Measuring the magnitude of misallocation goes halfway to understanding its role on aggregate TFP. Shedding light on the sources of misallocation, however, requires a systematic examination of the relative importance of different potential policy constraints. The results presented so far indicate that misallocation reduces aggregate TFP substantially. Correlation
results also show that misallocation is higher in low-income countries, suggesting that it contributes towards lowering per capita income. Moreover, there are large differences in the magnitude of misallocation across countries, the interquartile range of the TFP gap across countries being close to 2.
The large dispersion of misallocation possibly reflects differences in the policy environment that determines allocative efficiency. The goal of this section is to identify policy constraints that have been associated with misallocation in previous studies, and to conduct regression analysis in order to test their effect on misallocation. OLS regressions of economic outcomes on
institutional and policy variables are often subject to endogeneity problems. One major source of endogeneity stems from the fact that institutional design could be responsive to economic
outcomes, thus leading to serious reverse causation problems (Hall and Jones, 1999).
Fortunately, this issue is not of major concern for our analysis since our outcome variable,
misallocation, is measured using micro data, and thus is not directly observable to policy makers.
Measuring policy constraints
The list of policy and institutional factors that are likely to affect the efficiency of resource allocation across firms is long (Arnold et al., 2008). In general, policy constraints can affect allocative efficiency in two different ways. Some policy constraints reduce competitive pressure by lowering the entry of new firms, thus reducing the possibility of reallocation of inputs from inefficient incumbents to more productive new-entrants. Others induce misallocation by protecting inefficient existing plants (such as public firms) so that inputs are not reallocated towards more productive incumbents (Dollar and Wei, 2007).
Policies that affect allocative efficiency are also likely to affect technical efficiency. For example, increased competitive pressure not only facilitates efficient allocation of inputs across producers, but also pushes producers to use resources more efficiently and/or to adopt more efficient technologies. The effect of most policy variables on aggregate productivity is hence twofold; directly they determine the level of technical efficiency of producers, and indirectly they influence the allocation of inputs across producers. This sub-section reviews four groups of policy constraints that can affect allocative efficiency, and discusses their measurement.
i. Openness to external competition. Exposure to external competition because of openness to trade and foreign direct investment can enhance allocative efficiency. As highlighted by the seminal work of Melitz (2003), trade openness intensifies competition and increases aggregate productivity by allowing more productive firms to expand and the least efficient firms to exit.
There is an extensive empirical literature supporting the reallocative effect of trade. Among others, Bernard et al. (2006) show that low-productivity plants are more likely to die in
industries with falling trade costs, thus contributing to higher productivity growth. Eslava et al.
(2004) report that trade openness contributed to higher productivity in Colombia by facilitating reallocation of inputs from low- towards high-productivity businesses. Increased FDI has practically the same effect on local firms which face more competition following the entrance of multinationals (Crespo and Fontoura, 2007).
Openness to trade and FDI can also have a direct effect on the technical efficiency of firms.
Facing more intense competition, domestic firms are forced to use their existing resources more efficiently. In addition, FDI and trade openness expose domestic firms to new technologies, allowing them to improve their productivity through spillovers and imitation (Crespo and Fontoura, 2007).
In this paper, trade openness is measured using the level of trade intensity, calculated as imports plus exports expressed as a percentage of GDP. Although this measure is likely to understate trade in relatively large countries which rely less on inter-country trade and more on within-country trade, it is nonetheless the best available measure of openness to trade. Openness to foreign investment is measured as net FDI inflows as a percentage of GDP, which includes initial equity capital to acquire at least 10% stake in domestic enterprises as well as
reinvestments of earnings. Both measures are taken from the World Development Indicators database, and are averaged over the years 2001-2007 to reduce year-to-year fluctuations. As shown in the descriptive statistics given by Table 2, the average level of trade openness in our sample stands at 82.5% of GDP and the average net FDI inflow is 4.3% of GDP.
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ii. Level of domestic competition: entry and exit barriers. Regulatory constraints can exacerbate misallocation by hindering competition among existing firms or by discouraging the entry of new firms. Market regulations, often imposed to address public interest issues such as externalities and monopoly, can end up hampering competition and reducing allocative
efficiency (Djankov et al., 2002). Examples of regulatory barriers that reduce the entry of new firms include high registration costs, licensing restrictions, and government ownership of firms.
Several studies have found a negative relationship between entry barriers and aggregate TFP.
Barseghyan (2008) finds that entry costs have a negative effect on TFP and GDP per capita, and Barseghyan and DiCecio (2009) show how high entry costs can induce misallocation. Fang (2010) shows that financial frictions amplify the negative effect of entry costs on TFP. Scarpetta et al. (2002) find that entry regulations influence productivity by affecting entry. Klapper et al.
(2006) report that costly regulation in European countries has reduced the rate of new firm entry
and forced new entrants to be larger. Arnold et al. (2011) find that product market regulations that curb competitive pressures tend to reduce the productivity performance of firms. Fisman and Allende (2010) find that the level of entry regulations affect factor allocation within industries by determining whether growth opportunities are utilized by new entrants or existing firms.
In this paper, I use two indicators related to regulatory constraints to measure entry and exit barriers. The first indicator measures the level of entry barriers with the cost of starting a new business, expressed as a percentage of GDP. Originally developed by Djankov et al. (2002), this indicator includes all official fees, and fees for legal or professional services that are required for starting up a new business. This measure of entry barriers has been used in previous studies (see Barseghyan, 2008). The second indicator measures exit barriers using the level efficiency of insolvency proceedings. This indicator was initially developed by Djankov et al. (2008) and measures the percentage of financial assets that can be recovered by creditors from their total claims upon the closure of a business due to bankruptcy. The indicator is inversely proportional to the cost and length of insolvency proceedings, and has been found to be a robust measure of the efficiency of bankruptcy laws (Djankov et al., 2008).5 By encouraging investors to use legal proceedings, efficient bankruptcy laws allow bankrupt firms to exit the market or reorganize at lower cost, hence reducing exit barriers and improving allocative efficiency.
Both measures of entry and exit barriers are taken from the World Bank’s Doing Business Indicators database and are averaged over the years 2001-2007 to reduce year-to-year
fluctuations. The average level of entry cost in our sample is around 85% of GDP, which is almost twice as large as the average value Djankov et al. (2002) report for their sample of 85 countries. Democratic Republic of Congo has the highest level of entry cost in our sample (1201%), whereas Lithuania has the lowest level of entry cost (3.36%). With respect to exit
barriers, the average rate of recovery during bankruptcy proceedings is 25%. This is almost half as large as the average recovery rate of 52% Djankov et al. (2008) find in their data, suggesting that countries in our sample have relatively less efficient insolvency proceedings. Since this measure has a significant correlation of 0.47 with GDP per capita, the low efficiency of
insolvency proceedings in our data reflects the fact that our sample is dominated by low-income countries. Mexico has the most efficient insolvency proceeding in our sample, with 64%
recovery rate. Rwanda, Laos, Burundi and Cape Verde have the least efficient debt enforcement with zero recovery rate.
iii. Financial frictions. Financial frictions are among the most widely studied determinants of factor allocation (Arnold et al., 2008). The level of financial development in a market can affect allocative efficiency in two ways (Buera and Shin, 2010). Firstly, efficient financial markets lower the cost of financial capital by pooling risks and providing efficient intermediation. Lower cost of capital can boost the entry of new firms, thus intensifying competition and forcing inefficient incumbents to exit. Secondly, well-developed financial markets are more capable at identifying profitable firms and reallocating capital towards them. The allocation of capital based on market forces will drive inefficient firms out of the market, including publicly-owned firms that would thrive due to preferential credit access from public banks. The resulting reallocation of capital towards more efficient firms will improve allocative efficiency and boost aggregate TFP. Several influential studies find that improvements in financial access enhance the entry of new firms which are potentially more productive (Buera et al., 2011; Rajan and Zingales, 1998;
Aghion et al., 2007; Greenwood et al., 2010).
To measure financial frictions, I use an indicator of financial development. Specifically, I use the size of the financial sector measured as credit extended for the private sector as a
percentage of GDP. Since financially developed markets with lower financial frictions in general have a large financial intermediary sector, this measure is used in the literature as an indicator of financial development (Levine, 2005). The average level of private sector credit as a percentage of GDP in our data, averaged over 2001-2007, is 33%. Congo Democratic Republic has the lowest level of financial development (2%) whereas South Africa has the highest level of financial sector development (138%).
iv. Labor and capital market distortions. In addition to financial frictions, regulatory constraints related to labor and capital markets can also distort factor allocation. Hopenhayn and Rogerson (1993) show that labor market frictions such as firing costs hinder the creation of new jobs.
Lagos (2006) shows that labor market distortions can lower TFP. Eslava et al. (2004) find that labor market reform contributed to the reallocation of inputs towards more productive firms in Colombia. Bassanini et al. (2009) report that firing regulations have a negative effect on productivity growth. Likewise, a number of studies have looked into possible distortions in capital markets due to corporate taxes. High corporate tax rates can distort investment in the economy and affect the re-investment decisions of multinationals (Mooij and Ederveen, 2008).
Economic theory and empirical evidence also reveal that, when capital is perfectly mobile, the incidence of the corporate tax is almost entirely borne by labor (Nicodème, 2008).
In this paper, I consider two indicators related to labor and capital market regulations. I use an indicator of the cost of firing to measure regulatory constraints that induce labor markets frictions. First developed by Botero et al. (2004), this measure includes various costs of terminating redundant workers such as advance notice requirements, severance payments, and other penalties. The cost of firing is expressed in terms of weekly wages so as to make it comparable across countries. Secondly, I use the profit tax rate as an indicator of capital market
distortions. Both factors, firing costs and the profit rate, affect the cost of labor relative to capital and hence can induce misallocation by forcing firms to make suboptimal input mix decisions.
Data for both measures is taken from the World Development Indicators database. The cost of firing is averaged over the years 2001-2007, but the profit rate for the year 2010 is used since data is not available for other years. Firing workers costs an amount equivalent to 56 weeks of wage on average, which varies from 4 weeks in Oman and Jordan to 192 weeks in Sri Lanka.
The profit tax rate averages around 17%, and ranges from zero in Liberia, Lithuania and Bolivia to almost 60% in Congo Democratic Republic.
Regressions results
This section presents regression results that link the policy constraints discussed above with our measures of misallocation. Table 3 provides six regression results in which the dependent variables are the three measures of TFP gap that are discussed in the measurement section. The TFP gap indicators used in the first three regressions measure misallocation at country level and are calculated based on Equation 15. In regression 1, the dependent variable is the TFP gap in which both output and capital distortions are removed. Regression 2 is based on the measure of TFP gap in which only output distortions are removed, and regression 3 is based on the measure of TFP gap in which only capital distortions are removed. These partial measures of
misallocation capture the separate effect of output and capital distortions on aggregate TFP.
The dependent variables in the last three regressions are industry-level measures of TFP gap that are calculated using Equation 14. Although all policy constraints are measured at country-level, their effect on misallocation could vary across industries. It is thus important to test if the effect of policy constraints is the same when misallocation is measured at industry level and
when industry effects are accounted for. As in the first three regressions, the dependent variables in the last three regressions reported in Table 3 are measures of TFP gap in which output and capital distortions are removed simultaneously and turn by turn. Since the use of country-level independent variables can bias the standard errors downward, the error terms in these regressions are corrected for clustering by country groups. The TFP gap ratios from equations 14 & 15 are converted to percentage terms as indicated earlier, so that they measure the unrealized TFP due to misallocation as a percentage of the actual TFP. Therefore, the coefficients in all of the regressions in Table 3 can be interpreted as the amount of percentage points by which TFP changes due to a unit change in the explanatory variables.
From regressions 1 and 4, which are based on the TFP gap due to capital and output distortions, trade openness appears with the expected negative sign. The coefficients of trade openness in the two regressions are very close, indicating that the effect of trade is not industry-specific. The coefficient of trade openness in regression 1 indicates that a rise in total trade in GDP by 1 percentage point lowers the gap between actual and potential TFP by 0.6%. In other words, raising trade in GDP by 1 percentage leads to 0.6% rise of TFP by reducing the negative effect of misallocation.
The second row of regression 1 reveals that FDI inflow has a positive effect on the TFP gap, and hence on misallocation. The positive and significant coefficient in regression 1 is unexpected since FDI inflow should facilitate competition and increase allocative efficiency. The coefficient of FDI turns insignificant in regression 4 where industry effects are controlled, although it is still large and positive. The positive coefficients of FDI could indicate the high productivity
heterogeneity that results from openness to foreign investment. One of the most established findings in the FDI literature is that foreign firms are more productive than their domestic
competitors. This empirical observation is in fact the underlying theme of new trade theory which emphasizes that productive firms are selected into exporting, while the most productive firms engaged in FDI (Helpman et al., 2004). By facilitating the entry of multinationals that are more productive than their domestic competitors, openness for FDI might thus increase
productivity heterogeneity in the market. Since productivity differences can also induce differences in marginal products, FDI can have a positive effect on misallocation unless local firms are able to narrow their productivity differences. In the robustness section, I will further explore the conditions under which FDI could improve allocative efficiency.
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The two variables measuring entry and exit barriers – entry costs and recovery rate,
respectively – appear insignificant in regressions 1 & 4. This suggests that the level of domestic competition is not as important as openness to external competition. Similarly, domestic sector credit, which is our measure of financial development, and the profit tax rate also appear insignificant.
The cost of firing employees, however, enters with strongly significant coefficients in regressions 1 & 4. A rise in the cost of firing redundant employees by an amount equivalent to one week’s wage raises the TFP gap by more than 0.5%. This implies that a decrease in the cost of firing from Malawi’s level (the 75th percentile) to the level in South Africa (the 25th percentile) is associated with more than 20% increase in TFP (i.e. decrease in the TFP gap), which is
approximately one third of the standard deviation. Thus the cost of firing is not only statistically significant, but it also has a strong economic effect of lowering aggregate productivity.
Regressions 2-3 & 5-6 in Table 3 are based on the indicators of TFP gap that measure misallocation due to only output and capital distortions. The results of these regressions are
useful for identifying the separate effect of policy constraints on the two types of distortions.
Trade openness in these regressions enters with negative but small coefficients, indicating that its separate effect on misallocation due to output and capital distortions is weak. Trade openness thus appears to affect both output and input distortions, but with a larger combined effect as is seen in regressions 1 & 4. Similarly, the separate effect of FDI on the two types of misallocation is relatively small and insignificant.
The effect of entry costs is relatively large and significant in regression 3. However, the coefficient of entry costs is insignificant in regression 6, indicating that the effect is perhaps concentrated in a few industries. Similarly, profit tax seems to have a weakly significant, positive effect on input distortions from regression 6, although its effect is insignificant in regression 3.
Domestic credit is insignificant in all regressions.
The cost of firing, however, has especially stronger and significant coefficients in regressions 2 and 5 in which the dependent variables measure misallocation due to output distortions. This suggests that size-related distortions tend to rise in countries with high cost of firing, possibly because firing costs constrain productive small firms from growing by increasing their labor force.