rafmacalaba/data-use-mentions-extended huggingface dataset:
of output shifts them left on the economies-of-scale curve (Harrison 1994; Aitken and Harrison 1999; Crespo and Fontoura 2007). It is also more likely in the short term that foreign firms bid away high quality labor from domestic firms by offering higher wages and benefits (labor turnover effect), resulting in a potentially negative spillover effect (Sinani and Meyer 2004; Hoekman and Javorcik 2006; Crespo and Fontoura 2007).

Both effects can reverse in the medium to long term, if domestic firms become more productive due to increased competition and start to absorb skilled workers from multinationals. Given that our data sample only allows us to control for short-term effects, the negative coefficients on the foreign output share variable are not surprising. Moreover, they are in line with other studies rejecting the existence of positive intra-industry FDI spillovers (Havranek and Irsova 2011) or studies that find negative intra-industry spillovers (e.g. Aitken and Harrison 1999; Djankov and Hoekman 2000; Konings 2001, among others).<sup>18</sup> A sector’s average share of foreign ownership in a country ( _own_ ) shows a positive coefficient sign, but is insignificant (columns 1 and 5). The interaction terms with a sector’s average percentage of FDI sales to the domestic market in a country ( _market_ ) has a significantly positive effect on domestic firms’ productivity in the OLS regressions (column 2), confirming the hypothesis that market-seeking FDI is more likely not only to provide a higher spillover potential, but also to translate into more positive actual spillovers.

Since the _market_ variable might be influenced by the productivity of domestic competitors, we additionally instrument for the interaction term of foreign output share with the _market_ variable in column 7. We choose the sectoral average number of days to clear direct exports through customs in a country (in natural logarithms) from the Enterprise Surveys Indicator Database as an inverse instrument. One could argue that longer waiting times at customs motivate foreign firms to rely more strongly on the host market
807|227,129|
|Baseline mean|9.65|11.07|11.75|12.12|12.38|
|_Xijt_|x|x|x|x|x|
|_Firstborn Girl_ x Birth year FE|x|x|x|x|x|
|_Female_ x Birth year FE|x|x|x|x|x|
|_Female_ x State FE|x|x|x|x|x|
|_Female_ x Birth order FE|x|x|x|x|x|
|Birth order x Birth year FE|x|x|x|x|x|
|Birth order x State FE|x|x|x|x|x|
|State x Birth year FE|x|x|x|x|x|
|_Firstborn girl_ x State FE|x|x|x|x|x|
|_Firstborn girl_ x Birth order FE|x|x|x|x|x|

NOTES: Sample of second- and higher-order births. Each column is a separate regression. The outcome is an indicator for death before age 1, 2, 3, 4, 5 for column (1), (2), (3), (4), and (5) respectively. For each column, we drop children that do not have full exposure to the risk of mortality. We always control for _Female_ and fixed effects (FE) for birth year and birth order and for _Firstborn girl_ and state FE. The vector _Xijt_ comprises mother’s age at birth, household wealth quintiles, caste, religion, residence in a rural area, educational attainment of child’s parents, and mother’s birth cohort. Standard errors in parentheses are clustered by state. Baseline mean refers to the average likelihood of under-5 mortality in the pre-ultrasound period. Data: NFHS. *** 1%, ** 5%, * 10%.
**and** World Bank (1983)--is to compute employment densities per 1,000 population. Because density reasures **all** surveyed activities against a coumon yardstick, population, this procedure allows comparison of absolute intensity levels for all activities enumerated, even if a survey's coverage is only partial. This approach is taken throughouw much of this paper, especially where we explore features of structural transformation using comparative data from studies with differing survey methodologies.

The nonfarm employment densities in Table 3 provide tantalizing initial insights into differences in the Asian and African rural f- arm sec ors. The data in Panel B suggest that Asian rural nonfarm employment densities conaonly lie in the range of 80 to 140 per 1,000 population, while the African densities only reach this level in West Africa.

Elsewhere they are much lower, more frequently on the order of 40 to 50 per 1,000. Thus, initially, it appears that the Asian rural economy supports about double the nonfarm activity found in East, Central and Southern Africa.

# D. Female Particigation

Women account for a substantial proportion of both management and employment in African rural nonfarm enterprises. In rural Zambia, they own 60 percent of the nonfarn enterprises (Hilimo and Fisseha, 1985), while their share in nonfarm employment ranges between 25 and 54 percent in countries such as Benin, Ghana, Kertya, Nigeria and Zambia (Dahomey, 1964; Steel, 1977; Freeman and Norcliffe, 1985; Mueller and Zevering, 1970; Milimo and Fisseha, 1985).

Social and religious norm vary considerably among countries and in some cases tightly shape the economic options available to women. This is clearly the case in Islamic countries, as the Chad data in Table 1 indicate. Even in non-Islamic countries, convention frequently dictates that women participate in activities that can be done in the home, with low
Also, by age group Figures 5a-c display notable differences across and within regions. Peak poverty rates of 85 percent for children aged 5-9 in rural areas in the North West in wave 3 stand in stark contrast to urban poverty rates for the 10-17-year old in the South West of below 10 percent. Even though rural poverty rates appear based on this data in wave 3 higher in the North West than in the North East, which currently receives a lot of attention from donor agencies, it needs to be kept in mind that the data from the North East is biased towards accessible areas. In the inaccessible rural conflict affected areas child poverty rates may likely be higher than the already high 70% rural child poverty rates in wave 3 in the accessible areas.

# _5.1.2 Multi-Dimensional Poverty in Nigeria_

As poverty goes beyond monetary poverty, this section presents poverty estimates based on different deprivations using the four waves of the GHS and the MICS 2016/2017 data set. The results of the individual level deprivations (education, food and health) are presented for different age groups, followed by the child deprivations (water, sanitation, shelter, information) measured at the household level over the complete child age range<sup>21</sup> . For each deprivation, first national level figures are presented for each GHS wave and the MICS data, distinguishing by urban and rural sector. Subsequently, estimates by geopolitical zone are presented by survey and sector. Finally, state level estimates based on the MICS data are displayed, with a further breakdown by gender for severe education and food deprivation.

## Education Deprivation

By reaching the age category 15-17 years, according to Figure 6a, around 1 in 10 children has never been to school and is not currently in school with little change over time. However, educational deprivation seems very much a rural phenomenon. Moreover, rural and northern do children start schooling later. Among the 5-9-year olds living in urban areas, around 10 percent has not yet been to school
18 (bequest motives and age structure) independent variables in the regression specification. In addition, the incorporation of all these variables into a single specification allows a simultaneous test to the validity of all of these theories.

These panel regressions were estimated for the period 1975 to 1992 and both country and time fixed effects were used. Annual observations were used and countries did not have the same number of observations<sup>17</sup> . Estimating panel regressions calls for particular care in accounting for the dynamic properties of the variables. In addition to the control variables mentioned above, we also include a lagged dependent variable to reduce the serial correlation of the error terms.

# **Missing Data Technique**

A major problem with estimating cross-country regressions that use socioeconomic variables is that there will be many missing observations. In our case, data on the Gini coefficient and life expectancy at age 65 are often sparse and available at irregular intervals. The problem is exacerbated by the fact that it is usually the less developed countries with the missing data for these variables, and it is the less developed countries that are likely to exhibit bequest constrained behavior. Thus, the exclusion of countries due to the lack of socio-economic (or any) data is likely to introduce a systematic bias against the existence of negative bequest motives.

In an attempt to harvest all the information we can from our data set, we use a method developed by Griliches (1986) for the treatment of missing data or observations. From the data appendix, it will be seen that we have been able to work around the problem of having insufficient observations for elderly life expectancy by obtaining predicted values of elderly life expectancy by using data on life expectancy at birth, which is highly correlated to elderly life expectancy. Since we do not have a variable that is highly correlated with the Gini coefficient, we will apply this missing data technique to the Gini coefficient.

The technique involves three stages. In the first stage, we perform the “normal” estimation
|Heatwaveevent||Baselinemeasure|
|---|---|---|
|<br>1976—London,UK<br>|9.7% increase England and Walesand<br>15.4% Greater London|<br>31-daymovingaverage ofdaily mortality<br>insame year<br>|
|1981—Portugal|in Portugal,406 in Lisbon (month of<br>July)|Predictedvalues|
|1983—Rome, Italy|35% increase in deaths inJuly 83 in|Compared with deaths insame month in|
|1987—Athens, Greece||‘Time trend regression adjusted|
|1991—Portugal|997 excess deaths|Predicted values|
|July12-21|||
|<br>1995—London, UK|11.2% (768) in England and Wales,<br>23% (184) Greater London|31-day movingaverage ofdaily mortality<br>in previous two years|
|July 30-August 3|||
|1994—Netherlands|24.4% increase, 1057 (95% C1913,<br>1201)|31-daymovingaverageofprevious 2 years|
|July 19-31<br>2003—Italy,|3134(15%) in all Italian capitals|Deaths insame period in 2002|
|June1—August15|||
|<br>2003—France|14802 (60%)|Average ofdeaths forsameperiod inyears<br>2000 to 2002|
|August 1-20<br>2003—Portugal|1854(40%)|Deaths insame period in 1997-2001|
|2003—Spain|3166 (8%)|Deaths in same<br>period 1990-2002|
|August 1-31|||
|2003—Switzerland,|975 deaths(6.9%)|Predicted values from Poisson regression<br>model|
|June 1-August 31 (3 months)<br>2003—Netherlands|1400deaths|Numberofdegrees above 72°F multiplied<br>bytheestimated numberofexcessdeaths<br>perdegree
fer to the tax department.<sup>13</sup> A belief that international donors and NGOs have just the right amount of influence, rather than too little influence, corresponds to a percentage point increase of 2.8 in the predicted probability that one will defer to the tax department. Analyses point to a similar effect of domestic civic groups and NGOs on citizens’ willingness to defer to the tax department. The predicted probability that one will defer to the tax department is 2.9 and 4.1 percentage points higher for respondents who believe that international businesses and domestic civic groups and NGOs, respectively, have too much influence, rather than too little influence, over their government.

This finding supports either one of two arguments. Citizens may view the influence donors and non-state actors exert over their government positively. Alternatively, it may be the case that citizens who believe that donors and non-state actors exert “too much influence” want to strengthen the state. By quasi-voluntarily complying with government rules and regulations, citizens are increasing the amount of revenue flowing to the state, thereby increasing the strength of the state.

Next, I examine whether including country-level indicators of the provision of services by donors and non-state actors, including a measure of the number of NGOs and IGOs present in each of the 19 countries, as well as foreign aid flows, food aid, and health aid, improves model 2’s fit. None of the country-level measures are significant at the p _<_ 0.05 level when I include the variables in the full model. The variable indicating the number of NGOs and IGOs per country is positive but only significant at the p _<_ .01 level. Including these country-level variables do not affect the predicted probabilities and of individual-level measures of donors and non-state actors.

To summarize, findings from multilevel logistic regressions suggest that the relationships between government effectiveness, procedural justice and deference to the tax department do not change once I account for citizens’ perceptions of donors and non-state
al. (2015). Family background is measured by father’s schooling. Father’s Edu is father’s years of schooling. The normalized schooling measure was constructed for sons and daughters separately. The Sub-Group SD refers to standard deviation of father’s years of schooling for that specific sub-samples (sons/daughters). The rank in schooling is constructed based on combined sample (Sons+Daughters), following Chetty et al. (2014). Sample corresponds to children born between 1957 and 1962, or 1968 to 1972. Covariates include birth district district FE, year of birth×1971 enrollment, year of birth×1971 number of children, year of birth×water sanitation program, year of birth dummies, following Duflo (2001). The variable Inpres measures the number of Inpres schools per 1000 children at the district level divided by the highest number of schools received by one district. For the sake of parsimony, only intercept, linear, and quadratic terms are reported in this table; full table, with all coefficients, is available upon request. Data sources: Indonesia’s full count census 2000 and Duflo (2001).

58
369|5,364|5,359|259|
|_Controls in All Panels_|||||||
|Individual Covariates|Yes|Yes|Yes|Yes|Yes|Yes|
|Household Size in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Enterprise Property in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Contract in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Dwelling Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Income Type in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of Assets in Afghanistan|Yes|Yes|Yes|Yes|Yes|Yes|

_Notes:_ Dependent variables: Mental Issues Index is constructed using the average of the variables in columns (ii) to (vi). The average is calculated with the number of variables available per individual, the index do not take into account the variables with missing values. The Individual controls include: age, gender, employed, student status in Afghanistan, and number of friends before moving to Tajikistan. Household controls include the variables listed in Table 2. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.

65
newer, have smaller class sizes and better teacher attendance than public schools. On average, student performance in public and private schools tends to be comparable. In Morogoro, most private schools are financially stable but nearly half express willingness to partner with the government. As such, in the short term, there is space to expand enrollment through partnerships with these schools at relatively little cost. At the national level, estimates suggest that increasing the share of lower secondary enrollment that is under public-private partnerships would reduce the annual cost of lower secondary significantly (World Bank 2019b).

In 2015, families of students in private schools paid close to 9.5 times more in non-tuition fees at the pre-primary/primary level and 2.1 times more at the secondary level. These differences are likely to be exacerbated with the introduction of FBEP. However, parents are willing to pay for basic education if they are guaranteed better quality. Consequently, unless PPPs are explicitly designed to ensure access to quality basic education for disadvantaged students, existing inequalities may widen.

On the whole, Tanzania is likely to face significant supply shortfalls in basic education. At the same time, detailed micro-data from one region (Morogoro) show that private schools have the capacity, service delivery, parental demand, and willingness to partner with the government on basic education service delivery. This suggests several entry points to explore welfare-enhancing publicprivate partnerships in Tanzania’s secondary education.

18
Second, we find that employment generates benefits that are significantly greater than that of cash alone, which yields a statistically insignificant 0.06 unit increase in our psychosocial index, consistent with recent meta-analyses of the mental health impacts of cash transfers (Ridley et al. (2020); McGuire, Kaiser, and Bach-Mortensen (2020)). We can reject equality of effects between employment and cash for our mental health index ( _p_ = 0 _._ 000), reported physical health ( _q_ = 0 _._ 081), cognitive performance ( _q_ = 0 _._ 028), and risk tolerance ( _q_ = 0 _._ 028).<sup>3</sup> These differences are substantial: employment improves mental health at a magnitude four times greater than cash alone.

Third, we find a high willingness to supply labor among study participants even at a wage of zero, suggesting that individuals are able to internalize the psychosocial benefits of employment. Through an incentivized elicitation of reservation wages for an additional week of work, we find that the majority (69%) of individuals are willing to work an additional week for zero pay. Among these individuals, the vast majority (77%) are willing to forego an alternative low-effort activity offered by the NGO for which they can earn 200 BDT (approximately $2.5 USD, or average savings at baseline) to instead continue working for no pay. We elicit these measures after eight weeks of working, with the intent of both familiarizing participants with the nature of the work and eliminating novelty effects that might arise in the early days of new employment.

We consider two potential confounds to the experiment: expectations of future work and experimenter demand effects. While we cannot rule out that our work made participants hopeful of eligibility for future work, a randomization of a certificate, intended to provide an explicit boost to the resumes of our employees, has no measurable impact on psychosocial well-being. Nor do we see differences in expected or
Table 7: Vietnam: Private Rates of Return to Schooling by Level of Education (percent)

|Educational level|All|Males|Females|Public|Private|
|---|---|---|---|---|---|
|Primary (vs less than primary)|13||21||23|
|Secondary (vs primary)|5|4|6|6|4|
|Vocational (vs primary)|4|5|5|6||
|University (vs secondary)|11|10|12|11||

Source: Based on the earnings functions results presented in Table 6

# REGIONAL DIFFERENCES

Evidence of low returns to schooling in Vietnam is not surprising. Analysis of private enterprises (Ronnas 1992) revealed a negative correlation between education and earnings. This is attributed to large regional differences in general levels of education and income. The level of education in the North is higher than in the South, and wage levels in the South are much higher than in the North. Ronnas argues that the income differences are due to the generally higher capital-labor ratio and better endowment with machinery and equipment in the South. Other factors may also be at work such as differences in work discipline, infrastructure and transaction costs.

The VLSS confirms that schooling levels are higher in the North. On average workers in the North have about ten years of schooling, while in the South the average worker has less than seven years of schooling (Figure 1). In terms of earnings, however, workers in the South earn more than workers in the North: VND 182,495 ($17) versus VND94,174 ($9) per month. Therefore, workers in the South have a third less schooling but earn 50 percent more than workers in the North.

18
**Table D.9:** Water and Sanitation

||(1)<br>Control<br>Mean|(2)<br>Pooled|(3)<br>CM|(4)<br>NFA|(5)<br>N|
|---|---|---|---|---|---|
|**Water and sanitation index**(SUR)||0.110<br>(0.044)<sup>_∗∗_</sup><br>[0.037]<sup>_∗∗_</sup>|0.066<br>(0.051)<br>[0.210]|0.154<br>(0.051)<sup>_∗∗∗_</sup><br>[0.025]<sup>_∗∗_</sup>|5053|
|(1) Water and sanitation HH index|0.000<br>(1.000)|0.068<br>(0.058)<br>[0.089]<sup>_∗_</sup>|_−_0.022<br>(0.068)<br>[0.362]|0.159<br>(0.063)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(2) Water and sanitation village index|_−_0.016<br>(1.003)|0.175<br>(0.087)<sup>_∗∗_</sup><br>[0.073]<sup>_∗_</
be able to sponsor and receive compensation for doing so from the beneficiaries (Weinstein, 2002; DeVoretz, 2008; Posner and Weyl, 2018, chapter 4). These schemes are one-sided market mechanisms in that permits do not come at an opportunity cost to their sellers. In our paper, however, supply is driven by domestic workers’ choices to temporarily renounce their right to work in exchange for some financial compensation.<sup>1</sup> An upside of such double-sided market is the property that it is a self-targeting and progressive mechanism, whereby only the most vulnerable citizens would opt in. Tax policy is therefore the government lever to regulate the market for work permits and in doing so trades breadth off against the depth of this market-driven social protection scheme. On the other hand, the market for work permits will be affected by the existence of informal and unregulated labor markets, all the more than formal and informal labor markets are more fully integrated due to weak labor and migration law enforcement.

The rest of the paper is organized as follows. Section 2 presents our theoretical model of a market for work permits and section 3 uses data from the US and Mexico to calibrate it. In section 4, we discuss the results of the calibration under our preferred specification as well as under alternative parameter values. In section 6, we discuss the limits of the model in the presence of imperfect enforcement. Section 7 concludes.

# **2 Theoretical Framework**

To understand the implications of a market for work permits, we consider two countries indexed by _j ∈{o, d}_ , where _j_ = _d_ refers to the destination country and _j_ = _o_ refers to the origin country. Each consists of a continuum of workers of measure _Nj_ . Consumers-workers have utility where **c** is the vector of consumption and _l ∈_ [0 _,_ 1] is labor supplied.

> 1Lokshin and Ravallion (2022) discuss the policy issues further, including those related to implementation
have the resources to include all the poor).

Results show that when it comes to exclusion errors under a fixed poverty line, what matters the most by far are budgets (Figure A2 in the Addendum, available upon request). Indeed, applying a PMT performs nearly as a random selection in excluding households, and exclusion errors decrease at a constant rate as budgets increase. For inclusion errors, initial levels of poverty are a more important determinant of performance. Overall, given the high prevalence of poverty in our samples, inclusion errors remain very low, and PMT plays a greater role at low selection thresholds in avoiding these inclusion errors.

# _Insights from studying targeting methods in practice_

Program managers often simulate the performance of different PMT models to make decisions on how to select program beneficiaries. The academic literature is also largely based on the simulation of PMT methods, which themselves are drawn from nationally representative surveys, to assess their performance. Nonetheless, there are several reasons why simulation results may differ from those obtained in practice 19