rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 10826

# **Abstract**

This paper examines the effect of compensating a private sector provider of employment services for successful placement of jobseekers with employers. Within this program, active job matching, relative to only job counseling, contributes to a significantly higher rate of employment, and these effects are more pronounced for subgroups that are less likely to find employment without assistance, including youth and low-skilled workers. Among the jobseekers randomly allocated to matching, those who were judged by counselors to be “employable” were far more likely to be matched to employers. To understand the decisions made to rate jobseekers as employable, the paper makes use of both information directly observable to job counselors and characteristics and personality traits from a separate survey of the jobseekers. Evidence suggests that under incentivized matching, counselor assessments of employability and designation for more intensive effort are consistent with cream-skimming.

This paper is a product of the Development Research Group, Development Economics and the Social Protection and Jobs Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at merima.balavac@efsa.unsa.ba, jgiles@worldbank.org, shari@worldbank.org, and movadiya@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors
that education improves managerial skills, this is further evidence of the importance of good practices for efficient production and favorable work conditions.

As noted earlier, the type of PMR that are most crucial for aggregate labor market outcomes depends fundamentally on their political and economic context. While land, capital, and intellectual property rights are crucial to efficient firm operation globally, their weak enforcement in LMICs sometimes still impedes delegating tasks beyond kinship ties. Adamopoulos and Restuccia (2020) examine a 1988 land reform in the Philippines, which introduced limits to farm sizes, redistributed land to landless or smallholder families, and imposed barriers to trading property rights on the market. Not only was this reform another example of a size-dependent policy in the fashion of those discussed in Subsection 3.3.3, but it also had direct implications for the management of farms. To quantify the effects of the reform on production efficiency, the authors develop a structural model with endogenous occupational decisions and technology choices. They use farm-level microdata from before and after the reform to discipline the model, and estimate that the reform reduced average farm size and agricultural productivity by 34 percent and 17 percent, respectively. Around one-third of this decline in productivity can be explained by the assignment of land to untalented farmers. Besides hindering the allocation of talent, policies on ownership structures can distort the incentives of managers. In India, Banerjee and Iyer (2005) rely on historical differences in property rights 31
|**Table C.13. Estimated rho (**ρ**) from**<br>|**cross-sectional data**<br><br>||
|---|---|---|
|**Country**|**Period**<br>ρ|**Alternative**ρ|
||2012-2015<br>0.84|0.52|
|**Egypt, Arab Rep.**|2015-2017<br>0.89|0.46|
||2017-2020<br>0.79|0.61|
||2007-2009<br>0.54|0.56|
|**West Bank and Gaza**|2009-2011<br>0.62|0.66|
||2011-2017<br>0.34|0.59|
||2005-2010<br>0.57|0.67|
|**Tunisia**|2010-2015<br>0.73|0.65|
||2015-2021<br>0.89|0.61|
||2004-2008<br>0.77|0.57|
|**Mauritania**|2008-2014<br>0.63|0.56|
||2014-2019<br>0.70<br>|0.61<br>|
|**Iraq**<br><br>|2007-2012<br>0.68<br><br>|0.37<br>|
|**Jordan**<br><br>|2010-2013<br>0.63<br><br>|0.63<br>|
|**Note:** ρare estimated using Equati<br>education. Alternativeρ’s are estima|on (C5) for all countries with cohorts being defined by age i<br>ted
contrast, only 20 % of men travel on foot, and more than 50 % of men use some transportation. There is also evidence that women use slower modes of transportation to commute to work as faster modes are usually more expensive (Anand and Tiwari, 2006). Concurrently, female labor force participation is low in India, at a rate of 28.2 % of the working-age population as of 2019, significantly lower than the average of 46 % in low- and middle-income countries.<sup>2</sup> We leverage the _Pink Slip programs’_ state-wide roll-out in Punjab and Tamil Nadu in April and May 2021, respectively. While Delhi also introduced this program in November 2019, data limitations preclude us from including it in our sample.<sup>3</sup> Several media

> 1India’s 2019 National Time-use Survey (https://mospi.gov.in/time-use-survey).

> 2World Bank’s Gender Data portal (https://tinyurl.com/4a8rm5ye). According to the most recent female labor force survey by India’s Ministry of Statistics and Program Implementation, it increased to 37% (http://tinyurl.com/mu2vx7m3).

> 3The CMIE, our data source in this paper, started collecting information on individual time usage after Delhi started implementing the _Pink Slip Program_ . So, we are unable to include Delhi in our sample.

1
# **1 Introduction**

The estimation of labor supply elasticities plays a critical role in the design of tax policy and transfer programs. An early literature using annual wages and hours worked from salaried workers finds very small wage-elasticities.<sup>1</sup> These low elasticities could be the result of the limited ability of workers to freely choose the number of hours worked and the fact that wage changes are rarely transitory.

A more recent literature uses data from sectors where workers have discretion over the number of hours worked. By focusing on self-employment occupations (taxi drivers, vendors, etc.), researchers have re-examined whether individuals substitute leisure intertemporally to maximize their overall earnings (see for example, Camerer et al., 1997; Chou, 2002; Farber, 2005, 2008; Fehr and Goette, 2007 and more recently Dupas et al., 2015; Stafford, 2015).

While most of these studies focus on the decision of the choice of hours worked (intensive margin), an alternative and important margin is whether to work at all (extensive margin). In many occupations, individuals cannot decide the number of hours worked due to technological constraints, but they have full discretion about whether or not they work on a particular day. The few studies that have simultaneously analyzed both labor decision margins have typically found much larger labor supply elasticities on the participation margin compared to the hour margin (Heckman, 1993).<sup>2</sup> While empirically larger in magnitude, the main reason why the extensive margin of labor supply has been understudied is data availability, as most studies only include short employment spells which hinders the ability to study the extensive margin. For instance, the data used by Henry S. Farber in the influential studies on taxi driver behavior in New York City (Farber, 2005, 2008) comprises a non-random sample of the shifts worked. There is limited information on whether drivers worked during the previous days or whether they perceived earnings from alternative occupations.
146|0.086|0.334|
||(0.223)|(0.137)|(0.225)|(0.114)|(0.115)|(0.110)|(0.218)|
|_×_ Oil price|0.646<sup>_∗_</sup>|0.140|0.800<sup>_∗∗_</sup>|0.070|0.219|0.144|0.782<sup>_∗_</sup>|
||(0.376)|(0.223)|(0.321)|(0.158)|(0.135)|(0.128)|(0.394)|
|N|3408|3640|3640|3640|3640|3640|3628|
|R-sq|0.72|0.71|0.57|0.60|0.58|0.61|0.59|

The dependent variable is in inverse hyperbolic sine. The regressions include country and year fixed effects. Robust standard errors in parenthesis, and * stands for statistical significance at the 10% level, ** at the 5% level and *** at the 1% percent level.

on the right-hand side. While the results using SIPRI data suggest that both oil and gas discoveries lead to higher arms imports, the results using COMTRADE data suggest only gas discoveries are associated with larger arms imports. These are not statistically significant results however. This may be because, while both oil and gas discoveries probably can trigger arms imports, identifying their average effects separately requires a larger time period.

The second panel suggests that it might only be offshore discoveries that trigger arms imports. While this result is in line with the Angola and Mozambique anecdotes, it does not appear to be in line with the evidence from Nordvik
and destination countries in a macro dataset makes it possible to statistically assess the determinants of the various patterns in international migration which might not be easily captured in an analysis of a single corridor. Furthermore, a large number of cells in a migration matrix are filled with zeros (see the section below for a more detailed discussion of this critical issue.) The informational content of the empty cells (such as for the corridor between Cape Verde and Turkey) of migration flows or diasporas between country pairs is valuable. The presence of zeros reflects that the net gains of migration in those corridors are too low for potential migrants and/or . certain factors lead to high levels of migration costs. In other terms, while it creates additional statistical complications that need to be addressed, the inclusion of zero values in macro datasets tends to highlight and identify selection biases.

# 3.2 The widespread presence of zero observations

The Docquier, Lowell and Marfouk (DLM, 2009) dataset includes almost all origin countries in the world and 30 destination OECD countries. Many statistical properties of the migration flows and stocks can be easily constructed using DLM. The distribution of the migration flows turns out to be unimodal, highly left skewed with a large amount of zero values for both the (net) migration flows between 1990 and 2000 and the stocks in 1990 and 2000. For example, for the 8
4 adjustment, European bank leverage and reliance on wholesale funding remain relatively high compared to other regions where deleveraging also took place but started from lower levels. For many global European banks, the L/D ratios were above 100 percent and median asset values stood at almost 19 times equity, compared to around 10 times for their U.S. counterparts at end2010 (figure 2).<sup>2</sup>

**Figure 3. Foreign Claims of European Banks on Emerging and Developing Countries**

<!-- Start of picture text --> 1,600<br>Asia & Pacific Mar 2005–Dec 2011<br>1,400 Europe<br>Latin America/Caribbean<br>1,200 Middle East<br>Africa<br>1,000<br>800<br>600<br>400<br>200<br>-<br>US$billion<br>Mar.2005 Jun.2005 Sep.2005 Dec.2005 Mar.2006 Jun.2006 Sep.2006 Dec.2006 Mar.2007 Jun.2007 Sep.2007 Dec.2007 Mar.2008 Jun.2008 Sep.2008 Dec.2008 Mar.2009 Jun.2009 Sep.2009 Dec.2009 Mar.2010 Jun.2010 Sep.2010 Dec.2010 Mar.2011 Jun.2011 Sep.2011 Dec.2011<br><!-- End of picture text --> Source: Bank for International Settlements (BIS), Consolidated Banking Statistics, table 9D.

<!-- Start of picture text --> Figure 4. European Consolidated Banking System Figure 5. European Consolidated Banking<br>Leverage, 2007–11 System Loan to Deposit Ratios, 2007–10<br>28 27.9 13 200<br>181<br>175<br>171<br>162<br>26 152<br>25.0 12 150 147<br>138
>-0.049***<br>(0.008)<br>-0.687***<br>(0.178)<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>0.601<br>y and year and a constant are i<br>country_j_ in year_t_. Tariff_fjt_<br>l Herfndahl index, where the<br>foreign competitors is proxied<br>tural log of the GDP of count<br>standard errors clustered by f|
|Log export sales<br>(3)<br>(4)|1.441***<br>(0.117)<br>1.647***<br>(0.080)<br>-0.049***<br>(0.008)<br>-0.868***<br>(0.182)<br>No<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>34,327<br>34,327<br>0.600<br>0.574<br>ntry_j_ by frm_f_ in year_t_. Fixed effects by frm, countr<br> is the tariff measure faced by frm_f_ when exporting to<br>n competition_fjt_(N_◦_countries) variable is the frm-leve<br>is the frm-level Herfndahl index, where the number of<br>by each frm in the destination markets. GDP_jt_ is the na<br>onal Financial Statistics (IFS). Heteroskedasticity-robust|
|(2)|1.324***<br>(0.114)<br>1.579***<br>(0.079)<br>No<br>Yes<br
sup>∗∗∗</sup>|(2.9)|
|Owns toilets|43.9<sup>∗∗∗</sup>|(5.7)|45.3<sup>∗∗∗</sup>|(5.4)|24.6<sup>∗∗∗</sup>|(2.9)|
|Own more than 2 plots|10.6<sup>∗∗∗</sup>|(1.7)|7.9<sup>∗∗∗</sup>|(1.8)|11.4<sup>∗∗∗</sup>|(1.8)|
|**5. Aid received in the last**|||||||
|**year**|||||||
|<br>MASAF or education aid<br>**6. Geography**|3.7<sup>∗∗</sup>|(1.7)|3.9<sup>∗∗</sup>|(1.7)|1.9|(2.0)|
|Rural Household|-9.8<sup>∗∗∗</sup>|(3.4)|-14.0<sup>∗∗∗</sup>|(3.0)|-14.4<sup>∗∗∗</sup>|(3.0)|
|Distance to town (km)|0.0|(0.0)|-0.0|(0.0)|0.0|(0.1)|
|District FE|Yes||Yes||Yes||
|IHS round FE and Season FE|Yes||Yes||Yes||
|Observations
7 information delivered by the information campaigns. The analysis here advances this body of research by examining both the effects of exogenous media access by itself, independent of concerted campaigns, and the mechanisms through which media effects might be transmitted.

Other research on the role of information from sources other than media has focused on intensive grassroots information campaigns, showing mixed effects. In Banerjee et al. (2010), these campaigns alone had no impact on learning in public schools in India, though when combined with efforts to organize volunteers to hold remedial classes outside the public school system, gains in literacy were large. Looking at similar information-only campaigns, Pandey et al. (2009) find reductions in teacher absenteeism, but little impact on learning outcomes. In research by Andrabi et al. (2008), looking at Pakistan, by far the largest effects of increasing information were on the behavior of _private_ schools, which faced the risk of parents switching to better-performing schools. In contrast, in Uganda an intensive campaign to mobilize communities to improve public health services, in which project representatives not only provided information, but also actively promoted the organization of villagers, led to large improvements in services provided and in health outcomes (Björkman and Svensson, 2009). The analysis here focuses instead on media access in the absence of concerted and intensive information or mobilization interventions and examines household responses to radio access.

A separate stream of the literature examines redistributive policies: citizens with more media access are more likely to be recipients of targeted benefits from government programs (Besley and Burgess 2002, Strömberg 2004). Scholars offer an accountability explanation for this result: citizens with greater access to media are better able to punish governments for failure to deliver benefits. However, data limitations, particularly the absence of evidence from household surveys, have made it difficult to distinguish this mechanism from others, such as the behavioral channels that we identify in this analysis.
the Economic Intelligence Unit (EIU) country reports, by counting the number of times “uncertainty” is mentioned in proximity to a word related to trade. In principle, the fact that the WTU index is specifically designed to capture trade related uncertainty could give it an advantage over the EPU index for studies focusing on the trade policy uncertainty / trade link. Nevertheless, the WTU index is characterized by reduced variability over time compared to the EPU index, which is extreme for some countries. Germany’s WTU, for example, is zero throughout the whole period we are studying. Positive values of the index are concentrated in 2018 and 2019, likely reflecting lack of attention given to trade policy uncertainty in media reports in the preceding period.

# **4. Economic policy uncertainty, trade, and GVCs**

Table 2 presents the results of regressions using yearly data. Two alternative dependent variables are used: growth in volumes of goods and services imports and growth in volumes of goods imports. Specifications 1a and 2a include the contemporaneous policy uncertainty measure (computed for each year as an average of the EPU index from January through December), while 1b and 2b include policy uncertainty that is lagged by 2 months (computed for each year as an

> 3 The Russian Federation and Mexico behave as outliers; therefore, we exclude them from all specifications. We also exclude India and the Republic of Korea, for which we do not have data on the REER.

8
questionnaires were consistently answered, we derive only the background questions that were consistently answered in Malaysia and the 19 comparison countries.<sup>4</sup> These categorical variables are then coded as dummy variables and measured in percentage form. A full list of the variables used in the analysis is shown in Table A1 in Appendix A.

# **4. Conceptual Issues**

In this section, we discuss what the differences in test scores across various cohorts, ethnic groups, genders, and subjects may reveal about the impact of PPSMI.

The impact on the older cohort of PPSMI-affected students, who participated in TIMSS 2007, embodies: (i) the adjustment cost associated with learning and teaching from the switch in the language of instruction between primary and secondary education; (ii) the adverse effect on a student’s ability to learn in their non-native language during secondary education; and (iii) the adverse effect on a secondary school teacher’s ability to teach in a language they are not well trained to use. The differences between the Malaysian sample and the counterfactual sample reveal the average of these three effects.

The younger cohort of PPSMI-affected students, who participated in TIMSS 2011, learned the two subjects in English throughout their primary and secondary schools. Thus, the impact on this younger cohort of students would not include the adjustment cost a student experienced from switching the language of learning. The impact on the younger cohort of PPSMI-affected students would thus include: (i) the adverse effect on a student’s ability to learn in their non-mother tongue throughout primary and secondary education; and (ii) the adverse effect on a teacher’s ability to

> 4 The majority of the predictors of test scores are derived from the school background questionnaires. Although this can potentially pose validity problems, Hanushek (2005) shows that school quality and added school resources play an important and positive role in determining students’ achievements.

11
Figure 3: Validation with SICONFI data - commitment <!-- Start of picture text --> CE MG<br>2000<br>1500 4000<br>1000<br>2000<br>500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PB PE<br>600<br>2000<br>400<br>1000<br>200<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>PR RS<br>1500<br>1000<br>1000<br>500<br>500<br>0 0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25> <-25 -20 -15 -10 -5 0 5 10 15 20 25><br>SP % difference from SICONFI data<br>8000<br>6000<br>4000<br>2000<br>0<br><-25 -20 -15 -10 -5 0 5 10 15 20 25><br>% difference from SICONFI data<br>Frequency<br>Frequency<br>Frequency<br>Frequency<br><!-- End of picture text -->

**Notes:** This figure presents the percentage deviation in the total amount of budget commitments, at the municipality-year level, between our dataset and SICONFI, the public finance dataset of the Brazilian Treasury. Values are positive whenever the total amount in our dataset, aggregated from individual commitments, is larger than that of SICONFI. See Table 1 for more details on our
|Lithuania|Household Budget Survey|2000-06|
||LivingConditions Survey|2007|
|Macedonia|Household Budget Survey|2000,04,06|
||Income and Expenditure Survey|2003|
|Madagascar|Enquête auprès des ménages|2001|
|Malawi|Integrated Household Survey|2004|
|Maldives|Income and Expenditure Survey|2002|
|Mali|Enquête auprès des ménages|2003,06|
|Marshall Islands|Income and Expenditure Survey|2002|
|Mauritania|Enquête sur le conditions de vie|2000,04|
|Mauritius|Household Budget Survey|2001,06|

18
surprisingly little systematic analysis of how factor market misallocation and related policies vary across Indian states. This paper contributes to this policy-relevant research agenda by using Indian firm-level panel data from 1999-2014 to describe trends in factor market distortions across Indian states. We look at ‘permanent’ labor, temporary (‘contract’) labor, land and fixed capital, and establish stylized facts about how factor market distortions have evolved across Indian states.<sup>7</sup> Complementing the results of the data analysis with information learned through firms interviews in two states, we suggest areas of focus for future policy analysis. The results of this paper can also be a useful filter for identifying cases of successful reforms or good practices in individual states which could be shared with other states.

We adapt the cost-minimization approach (De Loecker and Warzynski, 2012) to measure trends in factor ‘adjustment costs’ over the years. The ‘adjustment cost’ of a factor is conceptualized as an implicit variable cost incurred by a firm in using that factor. This conceptualization is similar to the ‘implicit taxes’ on inputs modeled in Hsieh and Klenow (2009), a seminal study of factor misallocation in India. The potential causes of this implicit cost include market frictions, institutional factors and factor market regulations which make it harder for firms to employ or adjust their inputs.

The analysis indicates that at the all-India level, the adjustment costs for permanent labor, contract labor and land have fallen between 1999 and 2014. There was a small increase in the adjustment cost for permanent labor between 2004-2007, but the trend has been downwards since 2009. The decline is most

> 5 We are grateful to Amit Khandelwal for in-depth advice on how to estimate adjustment costs. We also thank Sam Asher, Leonardo Iacovone, Rinku Murgai and Bob Rijkers for helpful comments and suggestions. We thank Sarur Chaudhary, Ruifan Shi and Sargam Jain for superb research assistance.

> 6 See, for example, Chapter 10 in Government of India (2017).

> 7 We