rafmacalaba/datause-encoder-data huggingface dataset:
welfare indicators. Together with a rigorous estimation strategy, our analysis adds to the literature by examining the effect of transportation costs and conflict on a variety of measures of well-being in DRC, captured by a wealth index, the probability of being multi-dimensionally poor (as defined by an index of living standard, education and health dimensions), and an estimate for local GDP which uses data from nighttime lights. We test for heterogeneous effects of transportation costs, conflict and their combined effect on these indicators.

Using DRC’s Africa Infrastructure Country Diagnostic road data<sup>2</sup> and GIS road network data on both trunk and rural roads, we develop a new data set of travel costs. While our measure of transport costs is the most accurate possible given available data, it is still endogenous due to the non-random placement of rural infrastructure. That is, roads tend to be placed near developed or high economic potential areas. Similarly, conflict is endogenous as it is closely related to wealth: conflict negatively affects wealth, while by the same token low levels of income can trigger incidences of conflict. We address these sources of bias following an instrumental variables strategy.

We instrument for transport cost by constructing a novel instrumental variable, which we term the natural-historical path, which measures the walking time taken to reach markets using the natural path (i.e. the shortest route given local geography and land cover), as well as historical caravan routes which were used to transport slaves and ivory. To instrument for conflict, we use a measure of the level of local social fractionalization, as theory predicts that polarization generates higher levels of conflict (Esteban and Ray 1999). While we believe that our instrumentation methodology is sound, and represents a significant improvement over the current literature, we also conduct robustness checks under the assumption that our instruments do not perfectly satisfy the exclusion restriction assumptions. Using Conley Bounds, we demonstrate that our results all remain consistent when the exclusion restriction assumption is relaxed for each of our instruments.

Overall, we find higher transportation costs have a significantly negative impact on wealth and a significantly positive impact on the probability of being multi-dimensionally poor. We also find that the location of conflict is important in determining its effect. Conflict near households has a strongly negative impact on a household’s wealth, and conflict near markets has a large, positive impact on the probability of being multi-dimensionally poor. More significantly, we find that when there is high conflict near both the household and market, households farther away from the market are likely to have higher welfare indicators. In such cases improved transport infrastructure does little to improve, and may indeed worsen, well-being as distance and remoteness can presumably provide at least partial sanctuary from conflict.

> 2 http://www.infrastructureafrica.org/library/doc/597/democratic-republic-congo-roads

4
are benefits to promoting bank competition for firms’ access to finance. We leave to further research an investigation of the specific policies that policy makers in the GCC can implement to increase competition in the banking sector as well as an analysis of any potential trade‐off between increased financial access and systemic stability. Our results suggest, however, that relaxing bank activity restrictions, improving the credit information environment through wider coverage of public registries and private credit bureaus, and strengthening the institutional framework for bank competition policy are important in supporting firm access to finance and reducing the negative impact of low bank competition.

14
Table 2. Reservoir MCEM Criteria and Sub-Criteria

|Criteria / Weight|Indicators / Sub‐criteria|
|---|---|
|Economic<br>18.4%|Social VAN ($ million)<br>Investment ($ million) / hectare<br>Investment ($ million) / land plots|
|Social<br>34.1%|% households under poverty line<br>Surface area of subsistence farms / small farms <12 ha<br>Number of beneficiaries (population in irrigable zone)<br>Indigenous communities in the territory [0,1]<br>% growth of rural population during last inter‐census period<br>Extreme zone, border region, or undeveloped area [0,1]|
|Strategic<br>22.6%|Number of water shortage decrees in past five years<br>Number of jobs generated (landowners and relatives)<br>Number of irrigation association systems that can be connected<sup>7</sup><br>Electricity generation capacity (MWh/year)|
|Environmental /<br>Territorial<br>9.2%|Number of people required to relocate<br>Number of archaeological site affected<br>Hectares of native forest in flood zone|
|Management<br>15.7%|Interest/support of beneficiaries<br>Economic contribution of regional government (regional government<br>contribution / total project investment)<br>Organization (1‐4 indicating degree of incorporation / legal standing)<br>Number of land parcels required to expropriate|

Source: Ministerio de Hacienda, 2014. Minuta Matriz Multicriterio Plan de Pequeños Embalses

> 7 This measures the number of ‘asociación pequeños regantes’ (APRs) or small irrigation associations that can be linked.

16
girls’ school participation (Akresh et al., 2012a, 2012b; Akresh, de Walque, and Kazianga, 2013).

At round 2, when overall enrollment impacts were more muted because the cash transfers were introduced a few months late in the school year, we find that impacts on boys’ enrollment could already be measured: the four types of transfers already lead to increases in enrollment for boys. We cannot however reject equality between the coefficients for boys and girls.

For child labor (columns 3-4), we confirm a reduction in child labor for UCTs, which is present both for boys and girls. At round 3, cash given to fathers lead to more child labor than when the transfers are given to mothers for both boys and girls, but compared to the control group, the increase is only statistically different than zero among girls.

For weight-for-age (columns 5-6), as observed earlier, most of the impacts are concentrated at round 2 in 2009 which was a more difficult year for agricultural production. Strikingly, all impacts are concentrated among boys: CTFs significantly improve weight-for-age and outperforms CTMs. Similarly, for boys, CCTs also outperforms UCTs and improves weightfor-age. None of these impacts and differences are statistically significant in the sample of girls. It is difficult to determine whether this result might be due to a preference for boys or to the fact that anthropometric outcomes tend to be lower for boys at those ages (see the lower means in the control groups for boys) and therefore that there is more room for improvement in the presence of an intervention.<sup>24</sup> Illness in the last 30 days as reported by the parents offers interesting contrasts by child gender (columns 7-8). At round 2, for boys, cash given to mothers decreases illness episodes and outperforms CTFs. For girls, while CTMs and CTFs are not statistically different from each other, only CTFs lead to a significant reduction. For boys, both CCTs and UCTs lead to significant reductions in morbidity, but for girls only CCTs achieve such impact and the difference between both types of transfers is significant. At round 3, for boys, CTMs and CTFs have a similar impact in reducing illnesses, while for girls only transfers given to fathers lead to a reduction. For males, only CCTs reduce illnesses episodes and they outperform UCTs, while for girls only UCTs lead to a significant reduction (but the difference with CCTs is not statistically

> 24 For pre-school age children in Sub-Saharan Africa, the empirical evidence indicates that while malnutrition is widespread, girls are on average better off than boys. See Svedberg (1990) for an earlier analysis and Wamani et al. (2007) for a more recent analysis using DHS data. A similar pattern was found in Burkina Faso in an impact evaluation of school feeding programs (Kazianga, de Walque and Alderman 2014).

21
|(7)<br>wealth index<br>factor<br>score|-182.432<br>(440.164)<br>-504.890<br>(323.558)<br>-675.200*<br>(367.459)<br>-977.280**<br>(393.707)|4,421<br>0.712<br>Yes<br>Yes<br>Yes<br>383<br>3484|ch as televisions and<br> factor analysis (see<br>timates are reported<br>he start of the war.<br>include an indicator|
|---|---|---|---|
|(6)<br>manual<br>work|0.001<br>(0.002)<br>-0.003<br>(0.002)<br>0.002<br>(0.003)<br>-0.001<br>(0.004)|3,188<br>0.165<br>Yes<br>Yes<br>Yes<br>381<br>0.0781|wnership su<br> index using<br>e treated es<br>r to age at t<br>her controls|
|ent<br>(5)<br>Agri -<br>own<br>farm|0.006**<br>(0.003)<br>0.004**<br>(0.002)<br>0.004*<br>(0.002)<br>0.005*<br>(0.003)|3,188<br>0.382<br>Yes<br>Yes<br>Yes<br>381<br>0.722|uses asset o<br>ulate wealth<br>atment on th<br>he table refe<br>bo test. Ot|
|Employm<br>(4)<br>Professional<br>sales<br>clerical etc|-0.007**<br>(0.003)<br>-0.001<br>(0.002)<br>-0.005*<br>(0.003)<br>-0.004<br>(0.004)|3,188<br>0.300<br>Yes<br>Yes<br>Yes<br>381<br>0.200|f grade ten. DHS<br> facilities to calc<br> (10%) level. Tre<br>vel. All ages in t<br> serves as a place|
|cation<br>(2)<br>(3)<br>Completed<br>Employed<br>SLC<br>in last<br>12 months|-0.003<br>-0.005<br>(0.002)<br>(0.003)<br>-0.001<br>-0.003<br>(0.002)<br>(0.002)<br>-0.003<br>0.001<br>(0.003)<br>(0.003)<br>-0.006**<br>-0.002<br>(0.003)<br>(0.003)|4,421<br>4,421<br>0.215<br>0.259<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>Yes<br>383<br>383<br>0.0831<br>0.765|that everyone takes at the end o<br>pes of water access and sanitation<br>cates signicance at the 1% (5%) <br>d errors are clustered at village le<br>s a second comparison group that|
|Edu<br>(1)<br>Years<br>of<br>schooling|Age 22 to 29 _×_ Months of war<br>-0.015<br>(0.025)<br>Age 9 to 15 _×_ Months of war<br>0.003<br>(0.018)<br>Age 4 to 8 _×_ Months of war<br>0.007<br>(0.025)<br>Age0 to 3 _×_ Months of war<br>-0.020<br>(0.031)|Observations<br>4,421<br>Adjusted R-squared<br>0.440<br>Birth year and month xed eects<br>Yes<br>Regional trends and other controls<br>Yes<br>Village xed eects<br>Yes<br>Number of clusters<br>383<br>Control mean (Age 16 to 21)<br>2.472|Note: School leaving certicate (SLC) is a national exam<br>bicycles, materials used for housing construction, and ty<br>Rutstein, and Johnson, 2004 for detail). *** (**) (*) indi<br>based on a dierence-in-dierence specication. Standar<br>Comparison cohort is age 16 to 21 and cohort 22 to 29 i<br>for high caste and month of interview xed-eects.|

47
absolute effect on bilateral trade than the exporter factor costs across all models and the difference (of their magnitudes) is statistically significant. Finally, consistent with predictions of the gravity model, estimates of all models show that sector output is significantly positively correlated with export and the elasticity is close to 1.

# **4.2 Robustness Check**

In this subsection, we conduct sensitivity analysis using alternative model specifications. First, we consider the possibility that sector output may be endogenous to export value, especially for export-oriented sectors. As a robustness check, we move sector output to the left-hand side of equation (18) and redefine our dependent variable as export intensity, that is, the ratio of exports to sector output. The results are reported in Table 8. The resulting coefficients are almost identical to those of the main model. This is expected, since the estimated trade elasticity of sector output in the main model is close to 1.

Second, we consider the possibility that bilateral trade may not respond to price changes immediately as a result of market imperfections or adjustment costs. There could also be contemporaneous feedback from exports to energy prices if export-oriented sectors dominate domestic industrial energy consumption. To account for delayed responses and the potential endogeneity of energy prices, we control for one-year lagged explanatory variables rather than contemporaneous variables. The resulting coefficients, shown in Table 9 are very similar in significance and size.

Third, we consider alternative levels of sector disaggregation. In previous regressions, we mapped export, ICIO, and value added data into the 10 manufacturing sectors used by the IEA so as to match energy consumption data. To account for potential aggregation bias in the factor content of trade (Feenstra and Hanson, 1999), we reestimate trade elasticities according to the sector aggregation used by the ICIO tables (15 sectors) as well as a sector aggregation based on the three-digit ISIC Rev. 3.1 (54 industries). Because energy consumption data are not available at these more disaggregated levels, we assume that the energy intensity of industries at the two-digit and three-digit levels of ISIC Rev 3.1 is the same if they belong to the same IEA sector. Regression results based on the alternative disaggregation levels, reported in Tables 10 and 11, are fairly close to the main results, although with 18
Education, Science and Technology, 2018) and a spreadsheet-based tool was developed to guide districts in completing allocations according to the rules.

In this section, we employ administrative data to create lists of schools that would have been eligible to receive new teachers in each of the years 2017-2019 according to the new rules.

# **Maximizing the efficiency of allocation rules**

## **T1: One teacher per grade**

The first stipulation of the revised rules – to provide adequate teachers to schools to ensure that they have at least one teacher per grade offered – is intended to eliminate multi-grade teaching. This rule is most likely to be relevant in smaller schools, often in remote areas, which are the most likely to lack one teacher per grade owing to small enrollments.<sup>7</sup> Such schools are large in number and concentrated in particular districts. In 2017, one district, Mzimba South, required 423 teachers to meet this rule, and the top five districts combined required 1,039 new teachers – more than 40 percent of all the new teachers needed to fully attain T1 in the whole country. These districts, unless experiencing severe overall shortages of staff, are poorly allocating their existing teacher resources and may not be expected to appropriately allocate additional teachers. In addition, meeting this need entirely would be likely to severely reduce the potential for PTR reduction at more understaffed schools, and would entail allocating a significant number of teachers to schools with PTR below 60 which are otherwise adequately staffed. This may not be an effective use of limited resources, given overall constraints on teacher availability.

We therefore apply the following modifications to T1:

- Schools with a PTR of 60 or below are excluded from receiving new teachers.

- Schools whose PTR would drop to 60 or below on receipt of a new teacher, are excluded from receiving new teachers.

- Districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is above 75:1.

## **T2: Allocation to schools with high PTRs**

The second stipulation, T2, is expected to be applied following fulfilment of the modified T1. In a context of perfect information, the preferred approach to allocating teachers in order to reduce PTRs would be the “smallest achievable maximum PTR rule” (Walter, 2018), in which teachers are allocated in order to reduce the largest school-level PTR, nationally or in a district, as much as possible. However, this approach, while the most efficient way to reduce PTRs in theory, is difficult to implement in practice as it requires a complete and accurate ranking of schools by PTR. As this may be complicated to implement, in practice, many countries adopt a simpler approach of identifying a target level of PTR above which schools are considered understaffed and providing teachers to bring

> 7 Of schools in the most remote areas, known as ‘Category A’, 38 percent have fewer than eight teachers, versus only 18 percent of the least remote ‘Category C’ schools (Ministry of Education, 2022; see Asim et al., 2019, for categorization).

6
Table 6 shows the (rank) correlations of the various indicators to gauge the internal coherence of the set. The various indicators of gender differentials in health status are reasonably highly correlated: .77 between child mortality and the population sex ratio, and .36 and .40 between the health treatment disparity and child mortality and the population sex ratio, respectively. Between the enrollment ratio and the male-female literacy gap, the correlation is .57. The labor force participation of rural women is quite highly correlated with child mortality. The two indicators which are aggregates of various others, in particular, the "status of women" indicator which was created from various parts of the NFHS data, are (unsurprisingly) quite highly correlated with several of the other measures.

Table 6: Rank correlations among the various indicators of female _I_ male disparity

|||I|II|m|IV|v|VI|vii|VIIi|
|---|---|---|---|---|---|---|---|---|---|
|I|Child mortality'|1||||||||
|11|F**_I_**MPopulation Ratiob|.77|i|||||||
|m|F / M no treatment for ARI or fever'|.36|.40|1||||||
|IV|F_I_M school enrollment (ages 11-14)'|.22|.06|.31|1|||||
|V|M-Fdifference in rural literacyb|.31|.33|.23|.57|1||||
|VI|Rural female labor force participationb|.61|.23|.08|.20|.32|1|||
|VII|Status of women;|.75|.83|.28|.39|.64|.72|1||
|vii|Difference of Gender and unadjusted HDId|50|.45|.25|.64|1|.22|.68|1|

Sources: (a) This study (b) Agarwal (1997) (c) Srivastava (1997) (d) Kumar (1996) There has been a fairly large body of literature which argues about the patterns and causes of gender differentials in outcomes within India, and we will not delve much into the 19
# **Appendix Table 3: Kitagawa-Oaxaca-Blinder Decomposition of Labor Force Participation** **<u>(Paid+Unpaid Work) by Urban/Rural</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES||Urban|||Rural||
||Overall|Endow.|Coeff.|Overall|Endow.|<br>Coeff.|
|Male|0.781***|||0.888***|||
||(0.008)|||(0.005)|||
|Female|0.585***|||0.822***|||
||(0.010)|||(0.006)|||
|Raw Difference|0.196***|||0.065***|||
||(0.013)|||(0.008)|||
|Difference due to|0.031***|||-0.005|||
|Endowments|(0.005)|||(0.004)|||
|Difference due to|0.165***|||0.070***|||
|Coefficients|(0.014)|||(0.008)|||
|Individual Age||0.039***|0.836***||0.001|-0.086|
|||(0.004)|(0.158)||(0.002)|(0.127)|
|Individual Age Squared||-0.028***|-0.476***||0.005|0.036|
|||(0.003)|(0.074)||(0.008)|(0.060)|
|Number of Adults in HH||0.000|0.001||-0.000|0.024**|
|||(0.000)|(0.013)||(0.000)|(0.011)|
|Number of Children Aged 0-||<br>-0.001*|<br>0.003||<br>-0.001|<br>0.015**|
|5 in HH||(0.000)|(0.007)||(0.001)|(0.007)|
|Individual owns a Financial||0.019***|-0.005||-0.016|0.008**|
|Account||(0.003)|(0.013)||(0.024)|(0.004)|
|Individual is Married||-0.001|0.084***||0.010|0.026|
|||(0.001)|(0.019)||(0.017)|(0.018)|
|Individual is Divorced/Widowed/||-0.001|0.005***||0.002|0.001|
|Separated||(0.002)|(0.002)||(0.004)|(0.001)|
|Individual Completed Secondary||-0.009***|-0.024***||0.002|-0.000|
|Schooling||(0.001)|(0.006)||(0.003)|(0.001)|
|Individual owns a Vehicle||0.009***|-0.003||-0.008|0.007|
|(Bicycle, Motorcycle, Car)||(0.002)|(0.011)||(0.013)|(0.010)|
|Wealth PCA - Based on||0.002***|-0.018||0.000|-0.008|
|Home Condition||(0.001)|(0.012)||(0.001)|(0.007)|
|Wealth PCA - Based on||<br>0.001***|<br>0.018||<br>0.000|<br>0.006|
|Appliances Ownership||(0.000)|(0.017)||(0.000)|(0.007)|
|Individual has a Disability||-0.000***|-0.000||-0.000|0.000|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|Constant|||<br>-0.256***|||<br>0.041|
||||(0.071)|||(0.057)|
|Observations|9,861|9,861|9,861|12,799|12,799|12,799|

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

45
Table 4: Changes in Poverty Rates Using Various Food Shares and Prices (%)

|Share of food in inflation|Base<br>Case'|Usin|g CPI|Using Su<br>Prices f|senas Unit<br>or Food|
|---|---|---|---|---|---|
||1996|1999|Change|1999|Change|
|Method I: Actual share of<br>food in consumption<br>expenditures, at poverty line|9.75|15.28|5.53|16.87|7.12|
|Method II: Using the share<br>of food in the poverty basket|9.75|16.27|6.52|17.89|8.14|
|Method III: Using new food<br>poverty line and<br>recomputing non-food share#|9.75|20.33|10.58|22.35|12.60|

Notes: ' The base case of poverty rate 9.75 percent in 1996 is obtained using an iterative method. The algorithm of this iterative method is to obtain a poverty line which coincides with the central point of the reference population expenditures. The steps in this iterative method are outlined in the appendix and discussed in details in Pradhan _et al_ (2000).

9 This procedure is _methodologically_ consistent, but not _welfare_ consistent. _Methodologically consistent_ means that poverty basket is calculated using the same procedure each year. While, _welfare consistent_ means that individual is at the same level of utility (the same material standard of living) in the two periods. Methodologically consistent _is not_ necessarily welfare consistent and vice versa.

Table 5: Differences in Inflation Rates between CPI and Susenas Unit Prices

|_<br>_<br>_<br>_<br>_<br>_<br>-_|(|% )||
|---|---|---|---|
||Food share|Using CPI|Using Susenas Unit<br>Prices for Food|
|Food||160|168|
|Non-food||81|81|
|Total:||||
|- Method I|Actual|137|142|
|- Method II|0.8|144|151|
|- Method III|1.0|160|168|

Notes: The weights of each of the 52 commodities in the Susenas unit prices are based on their shares in the 1996 poverty line.

The second method (method II) uses the share of food in the poverty basket in 1996 as the weight for food in the estimate of inflation. Given the methodology used, the poverty line is the food poverty line (FPL) plus a non-food allowance, where the non-food allowance is the non-food expenditures of those at the food 16