rafmacalaba/datause-encoder-data huggingface dataset:
# **_5.2 Effects of policies on misallocation_**

To assess the effects of these reforms, we build a panel of industries across states and compute changes in output misallocation between 1989 and 2000. This is our dependent variables in the regressions reported in table 12. Note that relative to our analysis of services in section 4 or to the results presented in Duranton _et al._ (2015), we work with different units, industries within states instead of an aggregate of industries within districts. Below, we assess to what extent this different level of analysis affects our results.

Our main explanatory variables are the reforms described above, which we aggregate over 1985-1997. Hence, our estimations regress changes in misallocation between 1989 and 2000 on institutional and policy changes between 1985 and 1997. We also consider longer lags for changes in misallocation below. Because changes in misallocation are often noisy and because the initial level of misallocation may be correlated with what triggered some of the reforms, we also control for the initial level of misallocation in our regressions.

From the regressions reported in table 12, we can draw a number of conclusions regarding the effects of institutional and policy changes. The first one regards the effects of labor reforms. Stricter pro-worker regulations led to a worse misallocation. This result is robust across most It is when we break down our and specifications. only sample distinguish between the organized and the unorganized sector that the effect of labor reforms become insignificant. This is perhaps unsurprising since labor reforms are thought to have pushed many establishments into the unorganized (informal) sector (Besley and Burgess, 2004).<sup>13</sup> More generally, this negative effect of pro-worker labor reforms is fully consistent with the conclusion of Besley and Burgess (2004) who find that these reforms were generally harmful to industrial development in India.

The second strong result that emerges from table 12 regards land reforms. These reforms led to less misallocation in manufacturing. Like with labor reform, it is only when we look within the organized sector or within the unorganized sector that the effects are insignificant. As argued above, the net effect of land reforms is in theory ambiguous

> 13See Duranton, Gobillon, and Overman (2005) for a more detailed discussion of the possible pitfalls associated with looking at misallocation over only one part of the establishment size distribution.

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collateral requirements of financial institutions and as impacted by the contractual framework. If legal system reforms allow a “new” asset (e.g. movable assets) to be used as collateral, enterprise and household surveys can be used to calculate the share of potential borrowers that would become bankable as they are now able to use their assets as collateral, thus advising policy makers on the binding constraint and most beneficial reform in terms of higher outreach.

While we have developed and discussed the access possibilities frontier on a conceptual level, we hope to have convinced the reader that it can very well be operationalized for both empirical and policy work. Disentangling different access problems and the impact of policy reforms is much more difficult for lending services than for payment and savings services, as the assessment and management of risk adds a crucial additional element. Nevertheless, we are confident that this tool can be refined further to serve both the debate on the measurement of access to finance as well as the policy debate on how to improve outreach of the financial system and access to financial services.

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Haitians living in rural areas are always on the move, weeding fields, harvesting crops, fetching drinking water, or driving livestock to fresh pasture. The people cannot afford to be idle.

Unemployment is a serious problem, particularly in urban areas. The unemployment rate among household heads in Haiti reached 38.9 percent in 2001 according to the household survey. The rate is highest in urban areas; 48.8 and 36.7 percent in metropolitan and other urban areas respectively compared to 35.6 percent in rural areas.

# **Agricultural and Nonagricultural Employment**

It seems fairly clear that the economy must remain a central focal point for policy makers aiming to reduce poverty. Employment is therefore essential and both the farm and nonfarm sector play a key role. The nonfarm sector is heterogeneous and includes a great variety of activities and productivity levels across nonfarm jobs. Moreover it can reduce poverty in a couple of distinct but qualitatively important ways. First, highproductivity activities seem to provide the rural population with sufficient income to escape poverty. Second, vulnerable segments of the population, such as the poorest tend to be concentrated in the low or less productive rural nonagricultural activities—mainly due to skill and educational deficiencies and location disadvantages. These lowproductivity/return occupations nevertheless provide a critical contribution to their livelihoods preventing further destitution.

Labor markets can be analyzed in many ways. One way is to consider the agricultural and nonagricultural sector or the off-farm sector. Off-farm employment has traditionally been seen as a low productivity sector, producing low quality goods. The sector, in this view, is expected to shrink as the economy develops and incomes increase. However, recent research shows that the rural nonfarm sector has a positive role in absorbing a growing rural labor force and slowing rural-urban migration. Moreover, the nonagricultural sector contributes to national income growth and in promoting a more equitable distribution of income (Lanjouw and Lanjouw 2001). Lanjouw and Lanjouw also find that the nonagricultural sector is large and growing in developing countries. In Latin America alone 47 percent of the labor force in rural settlements and rural towns are employed in off-farm activities. Moreover, 79 percent of women in the Latin American rural labor force are employed in off-farm activities. In Haiti 52.4 percent of the workers are engaged in off-farm activities—98.7, 55.4, and 37.2 percent in metropolitan, urban, and rural areas, respectively, in 2001.

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Table 9: Estimating the effect of migrants’ legal status on monthly wages after return, unconditional on return migration

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>monthly wage|
|---|---|---|---|---|
|Age|0.013***<br>(0.000)||||
|Age squared|0.012***|-0.070**|-0.014**|0.016***|
||(0.001)|(0.034)|(0.006)|(0.005)|
|No education|-0.000***|0.001*|0.000***|-0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Primary or preparatory|0.019***|0.434***|-0.029|-0.403***|
||(0.004)|(0.115)|(0.021)|(0.027)|
|Secondary|0.032***|0.414***|0.050**|-0.349***|
||(0.004)|(0.121)|(0.022)|(0.026)|
|Rural|0.017***|0.345***|0.034**|-0.248***|
||(0.003)|(0.099)|(0.017)|(0.021)|
|Oil prices|0.009***|0.248***|0.009|-0.080***|
||(0.003)|(0.081)|(0.015)|(0.016)|
|Diplomatic exchange||-0.219**<br>(0.102)|||
|Confict|||0.046***<br>(0.013)||
|Illegal status||||-0.323***<br>(0.052)|
|Legal status||||0.207***<br>(0.041)|
|Observations|14,879|1,955|2,071|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.: 6.|780; Illegal mig.|=6.500; Legal m|ig.=7.072||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of monthly wages, unconditional on return migration. The predicted logarithm of monthly wages for non-migrants, illegal migrants, and legal migrants are reported in the last row.

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- 22 -

41. Effective Protection also varies widely between the types of plants (ISPs versus MSPs), products (flat versus long prodiucts) and steel quality (mild versus special steels/alloys). Table 10 assembles the fragmentary, and largely outdated, available data on EPRs.

**TABLE 10:** EFFECTIVE PROTECTION RATES

|Product|Year|NP|C|EPR|
|---|---|---|---|---|
|||Output|Input|a**)**|
|ISP: SAIL (public)|1985|1.30|0.89|74|
|ISP: TISCO (private)|1985|1.31|1.11|51|
|MSPs (Mild Steel)|1985|1.43|1.35|32|
|CR Coils|1985|1.61|0.89|112|
|CR Coils (MSP)|1987|1.70|1.52|174|
|Galvanized Plates/Sheets|1988|1.68|1.66|93|

- Sources: Pursell (for 1985) DFI subproject appraisal reports.

The value added content in ISPs is high, averaging about 50%. It should be noticed that the EPR for SAIL is higher than for TISCO, due to the former's access to inputs (coal, iron ore) at preferential prices. The value added content **is** quite low in MSPs. For example, the basic production parameters and prices for an MSP project to produce cold rolled (CR) coils in the late 1980s are as follows:

|Pri|ces (Rup|ees|Per ton)|
|---|---|---|---|
|Domestic|CIF||NPC|
|Output: CR Coils<br>14,750|8,950||1.65|
|Inputs:**HR**Coils (Local)<br>8,380|5,710||1.47|
|**HR**Coils (Imported)<br>10,700|5,710||1.87|
|Value added share in output at inter|nationa|l pr|ices:**16Z**|

42. It **is** quite likely that effective protection has decreased in the mild steel ISPs during recent years with the increased competitiveness of steel prices. Prices for the major inputs (iron ore, coke) expressed in foreign exchange (US$) have remained largely constant. On the other hand, prices of major mild steel products (rods/bars, plates/sheets, HR coils) expressed in foreign exchange have come down. Under such circumstances, the EPR of SAIL may presently be about **30%** and that of TISCO around 10%. In view of the high protection and prices of their output, effective protection in special steel/alloy producing MSPs most probably remains high. However, the
likely to live in Cairo and Upper Egypt, both urban and rural, whereas undocumented migrants are more likely to live in rural Lower Egypt. As for their job in 2012 upon return, undocumented migrants have greater job tenure by approximately 2 years and are significantly less likely to have a work contract compared to documented migrants. Along the spectrum of sectors of employment and economic activities, both documented and undocumented migrants seem to be equally employed in the different economic sectors and activities.

In Table A.2 in the Online Appendix, we examine additional differences between documented and undocumented migrants with respect to their migration spell abroad. Documented migrants are found to have significantly higher monthly earnings and savings while abroad. They are also more likely to remit and to send higher remittances compared to undocumented migrants. In addition, documented migrants are found to have longer migration spells relative to undocumented migrants, by one year on average. However, there is no significant difference in terms of number of migration episodes between the two groups—as they both seem on average to have one migration spell. In the 1980s, a significantly higher proportion of undocumented migrants migrated compared to documented migrants. By contrast, in the 1990s and in the 2000s, a higher proportion of documented migrants migrated for the first time compared to undocumented migrants. In terms of destination choice, undocumented migrants were significantly more likely to choose Libya and Iraq. However, documented migrants were found to be significantly more likely to migrate to Saudi Arabia, United Arab Emirates, Kuwait, and other destination countries.<sup>12</sup> Looking at the average hourly wages in 2012 for non-migrants, returnees, legal, and illegal migrants in Table 3, we find that the average hourly wage for returnees is higher than non-migrants.<sup>13</sup> We also find that documented migrants have higher hourly wages compared to undocumented migrants upon return. In the second column, we report the difference in hourly wages between each group and non-migrants. Indeed, we find that the difference in hourly wages between documented migrants and non-migrants is the largest, while we find that the difference in hourly wages between undocumented migrants and non-migrants is the smallest.

> 12 Other destination countries include: the United States, the United Kingdom, the Netherlands, France, Austria, Czech Republic, Italy, Greece, Cyprus, Bulgaria, Romania, Iceland, Uganda, Mozambique, Morocco, Algeria, Sudan, the Syrian Arab Republic, Lebanon, Palestine, the Republic of Yemen, Bahrain, Qatar, Oman, and Japan.

> 13 It is important to note that returnees include both legal and illegal migrants.

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and also allows us to measure the responses of major macroeconomic variables.

Our results confirm that the impact of a negative shock to Chinese real GDP on the Asian countries has significantly increased under the recent trade structures of 2005 and 2013 compared to the earlier trade structures of 1985 and 1995. This confirms the common understanding of Asia’s increased dependency on China. The GIRFs are significantly negative with the exception of the Philippines and New Zealand. All remaining Asian countries are negatively impacted by a real GDP shock to the Chinese economy at the 68% interval. China’s slowdown also curbs its demand for commodities, and we investigated whether this translates into commodity price drops. Our GIRFs show that a negative shock to the real GDP of China not only reduces crude oil prices, as some previous studies have shown, but also metals and agricultural prices. We also ran our model to test the impact of a potential US real GDP shock, and confirms that although the US has a stronger influence on Asian economies than China, these countries are more exposed to China than ever through increased economic ties.

The rest of the paper is organized as follows. In Section 2, we analyze the historical transition of the Chinese trade volume using trade data. In Section 3, we explain the standard GVAR model following past studies, and introduce several modifications, such as the time-varying trade weights, increasing the number of commodities, and inclusion of the “shift in intercepts” dummy variables to control the outliers. In Section 4, we estimate the model. In Section 5, we calculate the GIRFs, and investigate the effect of a Chinese economic shock on the Asian countries by comparing the shapes of the GIRFs with various settings. Section 6 summarizes our conclusions.

# **2 The transition of China’s trade share**

China’s membership of the World Trade Organization in 2001 dramatically altered the outlook for global trade and became a turning point for the country’s economic development.

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Table 10. Evaluation of the National PMT with Employment Information (OLS Regression)

|**Evaluation of Including Employment Information in the PMT**|
|---|

||**OLS Model w**<br>**Information**<br>**Poverty Line**<br>|**ith Employme**<br> **/ PMT Cutoff**<br>|**nt**<br>|**OLS Model w**<br>**Information**<br>**Poverty Line**<br>|**ithout Emplo**<br>**/ PMT Cutoff**<br>|**yment**<br> <br>|
|---|---|---|---|---|---|---|
|**Evaluation Criteria**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|**Bottom**<br>**20%**|**Bottom**<br>**40%**|**Bottom**<br>**60%**|
|Poverty headcount (observed)|19.96%|39.95%|59.89%|19.96%|39.95%|59.89%|
|Householdpovertyrate(observed)|19.36%|37.59%|54.92%|19.36%|37.59%|54.92%|
|Household poverty rate (predicted)|13.04%|39.16%|59.70%|12.81%|38.76%|59.60%|
|Total accuracy|83.18%|77.96%|79.43%|83.01%|77.89%|79.26%|
|Povertyaccuracy|40.24%|72.78%|85.63%|39.21%|72.15%|85.38%|
|Undercoverage|59.76%|27.22%|14.37%|60.79%|27.85%|14.62%|
|Leakage|6.51%|18.92%|28.12%|6.47%|18.65%|28.19%|

_Notes:_ This model includes all variables in Table 3, as well as the proportion of household members who have worked for income to the total household members where noted. The results are based on out-of-sample prediction on a random sample of 25% of households.

In this section, we explored the impact of different model specifications and the inclusion of location and employment variables on the targeting performance of the PMT. The results of a binary dependent logistic regression of the poverty status of the household were nearly identical to the results of the PMT based on OLS regression. Employment information did not impact the performance of the PMT in a meaningful way. And the inclusion of a complete set of location indicator variables marginally improved targeting performance of the PMT. However, in practice, social programs are likely to be administered at the state level with pre-determined budgets, rather than by using the PMT to determine the allocation of beneficiaries across states. Due to this, along with the minor improvements of the performance of the PMT with the inclusion of location variables, the Sudan national PMT excluding location indicators appears to perform well to meet program objectives.

# 6. Conclusion

In this paper we develop and propose a PMT to support the efforts of Sudan in the targeting of the Sudan Emergency Safety Nets Project (SESNP) to those most in need to improve the poverty impact and sustainability of the program.<sup>20</sup> Currently, there are efforts in Sudan to implement a new social safety net

> 20 Given that targeting can be politically sensitive (Del Ninno and Mills, 2015) and, particularly in FCV environments (Fragility, Conflict, and Violence) such as Sudan, political economy considerations and the costs of adopting a PMT should be examined. In this regard, when operationalizing the PMT, it will be important to coordinate with the Central Bureau of Statistics of Sudan and other government stakeholders on this work.

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# Optimal Public Sector Premium, Talent Misallocation, and Aggregate Productivity: Evidence from the Middle East and North Africa<sup>∗</sup>

Francisco Parro<sup>†</sup> Jesica Torres<sup>‡</sup>

**Keywords:** Misallocation; public sector employment; aggregate productivity; female labor force participation.

**JEL Classification:** E6; H3; J2; O1.

> ∗We have benefited from helpful comments and suggestions from Roberta Gatti, Aart Kraay, Daniel Lederman, Cesar Calderon, and Raul Morales Lema. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> †Universidad Adolfo Ib ́a ̃nez, School of Business. Email: francisco.parro@uai.cl

> ‡The World Bank. Email: jtorrescoronado@worldbank.org
Table 4. Model With Interaction Between Inequality and Initial Income

|Dependent Variable is:|||l|n(yit)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:|All Cou|ntries|Excluding<br>Coun|Transition<br>tries|Excluding<br>Coun|East Asian<br>tries|
|Inequalityit|9.26***|6.35***|10.11***|5.93***|5.54**|7.93***|
||(2.11)|(2.30)|(3.53)|(2.00)|(2.74)|(1.83)|
|Inequalityit|-1.19***|-0.78**|-1.32**|-0.72**|-0.65*|-1.04***|
|*ln(yit-1)|(0.31)|(0.35)|(0.51)|(0.29)|(0.39)|(0.28)|
|ln(yit-1)|0.70***<br>(0.06)|0.78***<br>(0.04)|0.71***<br>(0.06)|0.79***<br>(0.04)|0.73***<br>(0.04)|0.75***<br>(0.04)|
|Kleibergen Paap F-<br>Statistic|14|15|13<br>First Stage:|15<br>Inequalityit|10|15|
|Residual Inequalityit|0.76***<br>(0.03)|0.93***<br>(0.02)|0.75***<br>(0.03)|0.93***<br>(0.02)|0.81***<br>(0.04)|0.95***<br>(0.02)|
|Residual Inequalityit|0.01***|-0.00**|0.01***|-0.00**|0.01*|-0.01***|
|*ln(yit-1)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.01)|
||||First Stage: Ineq|ualityit*ln(yit-1)|||
|Residual Inequalityit|4.52***<br>(0.24)|6.52***<br>(0.19)|4.50***<br>(0.25)|6.53***<br>(0.21)|4.85***<br>(0.27)|6.70***<br>(0.17)|
|Residual Inequalityit|0.15***|-0.08***|0.15***|-0.08***|0.12***|-0.09***|
|<br>*ln(yit-1)|(0.02)|(0.01)|(0.02)|(0.01)|(0.03)|(0.01)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|768|768|725|725|738|738|

Note: The method of estimation is two-stage least squares. Bootstrapped standard errors are shown in parentheses. _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

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