rafmacalaba/datause-encoder-data huggingface dataset:
42 for estimating returns to schooling that are beyond the scope of this paper; they are discussed extensively elsewhfre (e.g. Griliches, 1977; Schultz, 1988.). One is the inadequacy of staistical controls to take account of individual ability (i.e. omitted input bias or selection by comparative advantage) (Willis and Rosen, 1979; Heckman and Sedlacheck, 1985). Another is the neglect of school quality in **the wage** fLunction, which **because** quality tends to be directly related to **the** quantity of schooling individuals receive, may overstate the returns to additional years of constant-quality schooling (Welch, 1966; Behrman and Birdsall, 1985). There is no consensus on precisely which variables should enter the wage function or how to statistically identify individuals who invest in schooling or work for **wages** in the labor force. Different strategies for constructing **the** comparison groups **may** imply private rates of return to schooling that are subtantially lower, or sometimes higher, than the simplest logarithmic wage function that includes only years of schooling in linear form and a quadratic in years of post schooling experience (Nincer, 1974). Controversies involving the methods' for estimating returns to schooling are, hopefully, less germane to my objective here, which is to compare the rates of return for women relative to those for men, rather than to assess precisely **the** absolute level of the returns to private and public resources invested in **the** education of women. 18 Labor Supply and **Rates** of Return to Schooling Several studies on returns to education by school level for smn and women are suimarized in Table 1. They **are** not entirely comparable. Several methodological problems **and** inconsistencies **arise** because of the different ways nalysts have handled the effects of time allocation on the computation of returns, and the potential bias due to sample selection because analysts can
- 32 -

9. Some researchers have preferred to use the interstate price deflator estimated by Bardhan (1974). This does, however, have some disadvantages for our purposes: Bardhan's data are for the early 1960s and only cover rural areas, and not even for all states. Rural price indices for 1983 based on Bardhan's data do, however, turn out to quite strongly correlated with those based on Bhattacharya et al. (1987) when both are up-dated by the CPIAL (r = 0.83 across the 15 comparable states). The interstate variability in real mean consumption is higher using the deflator based on Bardhan's study, though the difference is small (a population weighted CV of 4.2Z, versus 3.72 when based on Bhattacharya et al).

10. The appropriateness of CPIAL and CPIIW as price deflators for po'erty analysis has been recently questioned by Minhas et al. (1987, 1988) who also develop alternative price indices for the rural and urban middle three deciles. However, their study does not provide statewise price indices for rural areas, and in the event, using CPIAL and CPIIW seems to be the best that one can do.

11. The Minhas price index applied to the Dandekar-Rath line gives a 1983 poverty line within our interval.

12. The poverty estimates for rural Jammu and Kashmir (J&K) are lower than would be expected a priori; J&K is a relatively poor state by several socio-economic indicators, such as the rates of literacy or infant mortality. The lower poverty estimates are largely the result of a 'high' value of real mean consumption; 29.2 percent higher than mean consumption for rural India at 1983 all-India rural prices. But, even in terms of current prices, mean consumption for rural J&K is 14.5 percent higher than that for rural India. The rest is, of course,* accounted for by the difference in rural J&K and rural India. It could well be that the inter-state price relative constructed by Bhattacharya et al. (1980), which we use in this study, underestimates prices in rural J&K relative to other regions. However, as other studies of inter-state price variation have a much small regional coverage, we are unable to further address this issue.

13. These regressions should be interpreted as simplified representations of the underlying statistical relationship between these variables; all three variables (poverty measure, mean, and Gini) are of course derived from the same distribution. A dummy variable for urban areas was also tested but proved highly insignificant. Note that the logit transform avoids the truncation which arises in using P0 as the dependent variable (being bounded in the 0, 1 interval). This specification comfortably passed a Ramsey RESET test on functional form. Details are available from the authors.
Figure 7: Reasons behind the reduction of enterprise revenue by % of households reporting the reason <!-- Start of picture text --> 50.0<br>45.0<br>40.0<br>35.0<br>30.0<br>25.0<br>20.0<br>15.0<br>10.0<br>5.0<br>0.0<br>Business Business Seasonal No Lack of Cannot Other<br>closure closure closure customer raw transport reasons<br>due to other materials products<br>COVID-19 reason<br>Female headed Male headed Total<br>%<br><!-- End of picture text --> Source: Author’s calculation using HFPS 2020 Figure 8: Coping strategy adopted by households (% of total households) in response to any shock during COVID-19 <!-- Start of picture text --> male hh female hh male hh female hh<br>rural urban<br>Relied on savings Help from family/friends<br>Reduced consumption Bought cheaper food<br>Sale of cattle Took no strategy<br>100<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: Author’s calculation using HFPS 2020 Increases in food price and illness of income-earning members (both in rural and urban areas) and job loss and business closure (more in urban areas) are the most reported shocks by households. In urban areas, households are significantly more reliant on savings and help from family and friends as a coping strategy, whereas in rural areas, they are more likely to reduce consumption and sell animal livestock in response to the experienced shocks (Table 7). This variation in rural-urban coping strategies is not unexpected. Using similar COVID-19 phone surveys for four SSA countries, Josephson et al. (2020) estimate that rural 15
transformation and growth, as long as the economy has been able to build up human capital and accumulate fundamental capabilities that transform those services into high-productivity activities (like finance and business services). However, this typically happens rather late in the development process, after industrialization runs its course, and high-productivity (tradable) segments of services cannot absorb as much labor. As for labor-intensive tradable services (like tourism), they have typically spawned few links to the rest of the economy and have not produced much diversification.

One prominent exception is the success of Hong Kong. Its structural transformation picture looks just like that in Vietnam, except that the roles of agriculture and manufacturing are reversed (Figure O.10). In Hong Kong, it is manufacturing that has rapidly shrunk since 1990, releasing more than 20 percent of the economy’s labor force to other sectors. The displaced labor found employment in services (wholesale and retail trade, finance, insurance and business services, and so on), but at even higher levels of productivity. So deindustrialization was growth promoting. The difference with other countries is that Hong Kong first achieved significant levels of industrialization

**FIGURE O.10** A move from manufacturing to services in Hong Kong

<!-- Start of picture text --> Correlation between sectoral productivity and change<br>in employment shares in Hong Kong (1990–2005)<br>2<br>β = 2.8359; t -stat = 2.51 pu<br>1 fire<br>tsc<br>0<br>min wrt<br>cspsgs<br>con<br>–1 man<br>agr<br>–2<br>–0.2 –0.1 0.0 0.1<br>Change in employment share (∆Emp. Share)<br>Fitted values<br>productivity<br>productivity/total<br>sectoral<br>of<br>Log<br><!-- End of picture text --> Source: Author’s calculations with data from Timmer and de Vries (2009); McMillan and Rodrik (2011). Note: Size of circles represents employment share in 1990; _β_ denotes coefficient of independent variable in regression equation: ln( _p_ / _P)_ = _α_ + _β_ Δ Emp. Share; agr = agriculture; cspsgs = community, social, personal services, and government services; fire = finance, insurance, real estate, and business services; con = construction; man = manufacturing; min = mining and quarrying; pu = public utilities (electricity, gas, and water); tsc = transport, storage, and communications; wrt = wholesale and retail trade.

34
women may be less likely to own property and other assets, for example, differences can vary across and within countries, by region, institutional context, age, marital status, and other individual, household and local factors. Multi-topic household surveys are an ideal source of information on these different dimensions, and to better highlight the socioeconomic and demographic profiles of asset owners for accurate policy targeting. When included in surveys, however, variables on wealth (as well as other welfare aggregates such as consumption, on which national measures of inequality are based), are often collected at the household level (De Vryer and Lambert, 2021; OECD, 2013), or by a single member reporting for others (Hasanbasri et al., 2021b). Household-level measures of inequality, however, can inherently mask unequal resource allocation and welfare outcomes among individuals (Chiappori and Meghir, 2015; Haddad and Kanbur, 1990). Improved data in this area that allows for a disaggregated profile of asset owners and wealth is instrumental in constructing accurate estimates of wealth inequality, and in turn, shaping effective, distributionally-sensitive policies around asset ownership and rights — whether in such areas as land reform, expanding access to finance, or taxation.

Little is also understood as to which assets drive wealth inequality within countries, given data gaps on individual ownership of different asset classes, particularly in national surveys that could allow for a deeper understanding of inequality within populations. And even when these topics are covered in surveys, household members’ asset ownership and control are typically asked only of one person considered by survey enumerators to be knowledgeable about these issues. Recent evidence has shown that this more common survey approach, as opposed to surveying individuals directly and in private, leads to significant gender differences in reporting — in a study examining agricultural land ownership in Malawi, Kilic, Moylan and Koolwal (2021) find that asking one household member to report on others’ ownership, compared to having all members reporting themselves, leads to lower reporting of women’s land ownership and higher reporting for men.

3
parcel locations as defined by their longitude and latitude.<sup>5</sup> Calculating the Simpson Fragmentation Index and deviations in parcel size both require an accurate measures of parcel area _α_ . Most measures in the data were calculated using GPS coordinates. When GPS observations were missing, enumerators measured area using a rope-and-compass method. They also inquired as to the farmer’s own estimate of the field size. Across three rounds 10.4% of parcels were missing area measurements taken by GPS, the bulk of them in the first round. Where GPS measures were missing but ropeand-compass measures were available, we used the rope-and-compass measures of _α_ . This allowed us to recover half of the missing observations. In order to validate this substitution, we regressed GPS measured area on rope-andcompass area for those parcels with overlapping measures, and found them to be strongly correlated, with a _β_<sup>ˆ</sup> = 1 _._ 04 and _R_<sup>2</sup> = _._ 44.<sup>6</sup> We attempted to incorporate the self-reported measures, but many of these were expressed using traditional Ethiopian measures of area, such as the timad. Our attempts to convert these measures to standard hectares found them to be poorly correlated with GPS measures of area.<sup>7</sup> Furthermore, it is well documented that self-reported measures of parcel area suffer from non-random measurement error (Carletto, Gourlay, and Winters 2015).

Fragmentation measures and parcel characteristics across regions are reported in Table 2b, including average parcel size _α_ ̄ and the total area farmed by

> 5. The parcel coordinates are first flattened to cartesian space. A distance matrix is calculated for each household’s parcels, and fed into a traveling salesman minimization algorithm, specifying the home as the start and end point.

> 6. See appendix for details.

> 7. The LSMS Ethiopia documented district specific units of conversion from ‘Timad’ to hectare. We therefore attempted to convert these self-reported measures but produced a large number of outliers. As an alternative, we tried using a standard conversion for the ‘Timad’, treating it as 1/4 of an acre in line with the FAO standard. However, comparisons between self-reported area and GPS measurements when the two overlapped showed the former to be inconsistent. See appendix for further details.

11
# _8.6 The relation between declared trust and actual behavior of initiators_

The regression results indicate, as a persistent theme, that greater post-experiment declared trust is associated with larger amounts returned by receivers but _smaller_ amounts sent by initiators. The negative association for initiators is shown by the interaction coefficients to be due to the behavior of private-university initiators. The negative association seems unique for reported trust experiments.<sup>29</sup>

# _A signaling explanation_

With reported trust a true expression of identity, initiators who trusted more could have felt that they could substitute actions for beliefs when sending a signal of trust. This provides the prediction consistent with the empirical outcome that high-trust initiators, because of the substitution effect in signaling, transfer less, the more trusting they are. The income effect is that the more that is transferred, the greater the potential return to the initiator. The signaling explanation is contrary to the behavioral assumption that greater trust is displayed through greater willingness to transfer money.

# _Disappointment_

Initiators who expected greater returns than they received would have had reason in the post-experiment survey to express disappointment by declaring lack of trust in others. The more an initiator transferred and the less that was received back, the more disappointment there would have been and the less trust in others that would be declared. Participants from the private university, although they gained more in absolute terms than state university participants, because they gave more,

> 29 Johansson-Stenman, Mahmud, Martinsson (2011) combined a survey with a trust game using as subjects household heads in Bangladesh and found a positive association between declared trust and the amount sent by initiators. Glaeser et al. (2000) using undergraduate students at Harvard found no significant relation between declared trust and the amount sent but, as in our results, found that the amount returned was significantly explained by stated trust. Their experiments were nevertheless not anonymized, contrary to most others in the literature.

41
_Panel B. No-constant OLS regression of survey mean and GDP mean_

|||**All**||||**Consumpt**|**ion**|||**Income**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**Obs.**|**Coeff**|**S.E.**|**r2**|**Obs.**|**Coeff**|**S.E.**|**Error**|**Obs.**|**Coeff**|**S.E.**|<br>**Error**|
|All spells|16089 (155)|0.79 ***|0.05|0.39|5678 (127)|0.75 ***|0.05|0.75|10411 (67)|0.94|0.08|0.94|
|All 1 year spells|10139 (145)|0.80 ***|0.06|0.80|3398 (117)|0.76 ***|0.06|0.76|6741 (62)|0.93|0.07|0.93|
|All 1 to 3 year spells|3464 (109)|0.76 ***|0.08|0.76|1186 (65)|0.67 ***|0.08|0.67|2278 (62)|1.02|0.08|1.02|
|All 4 to 6 year spells|3239 (135)|0.77 ***|0.06|0.77|1243 (107)|0.73 ***|0.06|0.73|1996 (65)|0.96|0.09|0.96|
|Comparable spells only|8021 (136)|0.82 ***|0.05|0.82|2642 (92)|0.75 ***|0.05|0.75|5379 (62)|1.02|0.07|1.02|
|Comparable, bal, 1-3 year spells|2569 (87)|0.82 ***|0.06|0.82|808 (44)|0.72 ***|0.06|0.72|1761 (55)|1.02|0.08|1.02|
|Comparable, bal, 4-6 year spells|1969 (88)|0.79 ***|0.06|0.79|613 (46)|0.70 ***|0.06|0.70|1356 (56)|1.03|0.07|1.03|
|1980s|104 (26)|0.99|0.20|0.99|29 (8)|0.92|0.20|0.92|75 (19)|1.00|0.18|1.00|
|1990s|493 (84)|0.55 ***|0.09|0.55|187 (60)|0.51 ***|0.09|0.51|306 (42)|0.63 ***|0.12|0.63|
|2000s|<br>2097 (118)|0.88 *|0.06|0.88|<br>851 (77)|0.82 ***|0.06|0.82|<br>1246 (54)|1.17 *|0.10|1.17|
|2010s|2481 (103)|0.65 ***|0.09|0.65|741 (76)|0.51 ***|0.09|0.51|1740 (58)|0.98|0.11|0.98|
|Low income|474 (49)|0.78|0.17|0.78|460 (48)|0.79|0.17|0.79|14 (3)|0.70|0.26|0.70|
|Lower Middle Income|2676 (77)|0.64 ***|0.10|0.64|1363 (65)|0.60 ***|0.10|0.60|1313 (18)|0.81|0.25|0.81|
|Upper Middle Income|3135 (50)|0.99|0.08|0.99|999 (35)|0.90|0.08|0.90|2136 (28)|1.28 *|0.15|1.28|
|High Income|5955 (41)|0.94|0.09|0.94|817 (30)|0.79 **|0.09|0.79|5138 (39)|0.97|0.09|0.97|
|East Asia & Pacific|1129 (19)|0.65 ***|0.12|0.65|952 (15)|0.66 ***|0.12|0.66|177 (6)|0.77 ***|0.09|0.77|
|Europe & Central Asia|<br>8501 (48)|0.86 *|0.08|0.86|<br>3301 (46)|0.81 **|0.08|0.81|<br>5200 (33)|1.08|0.08|1.08|
|<br>Latin America & Caribbean|<br>4422 (23)|0.92|0.21|0.92|<br>133 (4)|0.12 ***|0.21|0.12|<br>4289 (22)|0.99|0.21|0.99|
|Middle East & North Africa|338 (12)|0.49 ***|0.10|0.49|212 (10)|0.45 ***|0.10|0.45|126 (3)|0.55|0.36|0.55|
|North America|<br>1114 (2)|0.63 *|0.19|0.63|<br>496 (1)|0.31 ***|0.19|0.31|<br>618 (2)|0.72 **|0.11|0.72|
|South Asia|175 (7)|0.83|0.15|0.83|175 (7)|0.83|0.15|0.83|||||
|Sub-Saharan Africa|410 (44)|0.75 *|0.13|0.75|409 (44)|0.77 *|0.13|0.77|||||

**Note:** The panels shows results for no-constant regressions of growth rates from surveys on growth rates from national accounts, as done by Ravallion (2003). Robust standard errors clustered at the country level (based on robust sandwich estimator). Number of countries (clusters) are shown in parentheses next to the number of observations. Statistical significance of difference from _one_ , denoted by *p < = .1, **p < = .05, ***p < = .01.
Table 9: Impact of the Intervention on Test Scores (Heterogeneity by total score at baseline)

||(1)<br>Math Score|(2)<br>English Score|(3)<br>Total Score|
|---|---|---|---|
|Treatment|4_._242|4_._126|4_._064|
||(7_._645)|(7_._954)|(7_._219)|
|Below Median BL Total Knowledge Score|_−_82_._778_∗∗∗_|_−_82_._831_∗∗∗_|_−_82_._374_∗∗∗_|
||(5_._827)|(5_._884)|(5_._223)|
|Heterogeneous Treatment Impact|16_._327_∗_|10_._494|13_._547|
||(8_._938)|(8_._988)|(8_._258)|
|Constant|539_._553_∗∗∗_|540_._607_∗∗∗_|540_._001_∗∗∗_|
||(4_._912)|(5_._170)|(4_._585)|
|Observations|4203|4203|4203|
|R-sqr|0_._085|0_._091|0_._103|

_Notes:_ This table looks at the heterogeneous impact of the school leadership on student level Test scores (columns 1-3) with respect to total score at baseline. The heterogeneous treatment impact is the estimated coefficient for the value of the treatment effect interacted with the variable of interest. The outcome variables are: Math Knowledge Score (column 1); English Knowledge Score (column 2); Total Knowledge Score (column 3). Analytical sample restricted to MLSS schools observed in baseline and endline. All regressions are adjusted by the value of the outcome variable observed at MLSS baseline. Robust standard errors (clustered at the school level) are reported in the second row (in parentheses). ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

31
**Figure 5.  Share of youths aged 15-18 considering migrating, by sex and current activity.**

**Figure 6. Share of youths aged 19-25 considering migrating, by sex and current activity.**

<!-- Start of picture text --> Ethiopia Malawi Nigeria Average Ethiopia Malawi Nigeria Average<br>100% 100%<br>87%<br>90% 90%<br>80% 80%<br>71%<br>70% 69% 64% 64% 67% 71% 67% 70% 60% 62%<br>60% 60% 57% 58%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female<br>NEETs Currently at school Working NEETs Currently at school Working<br><!-- End of picture text --> Note. Sampling weights used. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024.

<mark>Regarding migration destinations, our findings show that</mark> **<mark>both youths aged 15-18 and 19-25 prefer relocating to urban areas, such as capital cities and towns, or other countries rather than rural areas.</mark>** <mark>This preference is particularly evident among the youngest youths, while it appears more varied among the oldest youths. Specifically,</mark> **<mark>youths aged 15-18 are more inclined to consider migrating abroad, whereas youths aged 19-25 show a higher interest in moving to capital cities</mark>** <mark>.</mark> <mark>We further observe gender disparities in migration destinations between female and male youths.</mark> **<mark>Male youths aged 15-18 are more inclined to desire to migrate than their female peers, especially towards urban areas</mark>** <mark>. On average, 44 percent of male youths in this age group express a desire to migrate to another country, compared to 36 percent of their female peers.</mark> **<mark>However, as they age, the situation changes, and female youths aged 19-25 become more willing to migrate than their male peers.</mark>** <mark>For instance, 56 percent of female youths in this age group express a desire to move to capital cities, compared to 46 percent of their male peers.</mark> These trends hold at the country level, but with some specificity. Youths are more likely to migrate to the capital city in Ethiopia, to cities/towns in Malawi, and to another country in Nigeria. These preferences are consistent across both age groups.

13