rafmacalaba/datause-encoder-data huggingface dataset:
14 traditional healers also play a role in rural areas.

Table 5b presents additional detail from the 1987 SUSENAS on annual absolute utilization rates per person of modern health providers. This tells us how yearly per capita, total as well as provider specific, visits differ across consumption deciles and sectors. It is clear from Table 5b that the rate at which morbidity is treated varies across deciles and rises with consumption. The latter effect is more pronounced in rural areas where individuals in decile 1 visit one of the modern facilities an average of 1.4 times yearly, while individuals in the tenth decile do so 3 times. In urban Indonesia the variability is lower, ranging from 1.8 to 2.2 visits per person per year.

_Changes in Incidence Between 1978 and 1987_ Table 6 presents statistics on individuals reporting illnesses and where they were treated as recorded in both the 1978 and 1987 surveys. The table provides insights into how the kind of treatment different subgroups seek when ill has altered during the decade under review. Each number refers to the proportion of all those reporting ill in a certain subgroup (such as "poorest 40 percent in urban Java"), who seek treatment at each of the options. Each column thus adds up to 100 percent. For example, of all those reporting ill in urban Java in the lower 40% of the per capita expenditure distribution, 31.8 percent did not seek treatment outside the home in 1987.

Looking at each treatment option separately, the following observations can be made.

i) Self. family or no treatment: At both dates, the lowest income groups are less likely than others to seek treatment outside the home. Indeed, self, family or no treatment is consistently their most common course of action. But the use of facilities outside the home by the poorest 40 percent has clearly increased since 1978. For example, for the lower expenditure group in urban Java, self, famil.y, or no treatment declined from 58 to 32 percent. Urban residents are also, generally, more likely to obtain
_Preliminary and incomplete Comments very welcome_

# **Good Countries or Good Projects? Macro and Micro Correlates of World Bank Project Performance**

## **Cevdet Denizer (World Bank) Daniel Kaufmann (Brookings Institution) Aart Kraay (World Bank)**

<u>cdenizer@worldbank.org, dkaufmann@brookings.edu, akraay@worldbank.org. We are very grateful to Jaime Zaldivar and</u> Kartheek Kandikuppa for their assistance in retrieving project-level data from the World Bank’s databases, and to Martha Ainsworth, Antonella Bassani, Jaime Biderman, Jean-Jacques Dethier, Marguerite Duponchel, Homi Kharas, Alex McKenzie, Hari Prasad, Lant Pritchett, Veronika Penciakova, Luis Serven, Andrew Warner, and seminar participants at the World Bank and IMF for helpful discussions. Support from the Concessional Finance and Partnerships Vice Presidency of the World Bank is gratefully acknowledged. The views expressed here are the authors’, and do not reflect those of the Brookings Institution, the World Bank, its Executive Directors, or the countries they represent.
|7|Conditional cash<br>transfer: school<br>stipend|Nepal|SA|RCT|Attendance rate|**0.517**|Journal|Edmonds and<br>Shrestha 2014|
|---|---|---|---|---|---|---|---|---|
|8|Labelled cash transfer<br>for education|Morocco|MENA|RCT|Dropped out by<br>the end of year 2|**0.486**<br>(abs.<br>value)|Journal|Benhassine et<br>al. 2015|
|9|Village‐based school|Afghanistan|SA|RCT|Enrollment|**0.478**|Journal|Burde &<br>Linden 2013|
|10*|<sup>Private school</sup><br>subsidies forgirls|Pakistan|SA|RCT|Enrollment|**0.441**|Journal|Kim et al. 1999|

Note: Interventions marked with an asterisk are girl‐targeted.

**Table 4: The 10 least effective interventions to improve access to education for girls**

||**Program description**|**Country**|**Region**|**Evaluation**<br>**design**|**Outcome**|**Effect size**<br>**(SD)**|**Type of**<br>**publication**|**Reference**|
|---|---|---|---|---|---|---|---|---|
||School canteen||||Absenteeism|**‐0.200**|||
|1|*Conditional take‐<br>home rations for girls|Burkina Faso|SSA|RCT|Absenteeism|**‐0.182**|Journal|Kazianga et<br>al. 2012|
|2*|Unconditional cash<br>transfer togirls|Malawi|SSA|RCT|Attendance, Yr2|**‐0.152**|Journal|Baird et al.<br>2011|
|3*|Early financial<br>commitment|China|EAP|RCT|Dropout|**‐0.140**<sup>**1**</sup>|Journal|Yi et al. 2015|
|4*|Hygiene promotion +<br>water treatment|Kenya|SSA|RCT|Enrollment|**‐0.138**|Journal|Garn et al.<br>2013|
|5|School meal|India|SA|DID|Enrollment|**‐0.120**|Journal|Afridi 2011|
||Edcation cash savin||||Enrollment Y1|**‐0.110**|||
||u g<br>account with parent||||Attendance overall|**‐0.107**|Working||
|6|<br>outreach|Ud|SSA|RCT|Enrollment Y2|**‐0.107**|paper|Karlan and|
||Education cash saving<br>account without<br>parent outreach|gana|||Enrollment Y1|**‐0.049**||Leiden 2014|

29
Table 12: Impacts on borrowing from sources other than MFI (survey data)

||(1)<br>Other formal|(2)<br>Other formal|(3)<br>Other formal (corr)|(4)<br>Informal|(5)<br>Informal|(6)<br>Informal (corr)|
|---|---|---|---|---|---|---|
|SL - Subsidy eligible|-1398.2|-906.3|-2205.6|-1115.7|-1076.4|-980.6|
||(1964.2)|(1906.7)|(2049.2)|(1053.9)|(1013.3)|(1412.5)|
|SL - Subsidy ineligible|38.16|855.2|1941.0|-1492.6|-1427.3|2534.8|
||(2858.9)|(2724.6)|(3378.9)|(1341.3)|(1366.2)|(3004.9)|
|Subsidy eligible|3078.4|3094.0|3286.2|-1475.8|-1474.6|-1973.5|
||(2023.1)|(1966.8)|(2079.5)|(1267.8)|(1274.7)|(1478.0)|
|Strata FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Control for inconsistency|No|Yes|Yes|No|Yes|Yes|
|~~F~~<br>~~i~~nteraction|0.655|0.565|0.282|0.842|0.855|0.340|
|controlmean<br>~~n~~otel|17847.3|17847.3|18485.6|3310.3|3310.3|3359.0|
|controlmean<br>~~e~~l|17138.6|17138.6|17912.9|2587.5|2587.5|3068.7|
|N|856|856|856|856|856|856|

_Notes_ : Sample restricted to matched MF client households not having a toilet at survey baseline; Dependent variables are (1)-(3) amounts borrowed from formal sources other than MFIs (banks, NGOs, self-help groups, savings funds and cooperatives) and amounts borrowed from informal sources (money lender, relatives, friends, acquaintances, work); Amounts are in Indian Rupees (1 USD = Rs. 71.2); Columns (2) and (5) control for an indicator equal to 1 if there is an inconsistency between official credit bureau data and survey respondent report in terms of whether or not the household borrowed any money from an MFI; Columns (3) and (6) apply a correction to the reported amount, based on the extent of misreporting between the self-report and the credit bureau data; SL stands for sanitation loan treatment arm; Robust standard errors clustered at the village level are shown in parentheses; *, **, *** indicates; significance at the 10, 5 and 1 percent level; F-interaction stats show p-value of F-test of equality of subsidy eligible/ineligible interactions with SL; Covariates: Indicator for presence of a child aged 0-2 at baseline, ratio between number of sampled clients and village size, indicator for whether there is any uncertainty around the matching quality; Strata and interviewer fixed effects included Figure 5: Timing subsidy uptake versus timing toilet construction <!-- Start of picture text --> 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Year toilet construction<br>Notes: N = 301<br>2018<br>2017<br>2016<br>2015<br>2014<br>2013<br>Year subsidy receipt<br>2012<br>2011<br>2010<br><!-- End of picture text --> 38
Bertrand, M. Duflo, E. and Mullainathan, S. (2004). “How Much Should We Trust Differencesin-Differences Estimates?” Quarterly Journal of Economics 119 (1): 249–75.

Brunnschweiler, C. N, (2008). "Cursing the Blessings? Natural Resource Abundance, Institutions, and Economic Growth". World Development, Vol. 36(3): 399–419.

Caselli, F. and G. Michaels (2009). "Resource abundance, development, and living standards: evidence from oil discoveries in Brazil." NBER Working Paper 15550.

Chambers D, and J. Guo, (2009). "Natural Resources and Economic Growth: Some Theory and Evidence". Annals of Economics and Finance, 10(2): 367–389.

<mark>China National Statistical Bureau, accessed at http://data.stats.gov.cn/english/</mark> Chuhan-Pole. P, A. Dabalen, A. Kotsadam, A. Sanoh, A. Tolonen, (2015). The Local Socioeconomic Effects of Gold Mining: Evidence from Ghana. The World Bank Policy Research Working Paper (WPS 7250) <mark>Damgaard, C. and Weiner, J. (2000). "Describing Inequality in Plant Size or Fecundity." Ecology, 81(4): 1139 –1142.</mark> Erten B. and J. A. Ocampo (2013). "Super Cycles of Commodity Prices Since the Mid-Nineteenth Century." World Development, 44 (C): 14–30.

Fafchamps M, M. Koelle and F. Shilpi, (2015). Gold Mining and Proto-Urbanization Recent Evidence from Ghana. The World Bank Policy Research Working Paper (WPS 7347) Francis, M. (2007). ‘The Effect of China on Global Prices’. _Bank of Canada Review_ (Autumn): 13-25.

Gennaioli, N., et al. (2014 ). "Growth in regions." Journal of Economic Growth 19: 259–309.

Gylfason, T., et al. (1999). "A mixed blessing." Macroeconomic Dynamics 3(02): 204–225.

Hodler, R. and P. A. Raschky (2014). "Regional Favoritism." The Quarterly Journal of Economics: <mark>129(2): 995–1033</mark> .

Henderson, J. V., et al. (2013). "Is Urbanization in Sub-Saharan Africa Different?" World Bank Policy Research Working Paper 6481.

Henderson, J. V., et al. (2012). "Measuring Economic Growth from Outer Space." <u>American Economic Review 102(2): 994–1028.</u> Humphreys, D., (2013). "Long-run availability of mineral commodities." <u>Mineral Economics,</u> 26(1): 1–11.

Kotsadam, A. and A. Tolonen (2013). "Mineral Mining and Female Employment", OxCarre Research Paper 114.

23
# **Greening China’s Rural Energy:**

# **New Insights on the Potential of Smallholder Biogas**

Luc Christiaensen and Rasmus Heltberg<sup>1</sup> The World Bank

**JEL Codes** : Q42, Q56, I12

**Key words** : household energy, biogas, renewable energy, China, fuel switching, indoor air pollution

**Sector Board** : Luc Christiaensen (EPOL) and Rasmus Heltberg (SDV)

> 1 Luc Christiaensen (lchristiaensen@worldbank.org) and Rasmus Heltberg (rheltberg@worldbank.org) are Senior Economists at the World Bank. They are indebted to Anqing Shi for excellent research assistance, Prof. Liu Qiming of the China Agricultural University for collaboration with data collection and survey design, and Wendao Cao and Sari Soderstrom, task managers of the World Bank supported Ecofarming project under which the data have been collected. The research benefited from financial support under the Trust Fund for Environmentally and Socially Sustainable Development (TFESSD) funded by the governments of Norway and Finland. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors or the governments they represent.
**Figure 24: Sensitivity of the urban‐rural gap to spatial price adjustment**

<!-- Start of picture text --> Share of the extreme poor by urban‐rural<br>100<br>80<br>60<br>97.3 96.9 97.1 95.9 95.5<br>40 87.4 81.3 88.6 84.6 80.7<br>66.7<br>54.7<br>20<br>0<br>Nominal Spatially Nominal Spatially Nominal Spatially Nominal Spatially Nominal Spatially Nominal Spatially<br>deflated deflated deflated deflated deflated deflated<br>Bangladesh Bhutan Sri Lanka Maldives Nepal Pakistan<br>2010 2012 2012 2009 2010 2013<br>Rural Urban<br><!-- End of picture text --> How much could this bias affect the global estimate of the share of the poor living in rural areas? An important consideration is that a sizeable share – about 39 percent -- of the extreme poor live in India, China, and Indonesia. These three countries use separate urban and rural PPP conversion factors for calculating regional and international poverty estimates. Because of this, the share of poor that is rural in these countries is not overestimated, even when using nominal welfare aggregates, because any differences in prices between urban and rural areas should be captured by the PPP conversion factor. An upward bias of about 6 to 7 percentage points that applies to 60 percent of the extreme poor would lead the estimate to overestimate the share of extreme poor by about 4 percentage points, modestly reducing the share of the extreme poor in rural areas only from about 80 to 76 percent.

# **4.3.Sensitivity to missing data**

The degree of missing data in the GMD varies by variable. **(** Table 9). The variable most severely affected by missing data is sector of work. This variable is reported in only 64 out the 89 countries in the sample, representing 2.1 billion of the full sample’s 5.25 billion people. For the purposes of this exercise, we consider education across all ages, which is reported in 83 out the 89 countries. Education is not asked for very young children, however, with the cutoff age varying across countries. The education profile, therefore, only represents 4.7 of the 5.25 billion people covered by the full sample.

The results reported above use a separate sample for each profiling variable. To see if using a fixed sample would significantly alter the results, we construct key profile statistics based on two fixed 28
**Figure 2. ECDI rates by arsenic exposure (WHO Standard of >10ppb)**

<!-- Start of picture text --> 100% 94%<br>90% 90%<br>90% 86%<br>80%<br>72%<br>68%<br>70% 65%<br>58%<br>60%<br>50% No Arsenic<br>Arsenic<br>40%<br>30%<br>22%<br>19%<br>20%<br>10%<br>0%<br>ECDI Literacy Learning Physical Social<br>100% 94%<br>89%<br>87%<br>90% 85%<br>80%<br>71%<br>67%<br>70%<br>62%<br>60%<br>53%<br>50% No Arsenic<br>Arsenic<br>40%<br>30%<br>21%<br>18%<br>20%<br>10%<br>0%<br>ECDI Literacy Learning Physical Social<br>Households exposed to arsenic (%)<br>Households exposed to arsenic (%)<br><!-- End of picture text -->

**Figure 3. ECDI rates by arsenic exposure (Bangladesh Standard of >50ppb)**

19
# **5. Conclusion**

Remittance flows are rising worldwide and are an increasingly important source of income for households in lower-income countries. Economists are just beginning to understand how remittances affect local economic outcomes in the recipient countries. Using _municipio_ -level data for Mexico, this paper contributes to the literature by focusing on a question that has been largely ignored: how remittances affect banking sector breadth and depth. Mexico makes an excellent case study because remittance flows are large and geographically concentrated. Also, in Mexico, banks play an important role in the collection of remittances by recipients. They earn fees from these services and gain potential clients for other banking products.

We find that remittances are strongly associated with the depth and breadth of banking services in Mexico. The effects are significant both statistically and economically. The most robust impacts relate to the number of branches, accounts, and the deposit-to-GDP ratio. We sometimes find a positive impact on credit as well, though here the results are much less robust, and in particular, do not hold up to instrumenting for remittance receipts.<sup>24</sup> Will the expansion of banking services caused by remittances result in additional development in the remittance recipient communities? This is a critical question, and an area for future research. The work of Burgess and Pande (2005) suggests that we might expect to find important effects on poverty and growth. The fact that we find some evidence of an association between remittances and credit suggests that banks may allow households to leverage remittance incomes for the purchase of durable goods or for investment in enterprises. But a more complete answer to the question will require more detailed data on the use of banking services in communities receiving remittances.

> 24 Woodruff (2006) also finds a positive association between receipt of remittances and the likelihood of having a loan using household data from a sample of households which have accounts in non-bank financial institutions in Mexico.

20
help policy makers design coherent domestic labor market and migration policies to maximize development gains.

A large and growing body of literature documents numerous push and pull factors affecting the likelihood of international migration—from economic gains to social networks and to prevailing social norms in sending and destination countries. Nevertheless, many empirical papers are often constrained by the scarcity of detailed data on migration and potential drivers for migration. A large body of the empirical literature instead evaluates specific channels such as education (e.g. Docquier et al., 2020) or social networks (Cairn and Smyth, 2011). In the rare instances in which rich data is available in developing countries, researchers may face arbitrary choices of which variables to include and how to sequence their inclusion in the analysis. For example, in estimating the black-white wage gap in the United States, Gelbach (2016) demonstrates that the choice of covariates to include first led to different interpretations of what factors matter in explaining the wage differential and by how much.

To overcome this issue and systematically compare drivers of migration for women and men, we leverage rich household data on international migration from Indonesia and implement a data-driven approach to identify and rank the predictors of migration, separately for women and men. We analyze 301 potential determinants of migration with 5,131 men and 8,187 women in our sample. We use two different supervised machine-learning (ML) algorithms to identify the determinants of migration: (i) first, using a Random Forest algorithm to identify the top predictors based on the importance indicator, and (ii) second, using LASSO Stability Selection to identify the predictors with the greatest impacts on the variance by computing the rate of appearance of these top predictors in multiple iterations of LASSO. These methods have two key advantages in comparison to theory-driven empirical models: (1) they select variables that have important predictive powers; and (2) they do not require that we specify a prediction model beforehand which would require subjective choices—the models are indeed generated in a data-driven way which is a “guarantee against overfitting and specification hunting” (Cengiz et al., 2021).

Since we only have data on past migration, we use interest in migration as our main outcome variable. Although interest in migration is likely to include larger shares of individuals than actual migration, unlike actual migration flows, migration intentions are not plagued by self-selection in who can overcome mobility barriers and those who cannot (Fouarge & Ester, 2007; Gubert & Senne, 2016). Moreover, interest to migrate and subsequent migration are positively correlated (Creighton, 2013; 3
Table 8: Components of Self-Discipline Index

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable:|Be good at<br>what I do|Be disciplined<br>and push myself|Finish whatever<br>I begin|
|Goal-Setting|0.078*|0.025|0.077**|
||(0.043)|(0.032)|(0.036)|
|Goal-Setting + Recognition|0.074*|0.011|0.052|
||(0.041)|(0.037)|(0.035)|
|Observations|13,343|13,339|13,152|
|Baseline Outcome Controlled|No|No|No|

_Notes_ : This table reports the result of estimating equation 1 on individual components of SelfDiscipline Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

Table 9: Components of Confidence Index

||(1)|(2)|(3)|
|---|---|---|---|
|Dependent Variable:|Feel confdent<br>in Exam|Feel confdent<br>with friends|Feel confdent<br>when interacting<br>with teachers|
|Goal-Setting|0.016|-0.024|0.045|
||(0.031)|(0.034)|(0.029)|
|Goal-Setting + Recognition|-0.024|-0.032|0.031|
||(0.039)|(0.030)|(0.037)|
|Observations|13,177|13,169|13,392|
|Baseline Outcome Controlled|No|No|No|

_Notes_ : This table reports the result of estimating equation 1 on individual components of Confidence Index. All the components are discussed in Section 2. Standard errors are clustered at the level of school. *p<0.1; **p<0.05; ***p<0.01.

24
# **4.4 Response Biases**

Acquiescence bias may be more likely in rural low-income settings, compared to the typical highincome developed country environment for which Big Five questionnaires and lower-order noncognitive subscales were originally designed. The bottom panel in Figure 1, which shows a strong negative correlation between the acquiescence score and the cognitive index (left) or the educational level (middle), is suggestive in this regard. The gradients are steep, and the acquiescence score is twice as large for somebody with no education compared to somebody with 10 years of education. This is consistent with qualitative observations during piloting: “yasaying” was more likely when respondents did not fully understand a question, and this happened more often for lower educated individuals. Cross-country evidence comparing acquiescence scores of the Big Five across 18 countries similarly shows higher acquiescence scores in lower income settings, and for lower educated populations (Rammstedt, Kemper, and Borg, 2013). The right panel in Figure 1 suggests that the relationship between acquiescence bias and age is less clear, though the u-shape curve may suggest that controlling for a quadratic function of age can help control for some of the acquiescence bias.

Strikingly, the acquiescence score shows a strong negative correlation with yields (coefficient is - 6.15 of the average rank of maize yield), significant at the 5%, indicating that respondents with a higher propensity of agreeing with different statements have lower yields on average. The importance of acquiescence bias in the sample and its high correlation with both cognitive skills and outcomes of interest imply that the actual effects of the noncognitive skills may be confounded with response patterns when the later are not properly dealt with. Because acquiescence bias leads to observable contradictions in the responses of reversed and nonreversed items, it can be corrected for as we have done in this paper. But this requires balancing reverse and non-reversed items in all scales, a practice commonly used in psychology but often ignored by economists. As reverse items can be somewhat harder to understand or translate, they may require more adaptation and innovation (avoiding for instance double negations which can confuse the respondent). While it may be tempting to instead drop reverse items, the benefits of being able to measure and correct acquiescence bias seem to clearly outweigh the costs.

Of course, acquiescence bias is only one of the possible response biases. Other biases include “extreme response bias” and “middle response bias”. In this study, correcting for extreme response bias by standardizing the standard deviations of responses did not lead to improvements in validity or reliability, as has been found also in other psychometric studies. That said, the distribution of many of the positively-phrased noncognitive questions is highly skewed to the 26
# **Low-Cost Private Schools in Tanzania: A Descriptive Analysis**

**Shwetlena Sabarwal Celeste Sununtnasuk** The World Bank International Food Policy Research Institute

**Deepika Ramachandran** The World Bank

JEL Codes: I22, I28, J20, J24 Keywords: Private schools; Public-Private Partnership; Universal Access; Education Systems; Tanzania.

Acknowledgements: We are grateful for inputs from Mariam Adil, Samer Al-Samarrai, James Habyarimana, Kanishka Kacker, Dmitry Chugunov, Ravinder Casley Gera, Inaam Ul Haq, and Research In Improving Systems of Education (RISE) team. Financial support from the Strategic Impact Evaluation Fund and Multi-Donor Education and Skills Fund are gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.