rafmacalaba/datause-encoder-data huggingface dataset:
state. The transitional dynamics of each country are interesting and revealing in themselves. But rather than attempting to describe all these dynamics, we prepared a typology of responses that are prevalent in our sample.

In all countries, on impact, the real wage in Food and Beverages declines. The decrease in food prices causes a loss of profitability in the sector that translates one to one to nominal wages. The decline in the price index (CPI) is proportional to the food share (which is less than 1). Real wages increase in the rest of the economy, both in the residual sector and in other manufactures, because of the increase in purchasing power (with constant nominal wages and a lower CPI). There are sizeable differences in these initial responses because the weight of food in the price index varies across countries.

The resulting changes in intersectoral wage differentials create incentives for workers to move away from the food sector. The real wage in the sector thus gradually starts increasing. It is remarkable that, in most cases, real wages actually recover and are in fact higher in the new steady state than in the initial steady state. There are only four exceptions, namely Latvia, Romania, Costa Rica, and South Africa. There are, however, significant differences in the time it takes to recover. In Azerbaijan, Russia, or India, for example, the recovery occurs in only 2 years (after the initial wage decline—year 3 of the transition). In Peru, in contrast, it takes 12 years, and in Turkey, 10 years.

Employment in the shocked sector declines, as expected, because firms shrink. It is noteworthy that food workers flow to other traded sectors rather than to the residual sector. In fact, in no country does the residual sector grow. It is not easy to track down why this happens, because the result follows from a combination of the large size of the residual sector, which thus only slightly reacts, of both the initial wage and the compensation differentials, and of the wage responses themselves. The analysis of wage reactions shows that both wages increase (as already pointed out) and that the increase in the wage of the other traded sectors is always larger than the increase in the wage of the residual sector. Only India shows similar responses in these two wages after the second year of the transition. In addition, the real wage in other traded sectors first increases, but then declines. However, in no country are the responses reverted (so real wages in the new 14
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, Canagarajah and Coulombe (1997) report that "a child begins to work as early as five years in rural Ghana". Thus, at the outset the sample is Ghanaian youth 6
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_Table 6 Regression of annual consumption on treatment status. The dummy variable for treatment by T2, the diary arm, is the omitted category. Column 1 (2) shows a regression of (natural logged) per-capita at-home spending on treatment groups, column 3 (4) regresses (natural logged) per capita total food spending on treatment groups, and column 5 (6) regresses (natural logged) per capita total spending (including takeaway, health, housing and education spending) on treatment groups. All values are in 1000 VND._

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||FAFH|At-home|Total food|Total<br>consumption|Percent at-home items<br>answered|
|T1: 7-day one-line recall|-2,818***|991|-1,828*|-923|-0.00|
||(548)|(746)|(1,038)|(2,762)|(0.01)|
|T3: 7-day individual recall|-1,847***|1,815**|-33|161|-0.00|
||(549)|(746)|(1,039)|(2,764)|(0.01)|
|T4: 7-day HH||||||
|bounding/salience|-883|660|-224|1,876|-0.01|
||(549)|(746)|(1,039)|(2,765)|(0.01)|
|Constant|8,527***|20,272***|28,799***|64,747***|0.41***|
||(389)|(529)|(736)|(1,958)|(0.00)|
|Observations|1,904|1,904|1,904|1,904|1,903|
|R-squared|0.015|0.003|0.002|0.001|0.001|
|T1=T3|0.08|0.27|0.08|0.69|0.63|
|T1=T4|0.00|0.66|0.12|0.31|0.20|
|T3=T4|0.08|0.12|0.85|0.53|0.42|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 In an attempt to explain this puzzle, we explored potential explanations linked to field implementation. In the one-line (T1) and individual-level (T3) recall arms, at-home consumption was collected in the first and only visit to the household, before asking about FAFH. In the diary (T2) and bounding/salience (T4) arms, at-home consumption was collected on the last visit.<sup>18</sup> We thus might expect that the length of the survey, the order of modules, or other components of this setup to have an impact on the at-home responses (Kilic & Sohnesen, 2017). However, the difference in reporting only arises in one of the treatment arms 18 The suggested protocol was to implement the whole consumption module in the last visit, so that the reference periods for the different components coincide (i.e. at-home and FAFH consumption correspond to the same week). Everything but the consumption module should have been implemented in the first visit. In practice, however, some interviewers collected some consumption information in previous visits.

24
columns of table 4, the dependent variable is an indicator for whether the child is active (participates in market work or household production for at least 7 hours in the last week). In columns 3-8, the dependent variable is the hours worked per week in market work (4 and 7), household production (5 and 8), and combined (3 and 6). Columns 3-5 are OLS regressions while columns 6-8 contain results from a Tobit specification. Because the Tobit and OLS results are similar, the discussion focuses on the OLS results. Only OLS results are presented in the remainder of the paper.

Interpreting the results for participation in table 4 requires an understanding of how child labor supply varies with household composition in 1993 after controlling for variation in child labor associated with all of the regression controls in table 4. The coefficient on a non-interacted household composition variable is the relationship between the household composition variable and child labor when the log rice price is zero. The average change in child labor associated with the addition of a person in a given age group is computed by adding the regression coefficient in table 4 for that age group to the product of the coefficient on the interaction of that age group total with the mean log of rice prices (1.05 is the mean log rice price in the pooled 1993 and 1998 data). That is, based on column 2, the average change in the probability that a child is active with the addition of a person between the ages of 0 and 5 is 0.095-0.48*1.05, or each additional child 0-5 increases the probability that a child 6-15 is active by 4 percentage points. By any of the measures in table 4 (i.e. in each column of table 4), child labor is higher when there are more children 0-5 present and lower when there are more young adults (16-24) or prime age adults (25-64) present. With elders (age 65 plus) and persons in the same age category (6-15), more household members in each group is associated with reduced time in household 29
practices. Nevertheless, the actual amount of schooling of the student cohort affected by COVID19 will be compromised if no mitigation or remediation takes place. In addition, the economic shock is likely to affect student drop out, and we should expect long term consequences.

We used microdata from the latest available household survey for 130 countries to estimate country specific dropout-income elasticities using the observed cross-sectional variation between educational enrollment and welfare. Following the HCI framework, we estimated this relationship for pre-school and primary-age students (4-11) and secondary-age students (12-17) separately (for more information see Annex A.2). If a country did not have a household survey, we used the average values from the countries in the same income level classification. In alignment with the existing literature, on average, older-age students seem to be more vulnerable to income shocks than younger students. The patterns for high and upper middle income countries are distinct from those of low- and lower-middle income countries.

The primary pathway for this is the income shock _(γ)_ from reduced economic activity. Given the dropout-income elasticity _(d),_ this will lead to more families pulling their children out of school to work (which particularly affects children in the secondary school age group), or because they cannot afford schooling.

There are important differences across countries in terms of gender and being out of school. In low- and lower-middle income countries, girls are more likely to be out-of-school especially at the 12-17 age range. In contrast, in upper-middle and high-income countries boys are more likely to be out of school. Despite this, the estimations of dropout-income elasticities show no systematic differences between boys and girls (see Figure A.2.1).

As more data become available in terms of policy actions taken by government, the behavior of households, and the effectiveness of the proposed alternative modalities, we will be able to refine some of these assumptions. The availability of additional data is particularly important to better understand the intrahousehold dynamics and school safety concerns, both of which are likely to have significant effects of school dropouts and gender differences. Figure 5 illustrates the main transmission channels described in this section: 15
a different Local Government Area (LGA) within the country or in a different country.<sup>37</sup> Table 1 reports simple descriptive statistics that show how to perform some preliminary back-of-theenvelope calculations to assess and address the ‘rare event’ issue in household surveys. A ‘migrant individual’ is an individual that was present in the previous interview but has now moved either internally (at least to a different LGA) or internationally. A ‘migrant-sending household’ is a household where there is at least one migrant individual in a given survey wave. In the case of the NUPD there is a mere 3 percent of migrant individuals, implying that migration is a statistically rare event in the individual sample. Conversely, almost 20 percent of households sent at least one migrant, meaning household-level migration is roughly 7 times greater than individual-level migration. The household-level share cannot be considered a ‘rare event’ outcome if we use a rule of thumb of 10 percent threshold for the underrepresented class.

**Table 1: Migration-related descriptive statistics for Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|Migrant individual|0.030|0.170|0|1|121,690|
|Migrant-sending household|0.195|0.396|0|1|19,249|
|Household size|6.322|3.451|1|34|19,249|

This simple exercise makes an important point: it is almost always true that there is a ‘rare event’ issue in standard household surveys for the study of (climate) migration, but only when looking at _individual_ migrants. Shifting to the household as the unit of analysis, the ‘rare event’ issue will be much attenuated (and possibly even eliminated), allowing the achievement of adequate statistical power for estimating household-level migration responses to climatic stress. This technical case for using households as the statistical population of interest compounds with the theoretical reasons provided in the conceptual framework, which suggest that the mobility response to weather and climate shocks is a decision mostly taken at the household rather than at the individual level, both in the case of economically-motivated migration in the face of slow-onset changes, and for ‘survival’ migration in response to fast-onset shocks.

> 37 LGAs are the second administrative level in Nigeria, and we use them to capture internal migration and exclude absent individuals who simply went to live on their own nearby. Even though our definition encompasses international migration other than internal mobility, the number of international migrants is minimal: 3.1 percent of the migrant sample and 0.0009 percent of the full sample.

25
# _3.3. Inter-regional vs. intra-regional investments_

Despite its sizeable share in total EAP’s cross-border investments (considering investments with the North, the South, and with itself), intra-regional investments are on average smaller in size than interregional investments. But EAP’s intra-regional investments are larger than those with the South and, for some investment types, they are as large as (if not larger than) those involving North economies. For example, EAP’s intra-regional investments and investments with the South accounted for about 24 percent and 2 percent of EAP’s total cross-border portfolio investments in 2014, respectively (Figure 6). EAP’s investments with the North captured the rest, about 74 percent. Similar shares are observed for syndicated loans. Regarding FDI, EAP’s intra-regional investments and EAP’s investments with the South represented on average 44 percent and 10 percent of total cross-border M&A flows, respectively, for the 2003-2014 period, and 48 and 19 percent of total cross-border greenfield investments during the same period. Note that, akin to the trends described in Section 3.1, the share of EAP’s intra-regional investments and investments with South tends to be greater in FDI than in arm’s length investments.

Regarding the evolution of these shares over time, intra-regional investments grew on average faster than their respective inter-regional counterparts for all investment types during 2003-2014. Intra-regional investments also grew faster than investments with the North, though they grew slower than investments with the South. These trends suggest that EAP’s investments with the South have been catching up with intra-regional investments over time.

# **4. A gravity model of EAP’s cross-border investments**

Next, we analyze more formally the region’s financial integration patterns documented thus far. Specifically, we estimate a gravity model using the Poisson Pseudo-Maximum-Likelihood (PPML) 14
# AN EMPIRICAL ANALYSIS OF MEXICAN MERGER POLICY<sup>∗</sup>

Marcos Avalos<sup>†</sup> and Rafael E. De Hoyos<sup>‡</sup> †Faculty of Economics and Business, Anáhuac University ‡Development Prospects Group, The World Bank JEL Classification: L51, D43, C35 Keywords: Competition Commission, Mexican Merger Policy _._

> † Av. Lomas Anáhuac s/n, Fracc. Lomas Anáhuac Huixquilucan, México, C.P. 52786.. Tel. 52(55) 56 27 02 10 Ext. 8627 Fax 8120 Correspondance Author: <u>mbracho@anahuac.mx</u>

> ‡ Development Prospects Group, WB, 1818 H Street, NW, Washington, DC 20433Tel: 202-458 4823 Fax: 202-522 2578 <u>rdehoyos@worldbank.org</u>
**Table A9.3. The Effect of the PMT on the Probability of Stunting Among Children 24 to 60 Months: Using Exposed Share of Eligible Life to Measure Program Exposure**

||Poole<br>|d Linear<br>|Linear P<br>|robability<br>|
|---|---|---|---|---|
||Prob|ability|w/Com|munityFEs|
|Year (2000=1)|0.002|0.001|-0.003|0.001|
||[0.023]<br>|[0.023]<br>|[0.027]|[0.028]|
|_Exposed Share_*Year|0.045|0.047|0.069|0.058|
||[0.046]|[0.046]|[0.068]|[0.069]|
|Gender (boy=1)|0.014|0.014|0.006|0.006|
||[0.016]|[0.016]|[0.016]|[0.017]|
|Age|-0.003***<br>|-0.003***<br>|-0.003***|-0.003***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Mother education (years)|-0.006***|-0.006***|-0.005***|-0.005***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Father education (years)|0.001|0.001|0.001|0.001|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Mother height (cm)|-0.019***|-0.018***|-0.018***|-0.018***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Father height (cm)|-0.011***|-0.011***|-0.011***|-0.011***|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Head of HH (male=1)|-0.004|-0.006|0.010|0.009|
||[0.026]|[0.026]|[0.028]|[0.028]|
|Number of children 5-14 yo in HH|0.023***|0.021***|0.019**|0.019**|
||[0.007]|[0.008]|[0.008]|[0.008]|
|HH owns private toilet (Yes=1)|-0.040**|-0.038**|-0.041**|-0.040**|
||[0.017]|[0.017]|[0.020]|[0.020]|
|HH has sanitation (Yes=1)|-0.112***|-0.093***|-0.098***|-0.095***|
||[0.021]|[0.025]|[0.027]|[0.029]|
|Type of community (urban=1), C|-|-0.044*|-|0.011|
|||[0023]||0038|
|||.||[.]|
|Distance to district capital (km), C|-|0.000|-|-0.000|
|||[0.000]||[0.001]|
|Distance to bus station (km), C|-|-0.000|-|-0.000|
|||[0.001]||[0.002]|
|Land w/ technical irrigation (%), C|-|-0.059|-|-0.028|
|||[0.045]||[0.069]|
|Comm. has asphalt road (Yes=1), C|-|-0.019|-|<br>-0.069*|
|||[0.021]||[0.036]|
|Comm. has sewerage system (Yes=1), C|-|0.031*|-|0.003|
|||[0.018]||[0.029]|
|Comm. has piped water (Yes=1), C|-|0.000|-|<br>-0.005|
|||[0.019]||[0.029]|
|Constant|4.971***|4.880***|4.851***|4.922***|
||[0.319]|[0.321]|[0.347]|[0.355]|
|R-squared|0.11|0.11|0.21|0.22|

Notes: _Exposed Share_ is the share of a child’s eligible life exposed to the program and is calculated as described on page 11. n is 3221. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

48
**Table A6 – Impact of treatments on willingness to get vaccinated**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Expert advice treatment|0.0244|||0.0207|
||(0.0279)|||(0.0288)|
|Social norms treatment||0.0159||0.0159|
|||(0.0292)||(0.0300)|
|Relative safety treatment|||0.106***|0.102***|
||||(0.0298)|(0.0294)|
|Male|0.0956**|0.0553|0.0776*|0.0640**|
||(0.0302)|(0.0310)|(0.0315)|(0.0240)|
|Over 40 years|-0.00228|-0.0109|-0.0554|-0.0135|
||(0.0322)|(0.0322)|(0.0327)|(0.0249)|
|Highlands region|0.0704|-0.0358|-0.0188|-0.00437|
||(0.0494)|(0.0503)|(0.0524)|(0.0392)|
|Islands region|0.0154|-0.0722|-0.0816|-0.0796*|
||(0.0506)|(0.0529)|(0.0543)|(0.0404)|
|Momase region|0.00354|-0.0807|-0.0731|-0.0622|
||(0.0460)|(0.0473)|(0.0490)|(0.0365)|
|Secondary school or less|-<br>0.0959***|-0.0566*|-0.0611*|-0.0539*|
||(0.0274)|(0.0280)|(0.0288)|(0.0220)|
|Constant|0.167**|0.258***|0.249***|0.236***|
||(0.0525)|(0.0533)|(0.0555)|(0.0431)|
|Observations|917|878|896|1521|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

44