rafmacalaba/datause-encoder-data huggingface dataset:
# **1. The missing market**

Barriers to international labor migration are clearly a major factor in explaining intercountry gaps in the marginal products of labor, implying large economic gains from reducing those barriers.<sup>2</sup> The main barrier is the restriction on the entry of foreigners to a country. Almost everywhere, a foreigner needs official permission—typically in the form of a work permit (WP)—to take up employment in a host country. Binding quotas on the supply of WPs (or work visas) create an excess demand globally for permission to work in high-wage economies among people living in relatively low-wage economies. The rationing of access to employment creates costs (including long and wasteful waits for visas) and strong incentives for illegal migration, including human smuggling.

An explicit WP is not required for citizens (or officially-recognized permanent residents). They already have a legally-recognized entitlement to accept any job offer in their own country. (Implicitly, the citizenship/residency document is the WP.) Once one reaches the specified working age, citizenship invariably comes with an unrestricted right to take up jobs offered—the “right-to-work.”<sup>3</sup> Currently, this right is not something that a citizen can relinquish. It is a nonmarketable entitlement.

Yet, alongside the global excess demand for jobs in high-wage economies, there are times at which citizens in those economies do not want to work in their country for the wage they can get, or simply cannot do so. They do not need their right to take up a job when offered, though it still has a value to others. So, at any one time, there are both foreigners who want jobs at the higher wage rates on offer in rich countries and people in those countries who have something they would prefer to do other than work for a wage. We have a missing market.

Restrictions on international migration for work are the root cause of the missing market. Without those restrictions, citizens would still not be able to sell their right-to-work, but that would be a moot point since nobody would have an interest in buying that right. There would be no missing market. However, removing all such restrictions is a tall order. There is another policy option—to create the missing market. Exploring that possibility is the aim of this paper.

> 2 See, for example, Clemens et al. (2019) and the estimates surveyed in Clemens (2011).

> 3 This term has different meanings. Here we do not refer to a right to actual employment but simply the right to take up employment if offered.

2
**_Figure 1: Education Inputs_**

<!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>Panel A. Pupil-Teacher Ratio in<br>Primary Education<br>Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>6 0<br>4 0<br>in 20 06<br>2 0<br>P u pil-tea che r ra tio, se con da ry<br>0<br>6 0<br>40<br>in 2006 20<br>0<br>P upil-tea che r ra tio, prim a ry<br>-20<br><!-- End of picture text --> <!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>15<br>10<br>5<br>in 2006<br>Secondary education teachers (per 1000 people)<br><!-- End of picture text --> Panel B. Trained Teachers in Primary Education

**_Figure 1: continued_**

<!-- Start of picture text --> Mozambique<br>.1 .2 .3 1 10 30 50<br>GDP per capita in 2006<br>(1000 of constant 2000 US$)<br>linear fitted values 95% confidence interval<br>1 2 0<br>1 0 0<br>8 0<br>in 2 0 0 66 0<br>4 0<br>T ra in e d te a c h e rs in p rim a ry e d u c a tio n 2 0<br><!-- End of picture text --> Panel D. Secondary Education Teachers per Capita Panel C. Pupil-Teacher Ratio in Secondary Education Source: World Development Indicators.

12
# **Table 11: Import prices: Before and after being privatized to the Ben Ali family**

**The Evolution of Log Unit Prices Before and After Privatization** Dependent <u>Variable: log price</u>

|_Standard errors clustered by_|_Firm-product_|_Product_|_Product_|
|---|---|---|---|
||||Excluding outlier firm|
||(1)<br>**coef/se**|(2)<br>**coef/se**|(3)<br>**coef/se**|
|Post-Privatization|-0.003|-0.003|-0.008|
||(0.051)|(0.069)|(0.071)|
|Post-Privatization*Ben Ali Owned|-0.179**|-0.179|-0.556**|
||(0.080)|(0.230)|(0.280)|
|Firm*Product*Source Country FE|Yes|Yes|Yes|
|Porduct*Source Country*Year FE|Yes|Yes|Yes|
|N|55452|55452|38409|
|Firms|1016|1016|984|
|Privatized Firms|23|23|22|
|Privatized to the Ben Ali family|5|5|4|
|#products|365|365|295|
|#firm*prodcuts|9382|9382|6374|
|R2|0.849|0.849|0.857|
|AdjustedR2|0.794|0.794|0.794|

Note: Robust standard errors in parentheses. *** p=<0.01, ** p<0.05, * p=0.10. Sample confined to source-productyears in which (to be) privatized firms are importing, and source-product combinations imported by at least one privatized firm both at least once before and at least once after privatization. Competitor firms are included if they import at least twice from the same source-product country combination and operate in the same sector as (to be) privatized firms.

48
# **1. Introduction**

Officially recorded remittance flows to developing countries are estimated to have reached $351 billion in 2011 (Mohapatra et al., 2011), more than three times the total official development assistance going to developing countries.<sup>1</sup> However, policymakers and much of the migration literature have long worried that the majority of remittances are used for consumption purposes, not savings or investment, reducing their long-term development potential (e.g. Chandavarkar, 1980; Connell, 1980; Durand and Massey, 1992; and IADB, 2004). While there are studies which show positive impacts of remittances on education and investment (e,g, CoxEdwards and Ureta, 2003; Woodruff and Zenteno, 2007; and Yang, 2008), recent work by Ashraf et al. (2011) has shown remittance receivers are likely to save less than their remittance senders desire, while insights from behavioral economics suggest that many people in general may save less than rational financial planning would predict (Benton et al., 2007).

One of the main policy responses to try and increase savings from remittances and improve financial management among remittance receivers has been the introduction of financial literacy programs for migrants and/or their families. For example, the Government of the Philippines launched a financial literacy campaign based on the concern that migrant families enjoy substantial consumption gains while their family members are abroad, but then have nothing left when the migrants return.<sup>2</sup> New Zealand’s aid agency is funding financial education for seasonal workers from the Pacific Islands in New Zealand<sup>3</sup> , and the Inter-American Development Bank has conducted financial education programs for remittance receivers in Guatemala and Nicaragua.<sup>4</sup> The global financial education program of Microfinance Opportunities/Freedom from Hunger/Citi Foundation now has a specialized curriculum directed at remittance receivers, aimed at helping them better use the money they receive.<sup>5</sup> However, while there is a strong association between financial literacy and levels of savings in both developed (Lusardi, 2008) and developing countries (Cole et al., 2011), the limited evidence on the causal impacts of general financial literacy programs in developing

> 1 Total Official Development Assistance to developing nations is estimated at $90 billion for 2010 <u>http://stats.oecd.org/Index.aspx?DatasetCode=ODA_RECIP</u> [accessed 20 April, 2012].

> 2 <u>http://jedayang.com/2011/08/24/financial-literacy-campaign-and-the-philippine-government/</u> [accessed 20 April, 2012]

> 3 <u>http://www.aid.govt.nz/media-and-publications/development-stories/february-2012/vakameasina-trainingextended-rse-employees</u> [accessed 20 April, 2012]

> 4 <u>http://www.thedialogue.org/projects#Transnational_Families</u> [accessed 20 April, 2012].

> 5 <u>http://www.globalfinancialeducation.org/future.html#remittance</u> [accessed 20 April, 2012]. Our modules, though closely related to the MFO content, is not entirely a derivative since the MFO module on remittances was not yet available at the time of our study.

2
||Table 5.Det<br>|erminants ofv<br>|alueadded per<br>|worker<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|VARIABLES|VA|VA|VA|VA|VA-2SLS|
|**Labor**||||||
|Proportion of|0.304|0.292|0.465|0.386|0.510*|
|temporary worker|(0.388)|(0.361)|(0.354)|(0.304)|(0.309)|
|Avg. Education of|0.112|0.100|0.161*|0.165**|0.187*|
|Workers|(0.0737)|(0.0748)|(0.0706)|(0.0634)|(0.114)|
|**Size and Age**||||||
|Market share|17.35*|16.56*|11.63*|11.11*|33.78***|
||(7.408)|(6.749)|(5.508)|(5.007)|(5.100)|
|Young|-0.288|-0.295|-0.130|-0.115|-0.226|
||(0.198)|(0.177)|(0.192)|(0.195)|(0.264)|
|Mature|-0.0396|-0.0353|0.0630|0.0634|-0.0067|
||(0.0679)|(0.0641)|(0.0808)|(0.0830)|(0.0457)|
|**Access to finance**||||||
|Credit line or overdraft||0.494**|0.451**|0.337**|0.282***|
|Facility||(0.149)|(0.117)|(0.0838)|(0.0898)|
|**Investment Climate (%)**||||||
|Cost of insecurity|||0.0228|0.0151|0.0143|
||||(0.0236)|(0.0214)|(0.0157)|
|Cost power outage|||-0.0150**|-0.0137**|-0.0142***|
||||(0.00515)|(0.00491)|(0.00420)|
|Bribe|||0.0182*|0.0170*|0.011|
||||(0.00784)|(0.00806)|(0.0384)|
|Time Tax|||0.00595|0.00121|-0.0002|
||||(0.00671)|(0.00839)|(0.0075)|
|**Outward-orientation**||||||
|Exporter||||0.752***|0.843**|
|||||(0.177)|(0.377)|
|**Sector**||||||
|Manufacturing|-0.809|-0.697*|-1.216***|-0.924***|-0.877|
||(0.415)|(0.292)|(0.178)|(0.187)|(0.972)|
|Observations|2,811|2,807|1,430|1,430|1,302|
|R-squared|0.345|0.361|0.463|0.481|0.521|
|<br>Weak identification test|||||14.56|
|Region FE|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|

Source: Authors’ estimations based on the data from the Enterprise Surveys 2007/2009 and 2014.

Note: VA=value added per worker. Robust standard errors clustered at the States level in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All the estimates use the weights from the survey.

21
provides indemnity payments to small-scale farmers if the amount of accumulated rainfall within a specific time period falls below an exogenous and pre-established threshold. This allows the use of a quasi-experimental approach --using regression discontinuity design-- to credibly identify causal effects of government transfers on electoral results. Moreover, studying the case of the Mexican Weather Indexed Insurance (WII) allows us to compare voter response in areas that have similar and comparable levels of vulnerability. Second, we collected, constructed and use electoral data at the lowest aggregation level: the electoral section. As already described, the multiple confounding factors that potentially make it difficult to identify an effect of disaster spending even with the use of panel data are minimized in our setting as we use small units of analysis and compare electoral outcomes of a single election. To the best of our knowledge, this is the first study that exploits the key features of a weather-indexed insurance scheme using GIS methods to produce a complete dataset allowing the empirical test of voter response to government disaster spending.

Evidence in the context of developing countries is very limited with the exception of India (Cole, Healy and Werker, 2012). Our findings complement the existing literature and are consistent with the results in previous studies for the US context (Healy and Malhotra 2009, Chen 2013) and Germany (Bechel and Hainmueller, 2011).

Some have argued that voters are not collectively rational as they often respond to situations that are beyond politicians’ control such as economic crises or natural disasters. For example, Achen and Bartels (2004) --using historical data from the US-- find that voters punish incumbent governments for shark attacks and droughts, as long as they can find some “psychologically appealing connection” linking disaster and government.<sup>2</sup> Similarly, Cole, Healy and Werker (2012) --using the quality of the monsoon rains as an exogenous shock to welfare-- examine voters’ decisions in state elections in India and confirm that elected officials fare worse when natural disasters strike. They show that, on average, incumbent parties that run for reelection get punished for bad weather, losing more than three percent of the vote for each standard deviation that district level rainfall deviates from its optimum level. However, they also confirm that

> 2 They focus on American historical electoral politics in the early 20th century and use the particular case of President Wilson’s reelection.

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31
# **Table 5 - Effect of treatment on GBV: Non-IPV GBV in the past 12 months**

# <u>ITT (OLS)</u>

||Exposure to Phys.<br>Violence, in HH<br>nonpartner|Exposure to Sexual<br>Violence, in HH<br>nonpartner|Exposure to Phys.<br>Violence, outside<br>HH|Exposure to Sexual<br>Violence, outside<br>HH|
|---|---|---|---|---|
|RMG|-0.016|0.007|-0.012|-0.003|
||(0.024)|(0.022)|(0.027)|(0.026)|
|Control Group Mean|0.124|0.091|0.153|0.147|
|N (Obs.)|828|865|828|865|
|Adj R-sq|0.025|0.023|0.058|0.051|
|p-val|0.508|0.737|0.644|0.896|

# <u>ToT (2SLS)</u>

||Exposure to Phys.<br>Violence, in HH<br>nonpartner|Exposure to Sexual<br>Violence, in HH<br>nonpartner|Exposure to Phys.<br>Violence, outside<br>HH|Exposure to Sexual<br>Violence, outside<br>HH|
|---|---|---|---|---|
|RMG|-0.016|0.007|-0.012|-0.003|
||(0.024)|(0.022)|(0.026)|(0.026)|
|Control Group Mean|0.122|0.094|0.151|0.149|
|N (Obs.)|828|865|828|865|
|Adj R-sq|0.025|0.023|0.058|0.051|
|p-val|0.506|0.736|0.641|0.895|

Robust standard errors in parentheses. We control for the following baseline characteristics: age, a binary indicator for married, an indicator for ever attending school, indicators for ethnicity being Lao-Tai, Mon-Khmer, SinoTibetan, Hmong-Iumien or other, household size and monthly household income. Data on gender-based violence was not collected at baseline so we cannot control for exposure before the RMG program. All outcomes are binary outcomes where the outcome equals 1 if she has experienced this type of violence in the past 12 months. * p < 0.1, ** p < .05, *** p < 0.01.

23
be considered reliable (Figure A5, A6 in Appendix A). For administrative areas, both for urban and rural areas, the headcount measures are well above the coefficients of variation obtained from the survey (A3 ad A7 in Appendix A). Consequently, for administrative areas, we only consider the mean adult equivalent monthly consumption (A4 and A8 in Appendix A).

The standard errors associated with those estimates do not account for the possible model errors which are due to a misspecification of the model we have used for the imputation in the census. Estimates based on area units below 1,000 households will also have an error associated with them due to the low number of observations in each area. As far as the enumeration area is concerned, despite the fact that the procedure technically allows estimating welfare indicators for this level of disaggregation, the model errors associated with those estimate will be too high as several enumeration areas have less than 1,000 households. As far as the administrative area is concerned a quarter of the rural administrative units are area of aggregation above 1000 household, and for urban area about two-third of the urban administrative units are above 1000 household<sup>13</sup> . Hence administrative level results could only be considered reliable for those larger administrative areas.

Overall the rayon level estimates of the three chosen welfare indicators are to be considered robust, whereas for administrative areas only mean adult equivalent monthly consumption for larger area are to be considered fairly realiable. The decrease in robustness associated with higher disaggregation levels is probably due to the sampling procedure adopted in the HBS that resulted in limited clustering in the sampling process.

Figures B1-B4 in Appendix B show the spatial heterogeneity of poverty for rayon welfare estimates and compare those estimates and their associated confidence interval with the national level value.

> 13 186 out of 896 rural administrative area are larger than a 1000 households, whereas for urban area 93 out of 140 administrative area are larger than a 1000 households.

40
_Figure 5. Distribution of sector level values of AuROC: FDI data_ Note: The figure reports histogram of estimated AuROC values for each sector. Estimation and classification are performed over the in-sample 2015-19 period.

Econometric results meet expectations: the model picks a comprehensive set of non-sector specific variables that can be understood as impacting various costs, as well as demand drivers that will impact future investment returns. This confirms the initial intuition behind the empirical set up: the dataset was built by identifying, building on literature and expert knowledge, factors associated with firm decision to invest that could be linked to economy-wide indicators of demand, as well as the costs of factors, essential infrastructure, the efficiency of institutions in providing product market rules, a stable macroeconomic environment and a political stability. Heuristically, this approach is akin to an expected country-risk-adjusted profit function.

Because results only capture economy-wide effects, they cannot be construed as identifying all the variables (let alone the most important ones) associated with investment decisions since sector-specific dimensions which are controlled for in the regression may very well be the paramount criteria. For instance, the availability of arable land for investments into industrial agricultural production, or the existence of specific skills in many industries, etc. Therefore, a correct reading of the results is that the model identifies factors primarily associated at the economy-wide level with investment decisions or trade success, considering available sector-level information.

Table 2 reports the results for variables with statistically significant estimated coefficients (at the 10% significance level). For parsimony we do not report the estimated coefficients for the full set of potential indicators (190). Following the discussion in the data preparation section, note that we construct the sector level values of each indicator as a nonlinear transformation of the original country level indicator using the information on the country and sector level FDI performance over the corresponding 2010-14 period. Following the CBT methodology, the transformation profiles the value of each indicator for each country and sector pair with respect to the relative performance of that country vis-à-vis all other countries that have recorded success in attracting FDI in a sector.

Before the transformation, we profile each variable such that higher values of the variable imply positive effect on the likelihood of receiving the FDI in a given sector. Indeed, not all variables reflect conditions that create positive incentives for investments: while some variable scores (e.g., GDP growth or 18