rafmacalaba/data-use-mentions-extended huggingface dataset:
rule, however, is a mechanism to delay, but not to prevent such exits. We only consider the first exit of a family since we do not want to estimate any effects on re-entry.<sup>14</sup> If the head leaves the social registry CadUnico, but a different person from the same family receives the benefits and thus _converts_ to being the head, we classify this family as still _in BF_ .

**Formal labor market participation** We identify formal labor market participation by merging information from CadUnico and RAIS. We classify families as having at least one member in formal dependent employment if any adult family member can be found in RAIS in a given semester.

**Municipality-level employment growth** We calculate employment growth at the municipality level using RAIS data. RAIS reports the number of job links, which slightly differs from the number of workers since a small share of individuals holds more than one formal job. To calculate the current stock of employment, we use job link data for the private sector, additionally excluding public firms within it, as hiring policies in the public sector are expected to follow different mechanisms. Employment growth is measured as the difference between the current period’s number of private-sector employment links and the previous period’s number of private-sector employment links, divided by the number of private-sector employment links in the previous period.

# **3 Descriptive Statistics**

This section presents descriptive statistics of our sample. We start by laying out some characteristics of the individual data before describing some of the exit dynamics using the family data. In December 2012, the average age of individuals was 35. Individuals between 25 and 34 years old are the prevalent age group (32%), followed by individuals between 35 and 44 years old. In addition, 66% of individuals were female and 34% male, consistent with the targeting approach of the program (which privileges female household heads). The mean family size in 2012 was 3.6, and
89|0.75|0.00|
|Head of household|0.68|0.74|0.00|0.55|0.60|0.00|0.65|0.60|0.00|
|Household size|4.7|4.6|0.01|5.1|5.4|0.00|8.7|7.1|0.00|
|Number of dependents|1.7|1.5|0.00|1.7|1.7|0.27|3.7|2.8|0.00|
|Current migrants in the HH.|0.21|0.05|0.00|0.19|0.18|0.56|-|-|-|
|Rural|0.93|0.91|0.21|0.40|0.35|0.01|0.77|0.59|0.00|

_Source:_ Bangladesh HIES 2016-17; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. _Note:_ Sample is restricted to males aged 20-59. Sample weights are applied to summary statistics.
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between household income per capita and the schooling/work decision seems to exist for this case, either.

While there are differences in the various studies as reviewed above, there are some common points that stand out. Most convincingly, the existence of a gender gap in schooling - girls tending to be less likely to attend school than boys - seems to be a fairly well established result. Further, there does not seem to be consistent evidence of discrimination in child labor. Lastly, the often hypothesized relationship between poverty and child labor seems not to be well grounded in empirical studies. Together with other issues of the previously reviewed research as discussed above, these are issues that will be incorporated in this study.

# **3. Presentation of the Data and Preliminary Analysis**

In this section we present the data and perform a preliminary analysis of the incidence of child labor and primary schooling in Ghana.

# **3.1 The Data**

The data originates from the "Core Welfare Indicators Questionnaire" (CWIQ) 1997 for Ghana. The CWIQ is a household survey, collected by Ghana Statistical Service in collaboration with the World Bank in 1997. It covers a total of 14,514 households and 60,686 individuals. Among the information covered is age and relationship of individuals to the household head, the socioeconomic status of the household head, poverty quintile of the household, whether the household is engaged in agriculture and/or raising cattle or sheep, all of which are important factors in the analysis of child labor. To our knowledge, this sample has not been applied previously to study the issue of child labor in Ghana.

# 3.2 **Preliminary Analysis**

A child is defined as a person less than 15 years of age. This coincides with the end of the primary school age. Likewise, we define that the cut-off age between infancy and childhood is at the age of five. While, at least from a Western point of view, this may seem extreme
during the years 1999-2006, when lobbying expenditure from the financial sector is found to be positively associated to the probability that legislators switch their position in favor of deregulation of that industry. Also in the United States, Gilens and Page (2014) are able to show that economic and business elites are much more effective in having preferred policies passed (or unfavored policies blocked) than mass-based interest groups. Finally, Karabarbounis (2011) proves that more money is associated with more political power using a panel of OECD countries over the period 1975-2001, supporting the argument that political influence is not uniformly distributed across voters, but that richer individuals’ votes weigh more. But inequality trumps the political voice of the poorer also indirectly. In particular, Solt (2008) shows for the case of Europe that inequality depresses political interest and participation of everyone except the very affluent in the society. In fact, as argued in World Bank (2017), citizens experiencing a persistent status of deprivation and the unresponsiveness of the state with respect to issues of importance to them may decide to _opt out_ from the political process, therefore lowering further the probability that redistributive policies are passed.

10
to work in agriculture, compared to the unsuccessful self-employed (table 8). Men and women who are self-employed are equally likely to be successful, while self-employed who identify themselves as head of household are less likely to be successful than are spouses and other family members (table 9).

What happens to the unsuccessful self-employed as countries develop? As the per capita GNI of a country increases, the proportion of unsuccessful self-employed in both agriculture and non-agriculture falls, as the unsuccessful self-employed are absorbed into non-agricultural wage and salary employment and, to a lesser extent, as successful non-agricultural selfemployed (figure 10).

> 5 Households were identified as falling below the $2/day poverty line if the position in the distribution of per capita household consumption was less than the $2/day poverty rate reported by the POVCAL network of the World Bank. Where possible, we matched the reported poverty rate to the reported year of the survey.  Where this was not possible, we used the poverty rate calculated for the year before or year after.  Where there was a poverty rate reported in the POVCAL data for both the year after and the year before the reported year of the survey, we used the mean.

11
on the data summarized in Table 2, and measures described in the text.

# **4.4 Human Capital as an Alternative Mechanism**

A channel other than financial wealth accumulation that could foster self-employment after return is human capital accumulation abroad. The vast majority of temporary migrants from Bangladesh are employed in brawn-based, low-skilled occupations in the construction sector overseas – primarily in construction, with a minority in service activities such as retail or transport. While studies in other contexts suggest that some workers accumulate human capital overseas which benefits them in wage employment back home, this channel does not 21
to a drought as the supply effect dominates, but this is something we test in the analysis. The pattern may differ across crops depending on their elasticity of demand. For example, the increase could be more muted for teff, which has a high elasticity compared to sorghum and maize (Tafere _et al._ , 2010).

When markets are not autarkic, trade is possible, and hence prices are not determined by local market conditions only. Consider for example, when there is one other market, market _j_ , with which market _i_ can trade. When local conditions are such that the local market clearing price in market _i_ at time _t_ would be very high relative to market _j_ , trade from market _j_ becomes profitable— increasing supply and lowering prices in market _i_ . When local conditions are such that the local market clearing price in market _i_ at time _t_ would be very low relative to market _j_ , it becomes profitable to supply other markets with grain from market _i_ , reducing supply and increasing the price in market _i_ . Local supply and demand conditions determine prices within a band (the size of which is determined by the cost of trading between market _i_ and _j_ ). Outside this band, prices are determined by prices prevailing in other markets.

What does this mean for the impact of weather on prices? If there is some heterogeneity in weather conditions across markets, as is the case in Ethiopia where weather conditions vary considerably across the country, trade can limit the positive or negative impact of weather on prices.

With this in mind, we present four questions that we explore using a unique data set on weather shocks and prices in rural markets across Ethiopia from 1996 to 2012. The data set combines monthly retail price data for six grains collected from 82 rural markets with data on rainfall losses in the rural district surrounding the market.<sup>8</sup> In addition, data on participation of the district in a rural safety net program that started in 2005, and on market
al. (2000) also show that private fuel consumption is affected by availability (scarcity) and management institutions.

Although trees often have multiple uses, a received wisdom from the 1980s was that trees were seldom planted on private land for fuelwood purposes. There are indications that this situation is changing with increased demand for, and reduced access to, woodfuels. Arnold et al. (2006) report on evidence, primarily from East Africa, that tree management by farmers is on the rise resulting in increasing reliance on private fuelwood supplies. In a survey in the Ethiopian highlands, 72% of trees were planted primarily (37%) or partly (35%) to produce fuelwood. In a more recent survey from Ethiopia on the use of trees and forest (EEPFE, 2008), 50% of a sample of 600 households said that they

> 21 Households with fewer men and with more women had greaterWTP for a new plantation, indicating that more accessible fuelwood sources are especially important when less male labor is available.

17
can work with primary data but use similar data preparation steps to reduce the number of missing values and improve data quality.

Whoever processes the data, it is important to ascertain carefully what has been done in the “terra incognita” where neither origin nor destination has reported data. Have these values been set to zero as in the widely analyzed trade database used by SST (2006)? Or have modelbased predictions been introduced to fill out the dataset for their needs? Given the risk of bias— of unknown direction—from use of missing values identified in this paper, and the futility of reverse-engineering modelled data—excluding these data from the analysis seems likely to be preferable to their inclusion. When missing data have been set to zero, this does not upset the adding up conditions emphasized by Fally (2015).

The problem of unreported data remains important. Even though the number of economies reporting trade data to COMTRADE more than doubled between 1992 and 2014, over 60 economies identified as partners in the 2014 COMTRADE dataset did not report to COMTRADE. This problem is probably even more serious with dependent variables other than trade, such as foreign investment, foreign aid or migration (Artuc et al 2014). And the problem is not unique to curated data sets. Missing values masquerading as zeros arise when bilateral trade data arranged by importer-exporter are arranged into a bilateral trade matrix using tools such as an EXCEL pivot table.

# **VII. Summary and Conclusions**

Having good estimates of the key parameters for the gravity model has become critically important because of its increasingly wide use in economic theory, economic policy and 25
|
|Number of adult male age 15 to 60|1.190|0.899|1.189|0.900|1.193|0.898||
|Number of adult female age 15 to 60|1.303|0.917|1.280|0.880|1.339|0.971||
|Male members age 60 and above|0.116|0.321|0.116|0.320|0.117|0.322||
|Female members age 60 and above|0.122|0.330|0.130|0.341|0.108|0.311||
|Land holdings in hectares|0.932|1.742|0.909|1.805|0.968|1.639||
|Total number of households|22|68|13|69|88|6||

**Source:** Own computation from 2010 World Bank Land Tenure Regularization Survey

**Note:** Significance levels are reported for t-tests of the equality of the means for each of the variables between control and treatment areas (* significant at 10%; ** significant at 5%; *** significant at 1%).

19
of the Jordanian neighbor respondent’s grandparents are of non-Jordanian decent, indicating a likely family history of migration or displacement. The outcomes are as defined in table G1. The regressions have enumerator fixed effects, communitylevel controls (Irbid/Mafraq governorate and population quartile), refugee household-level controls (shelter program, baseline number of children, baseline number of children plus adults), the Marlowe–Crowne scale, and respondent gender, education, and age. In the parentheses are robust standard errors clustered at the locality level. Statistical significance represented by * (10%), ** (5%), and *** (1%).

Table (G15) Jordanian Neighbor Primary Outcomes Heterogeneity by: Proximity to Study Refugee Household

||(1)<br>Social Attitudes<br>and Perceptions|(2)<br>Economic Attitudes<br>and Perceptions|(3)<br>PolicyPreferences|(4)<br>Altruism to Syrians|
|---|---|---|---|---|
|Treat|-0.427**|-0.014|0.190|0.067|
||(0.204)|(0.191)|(0.215)|(0.285)|
|Treat*Above Median Distance|0.392|0.122|-0.018|0.283|
||(0.282)|(0.280)|(0.277)|(0.527)|
|Above Median Distance|-0.069|0.033|0.046|-0.036|
||(0.076)|(0.071)|(0.071)|(0.131)|
|p-value: T + T*Het|0.85|0.67|0.39|0.33|
|
the telecoms regulator and provide the conditions for a competitive market.

The monopoly of TDM was ended by decree and the government agreed to an auction of a second mobile license (the first being held by the incumbent). A second license was granted to Vodacom in August 2002. Meanwhile the EMPSO recognized, as a second tranche condition, the submission of a new telecommunications law to parliament which would provide for competitive allocation of licenses. The EMPSO operation was approved by the Bank’s Board in late August 2002 and the second tranche was released in 2004.

The effects in the real economy took but a matter of months. For the first time ever, TDM ran an aggressive advertising campaign, sending out trucks of young people in orange uniforms to hold banners promoting the TDM mobile operation MCel. Prices of mobile phones fell even

> 22 One way of demonstrating directly that it is the process that induces policy improvement would be to obtain data on analytical work done by the World Bank, the government and other instances and construct appropriate variables which could be inserted into the main equation (8).

23
<!-- Start of picture text --> 1<br>0,8<br>0,6<br>0,4<br>0,2<br>0<br>-0,2<br>-0,4<br>-0,6<br>-0,8<br>-1<br>Kingdom Korea Italy Lithuania Malta Spain Colombia Belgium Portugal AfricaSouth Denmark Latvia Canada Brazil Greece Uruguay France Australia Mexico Honduras United States Austria Costa Rica Japan Germany Peru Sweden Bolivia Philippines Norway Argentina Chile<br>United<br><!-- End of picture text --> _Source:_ Min. Economy, INDEC. _Notes:_ the cyclical components of GDP and tax rates have been estimated using the Hodrick-Prescott filter. A positive (negative) correlation indicates a countercyclical (procyclical) tax policy.

However, as previously mentioned, traditional taxation in Argentina is less representative due to the significant role played by a wide range of heterodox taxes, as depicted in **Figure 14** . Unusual sources of revenue, which are typically more distortive, contribute to almost one-third of taxation in Argentina. Unfortunately, the availability of data poses a constraint when attempting to expand the analysis to include all sources of taxation, including these heterodox taxes. Specifically, the turnover tax presents extreme complexity as it is collected in different ways by each of the 24 provinces. Furthermore, it varies across economic sectors, and historical data availability is limited. Consequently, it becomes challenging to establish a single historical series to measure the effect of cyclicality. However, for export duties and financial transaction taxes, both collected at the federal level, readily available information is accessible.

Export duties and financial transaction taxes in Argentina exhibit a negative correlation with the economic cycle. Historically, export duties were initially implemented as emergency measures to address the inflationary impact of significant peso depreciation, typically during balance of payment crises. In line with this, export duties
.263|0.686|-0.395|
|Summer|_Cold_|-0.321|-1.008|-0.912|
||_Mild_|0.2|-1.008|-0.912|
||_Hot_|0.174|-1.008|-0.912|
|Fall|_Cold_|-|0.685|0.0002|
||_Mild_|-|0.685|0.0002|
||_Hot_|-|0.685|0.0002|

**Table 5** . Share of energy demanded for heating and cooling purposes, by energy source and climate region.

Source: U.S. Residential Energy Demand Database (www.eia.gov)

||**Elect**|**ricity**|**Gas**|**Oil P.**|
|---|---|---|---|---|
|**Climate**|Heating|Cooling|Heating|Heating|
|_Cold_|8%|5%|72%|88%|
|_Mild_|9%|17%|56%|86%|
|_Hot_|7%|28%|48%|86%|

17
school and out of work: Evidence from the world’s largest humanitarian cash transfer program. _Journal of Development Economics 168_ , 103266.

- Bahar, D., A. M. Ibáñez, and S. V. Rozo (2021). Give me your tired and your poor: Impact of a large-scale amnesty program for undocumented refugees. _Journal of Development Economics 151_ , 102652.

- Baird, S., J. De Hoop, and B. Özler (2013). Income shocks and adolescent mental health. _Journal of Human Resources 48_ (2), 370–403.

- Baseler, T., T. Ginn, R. Hakiza, H. Ogude-Chambert, and O. Woldemikael (2023). Can redistribution change policy views?: Aid and attitudes toward refugees in uganda. _Center for Global Development, Working Paper 645_ .

- Beltramo, T., F. Nimoh, and O. Matthew (2024). Financial security, climate shocks and social cohesion. _Center for Economic Policy Research, Working Paper_ .

- Burke, M., S. M. Hsiang, and E. Edward Miguel (2015). Global non-linear effect of temperature on economic production. _Nature_ .

- Chetty, R., N. Hendren, and L. F. Katz (2016, April). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. _American Economic Review 106_ (4), 855–902.

- Crowne, D. P. and D. Marlowe (1960). A new scale of social desirability independent of psychopathology. _Journal of Consulting Psychology 24_ (4).

- DellaVigna, S., N. Otis, and E. Vivalt (2020). Forecasting the results of experiments: Piloting an elicitation strategy. _National Bureau of Economic Research_ .

- DellaVigna, S. and D. Pope (2017). What motivates effort? evidence and expert forecasts. _The Review of Economic Studies_ .

- Egger,
, less detailed, dataset to show that a similar negative correlation of migration and bribe paying exists also in data independent of ours. The so called _Barometer of Public Opinion_ of Moldova’s Institute for Public Policy, a well-regarded biannual survey which collects individuals’ opinions on a wide range of topics regarding politics, values, and related issues in Moldova, covered informal payments to authorities and migration status in the April 2013 survey.<sup>35</sup> Those individuals with migration experience to the West were more likely to have had contact with the justice system and were more likely to have been asked for bribes for the

> 33. There is no statistically significant correlation between any school budget variable and the migration share of pupils in the household survey. Also, if migrant parents were planning to send their children abroad and therefore stopped paying local teachers, there should be strong differences within schools.

> 34. Alternatively, the main effects of migration on the provision of educational inputs remain unchanged after including the perceived school quality variable as an additional control (available upon request). 35. The sample contains 1100 individuals from 76 communities and is nationally representative of the adult population. All results are available upon request.

16
the 2001 Population Census of India, 11% of India’s population consisted of individuals who had moved permanently to their current district from a different district at some point in the past. In the 2011 Census, this share had increased to 14 percent.<sup>6</sup> Using railway data, the Government of India’s 201617 Economic Survey Report estimates a doubling of internal migration rate from 1991-2001 to 2001-2011 (Government of India, 2017).<sup>7</sup> India’s reputation as a low internal migration country is also being reconsidered by economic historians (Cohen, 1995; Tumbe, 2018; Rajan and Sumeetha, 2019). Migration in the past two centuries consisted mostly of young men migrating via caste networks to areas of economic opportunity, returning every few years, and eventually settling back into their home villages. This type of migration was common place: Tumbe (2018) estimates that 150 districts experienced mass migration during the past 150 years (defined as more than 20% of households affected). Migration has increased in the last few decades and is more permanent in nature. The high-skilled are increasingly dominant in internal migration flows (Docquier and Rapoport, 2012). Yet, this recent increase in migration builds on the migration networks established in the past century and a half (Tumbe, 2018).

The contribution of this study is to link migration to changes in land tenancy. This link first became clear to us during a field visit in rural Utter Pradesh, North India, in 2008, when we spoke with landlords about their contract choice. We then confirmed these patterns with historical accounts from other parts on India. Reddy (1996), for instance, collected land contracts and interviewed farmers in Andhra Pradesh, South India, spanning the period 1850 to 1980. He repeatedly notes the unique role that absentee landlords play in the rural economy. His analysis suggests that concerns about moral hazard cause absentee landlord to prefer contracts involving simple, once-per-

>