rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1. Average marginal effects of subjective well-being over the lifespan: IVS data (1981–2009)**

<!-- Start of picture text --> ECA Western Europe<br>(1) squared (2) squared<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(3) cubic (4) cubic<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>(5) non-parametric (6) non-parametric<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>23 28 33 38 43 48 53 58 63 68 73 78 83 88 93 23 28 33 38 43 48 53 58 63 68 73 78 83 88 93<br>Age Age<br>Note: The panel numbers correspond to the regressions in Table 1<br>Linear Prediction<br>Linear Prediction<br>Linear Prediction<br><!-- End of picture text --> 9
# **Introduction**

Worldwide, almost one-third of women experience intimate partner violence (IPV) (WHO 2021). IPV has negative impacts on physical and mental health outcomes among victims, as well as their children (Aizer 2010; Neggers et al. 2004; WHO 2013). These health consequences lead to large direct costs, in addition to affecting labor market outcomes, such as employment stability, productivity, and performance (Showalter 2016). The lifetime costs of IPV have been estimated at $103,767 per woman victim in the United States; 59 percent of these costs represent medical costs (Peterson et al. 2018). Additional costs include those among children who have been exposed to IPV, more than $50,000 over a child’s life course (Holmes et al. 2018). IPV prevalence has been found to be somewhat greater in developing countries, suggesting that losses to physical health, emotional well-being, and productivity may impact a larger portion of the population in these settings (WHO 2013).

Economists theorize that women’s economic resources (for example, employment, wages, or government transfers), as well as those of their male partners, impact domestic abuse (Manser and Brown 1980; Aizer 2010; Anderberg et al. 2016; Buller et al. 2018). Some studies propose theoretical models predicting that an increase in women’s economic status will unambiguously decrease the incidence of IPV by increasing the bargaining power of the woman in the household (Anderberg et al. 2016; Manser and Brown 1980). Other studies incorporate the possibility that men may become more violent if they feel threatened by the greater economic independence of their women partners (Heath 2014; Buller et al. 2018). Similarly, men may use violence as a means of extracting their partners’ income (Bloch and Rao 2002; Bobonis, Gonz, and Castro 2013; and some qualitative evidence from Litwin, Perova, and Reynolds 2019). In either case, societies with fewer legal and police resources, such as in developing countries, may be unable to protect women from an increase in violence resulting from economic “empowerment.” Similarly, cultures with social norms that excuse violence against women (for instance, machismo) may be characterized by a greater portion of the population turning a blind eye to incidents of IPV (Cools and Kotsadam 2017).

This study contributes to the literature by examining the effects of natural fluctuations in the gender wage ratio on three administrative measures of violence against women in Brazil. It takes advantage of administrative microdata on the gender wage ratio measure and violence outcome measures. The data on wages are comprehensive microdata on the full universe of formal contracts in Brazil. The outcome data are taken from three administrative sources measuring violence at varying levels of severity. Previous studies on how women’s economic status influences IPV typically use only one data source to 2
|**Table**<br>|**18: Cheating o**|**n Taxes and I**|**nformality**|||
|---|---|---|---|---|---|
|Dependent variable:<br>||||||
|Employment Growth<br>(log difference, %,<br> <br>**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|annual)||||||
|<br>Informal Competition<br>(Country level cell<br>**-5.984**<br>**(8.295)**|**-18.979****<br>**(8.091)**|**-15.545****<br>**(6.109)**|**-6.861**<br>**(6.207)**|**-18.884****<br>**(8.278)**|**-15.630****<br>**(6.256)**|
|average)*Cheating on<br>Taxes is Justified<br>(dummy)||||||
|Informal Competition<br>-4.490|-2.633|-4.252|-40.759|27.078|11.011|
|(Country level cell<br>average)<br>(5.547)|(4.630)|(3.660)|(45.691)|(55.300)|(38.435)|
|Initial Employment<br>-3.476***|-3.701***|-3.995***|-3.654***|-3.703***|-3.994***|
|<br>(logs)<br>(0.747)|(0.737)|(0.685)|(0.738)|(0.738)|(0.685)|
|Cheating on Taxes is<br>2.926|5.530|3.550|2.576|5.483|3.610|
|Justified (dummy)<br>(4.340)|(3.837)|(3.045)|(3.589)|(3.955)|(3.144)|
|Informal Competition|||3.779|-3.169|-1.617|
|(Country level cell<br>|||(4.647)|(5.916)|(4.051)|
|average)*GDP per<br>capita (lagged, logs)||||||
|GDP per capita (lagged, logs)|-2.957***|-2.615***|-3.414*|-1.682|-1.979|
||(0.937)|(0.547)|(1.888)|(2.363)|(1.747)|
|Age of Firm (logs)||<br>-3.350***|||<br>-3.350***|
|||(1.228)|||(1.226)|
|Manager Experience (logs)||<br>0.355|||<br>0.365|
|||(0.425)|||(0.427)|
|Exports (proportion of sales)||<br>1.586|||<br>1.581|
|||(1.323)|||(1.320)|
|Female Top Manger Y:1 N:0||<br>-1.481|||<br>-1.478|
|||(1.839)|||(1.837)|
|Multi-establishment firm Y:1 N:0||<br>4.250***|||<br>4.251***|
|||(1.339)|||(1.339)|
|Crime losses Y:1 N:0||<br>0.215|||<br>0.219|
|||(1.448)|||(1.446)|
|Courts obstacle Y:1 N:0||<br>-0.059|||<br>-0.067|
|||(0.842)|||(0.841)|
|Power outages (hours)||<br>-0.009***|||<br>-0.009***|
|||(0.003)|||(0.003)|
|Finance obstacle (0-4 scale)||<br>-0.348|||<br>-0.353|

70
benefits of temporary migration for migrants (Ahmed and Bossavie 2021). Finally, domestic labor market conditions in South Asian countries tend to be poorer compared to contexts where return migration has been previously studied. An examination of the labor market outcomes of return migrants in the context of South Asia can thus bring new descriptive insights to the literature on the topic.

The paper examines the labor market outcomes of return migrants relative to nonmigrants by focusing on three major migrant-sending countries in South Asia: Bangladesh, Nepal and Pakistan where labor migration is low-skilled and temporary by design. The choice of these three countries is motivated by data availability and by the similarities of labor migration patterns from these three countries. The paper utilizes nationally-representative household surveys which collect rich information on workers’ current labor market outcomes and on past migration overseas. This allows to compare the labor market outcomes of return migrants to those of nonmigrants, including overall labor supply, sectoral choice, occupational choice, and earnings. For Bangladesh and Nepal, the data sets also collect information on household members who are currently away for work overseas, which allows to compare the characteristics of return migrants with those of migrants who are still overseas.

The paper examines systematic differences in the labor market outcomes of return migrants and nonmigrants along four main outcomes: (i) labor market status and labor supply (ii) sectoral choice (iii) employment type (iv) earnings. We examine these differences both unconditionally and conditionally on workers’ observable characteristics that may be associated with return migrant status. Labor market choices (i) to (iii) are modeled using multinomial choice models where past migration status is one of the regressors, along with workers’ other observable characteristics. Outcome (iv) is modeled using standard Mincerian regressions where past migration status is the main explanatory variable of interest. The estimations carried out in this paper only control for differences in observable charac– – teristics but not in unobservables between nonmigrants and return migrants. Therefore, the evidence reported in this paper on the association between past migration overseas and labor market outcomes should be interpreted as descriptive rather than causal. That been said, the inclusion of control variables in our estimations does not affect much the magnitude of the estimated coefficients. This suggests that selection in return migration plays overall a limited role in the South Asian context compared to other origin countries studied by prior literature.

Regarding labor supply, we find that return migrants are less likely to be employed than nonmigrants in all three countries. The employment gap is driven by both higher levels of involuntary unemployment and voluntary inactivity among return migrants, especially, largely driven by recent return migrants. The employment rate of returnees converges to that 3
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Task���� is the measure of task intensity for the standard employees in the initial point of the analysis. Task���� is the measure of task intensity for the standard employees in the ending point of the analysis.

# LATIN AMERICA AND THE CARIBBEAN

||||Δ NRCA|||
|---|---|---|---|---|---|
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Argentina|0.449|0.555|0.106|yes|yes|
|Brazil|0.543|0.4445|‐0.098|yes|yes|
|Chile|0.437|0.2148|‐0.222|yes|yes|
|Mexico|0.020|0.3242|0.304|yes|yes|
|El Salvador|0.555|0.6205|0.065|yes|yes|
||||Δ NRCI|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Argentina|0.349|0.474|0.125|yes|yes|
|Brazil|0.613|0.5886|‐0.025|yes|yes|
|Chile|0.352|0.2391|‐0.113|yes|yes|
|Mexico|0.048|0.1605|0.112|yes|yes|
|El Salvador|0.446|0.4539|0.008|yes|no|
||||Δ RC|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Argentina|0.376|‐0.118|‐0.494|no|yes|
|Brazil|0.016|‐0.0177|‐0.034|no|yes|
|Chile|0.144|‐0.2252|‐0.369|no|yes|
|Mexico|0.047|‐0.0632|‐0.110|no|yes|
|El Salvador|0.160|‐0.1782|‐0.338|no|yes|
||||Δ NRM|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Argentina|‐0.216|‐0.036|0.179|yes|yes|
|Brazil|‐0.726|‐0.6537|0.072|yes|yes|
|Chile|‐0.257|‐0.0151|0.242|yes|yes|
|Mexico|‐0.175|‐0.1381|0.037|yes|yes|
|El Salvador|‐0.424|‐0.0664|0.358|yes|yes|
||||Δ RM|||
||SE|Temporary|diff|Equal sign trend|Reject H0|
|Argentina|‐0.217|‐0.094|0.124|yes|yes|
|Brazil|‐0.781|‐0.7469|0.034|yes|yes|
|Chile|‐0.356|0.0081|0.365|no|yes|
|Mexico|‐0.094|‐0.1548|‐0.060|yes|yes|
|El Salvador|‐0.456|0.0481|0.504|no|yes|

Page 48 of 51
**Table 2: Croatia: General Government Spending, 2014**

|Spending Component|Millions of<br>HRK|% of<br>GDP|Included<br>in<br>analysis|% of<br>GDP|
|---|---|---|---|---|
|Total expenditures|158,037|48.1|60,816|18.5|
|Social Spending|78,847|24.0|60,816|18.5|
|_Social Protection_|46,749|14.2|28,977|8.8|
|Contributory social insurance benefits|35,976|11.0|25,341|7.7|
|Old age|21,054|6.4|21,054|6.4|
|Full-age retirement pension|19,106|5.8|19,106|5.8|
|Early-age pre-retirement pension|1,949|0.6|1,949|0.6|
|Other pension/benefits|14,921|4.5|14,921|4.5|
|Family pension|4,778|1.5|4,778|1.5|
|Disability pension|5,511|1.7|5,511|1.7|
|Unemployment benefit|1,496|0.5|1,496|0.5|
|Sickness benefit A|949|0.3|949|0.3|
|Sickness benefit B|345|0.1|345|0.1|
|Maternity leave benefit|1,842|0.6|1,842|0.6|
|Non-contributory benefits|9,386|2.9|3,636|1.1|
|Disability|949|0.3|949|0.3|
|Maternity, parental and child benefits|1,731|0.5|1,731|0.5|
|One-time grant for newborn children|92|0.0|92|0.0|
|Child benefit|1,639|0.5|1,639|0.5|
|Social assistance|956|0.3|956|0.3|
|Subsistence benefit|710|0.2|710|0.2|
|Housing benefit|171|0.1|171|0.1|
|Lump-sum assistance|75|0.0|75|0.0|
|HBDR/HRVI related benefits|5,750|1.8|||
|Benefits provided by local self-government units|1,387|0.4|||
|_In-kind transfers_|33,160<br>|10.1|31,839|9.7|
|_Education_|14,081|4.3|14,081|4.3|
|Kindergarten|2,149|0.7|2,149|0.7|
|Primary school|5,840|1.8|5,840|1.8|
|Secondary school|3,553|1.1|3,553|1.1|
|Tertiary schools|2,897|0.9|2,897|0.9|
|Other education|36|0.0||-|
|_Healthcare_|18,507|5.6|17,757|5.4|
|Health insurance fund|17,757|5.4|17,757|5.4|
|Other health|749|0.2|||
|_Other social spending_|325|0.1|||
|Income support to individual farmers|140||||
|Other expenditure(non-social)<br>Sources: World Bank staff based on Ministry of Finance|79,190|24.1|||

8
**Table 4: Log Value of Yield Oaxaca Decomposition by Gender of Plot Manager**

||**(1**|**)**|**(**|**2)**|
|---|---|---|---|---|
||**Parcel Cha**|**acteristics**|**Indiidal**|**Commnit**|
||<br>**Decomp**|<br>**osition **|**vu**<br>**Characteristics**|**uy**<br>**Decomposition **|
|**Panel A : Aggregate Decomposition**|||||
|Male Group Value of Yield (log)|4.345***||4.345***||
||(0.021)||(0.021)||
|Female Group Value of Yield (log)|4.169***||4.169***||
||(0.020)||(0.020)||
|Difference in Value of Yield|<br>0.175***||<br>0.175***||
||(0.029)||(0.029)||
|Explained Portion of Difference|-0.120***<br>(0.029)||-0.111***<br>(0.022)||
|Unexplained Portion of Difference|0.296***<br>(0.028)||0.287***<br>(0.030)||
|**Panel B : Detailed Decomposition**|||||
||**Explained**|**Structural**|**Explained**|**Structural**|
|**Parcel Characteristics**|||||
|Distance from Homestead to Plot|-0.000|0.100|0.000|-0.016|
||(0.001)|(0.064)|(0.001)|(0.057)|
|Land Quality|0.002|0.010|0.003|0.004|
||(0.002)|(0.029)|(0.002)|(0.020)|
|Topology|0.002|0.039|0.001|0.024|
||(0.003)|(0.044)|(0.003)|(0.029)|
|Water Source|-0.003<br>|0.008<br>|-0.002<br>|0.000<br>|
||(0002)|(0008)|(0002)|(0005)|
|Planted Acres (log)|.<br>-0.086***|.<br>-0.086**|.<br>-0.093***|.<br>-0.065*|
||(0.010)|(0.043)|(0.010)|(0.038)|
|Rented Parcel|-0.003*|0.004|-0.003*|0.010|
||(0.002)|(0.016)|(0.001)|(0.013)|
|Aggregate of Crop Selection|<br>0.024***|<br>-0.203***|<br>0.023***|<br>-0.164***|
||(0.008)|(0.054)|(0.008)|(0.053)|
|% Planted with Bananas|0.008***|-0.052***|0.008***|-0.047***|
||<br>(0.003)|<br>(0.014)|<br>(0.003)|<br>(0.014)|
|% Planted with Coffee|<br>0.016***|<br>-0.006|<br>0.014***|<br>-0.004|
||(0.004)|(0.006)|(0.004)|(0.004)|
|% Planted with Fruits|0.003*|0.001|0.003*|0.004|
||(0.002)|(0.004)|(0.001)|(0.003)|
|% Planted with Other Cash Crops|0.032***|-0.002|0.032***|-0.004|
||(0.005)|(0.005)|(0.005)|(0.004)|
|% Planted with Other|<br>-0.000|<br>0.001|<br>-0.000|<br>0.001|
||(0.001)|(0.001)|(0.000)|(0.001)|
|% Planted with Pulses and Oils|-0.009***|-0.033*|-0.010***|-0.029|
||(0.003)|(0.020)|(0.003)|(0.020)|
|% Planted with Roots|-0.033***|-0.070***|-0.032***|-0.072***|
||(0.005)|(0.021)|(0.005)|(0.021)|
|% Planted with Vegetables|<br>0.001|<br>-0.000|<br>0.003*|<br>-0.002|
||(0.001)|(0.004)|(0.001)|(0.004)|
|Intercropped|<br>0.005|<br>-0.042**|<br>0.005|<br>-0.012|
||<br>(0.004)|<br>(0.019)|<br>(0.004)|<br>(0.018)|
|Aggregate of Labor|<br>-0.032***|<br>-0.017|<br>-0.023***|<br>-0.168|
||(0.009)|(0.235)|(0.008)|(0.205)|
|Family Labor Days per Acre (log)|<br>-0.052***|<br>0.067|<br>-0.044***|<br>-0.080|
||(0.009)|(0.181)|(0.008)|(0.155)|

20
There are two points worth reiterating, given the strong similarity in the results for Argentina and Brazil. Again, the similarity is that more comprehensive measures of import protection beyond MFN and bilateral applied tariffs tend to _reverse_ any potential building block evidence from the MERCOSUR FTA period (1990-1994) and tend to _strengthen_ the stumbling block evidence arising during the MERCOSUR customs union period (1995-2001).

First, we recall from Section 2 that Brazil has been much less active in using TTBs than Argentina overall and thus, against external, non-MERCOSUR partners. In 1991-1995, there are 13 industries (out of the 93 that we have in table 4) in which Argentina imposed an AD measure against the ROW. In 19952001, there are 30 industries (out of 91) in which Argentina imposed a TTB (AD or SG) against the ROW. Brazil also imposed an AD measure against non-MERCOSUR partners in 13 industries in 1991-1995, but it imposed a TTB in only 17 industries in 1995-2001, about half as many as Argentina.

Second, Brazil utilized TTBs against Argentina with much less frequency, whereas Brazil has been a frequent target of Argentina’s TTB use. For example, in column 3 of table 5, there were no cases in which preferential liberalization was reversed by Brazil imposing AD against Argentina during 1991-1995. During 1995-2001, there are only four instances in which preferential liberalization was reversed by Brazil imposing a TTB (AD or SG) against Argentina (column 6). Meanwhile, during 1991-1995, there are 15 cases in which Argentina reversed its preferential liberalization by imposing an AD measure against Brazil; and during 1995-2001, there are 19 instances of such reversal by imposing a TTB.

To conclude this section, while the overall patterns of our results for Argentina and Brazil are similar, especially once we define the changes in import protection more broadly (to include additional policy instruments beyond tariffs), there are two subtle differences worth highlighting. The first difference is in how Argentina and Brazil arrived at this similarity – i.e., a comparison of columns (1), (2) and (3) in each table suggests that Argentina had potentially “more” of a building block effect (apparent in the tariff only data) for its TTBs to overcome than did Brazil. Second, in the raw data on TTB use, we have also observed a substantial difference in how each country utilized TTBs during this period (both overall and toward each other). We use the next section to explore the role of market power in potentially shaping these results. Before turning to the next section, a final point worth mentioning is that our results are robust to re-defining the measuring of TTBs in the dependent variable in the following way. We reestimated all the specifications from tables 4 and 5 but re-defining the dependent variable such that whenever there is a TTB imposed, it is considered as an increase in protection (i.e., in Table 2, the case in 23
20 to be temporary, a slowdown of investment growth from this source would be most likely in the future (as the opportunity to buy cheap comes to an end). The macroeconomic implication is that a cyclical pick-up in investment is unlikely to lead the recovery over the short term. However, a deepening of reform in the context of NAFTA, reduced political uncertainty after the 1994 presidential elections, and overall low price stability are likely, over the medium term, to prompt a recovery of investment.

# Digression: NAFTA. the exchanae rate and investment.

It is often argued that low Mexican wages will be the key to massive investment in Mexico (possibly including the relocation of many US firms) and that, in view of this investment, there is scope for substantial further real appreciation of the peso. However, while the unadjusted ratio of average USMexico manufacturing wages was 5.23 in 1991,22 the productivity-adjusted ratio was in the same year 1.026, i.e., the relevant wage differential (or unit labor cost differetial) was actually quite small or virtually nonexistent in 1992. This implies that a further real peso appreciation (measured through wages) could strongly erode Mexican external competitiveness

**22** This incLudes fringe benefits in the case of Nexico. The source for Mexico is IKEGI and the concept is average compensation per emptoyee in manufacturing C"Remuneracion media por persona ocupada en et sector manufacturero"); the 1991 figure was 72,088 pesos per day which, divided by the average peso-dolLar ER of 1991 (301B.4), is USS23.58. Hourly compensation in the US was obtained from the US Department of Labor (March, 1993) and was USS15.6 in 1991; we converted this into a daiLy figure by multiplying it by the 8 hours of a working day, so that the daily average compensation per empLoyee in manufacturing was US$124.8 in 1991.

23 Manufacturing output per employee in the US was in 1991 1SS70,384 and in Mexico USS13,824. The US figure is the ratio of value added by manufacturing (USStr 1.32205 in 1990 muLtiplied by the ratio of the 1991-90 US industriaL production index of IMF-IFS and multipLied by the ratio of the 1991-90 US producer price index INF-IFS, i.e., LUSStr. 1.29665 in 1991), and 1991 US manufacturing employment C1B,455,000). The Mexican figure is the ratio of manufacturing GDP in 1991 (pesos bn. 189399.8 divided by 3,018.4, the 1991 peso-dottar ER, i.e., USSmi.62,748), and mnaufacturing emptoyment (4,493,279, manufacturing empLoyment according to the 1990 Census, times the ratio of the 1991-90 nrniufacturing employment index of Bank of Mexico, i.e., 4,538,832).
# **D Additional Tables**

## Table D.1: **Household Survey** – Eligible Population and Sample Frame

The Table reports summary statistics of the sample population and sample frame. Panel A reports summary statistics on program beneficiaries by bank and district. Observations cover all beneficiary accounts from the largest six commercial banks and the state’s largest cooperative bank, accounting for 91% of eligible accounts in the districts covered by the survey. Panel B summarizes all accounts included in the sample frame and qualifying for a 100% waiver or 25% conditional debt relief. Observations in the sample frame are drawn from administrative data published by the largest six commercial banks and the largest cooperative bank in the state of Gujarat. Percentages refer to the proportion of total beneficiaries included in the sample frame.

_Panel A: Program Beneficiaries by Bank and District_

||District|||||
|---|---|---|---|---|---|
||Anand|Kheda|Gandhinagar|Mehsana|Total|
|Bank of Baroda|1,941|3,644|503|1,070|7,158|
|Bank of India|877|870|343|432|2,522|
|Central Bank of India|1,384|738|243|253|2,618|
|Dena Bank|654|366|794|803|2,617|
|State Bank of India|3,412|2,711|916|3,187|10,226|
|Union Bank of India|1,013|1,428|306|84|2,831|
|Kaira District Coop Bank||21,141|0|0|21,141|
|Total||40,179|3,105|5,829|49,113|
|Other banks||3,956|491|14,933|19,380|
|District total||44,135|3,596|20,762|68,493|

_Panel B: Sample Frame by Bank and District_

||District|||||
|---|---|---|---|---|---|
||Anand|Kheda|Gandhinagar|Mehsana|Total|
|Bank of Baroda|276|276|35|70|657|
|Bank of India|14%|8%|7%|7%|9%|
|Central Bank of India|84|95|33|34|246|
|Dena Bank|10%|11%|10%|8%|10%|
|State Bank of India|215|39|25|16|295|
|Union Bank of India|16%|5%|10%|6%|11%|
|Kaira District Coop Bank|1,442|1,170|||2,612|
|||12%|||12%|
|Total|2,515|2,117|410|512|5,554|
|||12%||13%|9%|

56
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