rafmacalaba/datause-encoder-data huggingface dataset:
2005-2016, the study uses a longer time series going back to 1991 to model the trend in unemployment rates and, thereby, to extract the cyclical component of unemployment using a Hodrik-Prescott Filter (Hodrik and Prescott, 1997).<sup>21</sup> The study also shows results with an alternative measure, the annual change in the logarithm of the unemployment rate.

Figure S1.1a shows a plot of the cyclical male unemployment vs. cyclical female unemployment in the sample with a 45 degree line imposed on top. This shows that the unemployment shocks are positively correlated, although there is variation around the 45 degree line. Figure S1.1(b) displays the pooled (across countries) mean of cyclical and log-differenced male and female unemployment over time for the country-years in our sample. Although shocks to male and female unemployment are positively correlated (Figure S1.1(a))<sup>22</sup> , shocks to female unemployment are larger and there is considerable variation in the relative size of each over time, as shown by the ratio and difference between the male and female cyclical unemployment (Figures S1.1(c) and S1.1(d)).

Figure S1.2(a) and (b) plot the unconditional association between country-year cyclical male and female unemployment and spousal physical violence rates. At the aggregate level, no clear pattern emerges from the data, particularly for female unemployment, though this unconditional association does not account for country fixed effects which the regression analysis shows it is important to account for (section 4).

> 21 The stata command –tsfilter hp- is used, with a smoothing value of 6.25 which is the recommended value for yearly data. Our results are robust to using instead either 25 or 100 as alternative smoothing parameters.

> 22 The correlation (covariance) between the aggregate cyclical male and female rates is 0.8463 (0.015, though note that the study uses variation within country over time in these series rather than cross-sectional variation). Though the sign of the shocks are often the same, this is not always the case and in a number of cases the sign of the shocks are opposing. Among examples are Dominican Republic in 2007, Malawi in 2010 and the Philippines in 2013. Co-movement of the two series is strongest in the later years in our sample (2014, 2015 and 2016). It has been confirmed that the results are robust to dropping these years from the analysis (results available on request).

9
two digits. However, from a welfare point of view it is especially relevant to assess the cost of catastrophes in terms of forgone output and consumption, more than determining their impact on the stocks.

This paper uses a broad set of data to quantify the cost of natural disasters, especially those related to climatic events, in terms of GDP per capita. The results indicate that disasters have modest but economically meaningful output consequences, resulting on a decline in output per capita of about 1 percent. To x ideas, this is larger than the typical impact of terms-of-trade shocks, which are frequently considered as important sources of uctuations. The paper also shows an increase in incidence of these disasters of about 30 percent in the last decades, which implies an increased expected output cost of about 0.3 percent of per capita GDP.

It is important to highlight in these nal remarks that these estimates come from a semistructural model that does not cover all the potentially relevant macroeconomic variables. This means that the estimated output responses are conditional on the endogenous responses taken to alleviate their consequences. Although the paper shows that the most obvious of these responses in terms of foreign aid ows reduces only marginally the impact of the shocks, it is not possible to extrapolate these results to other palliative measures coming, for instance, from local government spending. The estimates are therefore an accurate description of the reduced form cost of disasters but probably a conservative estimate of the cost that would be observed in the absence of any mitigation eort.

# References

- Ahmed, S. (2003). Sources of macroeconomic uctuations in latin america and implications for choice of exchange rate regime. Journal of Development Economics, 72:181202.

- Albala-Bertrand, J. (1993). Political economy of large natural disasters: with special reference to developing countries. Oxford University Press, USA.

- Bank, W. (2008). World development indicators. CD-ROM.

- Benson, C., Clay, E., and Bank, W. (2004). Understanding the economic and nancial impacts of natural disasters. World Bank Washington, DC.

- Broda, C. (2004). Terms of trade and exchange rate regimes in developing countries. Journal of International Economics, 63(1):3158.

- Deaton, A. and Miller, R. (1995). International commodity prices, macroeconomic performance, and politics in Sub-Saharan Africa. International Finance Section, Department of Economics, Princeton University.

- Dell, M., Jones, B., and Olken, B. (2008). Climate shocks and economic growth: Evidence from the last half-century. Technical report, NBER Working Paper 14132.

15
Table 1: Summary statistics

||B|RMS|||HIES||
|---|---|---|---|---|---|---|
||Return|migrants|Non-m|igrants|Curren|t migrants|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|
|**_Socio-econmonic characteristics_**|||||||
|Male_†_|0.96|0.20|0.48|0.50|0.96|0.19|
|Rural/Semi-urban|0.99|0.10|0.95|0.22|0.99|0.11|
|Age currently<br>|37.9|8.2|35.3|11.4|34.1|8.8|
|Age category<br>|||||||
|_18-24_|0.03|0.17|0.21|0.41|0.14|0.34|
|_25-34_|0.35|0.48|0.28|0.45|0.39|0.49|
|_35-44_|0.39|0.49|0.25|0.43|0.32|0.47|
|_45-54_|0.20|0.40|0.19|0.39|0.13|0.34|
|_55-59_|0.03|0.17|0.07|0.25|0.02|0.14|
|Years of schooling|6.6|4.0|5.6|5.0|7.3|3.6|
|Education level|||||||
|_Illiterate_<br>|0.16<br>|0.37<br>|0.30|0.46<br>|0.08|0.26<br>|
|_Some primary (1-5)_<br>|0.24|0.43|0.25|0.44|0.26|0.44|
|_Some secondary (6-9)_|0.36|0.48|0.21|0.41|0.38|0.48|
|_Above some secondary (10-12)_<br>|0.22|0.41|0.17|0.37|0.25|0.43|
|_Tertiary (16+)_|0.02|0.15|0.06|0.24|0.03|0.16|
|Married currently|0.88|0.32|0.77|0.42|||
|Married before migration|0.58|0.49|||||
|**_Migration costs_**|||||||
|Total costs, 2010 constant dollar|4,203|2,121|||||
|Costs by usage, 2010 constant dollar|||||||
|_Intermediary fees_<br>|2,318|2,195|||||
|_Visa/Passport_|998|1,544|||||
|_Government fees_|51|224|||||
|_Other costs_|746|1,386|||||
|Fraction of migrants who borrowed|0.56|0.50|||||
|Share of costs fnanced by borrowing*|0.76|0.30|||||
|**_Stay abroad_**|||||||
|Destination country|||||||
|_GCC**_|0.74|0.44|||0.62|0.49|
|_Southeast Asia_|0.17|0.38|||0.21|0.41|
|_Other country_|0.08|0.28|||0.17|0.38|
|Age at departure|28.5|7.2|||28.1|8.1|
|Duration of stay at destination|6.5|5.6|||6.3|5.2|
|Return earlier than planned|0.25|0.43|||||
|Annual income, 2010 constant dollar|5,299|4,703|||||
|**_After return/Currently_**|||||||
|Age at return|34.9|8.1|||||
|Years since return|2.7|2.6|||||
|Employment status currently***|||||||
|_Not working_|0.17|0.38|0.16|0.36|||
|_Waged worker_|0.29|0.45|0.56|0.50|||
|_Self-employed_|0.54|0.50|0.28|0.45|||
|Annual income, 2010 constant dollar|1,280|499|1,163|1,174|||
|Observations|4,|709|47,|137||3,571|

_Note:_ Sample is restricted to males aged 18-59. _†_ Here, the mean is the share of males in the full sample. *Conditional on having taken a loan to migrate. **For columns 3 to 6, the GCC does not include Bahrain, which is coded as “others” in the original HIES data. ***These statistics exclude individuals who have been back in Bangladesh for less than a year at the time of the survey. 12
the 1980 NTR liberalization. Nonetheless, the results with more disaggregated productlevel data are very similar to the baseline when we consider the periods 1974–88 (using the 8-digit TS-USA product-level aggregation) and 1990–2008 (using the HS-8 product-level aggregation), separately. Figure 8 plots the time-varying effect of the NNTR gap on exports from China obtained from equation (5) when we use the 8-digit TS-USA (Tariff Schedule of the United States) product-level aggregation for the period 1974–1987.<sup>21</sup> The pattern is strikingly similar to the baseline pattern with SITC aggregation. Figure 9 uses the HS-8 product-level aggregation for the period 1989–2007. Between 1989 and 1993, the negative effect is larger, rising from around _−_ 3 _._ 43 in 1990 to around _−_ 1 in 1993. After 1993, it remains relatively stable at _−_ 1. In the aftermath of China’s PNTR access, the effect halves and then becomes statistically insignificant by 2002.<sup>22</sup> Note the summation of the total elasticity _− − −_ under TS-USA ( 4), HS-8 ( 3), and the two years under SITC ( 2) yields a total elasticity of _−_ 9, which is close to the baseline estimate. More importantly, the time-varying pattern of the effect is also consistent with the baseline results. These results suggests our baseline findings are robust to the level of aggregation used.

**China Supply Effects.** Our baseline approach controls for U.S. demand shocks specific to particular goods but not for good-specific Chinese supply shocks. To study the importance of these potentially confounding effects, we use the World Trade dataset from Feenstra et al. (2005) (1974-2000) merged with the BACI Trade database (2001-08).<sup>23</sup> We estimate

where _δjgt_ controls for exporter supply shocks and _δigt_ for importer demand shocks (and trade

> 21The NNTR gap is not available at the TS-USA product aggregation. Instead of the NNTR gap, we use the mean TS-USA applied tariff on U.S. imports from China between 1974 and 1979 to proxy for the NNTR rate. Figure 5 shows applied NNTR rates are closely related to their schedule.

> 22Tables B.1 and B.2 in the appendix provide some robustness checks of the described pattern.

> 23Note this dataset is at the SITC 4-digit level. We include the 50 largest exporter countries in 2001 except Hong Kong SAR, China; Canada; Mexico; and the former Soviet Union members and aggregate the remaining countries into one. We further exclude goods subject to the MFA quotas as in our baseline. None of these restrictions change the results.

18
focus on public officials is appropriate even if well-designed policies may reduce the incentives of the private sector to engage in corruption. The argument is that a goverunent which cannot influence the incentives of its own officials is unlikely to be able to reduce private sector incentives to engage in corruption.

In the formulation above, an official's income does not have an effect on whether or not to engage in corrupt activities. Two concerns have been raised with this approach. One is that officials paid less than subsistence income are forced to undertake corrupt activities to survive, the second is that, at some high level of income, officials should be unwilling to risk that income to gain more through corrupt activities. Both arguments require an assumption that officials are motivated to follow the rule of law regardless of whether they see it as a threat to their income from corrupt activities. The argument put forward in this paper is that officials are not so motivated in countries in which the rule of law is weak.<sup>8</sup>

**Table 1: The Influence of Anti-Corruption Programs on Officials' Cost-Benefit Analysis**

|**Number Of**<br>**Corrupt**<br>**Transactions**|**Gross Gains From**<br>**Corruption**|**Probability Of**<br>**Paying**<br>**Penalty**|**Magnitude**<br>**Of Penalty**|**Actions Not**<br>**Influencing Cost**<br>**Benefit Analysis**|
|---|---|---|---|---|
|Bureaucratic<br>Culture -<br>Streamlining<br>Services|Economic Reform -<br>Improving<br>Competitive<br>Environment|Anti-<br>Corruption<br>Agencies|Rational-<br>ization of<br>laws|Raising<br>Awareness of<br>Public Through<br>Seminars|
|Creating or Raising<br>Public Service<br>Standards|Scaling Down<br>Individual Public<br>Projects|Parliamentary<br>Oversight||Public Opinion<br>Surveys|
|Reducing Public<br>Employment|Bureaucratic Culture|Ombudsman||Raising Public<br>Sector Wages|
|Reducing Public<br>Sector Size|Referenda on Large<br>Public Projects|Financial<br>Accountability||Reducing Wage<br>Compression|
|Financial<br>Liberalization||Media<br>Independence|||
|Increasing<br>Transparency||Judicial<br>Independence|||
|Decentralization<br>of<br>Public Services||Citizen<br>Participation|||
|Economic Reform<br>- Privatization||Rule Of Law|||
|||Ethics Office|||

A similar argument applies on the penalties side. The alternatives to public sector employment tend to be worse in countries with high levels of corruption. Given poor alternatives, we would expect public officials to be unwilling to risk their careers for small temporary gains. While in some cases an argument can be made for desperation on the part of public officials, more generally it seems likely that officials in highly corrupt societies do not see the rule of law as a risk to their careers.

5
notably (though not only) in India where the Maharashtra EGS started in 1973 and was long considered a model (Drèze, 1990; Ravallion, 1991). In 2005, India’s central government implemented a national version, now called the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS). This promises 100 days of work per year per rural household to those willing to do unskilled manual labor at the statutory minimum wage notified for the program. The work requirement is (more or less explicitly) seen as a means of assuring that the program is reaching India’s poor. The available evidence does suggest that the scheme is quite well targeted to poor rural households; see Alik-Lagrange and Ravallion (2012) for India as a whole and Dutta et al. (2013) for Bihar.

MGNREGS explicitly aims to reduce rural poverty and the promise has been great. Indeed, advocates have claimed that it could largely eliminate poverty in rural India. For example, Drèze (2004) claimed that the scheme “...would enable most poor households in rural India to cross the poverty line.” That might seem a tall order, but there can be no denying that this is an ambitious and well-intentioned effort to fight poverty in India and that, in principle, it has huge promise. The sheer scale is impressive; according to the administrative data, over 50 million households participated in MGNREGS in 2009/10.<sup>7</sup> The scheme is nation-wide. Here we focus on one state, Bihar, as discussed in the introduction. We refer to MGNREGS in Bihar as BREGS.

The first and most direct way this scheme tries to reduce poverty is by providing extra employment on demand in rural areas and in a way that is self-targeted to poor people. Intuitively, it is easy to imagine that low-wage manual labor, often under a hot sun, would not be attractive to anyone who is not in fact poor. However, we already know from the literature and field observations that there are a number of ways that the potential impact on current poverty of this scheme might not be realized in practice (Dutta et al., 2013):

- The supply side may be slow to respond to the demand for work on the scheme.

- Workers may be unable to meet productivity norms for earning the minimum wage.

- There may be delays in payment (random or purposive).

- Corruption may be present, whereby local leaders or officials take their cut.<sup>8</sup>

- There may be exploitation, stemming from the monopsony power of the village leader, essentially acting as a contractor.

> 7  See Government of India website for MGNREGS (http:\\nrnic.in).

> 8  For example, local officials may charge a “fee” for their services, such as providing wages in advance or collecting wages against “ghost workers.”

6
# **3.1. Merging and Matching Process**

The Raw Materials Database (RMD) of SNL Metals and Mining inform on the geolocation and the status of several mines in Africa. The database also informs on the year in which the most recent information was recorded. The two datasets are merged by first matching the spatial point coordinates (GPS) of the mines and primary sampling units (enumeration areas or districts or townville) in the survey data; and second, by matching the year of the survey data collection to the reference year for the latest information update on the mine. In cases where the year of the latest update does not correspond to the year of the Afrobarometer survey data collection, this latter is matched with the closest date in the SNL Metals and Mining data which in all accuracy corresponds to the previous or subsequent year. This consideration only applies to a few countries: Senegal (Round 3 and Round 4 of the Afrobarometer), Burundi (Round 5), Benin and E-Swatini (Round 6). In countries where the enumeration area is not coded in the Afrobarometer dataset, the districts or Townsville GPS coordinates are used to set the geographical boundaries.

From a cluster centre point, it is measured the distance to a mine and create a binary variable _‘Active’_ that equals to 1 if the sampling unit or district is located within 50 kilometres radius from an active mine. For the remaining units, it is created another binary variable _‘Inactive’_ which points to all respondents that reside within 50-km radius from an inactive mine. `Inactive’ refers to any mine with one of the following statuses: inactive, care and maintenance, on hold, rehabilitation, and under litigation. The pseudo control group thus include all enumeration areas located farther than 50 km radius from a mine (be it active or inactive). As the individual data are identified by their enumeration areas or districts or Townsville, it is feasible to relate their responses to their residential location at the time of the survey.

# **3.2. Dependent Variables**

The dependent variables are _(a)_ the residents’ appraisal of public goods and services, and _(b)_ their self-reported optimism about the future. The first set is operationalized from the following survey question.

_“How well or badly would you say the current government is handling the following matters, or haven’t you heard enough to say? “_ The different matters are: _(i)_ improving living standards of the poor, _(ii)_ creating jobs, _(iii)_ improving basic health services, _(iv)_ addressing educational needs, and _(v)_ providing water and sanitation services. For each of these items, the possible answers are on a four-step Likert-scale and range from ‘ _very badly’_ to ‘ _very well’_ . For policy matter, it is created a binary variable that takes the value of 1 if the individual responded _‘fairly well’_ or _‘very well’_ to the question and 0 for the responses _‘bad’_ or _‘very bad’._ Other responses such as ‘ _I don’t know_ ‘ or _‘haven’t heard about it’,_ 7
# 4.1 Comparing HIV Prevalence at Rounds 1 and 11

The data set of the General Population Cohort, which has collected data over 12 years, allows to make comparisons to estimate the impact of the information about the epidemic on HIV prevalence. The question that is central to this paper is how this impact might have differed with the level of education. In order to answer this question, this paper will start by comparing HIV prevalence among young individuals at rounds 1 and 11.

The intuition behind this comparison is that it allows a contrast, at identical ages, between individuals who have been exposed to the information campaigns and individuals who have not. Using age 15 as the potential starting point of the sexual activity,<sup>11</sup> individuals born before 1971 started their sexual life before the prevention campaigns. On the other hand, individuals born after 1971 had more access to information about the epidemic when they initiated their sexual activities. This separation in two distinct age groups will be used extensively in the next steps of the analysis. At round 1, from November 1989 to September 1990, except for a few individuals aged 18, everybody belongs to the group of individuals who have started their sexual life before the launching of the prevention campaigns. At round 11, from November 1999 to September 2000, the age group 18—29 is composed of people who have started their sexual life after the prevention campaigns. Moreover, because the average period between HIV infection and death is around 10 years (Morgan and others 2002), HIV prevalence re fl ects behavior that is on average fi ve years old. It is, therefore, reasonable to think that the prevalence at round 1 for young individuals re fl ects preinformation behavior, whereas HIV prevalence for the same age group at round 11 corresponds to postinformation behavior. In addition, this comparison holds the age group of the individuals constant, eliminating potential age pro fi le effects and is only marginally affected by mortality and the "survivor" bias. The assumption behind the strategy of comparing individuals who have been exposed to the information campaigns and individuals who have not is that, except for the HIV/AIDS information campaigns, there is no other period speci fi c effect.

The comparison between the two young age groups at rounds 1 and 11 is illustrated in fi gure 1: compared with individuals of the same age group at round 1, at round 11, the prevalence for individuals with no education decreased by 6 percentage points, by 5.9 percentage points for those

> 11 Average age of fi rst sexual activity is between 16 and 17. However, many individuals started earlier. But less than 10 percent started before age 15. I therefore chose 15 as the “potential” starting point of the sexual life.

10