rafmacalaba/datause-encoder-data huggingface dataset:
15 euros) cash transfer upon arrival to Dakar (see appendix A3). Once in Dakar, this money could be obtained from the migrant representative, and was intended to be used to help cover the initial costs of establishing oneself in Dakar. However, it was also sufficient to pay for the cost of returning to The Gambia if needed.

There are several pathways through which this second treatment may influence potential migrant behavior on top of that of treatment 1. First, youth who are planning on migrating the backway may not respond to information about riskiness alone, in the absence of seeing viable alternatives to improving their lives. This intervention should increase the relative attractiveness of Senegal as a destination choice by providing more information about it, increasing its salience and including role models who have made this journey, and lowering the cost of trying this alternative.

**_Treatment 3 (information and vocational training alternative)_** was shown the same information as the information and deterrence group, and additionally given the opportunity to enroll in a tuition-free vocational skill training program. Individuals in this group were given leaflets (see appendix A4) that enumerated the list of courses and instructions on how to register for the training. The courses included building construction (block work), carpentry and joinery, electrical installation, plastering and tiling, plumbing and gas fitting, refrigeration and air conditioning, small engine maintenance and repair, and welding and fabrication. Those that were given the training vouchers were instructed to send text messages indicating the courses they wanted to be trained on. In collaboration with The Gambia Technical Training Institute (GTTI), the training was scheduled to start in November 2019 at the Julangel Skills Training Center, URR, and to last for six months.

As with the Senegal alternative, the theory of change here is that providing a viable alternative to risky migration may change the cost-benefit calculation, as well as making youth more responsive to information on the risks. In addition, it is possible that providing the list of courses (which are linked to non-rural jobs typically available in urban areas of The Gambia, as well as in Senegal) made this type of jobs more salient, which could open new migration possibilities for the young men being offered treatment.

Finally, the **_Control Group_** was shown a video that explained the importance of exclusive breastfeeding. The video was obtained from the National Nutrition Agency (NaNa). This video is not expected to have any effect on migration outcomes.

17
<!-- Start of picture text --> Table 4b: Côte d'Ivoire<br><!-- End of picture text --> <!-- Start of picture text --> Employment by Industry<br>Industry Employment Industry Employment<br>2003 2014<br>Tobacco 313 Tobacco 295<br>Machinery 576 Furniture 432<br>Non-metallic metal 705 Paper 488<br>Other Transport 719 Other Transport 584<br>Paper 1,162 Non-metallic metal 1,522<br>Furniture 1,355 Machinery 2,557<br>Printing 1,936 Leather 3,249<br>Leather 2,347 Printing 3,403<br>Fabricated metal 2,805 Textiles 3,933<br>Rubber & Plastics 4,264 Fabricated metal 5,691<br>Textiles 4,918 Wood, not Furniture 10,118<br>Chemicals 5,105 Chemicals 12,204<br>Wood, not Furniture 13,140 Rubber & Plastics 13,257<br>Petroleum 14,329 Petroleum 13,324<br>Food & Bev 15,632 Food & Bev 20,316<br>0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,00015,000 20,000 25,000 30,000<br>Graphs by AN_EXO Graphs by AN_EXO<br><!-- End of picture text --> Table 5b: Côte d'Ivoire <!-- Start of picture text --> Employment Shares by Industry<br>Employment Share Employment Share<br>2003 2014<br>Tobacco 0.5 Tobacco 0.3<br>Machinery 0.8 Furniture 0.5<br>Non-metallic metal 1.0 Paper 0.5<br>Other Transport 1.0 Other Transport 0.6<br>Paper 1.7 Non-metallic metal 1.7<br>Furniture 2.0 Machinery 2.8<br>Printing 2.8 Leather 3.6<br>Leather 3.4 Printing 3.7<br>Fabricated metal 4.0 Textiles 4.3<br>Rubber & Plastics 6.2 Fabricated metal 6.2<br>Textiles 7.1 Wood, not Furniture 11.1<br>Chemicals 7.4 Chemicals 13.4<br>Wood, not Furniture 19.0 Rubber & Plastics 14.5<br>Petroleum 20.7 Petroleum 14.6<br>Food & Bev 22.6 Food & Bev 22.2<br>0 5 10 15 20 25 0 5 10 15 20 25<br>Graphs by AN_EXO Graphs by AN_EXO<br><!-- End of picture text --> 44
sectoral dimension _s_ and replace the vector _X_ with our location-sector specific variables of interest.<sup>8</sup> This gives us our first benchmark specification: _mijst_ is the observed migration rate between state _i_ and _j_ for sector _s_ in the household survey of year _t_ . It is simply defined as the number of migrants going from _i_ to _j_ divided by the number of stayers. Individuals are considered as migrants when they declare having lived five years ago ( _t_ – 5) in a different state than their current state of residence. Since we do not know the exact moment of migration, all independent variables are constructed as means over the years _t_ –4 to _t_ –1. This is indicated by the index _t_<sup></sup> .<sup>9</sup> Our main variable of interest is the market access gap between states _i_ and _j MA_  _<u>jst</u>_ for sector _s_ , ∆ _MAijst_  = _ln MA_  . An increase in this variable makes state _j_ relatively _ist_ more attractive, either because of i) a higher wage level or ii) new job opportunities (more or better jobs). We can isolate the second channel by including the wage gap, ∆ _wijst_ <sup>, in our benchmark specification. Adding the wage</sup> variable has an additional important advantage: it also captures other sector and time varying characteristics of the local labor market that we cannot observe but which are potentially correlated with foreign market access (e.g., sector-specific productivity differentials).

A lower number of available jobs typically also corresponds to a higher unemployment rate. But a higher unemployment rate can also reflect limitations on the labor supply side or a mismatch on the local labor market between vacancies and job seekers. While in some specifications we explicitly include regional differences in unemployment rates, our benchmark estimation includes _FEit_ and

> 8 Here we make the implicit assumption that workers do not switch sectors, and thus their migration decision depends only on state characteristics (e.g., price level) or the characteristics of their own sector (e.g., sectoral market access).

> 9 Our benchmark results hold also when specifying our independent variables as four-year lags instead of the mean over the previous four years.

7
-48-

# APPENDICES

# A THE SIZE AND THE BURDEN OF THE PUBLIC SECTOR

As in other countries that are at a similar stage of development, the share of the tax base in the economy is much smaller than in industrialized countries. The administrative resources that are available for the collection of revenues are also limited. Various indices illustrate that the tax base is very small with respect to the revenues that are required for the government policy. We consider here the size of the government labor force and the average effective tax rate.

_The Labor Force in the Public Sector_ According to the available data (Public Expenditures Survey, [1989]), the number of permanent civil servants who are not providing marketed services is about 110,000 in 1989. This number i; probably not very different from its value in 1985. For that year, the total number of tax payers is about 209,000. (The number of individuals who fill a tax form is about 10,000). The ratio between the two numbers is thus about 50 percent. To put the problem into perspective, could we imagine an industrialized country where one third of the labor force is employed in government activities that do not earn income?

_The Average Effective Tax Rate_
# accuracy of the forecasts on economic activity.

The pLanned le'%e1 of public expenditure as percentage of the maximum level permitted bv budgetary law declined sharply after 1977. During the Prem regime, the planned level of public expenditure was on average 90 percent of the maximum permitted level. After the hludgetary law was revised in 1974, the ceiling on the deficit bec.ame a binding (-nstraint on the level of proposed spending. As indicated in She last column of Table 4, the actual level of public expend.tures was well below the maximum level permitted by the budgetary law. Although the pericd 1975-1935 was during the time when the government accelerated the public spending, the increased spending never exceeded the limit, ranging from 84 to 98 percent of the maximum amount.

# Debt-service ratio and foreign debt ceiling

Regulation on foreign borrowing was made in 1960 by setting the ceiling oni debt-service ratio at 5 percent. In addition, foreign debt services must be less than 13 percent of planned revenue. The f3reign debt policy commission was alsc set up in 1960 to monitor and regulate foreign borrowing. The debt-service ratio and foreign debt service to planned revenue ratio were revised upward gradually in subsequent years to allow the government to finance its capital expenditure. In 1977, foreign debt policy commission was empowered to control public enterprises' foreign borrowing. In 1981, the ministry of finance was empowered to negotiate foreign loans for military procurement. The debtservice ratio was raised temporarily from 9 to 11 percent for the period 1984-1987 to accommodate refinancing program. The actual value of public debt service ratio as compared with the ceiling was presented in Figure 7. The debt service ratio declined after 1985 when the government realized the heavy burden of external debt. In 1986, the government was de -mined to reduce foreign debt by setting a one billion LIS dollars maximum amount of foreign borrowing within one year. This maximum amount was later raised to 1.2 billion US dollars in 1989.

# 3. DECOMPOSITION OF THE PUBLIC DEFICIT

An analvsis of the effects of public deficit on domestic macro variables will be discussed in subsequent sections by employing a small macroeconometric model. Since the size of the public deficit is in turn affected by the changing macroeconomic variables, from the partial equilibrium analysis, it is possible to identity some of the feedback effects of the domestic macro variable by using a single regression equation. In this section, the size of the consolidated public deficit wll be decomposed into domestic macro variables, foreign variables, and policy variables.

The contributions to the deficit of various variables will be 5
**Table 3. Panel B. Regressions with Savings Related Measures and TFP, ICA data**

|Dependent<br>variable|(1)<br>Operating<br>Income to<br>Sales|(2)<br>Any<br>Investment|(3)<br>Any<br>Investment|(4)<br>IK|(5)<br>IK|(6)<br>Ret.<br>Earnings<br>WK|(7)<br>Ret.<br>Earnings<br>WK|(8)<br>Ret.<br>Earnings<br>Invest|(9)<br>Ret.<br>Earnings<br>Invest|
|---|---|---|---|---|---|---|---|---|---|
|Small size|0.03**|-0.13***|-0.12***|-0.07***|-0.07***|21.69***|20.36***|-37.34**|-40.70**|
||(0.025)|(0.000)|(0.000)|(0.000)|(0.001)|(0.000)|(0.001)|(0.049)|(0.040)|
|Foreign|-0.00|0.03|0.02|0.03|0.02|-21.19**|-20.55*|-54.63*|-73.18**|
||(0.864)|(0.408)|(0.577)|(0.356)|(0.500)|(0.034)|(0.054)|(0.098)|(0.035)|
|Exporter|-0.03**|0.11***|0.11***|0.09***|0.09***|-6.17|-1.72|-3.09|5.30|
||(0.019)|(0.000)|(0.000)|(0.000)|(0.000)|(0.323)|(0.790)|(0.881)|(0.806)|
|Government|-0.09**|-0.04|-0.04|-0.03|-0.03|-30.19**|-23.52|-107.53**|-101.55**|
||(0.043)|(0.363)|(0.470)|(0.474)|(0.423)|(0.039)|(0.123)|(0.018)|(0.037)|
|TFP|0.11***|0.04***|0.04***|0.04***|0.04***|-5.98**|-10.04***|-0.51|-1.63|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.011)|(0.000)|(0.946)|(0.853)|
|Year 2005|0.03*|-0.18***|-0.19***|-0.17***|-0.17***|-5.31|-6.75|-311.01***|-304.94***|
||(0.057)|(0.000)|(0.000)|(0.000)|(0.000)|(0.406)|(0.312)|(0.000)|(0.000)|
|Year 2007|0.05***|0.04*|0.04*|-0.00|-0.00|4.02|0.24|-0.64|-3.52|
|Operating|(0.000)|(0.068)|(0.086)|(0.944)|(0.948)|(0.515)|(0.970)|(0.976)|(0.872)|
|Income to||||||||||
|Sales|||-0.03||-0.03||18.94**||26.73|
||||(0.294)||(0.379)||(0.033)||(0.353)|
|Constant|0.12***|||-0.14***|-0.14***|160.03***|159.09***|419.13***|414.72***|
||(0.000)|||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Observations|2673|2816|2598|2766|2563|2898|2673|2670|2464|
|R-squared|0.134|||||||||

35
Capital Flows.‖ _Claremont-KIEP Conference Volume on Monetary and Exchange Rate Arrangements_ , Seoul.

- Krugman, Paul. 1979. ―A Model of Balance-of-Payments Crises.‖ _Journal of Money, Credit and Banking_ , vol. 11, pp. 311-325.

Krugman, Paul. 1999. “Balance Sheets, the Transfer Problem, and Financial Crises.” Chapter 2 in _International Finance and Financial Crises: Essays in Honor of Robert P. Flood, Jr._ Edited by Peter Isard, Assaf Razin and Andrew K. Rose.

- Lee, Jaewoo. 2004. ―Insurance Value of International Reserves: An Option Pricing Approach,‖ IMF WP/04/175.

Obstfeld, Maurice. 1994. “The Logic of Currency Crises.” _Banque de France – Cahiers_ _economiques et monetaires_ No 43, 189-213.

Obstfeld M. and K. Rogoff. 1996. _Foundations of International Macroeconomics_ . Cambridge MIT Press.

Obstfeld, M., Shambaugh, J. C. and Taylor, A. M. 2010. ―Financial Stability, the Trilemma, and International Reserves.‖ _American Economic Journal: Macroeconomics_ , 2: 57-94.

- Ostry, Jonathan D. ,Atish R. Ghosh, Karl Habermeier, Luc Laeven, Marcos Chamon, Mahvash S. Qureshi, and Annamaria Kokenyne. 2011. ―Managing Capital Inflows: What Tools to Use?‖ IMF Discussion Note, SDN/11/06.

Pasricha, K. G. 2011. When are capital controls macro prudential tools? Manuscript, Bank of Canada.

Philippon, T. 2008. ―The Evolution of the US Financial Industry from 1860 to 2007: Theory and Evidence,‖ _NYU Stern Working Paper._

- Pinto, Brian. Forthcoming. _Sovereign Debt and Growth: Practical Lessons from Developing Country Experience._

- Pinto, Brian and Sergei Ulatov. 2011. “Financial Globalization and the Russian Crisis of 1998.” Forthcoming, Elsevier _Encyclopedia of Financial Globalization_ .

Prasad Eswar S., Raghuram G. Rajan & Arvind Subramanian. 2007. "Foreign Capital and

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7364. Cambridge, MA: National Bureau of Economic Research.

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30
where _INDCHARS_ is a vector of individual characteristics, _HHCHARS_ is a set of household characteristics, _GEOG_ is a set of geographical variables, and _SECTOR_ is a set of sectoral variables; ε is an error term and the prefix _d_ indicates changes. We are particularly interested in observing the size and sign of the dummies for changing sector. The results are shown in Table 8.

As is common in the growth regression literature, the log of real initial income has a negative coefficient, implying conditional income convergence. In fact, the data also show strong absolute convergence (Table 9) of income implying declining income inequality.<sup>17</sup> Individuals who were older in 1993 tended to have higher growth rates, although the size of this effect declines with age. Male workers also experienced much higher income growth than female workers, but those whose income came from wages experienced slower growth consistent with the impact of the crisis on formal wages. People with higher working hours in 1993 also experienced slightly lower income growth and the expansion of working hours is also associated with slower growth implying that it indicates stress rather than success.

Individuals who were better educated in 1993 tended to have higher subsequent income growth (around 6% higher growth per extra year of schooling). Households which were larger initially had faster income growth and growing households are also associated with higher income growth (although, of course, the causality here may run in the opposite direction). Having a large number of younger children is not only associated with lower income, but also with slower growth, although, as would be expected, increases in the number of young children are positively (but not statistically significantly) associated with growth. Having a larger number of men in the households is also associated with slower growth, although the effect is not statistically significant.

> 17 This is also consistent with the results obtained by Fields et al (2003) showing declining inequality for Indonesia between 1993 and 1997, as well as results reported in the World Bank’s _Poverty Assessment for Indonesia_ (2006b).

35
# coordinates of commune administrative centers.

# _3.1.2. Herbicide Report System_

In addition to the VNHS, measures of military herbicide exposure are constructed using the US Military Assistance Command Data Management Agency’s Herbicide Report System (HERBS) file. The data include but are not restricted to military operations conducted under Operation Ranch Hand. Both aerial (fixed wing, helicopter) and ground (truck, backpack) missions are recorded. A geographic information system (GIS) was further developed to facilitate the estimation of exposure from HERBS file data (Stellman et al., 2003; Stellman and Stellam, 1986, 2003, 2005). The GIS was initially designed to investigate the effect of Agent Orange and other herbicides on US veterans. The position of the majority of US military units is known with relative precision. These include largely those units that moved rarely or not at all, such as those assigned to fixed bases; locations are also known for many mobile units. For the Vietnamese population however, the issue is different; the current position of the household, let alone its position at the time spraying took place, is not accurately known so that exposure opportunity indexes computed by Stellman et al. (2003) and Stellman and Stellman (1986, 2003, 2005) cannot be used in the context of this investigation.

The GIS, nonetheless, allows counting the number of instances an herbicide spray application fell within .5, 1, 2 and 5 kilometers of a given reference point, and can also restrict counting to dioxin‐contaminated herbicide applications only. However, by design, the buffer zone cannot be arbitrarily set by the GIS user. As a commune in Vietnam has a radius of typically 5 kilometers, the available measures become problematic: most households would live at the border of such 5‐kilometer buffer zone, while herbicides have most likely been sprayed over rice fields and forests, further away from administrative commune centers. To overcome this shortcoming, buffer zones are extended by aggregating hit counts over a set of seven juxtaposed 5‐kilometer‐radius discs. The first disc ‐ central disc ‐ is centered on a commune center. The six other discs ‐ peripheral discs ‐ are centered on six points placed at a 10‐kilometer distance, forming a regular 20‐kilometer‐diagonal hexagon as illustrated in figure 1. For consistency, the six peripheral points are chosen so that the hexagon has one North‐ South diagonal at constant longitude. Aggregating hit counts over these seven discs gives an approximate account of the number of spray applications that occurred within 15 kilometers of a given reference location. This measure, the _herbicide hit count_ measure, is the main proxy 7
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**Table 6: Shares of Latin America's Economies in World Income, 1992**

|**Country**|**Aggregate Annual GDP**<br>**($b.)**|**Percentage of**<br>**World GDP**|
|---|---|---|
|Argentina|200|.98|
|Bolivia|_5_|.02|
|Brazil|425|2.05|
|Chile|37|.18|
|Colombia|45|.21|
|Costa Rica|6|.03|
|Ecuador|12|_.05_|
|El Salvador|6|.03|
|Guatemala|10|_.05_|
|Honduras|3|.01|
|Jamaica|3|.01|
|Mexico|285|1.41|
|Nicaragua|1|.01|
|Panama|6|.03|
|Paraguay|6|.03|
|Peru|21|.14|
|Uruguay|10|_.05_|
|Venezuela|59|.26|
|U.S.A.|5,904|28.50|
|MERCOSUR|641|3.41|
|CACM|26|.13|
|Andean Group|142|.71|

Source: Data from the World Bank Atlas
# **Table 1: Baseline Balance for Village Characteristics**

## **Means, clustered standard errors from OLS regressions in parentheses P-value of t-test of equality of means with relevant control group in braces**

||**Control**<br>**(1)**|**Treatment**<br>**(2)**|**Difference**<br>**(3)**|**Normalized**<br>**Difference**<br>**(4)**|
|---|---|---|---|---|
|**_A. Village Characteristics_**|||||
|**Number of villages**|50|150|||
|**Number of dwellings**|167|108|{.362}|-.121|
||[462]|[148]|||
|**Number of sampled adolescent girls**|28.6|28.4|{.943}|-.013|
||[13.4]|[9.30]|||
|**Poverty score (Mean across households)**|.353|.346|{.394}|-.094|
||[.055]|[.054]|||
|**Number of pre-existing NGOs**|2.32|2.17|{.610}|-.054|
||[2.09]|[1.74]|||
|**Christians as a share of village population**|.140|.140|{.952}|-.002|
||[.204]|[.219]|||
|**Distance from Freetown (miles)**|52.8|52.6|{.291}|-.005|
||[25.7]|[24.2]|||
|**Distance from Kailahun (miles)**|78.4|78.6|{.248}|.005|
||[19.6]|[18.5]|||
|**Distance from nearest PHU (miles)**|1.89|1.86|{.940}|-.012|
||[1.71]|[1.77]|||
|**Distance from nearest secondary school (miles)**|3.53|4.23|{.258}|.115|
||[3.34]|[5.10]|||
|**_B. Village Leader Survey_**|||||
|**"Girls who are visibly pregnant have a bad**<br>**influence on their non-pregnant peers"**<br>**[=1 if strongly agree]**|.960|.967|{.873}|.025|
|**"Girls should be allowed to continue their**<br>**education while pregnant"**<br>**[=1 if strongly agree]**|.120|.073|{.365}|-.111|
|**Pregnant girls allowed to sit exams in the nearest**<br>**secondary school**|.239|.319|{.270}|.132|
|**_C. Policy Responses_**|||||
|**Village was quarantined**|.060|.040|{.595}|-.065|
|**Village visited by contact tracing team**|.960|.933|{.455}|-.084|
|**Received Relief from NGO**|.780|.873|{.139}|.174|
|**Village received food aid**|.260|.213|{.504}|-.077|
|**Village received school supplies (excl. BRAC)**|.220|.207|{.818}|-.023|
|**Received relief from Government**|.800|.653|{.005}|-.234|

**Notes:** Data sources are the Village Census (Panel A), and the Village Leader Surveys (distance measures in Panel A, all outcomes in Panels B and C). Column 3 reports p-values from a test of equality of means carried out by OLS regression of each characteristic on a dummy for assignment to treatment. All regressions include strata (district) dummies and calculate robust standard errors. Normalized differences in Column 4 are computed following Imbens and Wooldridge [2009]. The Poverty Score (PPI) is calculated through scorecards and its value, ranging from 0 to 100, represents the likelihood of a household being below the poverty line. The number of pre-existing NGOs includes all organizations apart from BRAC. Distance from Freetown and Kailahun are computed from GPS data.