rafmacalaba/datause-encoder-data huggingface dataset:
|Govt. Funded Y:1|0.490|
|---|---|
|N:0*Number of||
|workers (logs)||
||(0.586)|
|Govt. Funded Y:1<br>|-0.052|
|N:0*Female owner<br>||
|present Y:1 N:0||
||(1.247)|
|Govt. Funded Y:1|29.313|
|N:0*Gender parity||
|in primary education<br>||
|enrollment||
||(26.603)|
|Govt. Funded Y:1|-0.078**|
|N:0*Gender parity||
|in workplace laws||
||(0.038)|
|Wage rate (real,<br>|0.002|
|logs)*Gender parity<br>||
|in workplace laws||
||(0.008)|
|Square of Females:<br>|1.217**|
|20-29 yrs. old (%)||
||(0.554)|
|Females: 20-29 yrs.|1.103|
|old (%)*GDP per<br>||
|capita (logs)||
||(1.073)|
|Females: 20-29 yrs.<br>|0.173|
|old (%)*Number of||
|workers (logs)||
||(0.229)|
|Females: 20-29 yrs.|-0.303|
|old (%)*Female<br>||
|owner present Y:1||
|N:0||
||(0.474)|
|Females: 20-29 yrs.|-52.807**|
|old (%)*Gender<br>||
|parity in primary||
|education<br>||
|enrollment||
||(24.971)|

78
# **_Table 7a: Heterogeneous Impact of COVID-19 School Closures by Men’s Beliefs on Gender Equality (Conservative versus Liberal Societies)_**

## **_Married Sample with child dependents_**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Busine|s closed||Ti|me Spent o<br>|n(Hoursp <br>|er day)...||
||<br>(1=Yes|<br>; 0=No)|Paid|Work|Care and<br>Respon<br>(Se|Domestic<br>sibilities<br>lf)|Care and<br>Responsibili|Domestic<br>ties (Spouse)|
||_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19 (Yes=1)|0.483**|0.025|1.196|-4.772**|7.505***|-0.080|6.792***|2.221|
||[0.22]|[0.14]|[3.65]|[2.39]|[2.86]|[1.54]|[2.25]|[2.10]|
|Men's Individual Beliefs (Share 0-1)|0.054|0.044|9.633***|1.033|-1.351|2.304*|3.300*|-5.397***|
||[0.14]|[0.12]|[3.09]|[2.01]|[2.66]|[1.40]|[1.95]|[1.87]|
|School closure due to COVID-19*Men's Individual Beliefs (Share)|-0.491**|-0.032|-1.074|5.710**|-7.448**|0.251|-7.390***|-2.044|
||[0.24]|[0.15]|[3.96]|[2.64]|[3.09]|[1.70]|[2.44]|[2.32]|
|GDP per capita (Ln)|0.006|-0.026***|0.055|0.377***|0.193**|-0.029|-0.302***|0.112|
||[0.01]|[0.00]|[0.10]|[0.09]|[0.09]|[0.05]|[0.08]|[0.07]|
|Constant|-0.094|0.183|-1.022|4.225**|3.288|1.456|3.077|8.758***|
||[0.16]|[0.12]|[3.25]|[2.12]|[2.53]|[1.39]|[1.94]|[1.92]|
|Observations|11692|23578|6794|13759|11692|23578|8806|17960|
|Adjusted R-squared|0.10|0.09|0.07|0.09|0.14|0.04|0.06|0.07|
|Mean|0.14|0.13|6.83|7.95|6.38|4.09|3.60|6.21|
|Sample<br>Notes:* significant at 10% level, ** significant at 5% level, *** significant at|FEMALE<br>1% level.|MALE|FEMALE|MALE|FEMALE|MALE|FEMALE|MALE|

[1] School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of survey wave. Men's Individual Belief is the proportion of men in the country who agreed with the statement that _men and women should have equal opportunities_ measured prior to the COVID-19 pandemic.

[2] OLS pooled regressions include Region of the world and Time Fixed Effects in 2020 and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents). Academic Break control is included in the regressions but not shown in the table.

[3] Sample is restricted to business owners who are married or living with a partner with any child dependents aged 18 years or younger in the household and is split by gender with FEMALE (Columns (1), (3), (5), and (7)) versus MALE (Columns (2), (4), (6), and (8)).

43
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27
- 21 -

In general the overviews indicate a mixed response to either chronically inadequate or reduced modern sector employment growth. Riveros (1988c), based on PREALC/ILO data, has found a rather small increase in informal sector employment in most countries in spite of the large macro fluctuations experienced in the 1980's. There are nonetheless some differences among countries, depending upon how the urban informal sector is measured. For instance, in both Argentina and Brazil, the urban informal bector has been a major absorber of the increased labor supply during the 1980's while the open unemployment rate has failed to increase. In Chile, informal sector employment actually fell between 1981 and 1983 (Edwards (1986)], while the open unemployment rate nearly doubled over the same period. In contrast, Uruguay and Mexico have experienced a sharp increase in urban informal employmeat in the 1980s. which in the case of the former has been accompanied by an expansion in open unemployment [Riveros (1988a, 1988c)]. In Bolivia, the informal sector absorbed part of the shortfall not taken up by above normal public sector employment growth, unemployment remained almost static, and the participation rate dropped significantly. This last finding, while consistent with the discouraged worker hypothesis discussed earlier, should be interpreted with some care as it may suggest also an increase in hidden urban unemployment [Klinov (1987)].10 Howeve;:, Bolivia seems to be the only country in the sample within which this effect v'as strong enough to offset a major part of the expected imbalance between the growth of labor supply and modern sector employment.

Inevitably, the evidence for other countries in the sample is somewhat scanty given a lack of suitable time-series data. In the Sub-Saharan African countries there is little doubt that informal sector employment has been expanding very rapidly. In Kenya, for example, the informal sector doubled its share of total urban employment between 1973 and 1984. In
# 2. **Indicators of Governance**

In this paper, we use data frorn 31 different indicators of governance constructed in 1997 and/or 1998. These indicators are drawn from 13 different sources and are grouped into three clusters corresponding to rule of law, government effectiveness, and graft. The key features of these indicators are summarized in Table 1, and a detailed description of the sources and variables can be found in Kaufmann, Kraay and ZoidoLobat6n (1999). In the first two columns of Table I we identify each source of governance data by abbreviation and by name. In the next three columns, we report the source of information for each measure (surveys of residents or polls of experts), the country coverage, and a measure of the extent to which the sample of countries covered by each indicator is representative of the population of countries in the world. In the remaining columns we report the specific concepts measured by each source in each of the three clusters. 3 A quick look at Table 1 shows that these indicators differ along several dimensions. First, even within clusters there is considerable variation in the particular concept measured by each indicator. For example, questions about graft range from the incidence of "improper practices' (WCY) to the likelihood that additional payments are required to "get things done" (WDR). Similarly, questions regarding the rule of law range from whether citizens can successfully sue the state to whether citizens are likely to resolve disputes extra-judicially. Despite this heterogeneity, we take the view that within each cluster, each of these concepts is an imperfect indicator of the corresponding broader concept of governance.

The second respect in which these indicators differ is in the nature of the respondents who provide the information. Slightly less than half of the indicators are surveys of businesspeople and/or residents of a country, while the remaining indicators are polls of experts who rate a set of countries according to various criteria. As we discuss in more detail later in the paper, this difference between these two types of

> 3 For a number of these sources, we use the average of several questions relating to the corresponding core concepts of govemance. As we discuss subsequently, we are reluctan **t** o include individual questions from a single source separately in our analysis, as the necessary assumption that measurement errors are uncorrelated across indicators is much more difficult to support for the case of multiple questions from a single source.

5
A few remarks are in order regarding our migration variable. First, a general caveat is that it measures whether a household sent one or more migrants in-between waves, but it does not incorporate detailed information regarding the nature of these migration decisions. Therefore, based on the conceptual framework illustrated in Section 2, we are assuming that most of the migration observed in the data is voluntary and economically motivated. To be able to disentangle economically motivated versus involuntary and low-return migration and thoroughly identify the respective transmission channels, we would need more detailed data on migrant individuals e.g., regarding the purpose of their migration, their current location, work status, and financial links with the origin household. Second, despite these limitations, our definition of migration is more precise than other recent studies using household survey data (e.g., Di Falco et al., 2023; Kafle et al., 2020; Mueller et al., 2020) which simply used changes in household size or short-duration absence from the households as proxies for migration. Third, consistent with the conceptual framework outlined in Section 2, it is defined at the household rather than individual level. This is important not only from a conceptual point of view (the household as the decision-making unit, rather than the individual, in line with the NELM perspective), but also for technical and practical reasons due to the rare-event nature of the migration phenomenon. One of the main concerns when using nationally-representative household surveys for studying migration is that the migrant sample size is often (too) small. While this is certainly a limitation if one wants to infer individuallevel relationships, it is a less serious problem for household-level statistical analysis. As noted by Carletto et al. (2023), collapsing individual migration data at the household level provides enough of a gain in statistical power and sufficient outcome variation needed for estimation. In our case, we observe just above 3% of migrant _individuals_ across the panel (2010-2019) but almost 27% of migrant-sending _households_ over the same period.

We then integrate household data with gridded geospatial weather information collected from third-party, publicly-available sources using spatially anonymized household GPS coordinates.<sup>9</sup>

> 9 Spatial anonymization is standard practice in georeferenced household survey data that are publicly available, to protect the privacy of the respondents. Michler et al. (2022) have recently shown that spatial anonymization methods currently implemented in longitudinal surveys supported by the LSMS do not exert any meaningful impact on estimates of the relationship between weather and agricultural outcomes. In particular, the authors conclude that researchers need not be concerned about potential inaccuracies that may be introduced by integrating spatially-

13
As is subsequently discussed, the empirical strategy relies on the geographical coordinates of communities. Consequently, the data are restricted to the communities (and associated households) for whom these data are available and correct.<sup>10</sup> Additionally, households without any consumption of food (purchased, free, or own production) or which had abnormally high holdings of land (>200 acres as compared to mean holdings of 3.7 acres with a standard deviation of 5.4) are not included.<sup>11</sup> The remaining analysis is based on 353 communities and 3,508 households for whom data were available.<sup>12</sup> The NUS data are supplemented with data from the Armed Conflict Location and Event Data (ACLED) for Uganda (Raleigh and Hegre 2005). The NUS data only include data on community level attacks in 1992, 1999, and 2004. By providing additional georeferenced data for the location of LRA attacks from 1997 until 2003, ACLED allow both for a larger set of instruments and a more accurate “map” of violence. Additionally, insofar as the behaviour of households changes based on their distance from violence, ACLED should result in more precise estimates of the effects of the risk of violence.<sup>13</sup> I use only events that are violent, involve the LRA, and occurred in 2003 or earlier. Additionally, since the precision of the geographical coordinates

> and risk. There are, however, also strong reasons to believe that the inclusion of wealthier households would lead to a larger impact of insecurity. In particular, in times of conflict and insecurity, their income generating activities would likely also suffer, and they would have more room to reduce their consumption before reaching minimal subsistence levels. Unfortunately, since the sample only contains current households, this hypothesis cannot be investigated.

> 10. For 33 communities, the recorded coordinates fall outside of the boundaries of Uganda, and therefore these communities have been dropped. Since this is based solely on the recorded coordinates, there is no reason to believe that any systematic differences exist in these observations.

> 11. While possible for households to live an entire week without receiving any food aid or food gifts, purchasing food, or consuming their own production, this is highly unlikely. Since this appears to be a clear coding error, these households are dropped. Their inclusion does not change the analysis.

> 12. The variables for the distance to the nearest attack are an exception. These were created using all the data—rural or urban—for which correct geographical coordinates were available.

> 13. The ACLED data are drawn from a variety of sources including press accounts, books, and humanitarian worker accounts. The data are disaggregated by event type, year, participants, and geographical coordinates.

9
Aside from access to regular economic data people need infornation on a variety of issues related to public sector activity. They need timely information on decisions related to various aspects of government activity, on how these decisions will be implemented, information on the consequences of these decisions and the process through which they are reached. Yet in many countries access to this type of information is very limited either because of the laws or regulations which restrict access, or simply because the administrative capacity to organize and disseminate information does not exist. Laws facilitating access to information held by the public sector can play an important role in increasing information flow and facilitating the monitoring government. This paper examines how the presence of Freedom of Information (FOI) laws may affect how countries govern. Of course, the extent to which better information will affect choices of course depends on how people can act upon their choices- many other laws affect this ability (e.g. insult and defamation laws). I focus on only one of the several possible relevant laws. Data

# The Transparency Index

In order to investigate the relevance of widely available economic data for the quality of governance, I created an index which I call the "transparency" index. I take 11 representative variables from 4 sectors: the real, fiscal, financial and external sectors for a total of 169 6
# Table of Contents

|I.|OBJEC|TIVE||1|
|---|---|---|---|---|
|II.|CONCE|PTUAL|FOUNDATION|2|
||A.|Intro<br>|duction<br>|2|
||B.|Altrui|sm|3|
||C.|Patern|alism|3|
||D.|The U|tility<br>Possibility<br>Function|5|
|III.|THE|INALYTIC|AL<br>MODEL|7|
||A.|Object|ive||
||B.|Descri|ption|7|
||C.|Method|ology|9|
||D.|Assump|tions<br>of the Model|11|
||E.|Soluti|on<br>Technique|13|
||F.|Develo|pment<br>of the Model<br>-<br>Paternalism|15|
|||with|Price<br>Subsidies<br>(One-Bundle<br>Case)||
|||1.|The Problem<br>of the Poor|16|
|||2.|The Problem<br>of the Rich|17|
|||3.|The Problem<br>of the Government|18|
|||4.|Comparative<br>Statics|19|
|||5|The Optimal<br>Subsidy|22|
|||6.|Cobb-Douglas<br>Application|23|
|||7.|Paternalism<br>with Cash Transfers|25|
|||8.|Subsidies<br>vs Cash Transfers<br>in General<br>Form|26|
|||9.|Subsidies<br>vs Cash Transfers<br>under|28|
||||the Cobb-Douglas<br>Model||
|||10.|Altruism|31|
||G.|Patern|alism<br>with<br>Subsidies<br>(Two-Good<br>Case)|32|
|IV.|EXTENS|IONS<br>|TO THE MODEL|35|
|V.|CONCLU|SIONS||37|
|VI.|REFERE|NCES||39|

This paper is part of a Ph.D. Dissertation in Economics at Georgetown University and motivated while at the World Bank LATIE Division. I wish to thank L. Bassi, T. Clements, J. Hicks, R. Kanbur, B. Kugler, J. Tybout, and K. Zheng for their useful comments.
Policy Research Working Paper 11000

# **Abstract**

This paper estimates the impact of bilateral labor arrangements on migration between two countries. It uses comprehensive data on bilateral migration and bilateral labor agreements across all country pairs for each decade from 1960 to 2020, and employs an empirical specification with a rich set of fixed effects. In the preferred and most stringent specification, the findings show that signing a bilateral labor agreement increases migration from an origin country to a destination country by 76 percent (0.57 log points) in the decade of signing. The effect persists for up to three decades. The impacts are higher for corridors without a pre-existing regular flow and for destinations in the Gulf Cooperation Council. In contrast, the effect is virtually absent for origin countries in Africa, driven by countries with weak government effectiveness. The estimates imply that bilateral labor agreements can lead to substantial welfare gains: low- and lower-middle-income countries can earn an additional US$120 million annually from a bilateral labor agreement. If countries in Sub-Saharan Africa were to experience similar impacts, the welfare gain from a single BLA could be as high as US$51 million per year for these origin countries.

This paper is a product of the Social Protection Department and Development Economics. 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 mshrestha1@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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 of the World Bank or the governments they represent._ Produced by the Research Support Team
24 The figure shows that recent growth has brought the urban poverty rate approximately on track to achieve the MDG by 2015. By the interpretation used here, the MDG target urban poverty rate for 2001 was 22.9 percent, while the estimate of the actual value from the 2000/01 HBS survey was 23.3 percent.<sup>13</sup>

# **Figure 13**

<!-- Start of picture text --> Poverty incidence and the MDG goals<br>simulated poverty based on two-survey approach, stratum distribution<br>1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>All urban areas (including DSM) Rural areas<br>MDG, urban target MDG, rural target<br>.45<br>.4<br>.35<br>.3<br>.25<br>.2<br>Fraction below poverty line.15<br>.1<br><!-- End of picture text --> The rural poverty rate remains substantially above the path necessary to achieve the MDG target. The MDG target for rural areas is attainable, but it will require sustaining growth at or above the rates achieved in 2001 and 2002. The rural GDP per capita growth rate was 2.6 percent in 2001 and 2.1 percent in 2002. A simulation forward from the 2000/01 HBS data, assuming distributionneutral growth, implies that the rural poverty reduction target will be met if a rural GDP growth rate of 2.3 percent per capita is maintained through 2015. The estimate of 2.3 percent is a lower bound, assuming no increase in inequality. If future growth is accompanied by increases in inequality, a growth rate greater than 2.3 percent in GDP per capita will be needed to achieve the MDG target. Assuming rural population growth net of migration continues at the rate of 2.5 percent annually and inequality does not increase (the national growth rate 1988-2002), rural GDP growth will need to reach 4.8 percent to achieve the MDG target.

Similarly, a simulation done for the entire country on the basis of a single national growth rate shows that annual GDP growth for the country as a whole will need to reach 5.1 percent to achieve the MDG target, assuming no increase in inequality and no change in the population growth rate. Figure 14 shows the GDP growth rates that would be needed under alternative population growth rates and inequality scenarios. The numbers underlying the figure were calculated from simulations assuming increases in the Gini coefficient in the form outlined in section 4.1. Because

> 13 Note that the urban poverty estimate for 2002 which is below the MDG path is from a one-year extension of the simulation, rather than from the HBS survey directly.