rafmacalaba/datause-encoder-data huggingface dataset:
45-degree line are more common. However, whether or not this is inequality decreasing or increasing depends on the country. For example, while male/female inequality would fall by more than 0.1 in Mali, Chad, and Niger—it would increase in Central African Republic and Morocco. Similarly, rich/poor inequality would fall by more than 0.1 in several countries—but it would increase by more than 0.1 in several others.

When enrollment is low for both poorer and richer children, it is perhaps not surprising that in some cases richer children might be first to take advantage of more schools availability. Lanjouw and Ravallion (1999) discuss a model of the timing of program capture and present empirical evidence for India showing that as average enrollment of children 6 to 11 increases, it increases more for poorer children. Younger (2003) uses data from Peru and shows that as secondary school enrollment increases, it increases more among the rich than the poor. He also shows that secondary school enrollment is no more or less responsive to the distance to the nearest school for children from richer or poorer households.

These results therefore suggest that increasing the availability of schools is a limited tool to increase enrollment among girls relative to boys. Figure 5 explores the issue further by focusing on the simulated impact of distances to school set to zero on gender inequality only among the poorest 50 percent. Again most points lie close to the 45-degree line: male/female inequality in enrollment would be largely unaffected by simply placing a primary, or even a primary and a secondary, school in every cluster, even among poor households. The countries where there would be impacts on gender inequality among the poor are generally the same as those where there would be impacts on average.

**Figure 5: Simulated gender enrollment inequality impacts of reducing the distance to schools among the poorest 50 percent**

<!-- Start of picture text --> Simulation for the poorest 50 percent: Simulation for the poorest 50 percent:<br>Distance to primary school reduced to zero Distance to primary and secondary schools reduced to zero<br>3.0 3.0<br>45 degree line 45 degree line<br>Morocco<br>2.5 Morocco 2.5<br>Niger 92 Niger 92<br>C.A.R<br>2.0 2.0 Senegal<br>C.A.RChad<br>Niger 98<br>Chad<br>1.5 1.5<br>Mali Mali Niger 98<br>Tanzania<br>1.0 1.0<br>0.5 0.5<br>0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5 2.0 2.5 3.0<br>Actual male/female ratio among the poorest 50 percent Actual male/female ratio among the poorest 50 percent<br>Simulated male/female ratio among the poorest 50 percent Simulated male/female ratio among the poorest 50 percent<br><!-- End of picture text --> _Note_ : The vertical distance from the 45<sup>o</sup> line shows the change in inequality associated with reducing the distance to school to zero. Labeled countries are those for which the absolute value of the change is greater than 0.1.

_Source_ : Author’s calculations from DHS data.

21
Retrieved from https://EconPapers.repec.org/RePEc:jda:journl:vol .50:year:2016:issue1:pp:289-304

- Pfister, S., Bayer, P., Koehler, A., & Hellweg, S. (2011). Projected water consumption in future global agriculture: Scenarios and related impacts. _Science of The Total Environment_ , _409_ (20), 4206-4216. Retrieved from https://www.sciencedirect.com/ science/article/pii/S0048969711007376 doi: https://doi.org/10.1016/ j.scitotenv.2011.07.019

- Rodella, A.-S., Zaveri, E. D., & Bertone, F. (2022). _The hidden wealth of nations : The economics of groundwater in times of climate change (english)_ (Report). World Bank Group. Retrieved from http://documents.worldbank.org/curated/en/ 099257006142358468/IDU0fb2550de013100434708d920a3e3bec6afb1

- Schonberger, S., & Wijnen, M. (2018). _Assessment of groundwater challenges opportunities in support of sustainable development in sub-saharan africa_ (Report). World Bank Group. Retrieved from https://documents1.worldbank.org/curated/en/ 420291533931251279/pdf/Assessment-of-groundwater-challenges -and-opportunities-in-Sub-Saharan-Africa.pdf

- Sekhri, S. (2014). Wells, water, and welfare: The impact of access to groundwater on rural poverty and conflict. _American Economic Journal: Applied Economics_ , _6_ (3), 76– 102. Retrieved 2024-05-10, from http://www.jstor.org/stable/43189490

- Suri, T., & Udry, C. (2022, February). Agricultural technology in africa. _Journal of Economic Perspectives_ , _36_ (1), 33-56. Retrieved from https://www.aeaweb.org/articles ?id=10.1257/jep.36.1.33 doi: 10.1257/jep.36.1.33

- Svendsen, M., Ewing, M., & Msangi, S. (2009). _Measuring irrigation performance in Africa:_ (IFPRI discussion papers No. 894). International Food Policy Research Institute (IFPRI). Retrieved from https://ideas.repec.org/p/fpr/ifprid/894.html

- Tesfaye, A., Bogale, A., Namara, R., & Bacha, D. (2008, 06). The impact of small-scale irrigation on household food security: The case of filtino and godino irrigation schemes in ethiopia. _Irrigation and Drainage Systems_ , _22_ , 145-158. doi: 10.1007/s10795-008-9047-5

- Tillie, P., Louhichi, K., & Gomez Y Paloma, S. (2018, July). _Does small-irrigation boost smallholder farming? Evidence from Niger_ (2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia No. 277088). International Association of Agricultural Economists. Retrieved from https://ideas.repec.org/p/ags/iaae18/ 277088.html doi: 10.22004/ag.econ.277088

- Torou, B. M., Favreau, G., Barbier, B., Pavelic, P., Illou, M., & Sidib ́e, F. (2013, July). Constraints and opportunities for groundwater irrigation arising from hydrologic shifts in the Iullemmeden Basin, south-western Niger. _Water International_ , _38_ (4), 465-479. Retrieved from https://ideas.repec.org/a/taf/rwinxx/ v38y2013i4p465-479.html doi: 10.1080/02508060.2013.817

- UNESCO and UN-Water. (2022). _The united nations world water development report 2022: Groundwater: Making the invisible visible_ (Report).

- WaterAid. (2022). _Groundwater: the world’s neglected defence against climate change_ (Re-

24
|**Table 7: Impac**|(1)<br>**t of EU accession o**|(2)<br>**n post-comm**|(3)<br>**unist countrie**|(4)<br>**s by period**|
|---|---|---|---|---|
|DiD in 1995|-0.587|-0.810|-0.479|-0.675|
||(0.582)|(0.598)|(0.644)|(0.650)|
|DiD in 2000|0.864|0.655|0.987|0.817|
||(0.706)|(0.726)|(0.749)|(0.749)|
|DiD in 2005|2.496***|2.284***|2.501***|2.333***|
||(0.811)|(0.839)|(0.841)|(0.827)|
|DiD in 2010|2.129***|1.850**|1.995**|1.731**|
||(0.788)|(0.832)|(0.829)|(0.837)|
|Cohort 1995|0.126|0.182|0.165|0.189|
||(0.958)|(0.932)|(1.058)|(1.013)|
|Cohort 2000|-0.515|-0.457|-0.770|-0.801|
||(0.826)|(0.772)|(0.924)|(0.841)|
|Schooling|-0.126|-0.117|-0.225|-0.233|
||(0.266)|(0.250)|(0.294)|(0.268)|
|Polity||0.145*||0.165**|
|||(0.077)||(0.078)|
|Trade|||0.009*|0.010**|
||||(0.005)|(0.004)|
|1995|0.996**|1.036**|1.008**|1.079**|
||(0.426)|(0.413)|(0.443)|(0.432)|
|2000|0.978|0.939|0.952|0.927|
||(0.644)|(0.649)|(0.637)|(0.641)|
|2005|1.529***|1.472***|1.518***|1.459***|
||(0.417)|(0.417)|(0.412)|(0.417)|
|2010|2.098***|2.106***|2.089***|2.105***|
||(0.454)|(0.427)|(0.443)|(0.418)|
|Constant|7.759***|6.516***|7.933***|6.562***|
||(2.335)|(2.278)|(2.351)|(2.269)|
|Prob > chi2|0.000|0.000|0.000|0.000|
|Observations|158|155|155|152|
|Groups<br>Clustered standard error<br>*** p<0.01, ** p<0.05,<br>"DiD in (year)" shows h<br>1995, 2000, 2005, and 2<br>Cohort 1995 comprises<br>agreement to the EU bet<br>and Romania.<br>Cohort 2000 comprises<br>agreement to the EU bet<br>Slovenia.|40<br>s at national level in par<br>* p<0.1<br>ow the treatment effect v<br>010 are time controls.<br>the ex-socialist/eastern<br>ween 1994 and 1995, na<br>the ex-socialist/eastern<br>ween 1998 and 1999, na|39<br>enthesis.<br>aries by period.<br>bloc countries th<br>mely: Bulgaria, C<br>bloc countries th<br>mely: Estonia, La|40<br>at signed their as<br>zech Republic, H<br>at signed their as<br>tvia, Lithuania, S|39<br>sociation<br>ungary, Poland,<br>sociation<br>lovakia, and|

20
recipients is _R H|T_ × _QH|T_ × _C_ . Parallel calculations can be done for the subsidy rate and value of subsidy received by the poor.<sup>4</sup>

The above framework addresses the distributional incidence of consumption subsidies. A similar framework was employed to assess the targeting performance of connection subsidies. When all unconnected households are assumed to be potential beneficiaries of connection subsidies,<sup>5</sup> the benefit targeting performance indicator Ω<sup>_C_</sup> takes the following form: The first term in equation 5 (X) is the ratio of the share of poor households without connections to the share of nonpoor households without connections<sup>6</sup> . The final three terms are the targeting rate ratio of the connection subsidy (T<sup>_C_</sup> ), the ratio of connection uptake rates (U<sup>_C_</sup> ), and finally the subsidy rate ratio (R<sup>_C_</sup> ). The U<sup>_C_</sup> ratio compares the share of poor households to the share of all households who are eligible for connection subsidies (those who do not initially have connections and are targeted for a connection subsidy) and decide to take up the offer and connect to the network.

# **Results and findings**

## **_Performance of quantity-based subsidies_**

Table 1 presents a snapshot of subsidy performance for subsidy cases in the sample that rely exclusively on quantity-targeting. The empirical results suggest that quantity-targeted subsidies do not effectively target the poor – many poor households are excluded, and the non-poor benefit disproportionately from the subsidies.

The four water IBTs presented in Table 1 all exclude more than half of the poor households. The electricity IBTs in the Africa case studies fail to reach more than 70 percent of the poor. Errors of exclusion are somewhat lower for electricity subsidies in India, but one-third of poor households are still excluded in most cases.

> 4  Note that unless all households are subsidy recipients (in other words, all (a) have access to the network, (b) have connections to that network, and (c) qualify to be subsidy recipients on the basis of the targeting procedure used), the average value of the subsidy received by subsidy recipients is less than the average value of the subsidy across all households.  The same is true for the average value of the subsidy provided to the poor.

> 5 In practice, this assumption might not hold.  For example, households in neighborhoods without access to the water network or electricity grid would not be able to connect unless the utility decided to extend services into their area. The implication of a violation of this assumption for subsidy performance is discussed in more detail below.

> 6 Using the notation from the consumption subsidy example, _XP_ is equal to 1 – ( _AP_ × _UP|A_ ) and _XH_ is equal to 1 –

> ( _AH_ × _UH|A_ ).

10
Multidimensional Well-Being Measurement Practices: A Review Focused on Improving Global Multidimensional Poverty Indicators<sup>∗</sup> Benoit Decerf DECPI, World Bank

**JEL** : I32.

**Keywords** : Multidimensional Well-Being, Poverty Measurement, Data constraints.

> ∗Acknowledgments : I am grateful to Paola Ballon, Mery Ferrando, Jed Friedman, Balint Menyhert and Berk Ozler for helpful suggestions. I thank all the participants at two internal seminars of the World Bank. This paper was prepared as part of the Research Frontiers in Poverty Measurement series organized by the Global Solutions Group Data for Policy of the Poverty and Equity Global Practice and DEC, led by Utz Pape and Jed Friedman. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequence of their use.
_50_

||top 10 performance bracket as per Laknonishok et al. (1992).|
|---|---|
|TOPIO*EXT:|Plan investments exclusively handled by ton 10 money managers.|
|_C. Pension Inves_<br>PRUDMAN:|_tment Practices_<br>Pension Board required to act according to "prudent man" rule.|
|INSTATE:|Fraction of pension investments which must be directed in-stae**_(%)._**|
|STKMAX:|There is a maximum limitation on the assets in the pension portfolio.|
|_D. Pension Repo_<br>INDINVPF:|_rting Practices_<br>Pension system obtains independent investment performance evaluafiors.|
|FREQVAL:|Frequency of independent performance evaluations.|
|REPSOLO:|System issues own financial report (not integrated with other budgets).|
|GIVERPT:|Plan participants receive annual financial report.|
|AMORTPER:|Amortization period for past service liabilities.|
|AMORTMS:<br>ACTUARUC:Pensi<br>_E. Pension Assum_<br>|Amortization period not stated.<br>on system uses unit credit method of computing pension liabilities.<br>_ptions_<br>|
|COLA:|Benefits are partially (or fully) indexed after retirement.|
|WDOT:|Future salary growth assumption required to compute PBO.|
|WDOTMS:|Salary growth assumption not stated.|
|PORTABLE:|Employees moving within state may carry benefit accruals to new plans.|
|NEXPROR:|Interest rate assumption used in computing PBO**_(%)._**|
|EXPRORMS:|Interest rate assumption not stated.|
|INFL:|Cost of living assumption required to compute PBO.|
|INFLMS:|Cost of living assumption not stated.|
|AVRETAGE:|Average retirement age used in computing PBO (yrs).|
|AVRETAGEMS:|Average retirement age not stated.|

Olivia S. Mitchell - Public Pension Governance and Performance
**_Energy demand models for policy_** **_<u>formulation</u>_**

locational effects overstate elasticities, particularly price elasticities; international cross sections are likewise undesirable because structural differences bias elasticities. Although national time series could avoid the cross-sectional difficulties but it suffers from multicollinearity and limited degrees of freedom (Hartman, 1979). Moreover, model results often suffer from little parameter robustness and over-estimation of long-run price elasticity. End-use models on the other hand require information on consumption behavior by income class, location and end-use types, technology-related information, information on economic and other drivers of demand, policy and scenario related data. While the nature of information is qualitatively different from that of an econometric approach, the information burden can be substantial.

Generally, the consumption behavior varies widely by income group and by location. This is more evident in larger countries but even in smaller countries this is visible. As the income distribution is generally skewed, the benefits of modern energies reach only a selected few and assuming an average level of consumption for the entire population does not fairly represent the demand behavior. Similarly, the supply is also skewed towards urban centers and accordingly, those who can afford to pay in rural areas may be deprived of modern energies due to inadequate supply facilities and resource availabilities. Thus using the idea of representative consumers or producers in the case of developing countries might produce biased results. More disaggregated analysis using detailed consumer characteristics is required but because such analyses are data intensive, often they are not attempted.

Inappropriate characterization of technologies and transition possibilities also affects the analysis. Although developing countries are characterized by their dependence on inefficient technologies, they can benefit from technological advances and leapfrog the technological ladder by adopting cleaner technologies. However, such technological transitions are not automatic and often require state intervention in the decision-making through appropriate institutional arrangements.

94
# **3 Data**

In this section, we first present the census data collected in Uganda in 2002. Besides the socioeconomic characteristics of the respondent, these data allow us to exploit two critical pieces of information: the employment status of non-migrants as well as the net in-migration rate in each region. We then describe the weather data that help us construct the instrument for this net in-migration rate and present additional region-specific controls. Finally, we comment on the descriptive statistics related to each of these variables.

## **3.1 Census data**

The population universe of the 2002 Uganda Census is composed of all persons living in the national territory. Respondents are the head of household or compound, or the person who has authority on the compound or the household. The dataset comprises 4,045,909 households (compared to a total of 24,442,084 inhabitants).

### **3.1.1 The employment probability**

The employment probability derives from a question that asks respondents to indicate their employment status during the week preceding the census. Respondents can report to be employed, unemployed, or inactive. The employed population consists of persons working for pay for an employer, self-employed persons, unpaid (usually family) workers engaged in the production of economic goods, and persons who have a job but were temporarily absent for some reason. Unemployed persons are those who report to actively seek work. The inactive population encompasses persons not actively seeking work, persons unable to work 11
**Making Education in China** Equitable and **Efficient** by

# **Shaikh** I. Hossain

This is a revised version of Chapter 1 of World Bank Report, "China: Social Sector Expenditure Review, 1996" I would like to thank Jere Behrman, Tamar Manuelyan and Carl Jayarajah for helpful comments. Comments from Mr Sun Ping Sheng, State Education Commission Beijing, and Mr Diao Yu Hua Provincial Education Commission, Zheng Zhou, Henan Province during a seminar held in Beijing were very helpful.
|Source/Target<br>Note<br>share of transmission due to consumption-related activities<br>consumption-related activity transmission<br>share of infection due to work-related activities<br>work-related transmission<br>proportion of infected population<br>community-based infection<br>Lei et al. (2020)<br>within-household infection<br>post-lockdown _R_0 in<br>impact of lockdown on<br>Flaxman et al. (2020), 7-country sample<br>community transmission<br>population infection rate of 2.6%<br>start date of the reference lockdown<br>lockdown length from<br>length of the reference lockdown<br>Flaxman et al. (2020), 7-country sample<br>GDP decline in 2020Q1-Q2 in<br>strength of the reference lockdown<br>the 7 countries from Flaxman et al. (2020)<br>semi-elasticity of child mortality w.r.t. income<br>Authors’ estimation using DHS data<br>low-income countries<br>Authors’ estimation using DHS data<br>lower-middle income countries<br>Authors’ estimation using DHS data<br>upper-middle income countries<br>Assumed based on literature<br>high-income countries<br>Walker et al. (2020)<br>COVID-19 mortality, type-_m_<br>Walker et al. (2020)<br>probability of hospitalization, type-_m_<br>World Development Indicators<br>number of hospital beds per 1000 people<br>WHO Global Health Repository<br>non-COVID mortality, type-_m_<br>World Population Prospects, 2020<br>mass of type-_m_<br>annual risk-free rate of 4 percent<br>weekly discount factor<br>Eichenbaum et al. (2021)<br>initial infected population<br>Eichenbaum et al. (2021)<br>efciency loss due to infection<br>steady-state labor supply = 1<br>disutility from labor supply<br>loss of type-_m_ reduces _u_(_·_) by _lm_<br>elasticity of substitution between household members<br>ASPIRE dataset<br>external transfer payment|the calibrated parameters discussed in the main text. Country-specifc parameters are presented in the appendix.|
|---|---|
|Value<br>country-specifc<br>country-specifc<br>country-specifc<br>0.27<br>2.32<br>country-specifc<br>7<br>38%<br>0.15<br>0.10<br>0.03<br>0.00<br>country-specifc<br>country-specifc<br>country-specifc<br>country-specifc<br>country-specifc<br>0_._961_/_52<br>0.1%<br>0.8<br>1.0<br>3.0<br>1.55% of GDP|te: This table lists|
|Name<br>_πI_1<br>_πI_2<br>_πI_3<br>_πI_4<br>_ξ_<br>-<br>-<br> ̄_μ_<br>_νj_(1)<br>_πd_(_m_)<br>_πb_(_m_)<br>_k_<br> ̄_πn_(_m_)<br>_l_(_m_)<br>_β_<br>_ε_<br>_φ_<br>_θ_<br>_σ_<br> ̄Γ_t_|No|

30
Zingales (1998) in two major ways. First, we consider modern business services sectors in addition to financial services. For most countries in our sample, business services as a share of GDP are generally on par with or greater than financial services. Second, even for financial services, we measure the intensity of its use in manufacturing sectors differently from Rajan and Zingales in order to maintain consistency with our measure of business services intensity. In particular, their measure of financial dependence is about the intrinsic needs for externally raised funds relative to total funding needs for long-term investment. In an input-output context, the financial services sector only provides financial services in value added terms, rather than the amount of external finance raised. Financial services may facilitate an investment deal, but it is different from investment. Therefore, their measures and ours reflect two different concepts, and the scatter plot in Figure 4 shows a weak correlation between the two measures.<sup>6</sup> Some more recent papers also examine the role of finance in the economy. Ju and Wei (2011) show in a general equilibrium model that, for economies with low-quality institutions, finance is a key driver of the real economy and a source of comparative advantage. Buera et al. (2011) demonstrate in a model that sectors with more financing needs are disproportionately vulnerable to financial frictions. A growing recent literature on credit constraints demonstrates that access to external finance helps to increase firms’ export performance (see, Amiti and Weinstein, 2011, among others). Business services cover a wide range of activities as listed in Appendix 3C. There are many case studies on how a certain type of business services promotes economic performance at the firm, state or national level (see USITC, 2013). However, comprehensive empirical analyses covering most of the major economies at a detailed industry level are rare, probably owing to the lack of detailed services data.

In the existing literature, the estimation of embodied services and the recent empirical analyses on their linkage to manufacturing export performance are somewhat disconnected. The former estimates the embodied services but does not examine empirically how services input

> 6 We compare our embodied financial services measures with the external financial dependence measures used by Rajan and Zingales (1998) for the U.S. and find a very weak correlation between them, using a concordance between ISIC Rev. 1 and the WIOD sectors (constructed by authors). The simple correlation coefficient is actually negative at -0.32 or -0.36, depending on whether we consider only embodied domestic financial services inputs or embodied domestic and foreign financial services inputs. A note of caution is that our sample period (1995-2007) differs from theirs (1970s and 1980s). Although we tried narrowing the gap as much as we can by picking their measure for year 1980 and ours for 1995, the weak correlation can be partially due to the different time coverages.

7