The incumbent's problem is given by:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Let [[??].sup.i] be the incumbent's optimal choice of technology. The solution to this problem establishes [[??].sup.i] as a function of the entry cost.
D. Definition of Equilibrium
A symmetric equilibrium is a set of prices (w,r,[p.sub.a],p), allocations ([c.sub.a], [c.sub.m], [h.sub.a],L, [z.sub.a],x, h,[y.sub.a],[y.sub.m]), technology [[??].sup.i], profit n, and functions ([[pi].sup.i]([A.sup.i]). [[pi].sup.e]([A.sup.e],[A.sup.i]), [[??].sup.e]([A.sup.i])), such that:
(i) [c.sub.a] and [c.sub.m] solve the household's problem;
(ii) [h.sub.a], L, and [z.sub.a] solve the agricultural firm's problem;
(Hi) x solves the manufacturing firm's problem;
(iv) In each industry, the equilibrium conditions for the game satisfy
* Stage 3 optimality: p = w/A,
* Stage 2 optimality: [[pi].sup.e]([[??].sup.e]([A.sup.i]),[A.sup.i]) [greater than or equal to] [[pi].sup.e]([A.sup.e],[A.sup.i]) [for all] [A.sup.i], [A.sup.e],
* Stage 1 optimality: [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] for which [[pi].sup.e] ([[??].sup.e] ([A.sup.i]) ,[A.sup.i]) -k [less than or equal to] 0,
(v) [PI] = [[pi].sup.i]([[??].sup.i]).
(vi) Markets clear: L = 1, [y.sub.a] = [c.sub.a], [y.sub.m] = [c.sub.m] + [phi]f([[??].sup.i]) + [z.sub.a], [h.sub.a] + h = 1, and x = [A.sup.i]h.
Condition (iv) describes the equilibrium conditions for the game. The first bullet requires that the incumbent charges the upper limit of the price that results from the existence of the competitive fringe. The second bullet requires that [[??].sup.e]([A.sup.i]) maximizes the potential entrant's profit for all [A.sup.i]. The third bullet requires that [[??].sup.i] maximizes the incumbent's profit subject to deterring entry. All other conditions are standard.
Appendix A establishes the existence and uniqueness of the equilibrium.
E. Properties of the Equilibrium
This subsection discusses how in equilibrium the entry cost affects the adopted technology. When the entry cost is infinite, entry is blocked automatically. Note that even in this case, the incumbent may adopt a technology better than [A.bar] to reduce the average production cost. Let [[??].sup.i] be the incumbent's optimal choice of technology in the economy with infinite entry cost. [A.sup.i] is also the incumbent's optimal choice as long as the no-entry constraint does not bind. Let [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] If k > [??], the no-entry constraint does not bind, and [A.sup.i] is the incumbent's optimal choice.
If, in contrast, k < [??], the no-entry constraint binds, and the incumbent must adopt a technology better than [[??].sup.i] to prevent that the potential entrant enters and steals the entire market. Moreover, as the entry cost decreases, the incumbent must adopt a better and better technology to make sure that the profit of the potential entrant does not exceed the decreasing entry cost. This suggests that there is an inverse equilibrium relationship between k and [[??].sup.i]: a lower k goes along with a higher [[??].sup.i]. The following Proposition formally establishes this:
PROPOSITION 1. (i) If k [greater than or equal to] [??], then [[??].sup.i] = [A.sup.i]; (H) If 0 [less than or equal to] k < [??], then [[??].sup.i] > [[??].sup.i] and [[??].sup.i] is decreasing in k.
Proof See Appendix B.
IV. QUANTITATIVE ASSESSMENT
In this section, I first calibrate the model and then assess how large the quantitative effects of entry barriers on TFP are. There are three possible ways of going about this. First, one may do the following: calibrate a benchmark version of the model to the U.S. economy; introduce higher entry costs and lower exogenous TFPs in the fringe and in agriculture to replicate key differences between poor countries and the United States; and evaluate what happens to the TFP of the poor countries if one decreases the entry costs from the level of poor countries to the level of the United States. Second, one may calibrate the model to the economies of poor countries and evaluate what happens to the TFP of the poor countries if one decreases the entry costs to the level that prevails in the United States. Third, one may calibrate the model to the U.S. economy and evaluate what happens to the TFP of the United States if one increases the entry costs to the level that prevails in poor countries. I follow the first procedure. The main reason for doing this is that the first procedure is essentially what Herrendorf and Teixeira (2011) did, which makes it easy to compare my results with their results. I have also experimented with the second procedure and found that, if anything, the effects of reducing entry costs on TFP are larger. The results are available upon request. I choose not to pursue the third procedure because conceptually it makes the least sense. The model developed above is about how the threat of entry determines the distance between the TFP of a poor country and the exogenously given frontier, which can be thought of as the TFP of the United States. The model developed above has nothing to say about what determines the TFP of the United States, which would require an endogenous growth model, such as Schumpeterian models. This point can be seen from the fact that the equilibrium of the economy calibrated to the United States turns out to fall into case (i) of the above Proposition where changing entry costs leaves TFP unchanged.
A. Calibration
This section calibrates the model to the U.S. economy. I normalize the technology frontier [A.sup.f] to be one. I set [[theta].sub.h] = 0.2, [[theta].sub.l] = 0.19, and [[theta].sub.z] = 0.61, following the estimates of the Economic Research Service (2909) in the U.S. Department of Agriculture. (6) There are no good estimates for the elasticity of substitution between agricultural and nonagricultural goods or the elasticity of substitution between intermediates. As a benchmark, I follow Ngai and Pissarides (2008) to set [sigma] = -9 and follow Parente and Prescott (1999) to set [epsilon] = 0.9. The sensitivity analyses of the two elasticity parameters are performed later.
I set f(A)=[A.sup.[gamma]]/[gamma][([A.sup.f]).sup.[gamma]]. This functional form has the property that it takes fewer resources to adopt the same technology as the technology frontier advances. This property is consistent with the development facts highlighted by Parente and Prescott (1994, Figure 1). The technology adoption cost in the model is the expenditure incurred by an intermediate-goods producer to raise its TFP. This cost can be linked to the expenditure on intangible investment, which includes but is not limited to R & D, advertising, training, and software expenditures. (7) Hence, the ratio of intangible investment to gross domestic product (GDP) can be used to pin down the parameter values in the adoption cost function.
I assume that the technology adopted in the United States is the frontier technology. I normalize [A.sub.a] in the United States to be one. The parameters left to calibrate are then [alpha], [A.bar], [[c.sub.a].bar], [phi], [gamma], and k. I calibrate these parameters jointly to match the following targets in the U.S. economy: the agricultural employment share, the agricultural consumption expenditure share, the subsistence expenditure as a share of GDP, the technology adopted, the ratio of intangible investment to GDP, and the ratio of entry cost to GDP per worker. GDP in the model is defined as the value added from all sectors, excluding the technology adoption cost because it is treated as business expense and is excluded from the National Income and Product Account (NIPA).
Although the parameters and targets are interrelated, some are more closely related than others, [alpha] and [A.bar] are primarily related to the shares of agricultural employment and agricultural consumption expenditure. As discussed in Section III, [A.bar] affects the price that the incumbent charges and thus affects the relative price between agricultural and manufacturing goods. The relative price and preference parameter a affect the shares of agricultural employment and agricultural consumption expenditure, as in standard models. The agricultural employment share is calibrated to 3% based on the study by the Bureau of Labor Statistics. (8) The agricultural consumption expenditure share is calibrated to 11%, which is the food expenditure share in Table Vb of the Penn World Table 96 (PWT96).
Using panel data from a sample of rural households in India, both Rosenzweig and Wolpin (1993) and Atkeson and Ogaki (1996) estimate a subsistence consumption need of approximately 33% of the average income of Indian villagers. GDP per worker in India is 7.6% of the value in the United States. This figure translates to a subsistence need of 2.5% of the average income or 23% of the total agricultural expenditure in the United States, which is used to identify [[c.sub.a].bar]. For robustness, I also perform sensitivity analyses using alternative values for [[c.sub.a].bar].
As noted above, the technology adoption cost in the model can be linked to the intangible investment in the data. Hence, [phi] and [gamma], the two parameters that affect the technology adoption cost, can be identified by the ratio of intangible investment to GDP and the technology adopted in the U.S. economy. The latter is assumed to be the frontier technology. I borrow the ratio of intangible investment to GDP from Conrado et al. (2012), which construct this ratio for a set of advanced economies and find a ratio of 11 % for the United States.
k is calibrated to the ratio of entry cost to GDP per worker. The World Development Indicators (WDIs) from the World Bank provide entry cost as a fraction of GDP per capita for a large set of countries. (9) Because I abstract from the complexity of the household sector and assume that the representative household supplies all of her labor endowment, the appropriate counterpart of output in the model is GDP per worker in the data. Hence, I use data on GDP per capita and GDP per worker from the Penn World Table 7.1 (PWT7.1) to convert the ratio of entry cost to GDP per capita to the ratio of entry cost to GDP per worker. The most recent value of the ratio of entry cost to GDP per worker for the United States is 0.72%.
The top panel of Table 1 summarizes the targets and the corresponding statistics generated by the model. The calibrated model can match the targets well. The top panel of Table 2 reports the calibrated parameter values.
B. The Quantitative Effect of Entry Cost
This section explores the quantitative effect of entry cost on TFP. For this purpose, I create a poor economy. The poor economy differs from the calibrated U.S. economy in three dimensions: the agricultural TFP [A.sub.a], the productivity of the competitive fringe [A.bar], and the entry cost k. These parameters are chosen to match averages of the agricultural employment share, the agricultural consumption expenditure share, and the ratio of entry cost to GDP per worker for the poorest 30% of countries in the world, as ranked by GDP per worker in PWT7.1 for 2008. The effect of the entry cost is assessed by reducing the entry cost from the poor economy to the U.S. level. The data for the agricultural employment share come from the Food and Agriculture Organization of the United Nations Statistical Yearbook Table A.3. The source of the data for the entry cost and the agricultural consumption expenditure share is the same as that for the United States.
The bottom panel of Table 1 summarizes the targets and the corresponding statistics generated by the model for the poor economy. The poor economy has a higher ratio of entry cost to GDP per worker and larger shares of agricultural employment and agricultural consumption expenditure. The bottom panel of Table 2 reports the calibrated parameter values for the poor economy. Using data from World Bank firmlevel surveys, La Porta and Shleifer (2008) study the productivity differences between formal and informal firms for a set of poor countries. They find that the average log difference in real output per worker between firms in the Informal Survey and the Enterprise Survey ranges from 1.95 in Cambodia to 6.16 in Niger, with a cross-country average of 4.29. The average log difference between firms in the Micro Survey and the Enterprise Survey ranges from -0.11 in Guinea to 5.02 in Namibia, with a cross-country average of 2.09. If the competitive fringe is interpreted as an informal firm, the corresponding statistic in the calibrated poor economy can be calculated as the log difference between [A.bar] and the technology adopted by the incumbent. The value is 3.12, which is in the range suggested by La Porta and Shleifer (2008).
This article exclusively focuses on the formal sector because the competitive fringe does not operate in equilibrium. There are several justifications for this. To begin with, I am using GDP per worker numbers from the Penn World Table that do not include value added produced by informal firms, so it is appropriate that the value added produced by informal firms is not included in the model GDP per worker. In addition, Bertrand and Kramarz (2002), Bruhn (2011), and Mckenzie and Sakho (2007) find that reductions in entry barriers do not lead to the formalization of informal firms. Given the fact that informal firms normally do not have access to better technologies, these papers suggest that the productivity of informal firms remains low even after entry barriers are reduced.
Table 3 reports the model outcomes for the poor economy. For comparison, the values are reported as the relative values to the United States. In the poor economy, the technology adopted (A), which can also be interpreted as the nonagricultural labor productivity in the model, is 51.21% of the U.S. level, and the agricultural labor productivity (Ag Productivity) is 1.74% of the U.S. level. This finding is consistent with the findings of Caselli (2005), Herrendorf and Valentinyi (2012), and Restuccia, Yang, and Zhu (2008), who find that the productivity gap between the United States and poor countries is larger in agriculture than in other sectors. In particular, using the data from Caselli (2005), I compute the average labor productivity for the countries included in the calibration for the poor economy and find a value of 31.22% for the nonagricultural labor productivity and a value of 1.49% for the agricultural labor productivity relative to the United States. Hence, the calibrated poor economy accounts for 61% of the observed difference in the nonagricultural labor productivity and 86% of the observed difference in the agricultural labor productivity between poor countries and the United States.
The model generates a higher agricultural relative price ([p.sub.a]) in the poor economy. Lagakos and Waugh (2013) compute the relative price between agricultural and nonagricultural goods for a large set of countries and find a negative relationship between agricultural relative price and GDP per worker. Using the data from Lagakos and Waugh (2013), I compute the average agricultural relative price for the countries included in the calibration for the poor economy and find a value of 2.35 relative to the United States. Although the relative price is not targeted, the model value of 1.94 is close to the value in the data.
Estimates of the ratio of intangible investment to GDP are not available for poor countries. Corrado et al. (2012) estimate this ratio for 28 European countries and the United States. The estimate tends to be smaller for poorer European countries. For instance, the average estimate for Mediterranean countries between 1995 and 2009 is 4.21%, whereas the average for richer European countries is over 7%. The estimates for Eastern European countries are even lower. Bulgaria, Latvia, and Lithuania have estimates of approximately 3%, and Romania has an estimate of 1.9%, the lowest among studied countries. The model generates a value of 1.55% for the adoption-cost-to-GDP ratio (Adoption/GDP) in the poor economy, which is 14% of the level in the United States. This result is qualitatively consistent with that of Corrado et al. (2012).
Table 4 reports the results from the counterfactual experiment of reducing the entry cost to the U.S. level in the poor economy. The values are reported as the values relative to the calibrated poor economy. When the entry cost is reduced, the technology adopted, or, equivalently, the nonagricultural labor productivity increases by 27%. The increase in the technology adopted leads to a sizeable increase in the adoption-cost-to-GDP ratio. GDP, or, equivalently, the aggregate labor productivity increases by 12%, as measured by U.S. prices. All of the other statistics hardly change. (10)
My findings are closely related to those of Herrendorf and Teixeira (2011). They find that higher entry barriers in poor countries account for a difference in TFP of more than a factor two, which is more than half of the differences between the United States and poor countries. In contrast, I find that higher entry costs account for a difference in TFP that is an order of magnitude smaller. The main reason for the difference is that I examine one measurable entry barrier, namely the regulatory cost of setting up a new business, and thus my analysis provides a lower bound on the effects of all entry barriers on TFP. In contrast, Herrendorf and Teixeira calibrate the entry barriers, so their concept of entry barriers is much wider than mine, and I suspect that their calibration might also pick up things that we do not normally associate to entry barriers. Note also that Herrendorf and Teixeira find that the effect of entry barriers on GDP per worker is strongly amplified through capital accumulation. In contrast, I do not have such an amplification mechanism in my model because I abstract from capital accumulation.
C. Robustness
This section explores the robustness of the quantitative results. For this purpose, I recalibrate the model for each of the following cases and report the effect of reducing the entry cost in Table 5.
Elasticity' of Substitution between Agricultural and Nonagricultural Goods. Ngai and Pissarides (2008) argue that the upper bound for [sigma] is -2.33. Table 5 reports that the quantitative result for [sigma] = -2.33 is similar to that in the Benchmark case.
Larger Adoption Cost for Poor Countries. It may be more costly to adopt better technologies in poor countries because of a lack of human capital or red-tape regulations. The second column of Table 5 shows that the quantitative effect is similar to that in the benchmark case, when [phi] in the poor economy is calibrated to be twice as large as that in the United States. (11)
Elasticity of Substitution between Intermediates, e controls not only the elasticity of substitution between intermediates but also the price elasticity of demand for intermediates. There are no available estimates for the elasticity of substitution between intermediates. In the model, -[epsilon] is the price elasticity of demand for intermediates. The price elasticity of goods varies substantially in the data. Gasoline is normally viewed as a good with very low price elasticity and may thus provide a lower bound for [epsilon]. Brons et al. (2008) find that the mean long-term price elasticity for gasoline is -0.84. (12) Column 3 reports that for [epsilon] = 0.84, the increase in the technology adopted is 21%.
Subsistence Level. 1 perform two sensitivity tests with alternative values for [[c.sub.a].bar]. In the first case, I set [[c.sub.a].bar] to zero. In the second case, I set [[c.sub.a].bar] to 7.6% of the income per worker in the United States, which is equivalent to the average income per worker in India. The two cases set lower and upper bounds for the subsistence level. As the entry cost is reduced, the technology adopted in both cases is close to that in the benchmark case.
Decreasing Returns to Scale. Because I abstract from capital accumulation, I can alternatively assume decreasing returns to scale for agricultural production. The Economic Research Service in the U.S. Department of Agriculture reports a share of 0.15 for machinery in agricultural production. Excluding the share of machinery gives a value of 0.46 for [[theta].sub.z]. The effect of entry cost in an economy with [[theta].sub.z] = 0.46 is almost identical to that in the benchmark case.
Different Agricultural Input Shares for Poor Countries. Fuglie and Rada (2013) estimate a constant-returns-to-scale Cobb-Douglas production function for Sub-Saharan African countries, which represent a major group of poor countries for the calibration. The input shares estimated are [[theta].sub.h] = 0.25, [[theta].sub.l] = 0.32, and [[theta].sub.z] = 0.43. (13) Recalibrating the model using these shares for the poor economy generates an increase of 21% for the technology adopted.
V. CONCLUSION
This article has developed a model that generates a negative relationship between entry barriers and technology adoption. The model is used to study the quantitative effect of the regulatory cost of setting up a new business on TFR I find that reducing the entry cost from the average level in the poorest 30% of countries in the world to the U.S. level can lead to an increase of 12% in the aggregate TFP and an increase of 27% in the nonagricultural TFP.
Although the model environment is a closed economy, the analysis in this article may also have implications for the effect of restricting foreign direct investment (FDI). If the potential entrant is a foreign firm, the entry cost may be even larger because there are normally more regulatory restrictions on FDI, and therefore, the negative effect on income and TFP may be even larger. In this case, the foreign entrant may also have access to a higher technology frontier than the domestic incumbent. Studying this extension is left as an interesting project for future research.
Appendix S1. A model with quality improvement ABBREVIATIONS FDI: Foreign Direct Investment GDP: Gross Domestic Product NIPA: National Income and Product Accounts PWT96: Penn World Table 96 TFP: Total Factor Productivity WDI: World Development Indicators
APPENDIX A
EXISTENCE AND UNIQUENESS OF EQUILIBRIUM
A. 1 Equilibrium Conditions for k [greater than or equal to] [??]
When k = [infinity], the no-entry constraint does not bind. Substituting p = w/[A.bar] into the incumbent's first-order condition for [A.sup.i] yields:
(A1) B[[A.bar].sup.[epsilon]] [w.sup.1-[epsilon]] [([A.sup.i]).sup.2] - [phi] ([partial derivative]f ([A.sup.i]) /[partial derivative][A.sup.i]) = 0
In the equilibrium, x =[A.sup.i]h and p= 1. This implies B=x = [A.sup.i]h and w = [A.bar]. Hence (A. 1) can be rewritten as:
(A2) h - ([A.sup.i]/[bar.A]) [phi] ([partial derivative]f ([A.sup.i]) /[partial derivative][A.sup.i]) = 0
Combining (4) and (6) gives:
(A.3) [z.sub.a] = w[h.sub.a] [[theta].sub.z]/[[theta].sub.h]
Solving [p.sub.a] from (6) and combining with (A3), (3), and market clear conditions give:
(A4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
The equilibrium is characterized by (A2) and (A4), which are two functions with two unknowns ([A.sup.i],h). Denote the left of (A2) by F([A.sup.i],h) and the left of (A4) by G([A.sup.i],h). It is easy to show that [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] Hence,
(A.5)[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
This implies that the solution to the system of Equations (A2) and (A4) is unique. Hence, the equilibrium exists and is unique for k = [infinity]. For future reference, denote the equilibrium solution for k = [infinity] by ([[??].sup.i],[??]). [[??].sup.i] is then the incumbent's optimal choice as long as the no-entry constraint does not bind in the equilibrium. Define [??] as:
(A6) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
When [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] This proves the existence and uniqueness of the equilibrium for k [greater than or equal to] [??].
A.2 Equilibrium Conditions for 0 [less than or equal to] k < [??]
From Section VI.A, [A.sup.i]=[[??].sup.i] cannot be supported as an equilibrium for k < [??]. This implies that for k < [??], the constraint binds in the equilibrium. Hence, the equilibrium is determined by (A4) and [[pi].sup.e] ([[??].sup.e] ([A.sup.i]), [A.sup.i]) - k = 0. Using the equilibrium conditions, [[pi].sup.e] ([[??].sup.e]([A.sup.i]),[A.sup.i]) - k = 0 can be rewritten as:
(A7) [[A.bar].sup.1-[epsilon]] [([A.sup.i]).sup.[epsilon]] (1 - ([A.sup.i]/[A.sup.e]))h-[phi]f([A.sup.e])-k = 0,
where [A.sup.e] is determined by the potential entrant's first-order condition:
(A8) [[A.bar].sup.1-[epsilon]][([A.sup.i]).sup.1-[epsilon]] h/[([A.sup.e]).sup.2] -[phi]([partial derivative]f([A.sup.e])/[partial derivative][A.sup.e]) = 0.
Denote the left of (A7) by M([A.sup.i], h). CONDITION 1.
(A.9)[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
If condition 1 holds, the solution to the system of Equations (A4), (A7), and (A8) is unique.
APPENDIX B
Proof of Proposition I
The first part of this proposition follows directly from Section A.1. I now prove the second part. When 0 [less than or equal to] k < [??], the equilibrium can be represented by:
(A10) M([A.sup.i],h) = 0,
(All) G([A.sup.i],h)= 0,
where the two functions M and G are defined as above. Straightforward calculations allow one to derive and sign the partial derivatives as follows: [partial derivative]M/[partial derivative]h > 0, [partial derivative]G/[partial derivative]h > 0, [partial derivative]M/[partial derivative]k < 0, and [partial derivative]G/[partial derivative]k = 0. If Condition 1 holds, the implicit function theorem implies [partial derivative][A.sup.i]/[partial derivative]k < 0. Continuity then implies [A.sup.i] > [[bar.A].sup.i].
LEI FANG, I would like to thank Richard Rogerson, Berthold Herrendorf, Edward C. Prescott, and Yan Bai for their comments and suggestions. I thank the seminar participants at Arizona State University. Federal Reserve Bank of Atlanta, Southern Methodist University, Southern Economic Association Annual Meetings 2008, and Econometric Society Winter Meetings 2009. I also thank the editor, Martin Gervais, and one anonymous referee for their patience and suggestions. The views in this article represent those of the author and are not those of either the Federal Reserve Bank of Atlanta or the Federal Reserve System.
Fang: Economist, Research Department, Federal Reserve Bank of Atlanta, Atlanta. GA 30309. Phone 404-4988057. Fax 404-498-8956, E-mail lei.fang@atl.frb.org
doi: 10.1111/ecin.12391
REFERENCES
Aghion, P., and P. Howitt. "Growth with Quality-Improving Innovations: An Integrated Framework," in Handbook of Economic Growth, Vol. 1 A, edited by P. Aghion and S. N. Durlauf. Amsterdam: North-Holland, 2005, 67-110.
Aghion, P., R. Blundell, R. Griffith, P. Howitt, and S. Prantl. "The Effects of Entry on Incumbent Innovation and Productivity." Review of Economics and Statistics, 91(1), 2009, 20-32.
Atkeson, A., and M. Ogaki. "Wealth-Varying Intertemporal Elasticities of Substitution: Evidence from Panel and Aggregate Data." Journal of Monetary Economics, 38(3), 1996,507-34.
Barseghyan, L. "Entry Costs and Cross-Country Differences in Productivity and Output." Journal of Economic Growth, 13, 2008, 145-67.
Barseghyan, L., and R. DiCecio. "Entry Costs. Industry Structure, and Cross-Country Income and TFP Differences." Journal of Economic Theory, 146(5), 2011, 1828-51.
Bertrand, M., and F. Kramarz. "Does Entry Regulation Hinder Job Creation? Evidence from the French Retail Industry." Quarterly Journal of Economics, 117. 2002, 1369-413.
Bloom, N., M Draca, and J. Van Reenen. "Trade Induced Technical Change? The Impact of Chinese Imports on Innovation, IT and Productivity." Review of Economic Studies, 83(1), 2015,87-117.
Brons, M., P. Nijkamp, E. Pels, and P. Rietveld. "A Meta-Analysis of the Price Elasticity of Gasoline Demand. A SUR Approach." Energy Economics, 30, 2008, 2105-22.
Bruhn, M. "License to Sell: The Effect of Business Registration Reform on Entrepreneurial Activity in Mexico." Review of Economics and Statistics, 93(1), 2011, 382-86.
Caselli, F. "Accounting for Cross-Country Income Differences," in Handbook of Economic Growth. 1st ed., Vol. 1, Chapter 9, edited by P. Aghion and S. N. Durlauf. Amsterdam: North-Holland. 2005, 679-741.
Corrado, C., J. Haskel. C. Jona-Lasinio, and M. Iommi. "Intangible Capital and Growth in Advanced Economies: Measurement Methods and Comparative Results." Working Paper, 2012. Accessed August 2, 2016. www.INTAN-Invest.net.
De Soto, H. The Other Path. New York: Harper and Row, 1989.
Djankov. S., R. La Porta. F. L. de Silanes, and A. Shleifer. "The Regulation of Entry." Quarterly Journal of Economics, 117,2002, 1-37.
Economic Research Service (ERS). "Agricultural Productivity in the United States, Agricultural Research and Productivity Briefing Room." Washington. DC: Economic Research Service, 2009. Accessed August 2, 2016. http://www.ers.usda.gov/Data/AgProductivity/.
Foster, L., J. Haltiwanger, and C. J. Krizan. "Aggregate Productivity Growth: Lessons from Microeconomic Evidence," in New Developments in Productivity Analysis, edited by C. R. Hulten, E. R. Dean, and M. J. Harper. Chicago: University of Chicago Press, 2001. 303-72.
Fuglie, Keith O. and Nicholas E. Rada, "Resources, Policies, and Agricultural Productivity in Sub-Saharan Africa." United States Department of Agriculture, Economic Research Report 145, 2013.
Hall, R., and C. I. Jones. "Why Do Some Countries Produce So Much More Output Per Worker Than Others?" Quarterly Journal of Economics, 114, 1999, 83-116.
Herrendorf. B., and A. Teixeira. "Barriers to Entry and Development." International Economic Review, 52(2), 2011, 573-602.
Herrendorf, B., and A. Valentinyi. "Which Sectors Make the Poor Countries So Unproductive?" Journal of the European Economic Association, 10(2), 2012, 323-41.
Klenow, P., and A. Rodriguez-Clare. "The Neoclassical Revival in Growth Economics: Has It Gone Too Far?" in NBER Macroeconomics Annual 1997. edited by B. Bernanke and J. Rotemberg. Cambridge, MA: MIT Press, 1997,73-102.
La Porta, R., and A. Shleifer. "The Unofficial Economy and Economic Development." Brookings Papers on Economic Activity. Fall, 2008.
Lagakos. D., and M. E. Waugh. "Selection. Agriculture, and Cross-Country Productivity Differences." American Economic Review, 103(2), 2013, 948-80.
Lewis, W. W. The Power of Productivity: Wealth, Poverty, and the Threat to Global Stability. Chicago: University of Chicago Press, 2004.
McKenzie. D., and Y. S. Sakho. "Does It Pay Firms to Register for Taxes? The Impact of Formality on Firm Profitability." Journal of Development Economics, 91(1), 2007, 15-24.
Moscoso Boedo, H. J., and T. Mukoyama. "Evaluating the Effects of Entry Regulations and Firing Costs on International Income Differences." Journal of Economic Growth, 17, 2012, 143-70.
Ngai. R., and C. A. Pissarides. "Trends in Hours and Economic Growth." Review of Economic Dynamics, 11. 2008, 239-56.
Nicoletti. G., and S. Scarpetta. "Regulation, Productivity and Growth: OECD Evidence." Economic Policy, 18(36), 2003, 9-72.
--. "Regulation and Economic Performance: Product Market Reforms and Productivity in the OECD," in Institutions, Development and Growth, edited by T. Eicher and C. Garcia-Penalosa. Cambridge, MA: MIT Press, 2006.
Parente, S. L., and E. C. Prescott. "Barriers to Technology Adoption and Development." Journal of Political Economy, 102(2). 1994, 298-321.
--. "Monopoly Rights: A Barrier to Riches." American Economic Review, 89, 1999, 1216-33.
Pavcnik, N. "Trade Liberalization, Exit, and Productivity Improvement: Evidence from Chilean Plants." Review of Economic Studies, 69(1), 2002, 245-76.
Prescott, E. C. "Needed: A Theory of Total Factor Productivity." International Economic Review, 39(3). 1998, 525-51.
Restuccia, D., D. T. Yang, and X. Zhu. "Agriculture and Aggregate Productivity: A Quantitative Cross-Country Analysis." Journal of Monetary Economics, 55(2), 2008, 234-50.
Rosenzweig. M. R., and K. I. Wolpin. "Credit Market Constraints, Consumption Smoothing, and the Accumulation of Durable Production Assets in Low-Income Countries: Investment in Bullocks in India." Journal of Political Economy, 101(2), 1993, 223-44.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article:
(1.) See, for example. Hall and Jones (1999), Klenow and Rodriguez-Clare (1997), and Prescott (1998).
(2.) See De Soto (1989), Djankov et al. (2002), Lewis (2004), and Nicoletti and Scarpetta (2003, 2006).
(3.) In reality, the adoption cost might be different for the incumbent and the potential entrant. For example, if adopting the new technology requires considerable reorganization, the potential entrant will have a lower adoption cost. In contrast, if experience is crucial for adopting the new technology, the incumbent will have a lower adoption cost. A simple way to model differences in the adoption cost is to have different tp for the incumbent and the potential entrant. This is not crucial for my results, as the key equilibrium properties proven below in Section III.E would continue to hold.
(4.) This is as in models of monopolistic competition.
(5.) Note that I assume that if multiple players post the same price in the Bertrand game, the incumbent will capture the entire market.
(6.) [[theta].sub.z] includes the shares of animal stock, fertilizer, and machinery.
(7.) See Corrado et al. (2012) for more discussion of the intangible investment.
(8.) Please refer to the following webpage: www.bls.gov/ emp/ep_table_201.htm.
(9.) The data can be found at the following webpage: http://databank.worldbank.org/data/views/variableSelection/selectvariables.aspx?source = worlddevelo pment-indicators.
(10.) When the entry cost is reduced, only the nonagricultural productivity increases; the agricultural productivity stays constant. A simple modification of the model provided in Appendix S1 shows that entry costs may also affect agricultural productivity. In this case, the reduction in the entry cost leads to a 38% increase in the technology adopted and an 88% increase in the aggregate TFP.
(11.) If the reduction in the entry cost is accompanied by a reduction in red-tape regulations, the quantitative effect will be larger than reported. In particular, if cp and k are reduced to the U.S. level simultaneously, the technology adopted will increase by 41%.
(12.) The long-term price elasticity is preferred because the analysis performed in this article is essentially a steady-state analysis.
(13.) The shares are reported in Table A.2 in Fuglie and Rada (2013). The manufacturing good share is the sum of the animal-stock share, fertilizer share, and machinery share. TABLE 1 Calibration U.S. Statistics Target Model Agricultural employment share 3% 3% Agricultural consumption 11% 11% expenditure share Entry cost to GDP per worker 0.72% 0.72% ratio A. Adopted 1 1 Subsistence to GDP per worker 2.5% 2.5% ratio Intangible investment to GDP 11% 11% ratio Poor Country Statistics Target Model Agricultural employment share 62% 62.17% Agricultural consumption 32% 31.93% expenditure share Entry cost to GDP per worker 36.16% 35.98% ratio Note: The table reports the calibration targets and the corresponding statistics generated by the model. GDP, gross domestic product. TABLE 2 Parameter Values U.S. Parameter [alpha] [[c.sub.b].bar] [PHI] Value. U.S. 2.46e-l1 0.02 0.65 U.S. Parameter [gamma] [A.bar] k Aa Value. U.S. 6.47 0.67 6.50e-3 1 Poor Country Parameter [A.bar] k K Value, Poor 0.02 0.08 0.25 TABLE 3 Calibrated Poor Economy Variable A 51.21% Ag Productivity 1.74% [p.sub.a] 1.94 Adoption/GDP 14% Note: The table reports the model results for the calibrated poor economy. The values are reported as the relative values to the United States. GDP. gross domestic product. TABLE 4 Effects of Entry Cost Variable A 1.27 Ag productivity 0.99 [p.sub.a] 1.01 Adoption/GDP 1.61 GDP 1.12 [h.sub.a] 1.06 Ag Exp share 0.95 Note: The table reports the results of reducing the entry cost from the calibrated poor economy to the level in the United States. The values are reported as the relative values to the calibrated poor economy. GDP, gross domestic product. TABLE 5 Sensitivity Analysis [[c.sub.a].bar] = 7.6% Variable [sigma] [[PHI].sub.p] = [epsilon] [[c.sub.a]. = - 2.33 2[[PHI].sub.us] = 0.84 bar] = 0 A 1.27 1.26 1.21 1.25 Ag productivity 0.99 0.99 0.99 0.98 [p.sub.a] 1.01 1.01 1.01 1.02 Adoption/GDP 1.62 1.54 1.22 1.58 GDP 1.13 1.12 1.10 1.07 [h.sub.a] 1.06 1.06 1.05 1.08 Ag Exp share 0.95 0.95 0.96 1.01 [[c.sub.a].bar] = 7.6% Variable [GDP.sub.us] [[theta]. Fuglie sub.2] = 0.46 and Rada A 1.29 1.25 1.21 Ag productivity 1.00 0.98 0.98 [p.sub.a] 1.00 1.02 1.02 Adoption/GDP 1.67 1.52 1.27 GDP 1.19 1.10 1.09 [h.sub.a] 1.03 1.07 1.06 Ag Exp share 0.86 0.97 0.97 Note: The table reports the results of the sensitivity tests. The values reported are the effects of reducing the entry cost from the calibrated poor economy to the level in the United States. The values are reported as the relative values to the calibrated poor economy. GDP, gross domestic product.
