rafmacalaba/datause-encoder-data huggingface dataset:
# 4. POLICY IrPLICATIONS AND CONCLUSIONS

For the purposes of policy making, the main weakness of this paper is that it does not forcefully address the issue of causality between the structure of production (shares of agriculture, industry, and services) and education. That is, the empirical analysis cannot determine whether changes in the structure of the economy cause increases in the demand for education, or whether increases in education levels facilitate a largely exogenous transition from an agrarian to an industrial/service economy. To determine policy, strong correlations between education and production structure are not enough; we need to know the direction of causality.

This paper tries to finesse this problem by using enrollment rates in primary and secondary school, and not education levels of adults. In the case of health investments, similarly, we use life expectancy at birth and not health stocks of working adults. Gill (1990) also addresses this issue of causality by estimating the demand for education in Peru during the 1980s by age group. The schooling of children (younger groups) is strongly correlated with structure of production, but this correlation is found to be weak for older workers. It is therefore likely that the causality runs from production structure to schooling demand, and not the other way round.

If this issue of causality is resolved in favor of the views expressed in this paper, some interesting policy implications emerge. The main implication is that service sector expansion helps to reduce gender inequity at the same time as fostering growth. This runs counter to the policy advocated by the World Bank and the International Monetary Fund that 40
who is a cadre or enterprise manager is less likely to be credit constrained, and the parent is likely to have high ability and be better situated to influence high school admissions decisions. Similarly, a child whose father ever enrolled in high school is 16.9 percent more likely to enroll in high school (column 1, row 6). Children with parents who have some high school education will also be in families that are likely to have higher income and preferences for more education.

A lower skilled family will face fewer constraints to enrolling a child in high school if a father has off-farm employment experience. This is because off-farm employment is associated with higher income in most analyses of household income in rural China, including those using this data sources (e.g. Benjamin, Brandt and Giles 2005). Nonetheless, we observe a negative but statistically insignificant coefficient on _father ever had off-farm employment_ . Parents with offfarm employment experience not only have higher earnings, but also more familiarity with the off-farm employment opportunities for middle school graduates.<sup>8</sup> We examine the heterogeneous impact of migrant opportunity on the high school enrollment decision by allowing the effect of village out-migration to vary with father occupational and educational background. We interact the migrant share of the village workforce with the four indicator variables for father characteristics in columns (2) through (5). Each interaction term is treated as endogenous, and interactions of the dummies with the quartic in years-since-IDs are used as instruments. As the migrant share of the village labor force increases, the probability that a middle school graduate with a professional father will enroll in high school falls significantly (Table 13, column 2). At the mean level of migration in the sample, the predicted effect of father being a professional on high school enrollment drops to 10.2 percent, or 30 percent lower than the estimated effect shown in column (1). If one had been concerned that the earlier estimates reflected the enrollment decisions of individuals with less ability to attend high school, this estimate is quite important, as it implies the effect of migrant opportunity is actually stronger on the high school enrollment decision among children who were otherwise more likely to attend high school.

We find a similar result when examining the effect of migrant opportunity on children with a father working in migrant or local wage employment in the previous year. Since households that 8We use the “father is professional” and “father ever had off-farm employment” variables to proxy for household wealth because including more direct wealth measures would risk introducing endogeneity bias.

24
# **Table 5. The effect of victimization on consumption by event and perpetrator type**

|||Event t<br>|ype<br>|||P<br>|erpetrato<br>|r type<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|VARIABLES|||||||||||
|Property|-0.087**<br>(0.037)|-0.066**<br>(0.032)|||||||||
|Violence|||-0.025<br>(0.033)|-0.034<br>(0.035)|<br>||||||
|Insurgents|||||-0.040**<br>(0.020)|-0.049***<br>(0.016)|||||
|Bandits/criminals|||||||-0.068<br>(0.073)|-0.015<br>(0.071)|||
|Pastoralists/nomads|||||||||-0.039<br>(0.024)|-0.015<br>(0.020)|
|Observations|2,125|2,075|2,125|2,075|2,125|2,075|2,125|2,075|2,125|2,075|
|R-squared|0.162|0.321|0.158|0.319|0.161|0.322|0.159|0.319|0.159|0.319|
|Number of households|717|717|717|717|717|717|717|717|717|717|
|Controls|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|
|Year FE|YES|NO|YES|NO|YES|NO|YES|NO|YES|NO|
|HH FE|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Region-round FE|NO|YES|NO|YES|NO|YES|NO|YES|NO|YES|

_Note:_ Dependent variable is household log total consumption. Data used are from the three waves of the GHS and telephone survey for conflict. All regressions are conducted using weights. Controls include all household and geographical variables listed in appendix table C.1. Standard errors clustered at the local government area level. Significance: *** _p_ < 0.01; ** _p_ < 0.05; * _p_ < 0.1 37
Table 2. Model Without Interaction Between Inequality and Initial Income (First Difference Specification)

|Dependent Variable is:||||Δln(yit)|||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:|All Cou|ntries|Excluding<br>Coun|Transition<br>tries|Excluding<br>Coun|East Asian<br>tries|
|ΔInequalityit|1.61***<br>(0.22)|1.47***<br>(0.23)|1.62***<br>(0.23)|1.45***<br>(0.23)|1.42***<br>(0.22)|1.25***<br>(0.23)|
|Δln(yit-1)|0.23***<br>(0.05)|0.26***<br>(0.05)|0.23***<br>(0.05)|0.26***<br>(0.05)|0.16***<br>(0.05)|0.19***<br>(0.05)|
||||First Stage for|ΔInequalityit|||
|ΔResidual Inequalityit|0.81***<br>(0.02)|0.88***<br>(0.02)|0.81***<br>(0.02)|0.88***<br>(0.02)|0.83***<br>(0.02)|0.90***<br>(0.02)|
|Kleibergen Paap F-<br>statistic|1424|2823|1340|2748|1338|2855|
|Country FE|No|No|No|No|No|No|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|622|622|591|591|562|562|

Note: The method of estimation is two-stage least squares. Standard errors are shown in parentheses. Δ _Residual Inequalityit = ΔInequalityit – αΔln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

27
8 of 1 through _5_ has a well-defined meaning, and a serious effort is made by the NC to apply the rankings consistently.<sup>1</sup> 3 These four factors make up the subset that we feel is both relevant and measurable. In order to adjust for the importance of any relevant but _uzneasurable_ factors, we later define a "residual" component of existence value and attempt to estimate the effect of its omission from the regressions. This artificial construction will be explained in Section 4, where it plays an important role in interpreting the pattern of spending decisions.

# 3. The Listing Decision

## A. Background: The Endangered Species Act of 1973

The Endangered Species Act of 1973 ("the Act') created a framework for the preservation of endangered plants and aiimals in the United States. This framework is administered primarily by the United States Fish and Wildlife Service (FWS), an agency of the Department of the Interior, which oversees the recovery of all terrestrial and freshwater species of plants and animals.<sup>14</sup> The term "species", although having a fairly precise technical meaning to taxonomists, is defined in the Act to include subspecies, varieties (for plants), and populations (for vertebrates), in addition to 'true' species in the technical "The Nature Conservancy distinguishes between global ranks, called 'G-ranks', which are given to full species, and 'T-ranks", which are given to subspecies or populations. In our data set, we use the raoltng relevant to the taxonomic unit being studied, i.e. G-ranks for full species and T-ranks for subspecies. The definitions of G-ranks given by the NC are: _G1_ - critically imperiled throughout their range and typically have fewer than 6 occurrences in the world, or fewer than 1000 individuals; 62 - imperiled throughout their range and typically have between 6 and 20 occurrences, or fewer than 3000 individuals; G3 - vulnerable throughout their range and typically have fewer than 100 occurrences, or fewer than 10000 individuals; G4 - apparendly secure throughout its range (but possibly rare in parts of its range); G5 - demonstrably secure throughout its range (however, it may be rare in certain areas). See National Heritage Data Center (1992) and (1993a).

'Me National Marine Fisheries Service is responsible for the administration of the Act for most marine species. In this paper, we focus our attention exclusively on the species monitored by the FWS. We focus on the FWS because the National Marine Fishery Service does not publish data comparable to our FWS sources. Since the vast majority of recovery programs are managed by the FWS, this restriction does not play a role in our results.
the Gini makes it a convenient way to model changes in the Gini. We can express the final incomes as a function of the initial income, mean income, and changes in the Gini: + τ ], (2) ii<sup>∗</sup> ii yy where = (1 + γγ)[(1 −ττ)yy is the mean income in the initial period. Using (2) and (1), it can be shown that the corresponding GIC takes the following form: ττ<sup>1</sup> (3) ggii = (1 −ττ)(1 + γγ) −1 + [ττ(1 + γγ)ττ] yyii This GIC is a convex, decreasing function (when ) along the percentile groups. It attributes high growth rates at lower percentiles, while it becomes flatter at higher percentiles. It is decreasing ττ> 0 throughout, meaning that the growth rate will be lowest for the richest percentile groups.

Another way of simulating a change in the Gini index uses a linear GIC. Such a GIC takes the following form: (4) ii ii gg Substituting (4) into (1), we can obtain the following expression for the income of percentile group = δδ−θθpp in the final period ii (5) ii<sup>∗</sup> ii ii ii This linear GIC can be obtained by taxing everyone in proportion to both their income and rank – the yy = (1 + δδ)yy −θθyy pp poorest person is taxed at a rate of and the tax increases proportionally with the rank – combined with a transfer where every person receives a share of their income. Unlike the convex GIC, whose central θθ parameter is directly related to percentage changes in the Gini index, there is no functional relationship δδ between the percentage change in the Gini index, , and the parameters of the linear GIC. We thus use an algorithm that iteratively changes the slope of the GIC until it matches the desired . αα To illustrate how the convex and linear GICs could look in practice, we use the welfare distribution from αα To illustrate how the convex and linear GICs could look in practice, we use the welfare distribution from a survey in Côte d’Ivoire from 2015. From 2015 to 2016 IMF data suggest that GDP per capita in Côte d’Ivoire grew by 5.6%. Figure 1 explores how this growth can be distributed if inequality stays unchanged or if the Gini increases or decreases by 1% (ignoring for the moment that only part of this growth is passed through to the consumption observed in surveys). The initial Gini in Côte d’Ivoire was 41.5, meaning that a 1% drop ( ) would bring the Gini to 41.1, while a 1% increase ( =0.01) would bring it to 41.9. Lowering the Gini by 1% does not have to impose a large cost on the top of the distribution. Because of αα= −0.01 αα Lowering the Gini by 1% does not have to impose a large cost on the top of the distribution. Because of the large income share of the top of the distribution, the reduction in the growth rate of the wealthiest individuals necessary to ensure that the bottom grows substantially faster than the mean is relatively small. For example, in the case of Côte d’Ivoire, a convex growth incidence such that the Gini decreases by 1% means that households at the 10<sup>th</sup> percentile grow 2.5 pp faster than the mean, yet only reduces the growth at the 90<sup>th</sup> percentile by 0.5 pp.

5
ban households and all households together, the change with respect to the baseline is overestimated in the short run (by approximately 5 percent) and underestimated in the long run (also by approximately 5 percent). If the household survey is not reweighted, the decrease in inequality is slightly underestimated; the difference between the two approaches is again very small in absolute terms but large in relative terms.

If we compare the micro-accounting framework with the Beta distribution approach, our results again show that the latter indicates always the same direction of changes in P0 and P1 as the former (except for P1 in period 1 for unskilled workers in the urban formal sector). Figure 7 shows that for workers in the rural sector the Beta distribution approach indicates for P0 and P1 in the short term a much higher change, and then in the medium term a much lower change, with respect to the baseline. However, at the end of the adjustment period both methods lead almost to the same change. For unskilled workers in the urban informal sector, changes in P0 and P1 are overestimated, for P0 in the long run by more than 30 percent, and for P1 by 20 to 30 percent throughout the adjustment period. For unskilled workers in the urban formal sector, the change of P0 fluctuates around the change indicated by the micro-accounting framework and amounts in some periods to more than 300 percent of the latter. The indicated direction of the change of P1 is on impact even the opposite of that indicated by the micro-accounting method; it goes afterward in the correct direction, but it overestimates continuously by almost 100 percent. For skilled workers in the urban formal sector, changes in both poverty indicators as measured by the Beta distribution method are more than 2 to 10 times higher than those measured by the micro-accounting method, but the deviations decrease over time. For capitalists and rentiers, the micro-accounting method shows a slight increase of P0 and P1, except through periods 6 to 8, the Beta distribution approach shows no change at all after the second period.

Both experiments, the cut in the minimum wage and the increase in the employment subsidy, lead to qualitatively similar results. A low impact on aggregate output, an increase of rural poverty and a decrease of urban poverty. In rural areas the increase in labor supply, resulting from lower incentives to migrate to urban areas, puts pressure on wages. In urban areas lower unemployment leads to less poverty. In sum, both policies result in a slight decrease of poverty on the economy wide level. These changes are indicated in a qualitative, but not in magnitude, similar way by the three macro-micro linkages we compared.

29
Figure 1: Household business outcomes: monthly averages <!-- Start of picture text --> (a) Monthly Suspension Filings (HBTC) (b) Monthly Closure Filings (HBTC)<br>Average Commune Monthly Suspension Filings Average Commune Monthly Closure Filings<br>Apr<br>May<br>Aug<br>Aug<br>2016 2017 2018 2019 2020 2016 2017 2018 2019 2020<br>Month Month<br>Average Suspension Filings per Commune Average Closure Filings per Commune<br>Pre-Covid Mean Pre-Covid Mean<br>(c) Weekly Work Hours (LFS) (d) Monthly Income (LFS)<br>Average Weekly Working Hours Average Monthly Income (Inflation Adjusted)<br>Sept<br>Sept<br>Apr<br>May<br>2018 2019 2020 2018 2019 2020<br>Month Month<br>Average Weekly Working Hours Average Monthly Income (Individual)<br>Pre-Covid Mean Pre-Covid Linear Trend<br>40<br>30<br>2<br>20 1.5<br>1<br>10<br>Number of Closure Filings .5<br>Number of Suspension Filings<br>0 0<br>50<br>6500<br>45 6000<br>40 5500<br>5000<br>35<br>Average Weekly Working Hours<br>Average Monthly Income (thousands) 4500<br><!-- End of picture text --> Note: This figure shows monthly-average plots of four commune-level household business outcomes: monthly suspension filings (panel A), monthly closure filings (panel B), weekly work hours (Panel C), and individual monthly income (Panel D). Blue/solid lines indicate monthly average values. Red/dashed lines represent pre-COVID means (2018-19). Data on household business suspension and closure filings are obtained from the Household Business Tax Census. Data on individual work hours and income are obtained from the Vietnamese Labor Force Surveys.

16
Table A.14: Robustness checks, using 2018 data. Estimating the effect of migrants’ legal status on wages after return, unconditional on return

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|-0.001|-0.042|0.009|-0.002|
||(0.001)|(0.037)|(0.009)|(0.005)|
|Age squared|0.000**|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.002|0.444***|0.155***|-0.332***|
||(0.002)|(0.127)|(0.035)|(0.026)|
|Primary or preparatory|0.001|0.243|0.133***|-0.325***|
||(0.003)|(0.151)|(0.039)|(0.027)|
|Secondary|0.001|0.258**|0.099***|-0.219***|
||(0.002)|(0.112)|(0.028)|(0.021)|
|Rural|0.003|-0.064|-0.036|0.003|
||(0.002)|(0.095)|(0.026)|(0.016)|
|Oil prices|0.004***<br>(0.000)||||
|Diplomatic exchange||-0.704***<br>(0.197)|||
|Confict|||0.232***<br>(0.048)||
|Illegal status||||-0.594***<br>(0.070)|
|Legal status||||0.130***<br>(0.044)|
|Observations|14,103|1,940|1,849|8,269|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=2.|282; Illegal mig|.=1.672; Legal|mig.=2.395||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Data comes from the 2018 ELMPS survey. Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

61
Table 6b: Impacts of PROSPERA on School Enrollment All 6th Graders in Medium, High and Very Highly Marginalized Communities School Fixed Effects

||7th grade|9th grade|12th grade|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary 2008|0.076***|0.091***|0.046***|
||(0.005)|(0.006)|(0.006)|
|Female beneficiary|0.008|0.004|-0.002|
||(0.007)|(0.007)|(0.007)|
|Female|-0.016***|-0.025***|-0.014**|
||(0.006)|(0.006)|(0.006)|
|Constant|0.878***|1.050***|0.551***|
||(0.031)|(0.033)|(0.034)|
|Observations|68,871|68,871|68,871|
|R-squared|0.428|0.332|0.245|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 6c: Impacts of PROSPERA on School Enrollment: All 6th Graders in Medium, High and Very Highly Marginalized Communities 6<sup>th</sup> graders in 2008 Nearest Neighbor Matching Models

|||Enrollment||
|---|---|---|---|
|Variables|7thgrade 2009-2010|9thgrade 2011-2012|12thgrade 2014-2015|
|Propensi|ty score matching with exact|matching on age and state (nu|mber of matches)|
|Girls|0.0688***|0.080***|0.0480**|
||(0.018)|(0.0187)|(0.0231)|
|Observations|6,654|6,654|6,654|
|Boys|0.0670***|0.0888*|0.0447**|
||(0.0212)|(0.022)|(0.0222)|
|Observations|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Overall, these results are highly consistent with Schultz, 2004; Behrman, Sengupta and Todd, 2005, where enrollment was measured by self-reported household data. With respect to gender, the results do not show significant differences by gender in the enrollment impacts, that is, program impacts on enrollment are similar for both genders. Note that both Schultz, 2004 and Behrman, Sengupta and Todd, 2005 demonstrate higher effects on enrollment at the secondary level for girls than boys in the randomized evaluation sample carried out in 506 communities in seven states in the initial years of this evaluation. Nevertheless, follow-up results of this sample 20
Table 4: Family education expenditure<sup>+</sup> per child, by total family expenditure quintile and level of education (public <u>schools only), in $</u>

|Quintile|Primary|Secondary|Tertiary|All levels|
|---|---|---|---|---|
|1st|70|111|92|77|
|2nd|136|172|88*|144|
|3rd|160|309|272*|187|
|4th|307|374|386|340|
|5th|547|889|1,075|767|
|All (mean)|238|484|613|338|
|N|340|146|47|533|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages

* less than 5 observations; + some values on household education expenditure are missing

In the case of children attending private schools (Table 5), the overall family education expenditure per child is slightly more than double that of children in public schools. Otherwise, the distribution of family education expenditure per child across expenditure quintiles is very similar to that of children in public schools, with the education expenditure of families in the top quintile exceeding that if families in the lowest quintile by a factor of 8 to 1.

Table 5: Family education<sup>+</sup> expenditure per child, by total family expenditure quintile and level of education (private <u>schools only), in $</u>

|Quintile|Primary|Secondary|Tertiary**|All levels|
|---|---|---|---|---|
|1st|158|245|91*|181|
|2nd|289|332|379*|317|
|3rd|760|912|-|827|
|4th|652|1,347*|898|917|
|5th|1,236|1,590|2,425*|1,450|
|All (mean)|615|902|869|750|
|N|42|29|11|82|

Source: Own calculations using the 1998 Enquête Niveau de Vie des Ménages

* less than 5 observations; ** Professional (technical) education institutions only as there are no private academic institutions; + some values on household education expenditure missing

14