rafmacalaba/datause-encoder-data huggingface dataset:
10 _Table 3: Decomposition results—Vietnam 1993-98_

|Bandwidth(Dong)|_V_|_H_|_R_|_V%_|_H%_|_R%_|
|---|---|---|---|---|---|---|
|30|-0.0694|0.0015|0.0802|-565%|12%|653%|
|40|-0.0718|0.0017|0.0824|-585%|14%|670%|
|50|-0.0717|0.0025|0.0814|-583%|20%|663%|
|60|-0.0718|0.0024|0.0816|-584%|20%|665%|
|70|-0.0727|0.0028|0.0821|-592%|23%|669%|
|80|-0.0729|0.0040|0.0811|-593%|33%|660%|
|90|-0.0727|0.0044|0.0806|-592%|36%|656%|
|100|-0.0729|0.0043|0.0809|-594%|35%|658%|
|110|-0.0729|0.0042|0.0810|-593%|34%|659%|
|120|-0.0727|0.0048|0.0802|-592%|39%|653%|
|130|-0.0729|0.0050|0.0802|-593%|41%|653%|
|140|-0.0726|0.0056|0.0793|-591%|45%|646%|

Whatever the bandwidth, the signs of _V_ and _H_ and the overall conclusions are the same.

In neither country are the small increases in income inequality due to small amounts of prorich vertical redistribution. Rather, they reflect two opposing forces: pro-poor (inequality-reducing) vertical redistribution; and the offsetting effects of inequality-increasing horizontal redistribution and reranking. If the only differential income growth in China and Vietnam had been _across_ groups of base-period equals, income inequality would have fallen during these periods of rapid economic growth. In the case of China, the reduction in income inequality would have been considerable: at the optimal bandwidth, _V_ equals –839% of _∆ G_ . Thus in the absence of differential income growth _within_ groups of base-period equals and the group reranking resulting from this, the Gini coefficient would have fallen by more than _eight times_ the observed absolute rise in the Gini. In Vietnam a similar story emerges, albeit less pronounced.

# V. **DISCUSSION**

Both China and Vietnam experienced rapid income growth during the periods studied (1989-97 and 1993-98, respectively) as they continued their transitions toward a market economy. In neither, however, did income inequality increase markedly. Section II shows that
|HH Distance to Nearest Road|Household Distance in (KMs) to Nearest Road|9.84|9.85|
|---|---|---|---|
|HH Distance to Population Center|Household Distance in (KMs) to Nearest Population Center with +20,000|37.67|17.95|
|_ganyu_wage salary in the EA (MKW)|Value of daily_ganyu_wage salary in the EA (MKW)|566.22|599.87|
|Price of maize grains in the EA (MKW)|Price of maize grains in the EA (MKW)|198.59|112.66|
|Price of fertilizer in the EA (MKW)<br>|Price of fertilizer in the EA (MKW)<br>|62.27|39.34|
|EA neighbor’s wage employment participation|Participation rate in non-farm wage employment by other household in the<br>same EA|15.11|14.90|
|EA neighbor’s self-employment participation|Participation rate in non-farm self-employment by other household in the<br>same EA|21.58|15.27|
|Share of total land cultivated in crops (crop mix)<br>||||
|Grains|Share of total land cultivated in grains|64.53|26.22|
|Legumes|Share of total land cultivated in legumes|23.00|22.86|
|Tubers<br>|Share of total land cultivated in tubers<br>|1.55|7.75|
|Oils crops|Share of total land cultivated in oil crops|0.43|3.86|
|Horticulture crops|Share of total land cultivated in horticulture crops|4.03|11.10|
|Cotton|Share of total land cultivated in cotton|1.29|7.29|
|Tobacco|Share of total land cultivated in tobacco|4.19|11.79|

Source: Generated by authors using LSMS data Note: PSLC=Primary School Leaving Certificate. JCE=Junior Certificate Examination. MSCE=Malawi School Certificate of Education Examination.

37
rather than the income effects associated with the coffee mills. This is important because mass construction of schools could, for instance, not only expand access to schooling opportunities but also have a positive impact on the quality of teaching and learning by reducing overcrowding.

To address this issue, we combine data on primary school construction in Rwanda with the data on coffee mills and explore the association between the rollout of coffee mills and changes in school access. Specifically, using data geolocation and the year of construction of the universe of primary schools in the country, we compute the number of (new) primary schools as well as the cumulative number of primary schools operational per year and correlate it with the rollout of coffee mills at the municipality level.<sup>17</sup> The results as shown in Table A2 in the (online) Appendix, reveal no association between the rollout of the coffee mills and expansion in primary schools. Thus, we can rule out the possibility of a contemporaneous expansion in school access contaminating our results.

# **6 Mechanisms**

In this section, we explore the potential pathways through which exposure to coffee mills influences student performance. We hypothesize and test three key channels: income, health, and school attendance. To test these channels, we combine data from the Rwanda DHS with data on the location and operational status of the coffee mills. However, unlike the main analysis where we define the catchment area using a 2.5km radius from a coffee mill, we are unable to use the same distance threshold with the DHS data, due to the inherent displacements of the coordinates in the DHS survey. Due to privacy concerns, the geo-coordinates of the DHS are displaced by up to 2km in urban centers and 5km in rural areas.<sup>18</sup> As a result, defining treatment and control locations based on distance thresholds below 5km, especially in the context of our study where 88% of the sample are rural, is likely to induce significant measurement errors. Therefore, for the analysis on the mechanism, we define the catchment using the alternative measure, which is the 5km radius from the coffee mill. Given that the results in Section 5.3.4 confirm our baseline results, we argue that the benefits of using the 5km threshold for the analysis based on the DHS data far outweigh the potential measurement errors associated with defining treatment and control using the more restrictive distance threshold, i.e., 2.5km.

Consequently, we estimate the following specifications:

> 17Municipality here refers to the third administrative region, otherwise referred to as "sectors". 18https://dhsprogram.com/pubs/pdf/SAR7/SAR7.pdf

16
**Table 7: Intrahousehold heterogeneity of impacts by gender and age: Beneficiaries from productive investment package versus control**

**<u>Fixed Effects Model</u>**

||||Nu|mber of hours|per week chil|d between 6|and 15 years wo|rked in :||
|---|---|---|---|---|---|---|---|---|---|
|COEFFICIENT|nr obs/hh|non-<br>agricultural|agriculture &<br>livestock|agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|male*itt|2143/806|-0.0680|-1.480**|-0.914|-0.558**|0.0654|-0.0643|-1.760**|-1.759*|
|||(0.23)|(0.62)|(0.58)|(0.23)|(0.35)|(0.33)|(0.67)|(0.93)|
|female*age1015*itt|2143/806|0.915***|0.665|0.0801|0.561*|-0.245|0.946**|1.527**|2.228**|
|||(0.307)|(0.640)|(0.585)|(0.299)|(0.397)|(0.431)|(0.725)|(1.047)|
|male*age1015*itt||0.0499|-3.319***|-2.404***|-0.910***|-0.390|-0.0754|-3.254***|-3.720***|
|||(0.197)|(0.875)|(0.807)|(0.306)|(0.461)|(0.324)|(0.897)|(1.096)|
|male*itt||0.430*|0.853|0.545|0.300|0.154|0.525|1.031|1.710|
|||(0.247)|(0.682)|(0.647)|(0.286)|(0.485)|(0.450)|(0.782)|(1.126)|
|P-value female*age1015*itt = male*|age1015*itt|0.184|0.002|0.031|0.001|0.984|0.177|0.000|0.001|

# **<u>Random Effects Model</u>**

|||Nu|mber of hours|per week chil|d between 6 an|d 15 years wor|ked in :||
|---|---|---|---|---|---|---|---|---|
|COEFFICIENT<br>nr obs/hh|non-<br>agricultural|agriculture &<br>livestock|agriculture|livestock|chores|domestic<br>work|all econ<br>activity|all work|
|male*itt<br>2143/806|-0.205|-1.586***|-1.169**|-0.413**|0.482|0.0997|-1.806***|-1.186|
||(0.19)|(0.54)|(0.52)|(0.19)|(0.30)|(0.29)|(0.59)|(0.77)|
|itt|0.621***|-0.0413|-0.292**|0.265|-0.963***|-0.143|0.539|-0.595|
||(0.15)|(0.26)|(0.15)|(0.23)|(0.37)|(0.27)|(0.34)|(0.67)|
|female*age1015*itt<br>2143/806|0.895***|0.0848|-0.124|0.271|-0.402|1.026***|0.982**|1.748**|
||(0.236)|(0.362)|(0.284)|(0.276)|(0.355)|(0.396)|(0.445)|(0.794)|
|male*age1015*itt|0.314**|-3.699***|-2.779***|-0.928***|-0.335|0.163|-3.266***|-3.503***|
||(0.153)|(0.867)|(0.847)|(0.269)|(0.449)|(0.254)|(0.886)|(1.068)|
|female*itt|0.0920|-0.0959|-0.222|0.103|-0.728**|-0.746***|-0.0460|-1.631**|
||(0.0860)|(0.273)|(0.148)|(0.238)|(0.350)|(0.235)|(0.330)|(0.669)|
|male*itt|0.233***|0.515|0.147|0.389|-0.283|-0.144|0.622|0.241|
||(0.0805)|(0.363)|(0.272)|(0.243)|(0.366)|(0.203)|(0.435)|(0.664)|
|P-value female*age1015*itt = male*age1015*itt|0.049|0.001|0.007|0.017|0.884|0.015|0.000|0.000|
|_NOTE:_Standard errors corrected for clustering at co<br>regression of outcomes in control and on series of ag<br>least 2 children between 6 and 15. age1015 is a dum<br>controls for household random effects.|mmunity level in<br>e dummies for e<br>my variable = 1 f|parentheses. *<br>ach gender. All<br>or children bet|** p<0.01, ** p<br>estimates includ<br>ween 10 and 15.|<0.05, * p<0.1.<br>e a gender dum<br>Fixed effects m|Dependent varia<br>my, and interact<br>odel controls fo|bles corrected fo<br>ions for gender a<br>r household fixed|r gender-specific<br>nd age. All house<br>effects, random|age trends by<br>holds with at<br>effects model|

27
district-of-birth characteristics ( _Zd_ ) and the indicator for belonging to the most exposed cohorts in order to alleviate concerns that other district-of-birth characteristics and not INPRES intensity could explain the results we find.<sup>13</sup> Standard errors are clustered at the district of birth of mothers.

Another potential threat to identification is related to the migration of mothers. In our data, mothers’ district of residence in 2013 is limited to the nine rural districts in the Indonesia ECED data. Thus, we may be concerned that migration from high INPRES birth districts to our nine districts reflects particularly aspirational individuals (i.e., those likely to obtain more education and to experience upward geographic mobility) whereas migration from low INPRES birth districts to our nine districts selects on particularly low-aspirational individuals (i.e., those likely to obtain less education and to experience downward geographic mobility). While we cannot directly rule out this possibility, our analysis of migration patterns among mothers in our data suggests that this is unlikely to be a major threat to validity. Figure 6 presents the mobility pattern across districts by whether the district experienced low intensity or high intensity INPRES. Each dot is a birth district, sorted by levels of Human Development Index (a measure of overall development). Birth districts that are more (less) developed are on higher (lower) values of the y-axis. For each birth district, we calculate the median HDI of the district where mothers are residing in 2013. The difference between this median 2013 HDI and the birth district HDI (x-axis) indicates the level of mobility experienced by mothers coming from each birth district. The figure clearly shows similar and consistent migration patterns across low and high intensity INPRES districts: mothers born in higher HDI districts experience larger downward mobility, while mothers born in lower HDI districts experience larger upward mobility. This suggests that differential migration patterns between low and high intensity INPRES districts are unlikely to bias our results.

We expand the empirical model above to investigate the effect of maternal education on children’s outcomes. Specifically, we consider children’s outcomes to likely be a function of the age and gender of the focus child in our study. We do so with alternative controls ( _Xicd_ ) for age and an indicator function for child gender. Therefore, the model taken to the data is:

> 13These are the same robustness exercises conducted in Duflo (2001).

17
In Thailand, the life sector has reverted to data set averages recently. The non life sector has seen flat Herfindahl values despite being at lower than average levels.

<!-- Start of picture text --> 2,010<br>1,810<br>1,610<br>1,410<br>1,210<br>1,010<br>810 Life Insurance<br>610 Non Life Insurance<br>410<br>210<br>10<br>1991 1996 2001 2006<br>Year<br>In Indonesia, both sectors have been persistently<br>below the data set average. The life sector reduction<br>is, therefore, counter to the view that the direction of<br>change would be toward the data set average but the<br>non life sector has shown some tendency to increase.<br>3,510<br>3,010<br>2,510<br>2,010<br>1,510<br>1,010<br>510 Life Insurance<br>Non Life Insurance<br>10<br>1991 1996 2001 2006<br>Year<br>Herfindahl Value<br>Herfindahl Value<br><!-- End of picture text --> In Indonesia, both sectors have been persistently below the data set average. The life sector reduction is, therefore, counter to the view that the direction of change would be toward the data set average but the non life sector has shown some tendency to increase.

<!-- Start of picture text --> 3,510<br>3,010<br>2,510<br>2,010<br>1,510<br>Life Insurance<br>1,010 Non Life Insurance<br>510<br>10<br>1991 1996 2001 2006<br>Year<br>Herfindahl Value<br><!-- End of picture text --> In the Philippines, the life sector has reduced to levels now below the sector average and then remained stable. For the non life sector, after a period at particularly low levels, gradual increases are starting to be recorded.

<!-- Start of picture text --> 1,610<br>1,410<br>1,210<br>1,010<br>810<br>610<br>410<br>210 Life Insurance<br>Non Life Insurance<br>10<br>1991 1996 2001 2006<br>Year<br>Herfindahl Value<br><!-- End of picture text --> In Egypt, the life sector has fallen in line with the hypothesis that movement would be toward the data set average whereas the non life sector has seen increased concentration to levels somewhat counter to expectations.

Source: Staff Analysis As a result, an initial proposition based on these individual cases and the wider data set would be that a market where the Herfindahl Index observation is above or below the 8
CÔTE D’IVOIRE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 20 Potential gains from greater operational efficiency**

||ICT|Irrigation|Power|Transport|WSS|Total|
|---|---|---|---|---|---|---|
|Under-recovery of costs|-|n.a.|188|32|38|258|
|Overstaffing|n.a.|-|42|n.a.|0|42|
|Distribution losses|-|-|132|n.a.|5|137|
|Under-collection|-|n.a.|14|16|0|30|
|Low budget execution|0|n.a.|0|7|3|10|
|Total|0|0|376|55|46|477|

Source: Derived from Foster and Briceño-Garmendia 2009. — = not applicable; n.a. = not available.

In the water sector, the estimated average total cost of producing utility water is $1.17 per cubic meter, while the average effective tariff is only $0.89, which covers operating and maintenance costs but makes only a partial contribution to capital costs. As a result, the water utility covers only 76 percent of costs. The associated financial burden is approximately 0.2 percent of GDP. Although the burden of under-pricing for both power and water is very substantial, it is nonetheless lower than what is found elsewhere in Africa (figure 19).

**Figure 17 Underpricing of water in Côte d’Ivoire**

Financial burden of under-pricing in 2008-2009, as percentage of GDP <!-- Start of picture text --> Côte d'Ivoire fragile states<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia and others 2009.

Like in many other African countries, access to power and piped water is much greater among the rich in Côte d’Ivoire than among the poor (figure 20). As a result, the highest income quintile receives the greatest benefit from subsidized tariffs. Recent empirical analysis shows that poor households capture only about half their fair share of power and water subsidies in Côte d’Ivoire based on a measure of distributional incidence.While this is slightly better than in other African countries, it is a poor performance in absolute terms (figure 21).

36
22

# Table 2: Results from hybrid efficiency measurement method

||Californi<br>Single|a school districts<br>Multi-outcome|OECD P<br>Internatio<br>Assessm<br>Single|rogram for<br>nal Student<br>ent (PISA)<br>Multi-<br>outcome|OECD he<br>Single|alth systems<br>Multi-<br>outcome|Vietnam dis<br>Single|trict hospitals<br>Multi-<br>outcome|
|---|---|---|---|---|---|---|---|---|
|Inefficiency|3997|1931|2318|1721|868|1056|172|172|
|t-stat|27.29|6.19|8.26|2.95|16.38|20.94|18.96|15.88|
|Caliper|9.0|15.0|10.0|12.0|9.0|5.0|7.5|4.0|
|Step|9.0|20.0|10.0|16.5|9.0|2.5|7.5|4.0|
|No. of datapoints on frontier|66|67|25|19|44|50|66|65|
|% on frontier|3.5%|3.6%|21.2%|16.0%|4.7%|5.3%|6.4%|6.3%|

Notes: t-stat is derived from the standard error obtained via bootstrapping with 250 replications. Actual caliper and step used are 1/100 of the values shown in the case of the singleoutcome exercise, and 1/10 of the value shown in the case of the multi-outcome exercise. The caliper and step are both defined in terms of standard deviation of the outcome variable. The calipers and steps in the single- and multiple-outcome cases are chosen to allow for similar number of datapoints on the frontier.
the measurement of these rights, and the near-negligible incidence of documented agricultural land ownership in Malawi, the estimate of the SDG indicator 5.a.1 surges under the business-as-usual approach for both male and female adults.

**Table 4. Effect of Business-As-Usual Survey Approach (T1/IHS4) on Agricultural Land Ownership and Rights**

||||**Reported**<br>|||**Economic**<br>||
|---|---|---|---|---|---|---|---|
|**Sample**|**_N_**|_Overall_|_Exclusive_|_Joint_|_Overall_|_Exclusive_|_Joint_|
|_Overall_|25,967|0.017<br>[0.96]|0.052***<br>[4.12]|-0.050***<br>[-3.18]|-0.017<br>[-0.91]|0.022**<br>[2.50]|-0.045**<br>[-2.55]|
|**All adults**<br>_Male_|11,787|0.064**<br>[2.44]|0.087***<br>[4.74]|-0.037*<br>[-1.71]|0.014<br>[0.54]|0.038***<br>[3.53]|-0.024<br>[-0.92]|
|_Female_|14,180|0.001<br>[0.02]|0.015<br>[0.81]|-0.031**<br>[-2.11]|-0.020<br>[-0.92]|0.004<br>[0.28]|-0.033*<br>[-1.91]|
||**SDG Owner **||**Sell**|||**Bequeath **||
|**Sample**|_Overall_|_Overall_|_Exclusive_|_Joint_|_Overall_|_Exclusive_|_Joint_|
|_Overall_|0.052***|0.036***|0.001|0.030***|0.051***|0.007|0.038***|
||[3.07]|[2.66]|[0.11]|[4.10]|[3.02]|[0.56]|[4.14]|
|**All adults**<br>_Ml_|0.054**|0.037|-0.004|0.039***|0.039|-0.006|0.037***|
|_ae_|[2.09]|[1.64]|[-0.22]|[3.95]|[1.52]|[-0.28]|[2.63]|
|_Fl_|0.062***|0.042***|-0.004|0.039***|0.068***|0.004|0.060***|
|_emae_|[3.36]|[2.75]|[-0.28]|[5.05]|[3.73]|[0.23]|[6.16]|

<u>Notes:</u> (1) The sample is comprised of individuals 18 and older, and of those involved in agriculture. The results are from linear probability models, weighted by the response weight. ***=p<0.01, **=p<0.05, *=p<0.10. T-statistics are presented in brackets, and account for clustering at the enumeration area level.

(2) SDG owner is equal to 1 1 if the individual is a documented owner of any parcel or has the rights to sell or bequeath any parcel, and 0 otherwise.

Aside from differences in survey design, one potential reason for higher exclusive ownership among men in the business-as-usual approach may be due to the association between headship and land ownership/rights. Among all age-eligible adults, men having ownership or rights across survey approaches are almost always household heads (Figure 4), in contrast to women with ownership/rights whose household status is more varied. And, as discussed earlier, the share of male household heads overall is significantly higher in the IHS4 sample. Among the “most knowledgeable” respondents in the IHS4 parcel data who reported on household members’ land ownership under the business-as-usual approach, 96 percent of men respondents were household heads, compared to 49 percent of women respondents (Appendix Table A3a). Appendix Table A3b also presents OLS regressions on correlates of the “most knowledgeable” respondent, showing that household headship is highly correlated with being the selected respondent in T1, across both samples of women and men (and for the combined sample, women household heads were significantly more likely to be selected). Spouses were also significantly likely to be surveyed as the main respondent in T1, although to a lesser extent compared to household heads.

21
Reading: Deacon and Murphy 1997, Hansen 1989 See also: Debt Swap

# **Diaspora Bonds**

A diaspora bond is a sovereign debt instrument to raise financing from a country’s overseas diaspora. Israel and India have raised US$35–$40 billion with diaspora bonds. Diaspora bonds can have two objectives: they can keep the diaspora affiliated with their original home country and they can offer countries possibilities to raise funds that are partially concessional because a diaspora member is likely to accept under-market interest rates for patriotic reasons.

Reading: Chander 2001, Ketkar and Ratha 2007

# **Debt2Health**

Debt2Health is a debt conversion scheme piloted by Germany, Indonesia, and the Global Fund to Fight AIDS, Tuberculosis and Malaria. In the first Debt2Health operation, Germany cancelled Indonesian debts of €50 million, Indonesia paid €25 million to the Global Fund, and the Global Fund in turn agreed to spend this contribution on projects in Indonesia. The novel aspect of this mechanism is that a multilateral organization can more ensure appropriate usage of funds received from debt conversion. Another Debt2Health Swap with Pakistan, Germany, and the Global Fund has been implemented. Debt2Health initiatives with Kenya and Peru and the Global Fund and Germany are planned.

Further information: <u>http://www.theglobalfund.org/en/funds_raised/ innovative_financing/initiatives/debt2health/</u> Reading: Cassimon, et al. 2008 See also: Debt Swap Debt Relief

# **Debt Swap**

A debt swap (also called debt-for-development swaps) provides development financing “through the exchange of a foreign-currency-denominated debt for local currency, typically at a substantial discount. The process normally involves a foreign NGO that purchases the debt from the original creditor at a substantial discount using its own foreign currency resources, and then resells it to the debtor country government for the local currency equivalent (resulting in a further discount). The NGO in turn spends the money on a development project, previously agreed upon with the debtor country government.”<sup>91</sup> Reading: Edwards 1992, Kaiser, et al. 1996 See also: Debt2Health Debt Relief

> 91 OECD Glossary of Statistical Terms: http://www.imf.org/external/pubs/ft/eds/Eng/Guide/index.htm.

68
|R<sup>2</sup>|0.14|0.21|0.18|0.17|0.2|0.19|0.28|0.22|0.29|
|---|---|---|---|---|---|---|---|---|---|
|# obs.|18751|18751|18751|11992|11992|11992|6284|6284|6284|

Significance level, *: p<0.10, **: p<0.05, ***: p<0.01. KCPE = Kenya Certificate of Primary Education.

31