rafmacalaba/datause-encoder-data huggingface dataset:
Croatia (0.542) and the United States (average score, 0.62).<sup>18</sup> _Second_ , although the fat left tail and the thin right tail in the distribution of management score is evident in not only Russia, but also in Croatia, Mexico and Pakistan, the comparison with the US is starker. In the US, 18 percent of the establishments have a score higher than 0.75 and barely a quarter have a score lower than 0.50. _Third,_ among the components of management scores, an average surveyed Russian firm scores much lower on the structured management practices related to data driven performance monitoring relative to Croatia, Mexico, Pakistan and the United States. By comparison, adoption of structured management practices on human resources and target setting for an average surveyed is higher in Russia relative to that in Mexico.<sup>19</sup>

# **_Table 2: Russia not only far from frontier but also has large share of poorly managed firms_**

18 For comparator countries, the sample frame varies significantly. For example, MOPS in the US used the sample frame of the Annual Survey of Manufacturing, comprising of around 50,000 plants in each wave for the years 2010 and 2015. Nearly 74,000 responses for the two years were received, that is, an average response rate of 74 percent. Likewise, in Mexico the survey was conducted for nearly 25,000 firms in manufacturing and services, with a response rate of 96 percent. The survey in Russia is perhaps closest to that in Croatia where 727 firms were interviewed face to face across five broad regions, with response rate similar to that in Russia (about 17 percent). Although the survey in Croatia covered both manufacturing and services, in this paper we present comparisons with manufacturing only. It is also worth reminding that although much of the lagging regions in Russia is not surveyed, the survey also skips bigger cities and agglomerations such as Moscow, Moscow Oblast, Saint Petersburg and Leningrad Oblast. Due to restricted coverage on location and size of the firms in Russia, management score could go in either direction. For example, we do not cover smaller firms (less than 25 employees) in Russia (which are covered in both Mexico and the US), and likewise, we do not cover much of the lagging regions in Russia as well. A broader coverage could therefore also pull down the current management score, depending on the share of firms that is added on either end of the location or size distribution. Furthermore, as we will learn later in the paper, a low response rate in Russia is likely leading to an overestimation of the observed management score.

19 This interesting finding is explored more in Annex table A.2, where we compare the average score of firms surveyed in Russia in each of the 16 questions on management practices to the scores of the US firms as detailed in Bloom et al. (2019a).

10
Cox, D. 1987. “Motives for Private Transfers.” _Journal of Political Economy_ 95 (3), 508–46. Cox, D., Z. Eser, and E. Jimenez. 1998. “Motives for Private Transfers over the Life Cycle: An Analytical Framework and Evidence for Peru.” _Journal of Development Economics_ 55 (1), 57–80.

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34
Table 2: Relationship between management sub-scores and post-COVID outcomes; manufacturing.

||(1)<br>∆Sales|(2)<br>Temp.|(3)<br>Perm.|(4)<br>Furloughed|(5)<br>Laid Of|(6)<br>Pivot<br>Product|(7)<br>Remote<br>Work|(8)<br>Arrears|
|---|---|---|---|---|---|---|---|---|
|MS: Operations|0.004|-0.074|-0.124|-0.015|0.044|0.032|0.008|-0.001|
||(0.007)|(0.079)|(0.076)|(0.040)|(0.043)|(0.034)|(0.034)|(0.037)|
|MS: Monitoring|0.005|-0.022|-0.103|-0.103<sup>_∗∗_</sup>|0.015|0.032|0.090<sup>_∗∗_</sup>|-0.035|
||(0.007)|(0.077)|(0.093)|(0.044)|(0.049)|(0.038)|(0.037)|(0.042)|
|MS: Targeting|0.003|0.012|-0.041|0.042|0.024|0.017|-0.013|-0.048|
||(0.008)|(0.081)|(0.081)|(0.050)|(0.052)|(0.042)|(0.041)|(0.045)|
|MS: Incentives|0.025<sup>_∗∗∗_</sup>|-0.289<sup>_∗∗∗_</sup>|-0.095|0.043|-0.027|0.094<sup>_∗∗_</sup>|0.114<sup>_∗∗_</sup>|-0.159<sup>_∗∗∗_</sup>|
||(0.009)|(0.096)|(0.093)|(0.054)|(0.063)|(0.048)|(0.047)|(0.052)|
|Size (log)|0.001|-0.051|0.141<sup>_∗_</sup>|0.208<sup>_∗∗∗_</sup>|0.117<sup>_∗∗_</sup>|0.027|0.110<sup>_∗∗_</sup>|0.020|
||(0.008)|(0.080)|(0.079)|(0.052)|(0.055)|(0.043)|(0.043)|(0.047)|
|Sales (log)|0.024<sup>_∗∗∗_</sup>|-0.093<sup>_∗∗_</sup>|-0.131<sup>_∗∗∗_</sup>|-0.122<sup>_∗∗∗_</sup>|-0.069<sup>_∗_</sup>|-0.020|0.081<sup>_∗∗∗_</sup>|-0.074<sup>_∗∗_</sup>|
||(0.005)|(0.043)|(0.047)|(0.034)|(0.036)|(0.028)|(0.028)|(0.030)|
|Observations|2063|1958|1551|1567|2108|2113|2115|1954|
|Adjusted _R_<sup>2</sup>|0.187||||||||
|Pseudo _R_<sup>2</sup>||0.166|0.150|0.160|0.099|0.111|0.109|0.106|
|df<br>~~m~~|27|24|21|25|27|27|27|27|

Notes: Standard errors in parentheses All specifications include controls for country, sector, number of days from lockdown, firm age, and export dummy. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01

**14**
**Table 4:** Forecast error GVD for **Eurozone [1996-2011]** : percent of variation in _CA_ explained by column variable

||Ba|seline VA|R|||
|---|---|---|---|---|---|
|Years ahead|_R_|_REER_|_GR_|_YGAP_|_CA_|
|2|7.25|0.38|7.23|1.26|83.87|
|4|7.79|0.42|7.29|1.26|83.23|

|Altern|ative VAR|_(replacin_|_g ULC by _|_REER)_||
|---|---|---|---|---|---|
|Years ahead|_R_|_ULC_|_GR_|_YGAP_|_CA_|
|2|6.34|1.82|9.11|1.53|81.21|
|4|6.82|2.40|9.06|1.53|80.20|

The estimation was finally conducted separately for the Eurozone core and periphery countries over the 1996-2011 period (Table 5). The GIR remain qualitatively similar to the ones for the Eurozone sample as a whole (Figure 8) and are not shown separately (available upon request). The GVD for the current account of core countries over this more recent period reveal, however, significant differences compared to the full Eurozone sample: First, _REER_ , _GR_ , and _YGAP_ are now equally important in explaining the external balance fluctuations in the Eurozone core (each variable accounting for around 4, 6 and 5 percent, respectively). Second, the contribution of interest rate shocks to the variance of current account changes is much lower compared to the full Eurozone sample (less than 3 percent after four years compared to 8 percent for the full sample). When we compute the GVD using the alternative VAR, the qualitative results remain unchanged. However, the contribution of _ULC_ increases further and becomes equal to the total contribution of growth and convergence (sum of _GR_ and _YGAP_ ).

Similarly, the GVD for the current account of periphery countries over this more recent period present also significant differences compared to the full Eurozone sample: Importantly, the contribution of changes in the four variables ( _R_ , _REER_ , _GR_ , and _YGAP_ ) to the variance of current account changes is much higher, reaching 50 percent after two years. Changes in growth and relative per capita incomes explain, as earlier, a major part of _CA_ variance, now exceeding 30 percent for _GR_ and _YGAP_ combined. Moreover, the contribution of changes in real interest rates is much higher, reaching 16 percent. In line with our previous finding for the full sample, the strong role of _R_ in _CA_ fluctuations in the periphery is explained by the stronger transmission of real interest rate shocks rather than the larger variance of these shocks, indicating possible savings elasticity changes to interest rates.<sup>17</sup> As in the previous estimations, the contribution of real exchange rates to _CA_ variance remains minor, reaching only about 3 percent. The alternative

> 17 Indeed, the standard deviation for the R shocks is 1.81 for 1996-2011, compared to 2.12 for the entire period [1975-2011].

20
_Figure 3: Inclusion errors_ <!-- Start of picture text --> 57%<br>60%<br>50% 45%<br>42%<br>39%<br>40%<br>30%<br>20% 14% 15%<br>11%<br>10% 6%<br>0%<br>Inclusion errors [based on Inclusion errors [based on<br>poverty line] program eligibility threshold]<br>CBT FCS PMT Universal<br><!-- End of picture text --> Note: Table A.2 in supplementary appendix summarizes the results.

Importantly, exclusion errors based on the national poverty line are large. This is driven by the overall program coverage due to its limited funding (as opposed to targeting performance). While 85% of households in the project areas are poor, the program can only cover about half of them (43%). This means that, in the best‐case scenario, if the program were to correctly identify all the poor, exclusion errors would still be 49%. Given the limited program coverage relative to needs, none of the targeting methods can substantially reduce exclusion errors.<sup>22</sup> The various methods have similar efficiency against welfare measures other than consumption and poverty. Table 2 shows that there are no robust statistical differences in average characteristics of beneficiary households selected by each method across other welfare benchmarks such as food insecurity, an asset index, a livestock index, income per capita, food stock coverage, as well as stunting, wasting and underweight of children under the age of 5. There are no statistical differences between households selected by CBT and FCS, aside from a slightly higher share of severely wasted children in households selected by CBT. There are no statistical differences in the profile of households selected by PMT and CBT either, aside from a slightly higher share of severely underweight selected by CBT. There are only a couple of small differences between households selected by FCS and PMT, with FCS households slightly less food insecure, and having slightly more food stock. The few observed differences are small in magnitude, only significant at the 10 percent level, and with no clear or robust patterns.

Based on subjective well‐being indicators, PMT and CBT perform equally well. For instance, 62 percent of households selected by CBT are considered to belong to the bottom 43 percent of the welfare distribution based on poverty perceptions from other households. Results are largely similar when considering the distribution based on the self‐perception of basic needs, with 64 percent of CBT selected households belonging to the bottom 43%. Similar results are observed for PMT: 59 percent of households selected by PMT are considered to belong to the bottom 43% based on the two subjective well‐being indicators. These

> 22 Table A.2 in the supplementary appendix provides the results.

14
17 _References._

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implication of regulations in more detail would be of importance as removing unjustified interventions is likely to go a long way towards improving resource allocation in agricultural systems characterized by very unequal land distribution. Most rental markets in developing countries involve some form of share tenancy. While this arrangement does not lead to full efficiency, it is a second best solution given risk and imperfect capital markets. The sections below elaborate these points and review relevant evidence.

# **3.1 KEY DETERMINANTS OF LAND MARKET PARTICIPATION**

The shadow price of land for different types of heterogeneous agents is determined by the agricultural production function, the households' inherent managerial ability, and by a number of imperfections in labor, credit, and land markets that are common in rural areas. If credit and land rental markets were perfect, the supervision costs associated with the use of hired labor would make smaller farms more productive, and would lead households to lease in or lease out the amount of land required to maintain a uniform ratio of family labor endowment to operated area, irrespectively of the land ownership distribution (Feder 1985). However, imperfections in other markets may change this, with implications for the functioning of land rental and sales markets. If, in the presence of credit market imperfections, supply of working capital depends on the amount of land owned, the optimal size of the operational holding would vary systematically with size of the owned holding even if land rental markets were perfect. While the magnitude (and direction) of this effect would depend on the elasticity of output with respect to effective labor and of labor effort with respect to supervision, it can overwhelm the productivity advantage of family farmers and give rise to a positive relationship between owned farm size and productivity. In addition to this, capital and insurance market imperfections may also affect the production activities of poor producers - possibly leading them to pursue less risky but also less productive activities. Below we review the factors which affect the productivity of farmers, and thus determine their demand for land.

**_Economies of scale:_** The presence or absence of economies of scale would systematically affect the shadow price of land for different farm-size classes. Possible economies of scale could arise from the presence of indivisible factors of production or cost elements leadingto an initial range of farm size where the average cost of production declines with farm size. In cases where other markets function reasonably well, optimal farm sizes tend not to exceed the scale at which family labor is fully occupied (utilizing seasonal hired labor for specific tasks).<sup>2 4</sup> Agricultural activities where significant economies of scale in the production process

> **24** A large number of empirical studies (e.g. Olson-Lanjouw 1995 for India, Feder et al. 1992 and Burgess 1997 for China, Olinto 1995 for Paraguay) are indeed unable to reject the hypothesis of constant retums to scale in agricultural production.

16
# The Unintended Consequences of Deportations: Evidence from Firm Behavior in El Salvador<sup>_∗_</sup>

Antonella Bandiera<sup>_†_</sup> Lelys Dinarte<sup>_‡_</sup> Sandra V. Rozo<sup>_§_</sup> Carlos Schmidt-Padilla<sup>_¶_</sup> Mar ́ıa Micaela Sviatschi<sup>_∥_</sup> Hernan Winkler<sup>_∗∗_</sup>

**Keywords:** Deportations, Firms, Informality. **JEL Classification:** D22, J61, O17

> _∗_ We are grateful for comments from Juan Blyde, Mat ́ıas Busso, Marisol Rodr ́ıguez Chatruc, Ana Mar ́ıa Iba ̃nez, Anna Mar ́ıa Mayda, Juan Vargas, and participants at the Inter-American Development Bank Discussion Seminars. We are also grateful to Sarita Or ́e, Horacio Vera, and Sylvia Yin for their excellent work as research assistants. We appreciate the support from the Salvadoran Social Security Institute ( _Instituto Salvadore ̃no del Seguro Social_ ) and the Ministry of Justice and Public Security of El Salvador for granting us access the data. We acknowledge funding by the Inter-American Development Bank and the Research Support Budget from the World Bank. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers, funding agencies, or governments. There are no conflicts of interest or financial support to disclose.

> _†_ ITAM. Email: antonella.bandiera@itam.mx

> _‡_ The World Bank. Email: ldinartediaz@worldbank.org

> _§_ University of Southern California. Email: sandra.rozo@marshall.usc.edu

> _¶_ University of California at Berkeley. Email: cschmidtpadilla@gmail.com

> _∥_ Princeton University. Email: msviatschi@princeton.edu

> _∗∗_ The World Bank. Email: hwinkler@worldbank.org
Table 4b. Key <u>Correlates of Positive Youth Behaviors, Chile</u>

||Not in|active<sup>a</sup>|Sch<br>comp|ool<br>letion|Older ag<br>jo|e at first<br>b<sup>b</sup>|Safe<br>beha|sexual<br>vior<sup>c</sup>|Older<br>fi<br>preg<br>paren|age at<br>rst<br>nancy/<br>thood<sup>b</sup>|Partic<br>acti|ipate in<br>vities<sup>c</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|
|**Macro**|||||||||||||
|Rural residence|--|-0.63<br>(0.14)|-0.46<br>(0.17)|--|-0.09<br>(0.02)|--|--|--|--|--|--|--|
|**Micro**|||||||||||||
|Low parental<br>education level|-2.02<br>(0.26)|-1.47<br>(0.23)|-4.18<br>(0.70)|-2.76<br>(0.31)|-0.28<br>(0.03)|-0.11<br>(0.03)|-0.79<br>(0.18)|-1.09<br>(0.18)|--|-0.11<br>(0.02)|-0.83<br>(0.16)|-1.06<br>(0.18)|
|Positive<br>relationship with<br>father|--|--|0.61<br>(0.19)|--|--|0.07<br>(0.02)|--|--|--|0.06<br>(0.01)|--|--|
|Positive<br>relationship with<br>mother|0.76<br>(0.25)|0.75<br>(0.19)|--|1.59<br>(0.21)|--|--|--|0.98<br>(0.18)|--|0.10<br>(0.02)|--|--|
|Abuse in<br>household|--|--|--|--|--|-0.07<br>(0.02)|--|-0.67<br>(0.15)|--|--|--|--|
|Substance abuse<br>inthehome|--|--|--|--|--|--|-0.70<br>(0.14)|--|--|--|--|--|
|Social exclusion|--|-1.29<br>(0.10)||-1.54<br>(0.12)|--|-0.07<br>(0.01)|0.37<br>(0.13)|-0.98<br>(0.10)|-0.04<br>(0.01)|-0.16<br>(0.01)|-0.85<br>(0.12)|-1.20<br>(0.11)|
|Trust in<br>governmental<br>institutions|--|--|--|-0.87<br>(0.24)|0.07<br>(0.03)|--|0.78<br>(0.15)|--|--|--|--|--|
|Connectedness|--|--|0.49<br>(0.16)|0.60<br>(0.17)|--|--|--|--|--|--|--|--|
|Connection with|--|--||--|--|--|--|--|--|--|0.43|--|

3
who fail to optimally use the resources available in their country. Crucially, the products whose factor intensity puts them at comparative disadvantage exit the US market even faster if the exporting country has a high share of bank credits to the GDP. Our evidence therefore suggests that a strong banking sector can prevent a sub-optimal use of resources by enforcing an e¢ cient export composition before a competitive foreign market does so. A well-developed domestic ...nancial system helps to push the country’s exports towards products congruent with its comparative advantage.

The paper makes three main contributions. First, it introduces a new channel through which ...nance improves resource allocation in the real economy, extending the existing work of developmental and ...nancial scholars. Exports violating the notion of comparative advantage represent an important and rather underresearched facet of resource misallocation. Moreover, the existing work on misallocation su¤ers from the lack of internationally comparable production data at the level of highly disaggregated goods. In contrast, the richness of available trade data permits a detailed and thorough empirical analysis of resource misallocation in a broad sample of countries. As for the ...nance literature, it has traditionally focused on capital misallocation and its consequences for economic growth (Lang et al. 1996, Wurgler 2000). The Heckscher-Ohlin theory compares the overall factor intensity of a product with factor endowment of the exporting country. This framework therefore allows us to examine the role of ...nance in the wider context of resource allocation.<sup>3</sup> Second, the paper contributes to the literature on the e¤ects of ...nancial factors on trade (Beck 2002, 2003; Ju and Wei 2005, Greenaway et al. 2007, Muûls 2008, Manova 2008, Manova et al. 2009). This recently growing line of research shows that ...nancial development improves the export performance of a given country. Finance especially bolsters exports of ...rms that come from ...nancially vulnerable industries or face credit constraints. These are important results, but their implications for overall allocative e¢ ciency might yet prove elusive. What if ...nancially constrained ...rms specialized in products whose factor intensities match poorly

> 3Bernard et al. (2006, 2007) investigate the resource reallocation alongside the lines of comparative advantage following a trade liberalization. However, they do not examine the role of ...nancial factors in their work.

3