rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Correlations among various proxies for financial development**

||[1]<br>Commercial<br>bank branches|[2]<br>Domestic credit<br>to priv. sect.|[3]<br>ATMs|[4]<br>Account<br>ownership|[5]<br>Financial<br>development|[6]<br>Financial<br>institutions|[7]<br>Financial<br>markets|
|---|---|---|---|---|---|---|---|
|Commercial bank branches<br>(per 100,000 adults)|1|||||||
|Domestic credit to private<br>sector (% of GDP)|0.476***|1||||||
|Automated teller machines<br>(ATMs; per 100,000 adults)|0.541***|0.578***|1|||||
|Account ownership<br>(percent of age 15+)|0.529***|0.677***|0.682***|1||||
|IMF’s “financial<br>development” index|0.539***|0.809***|0.754***|0.799***|1|||
|IMF’s “financial institutions”<br>development index|0.689***|0.802***|0.792***|0.827***|0.928***|1||
|IMF’s “financial markets”<br>development index|0.366***|0.733***|0.648***|0.696***|0.956***|0.777***|1|

Notes: Global Financial Development Database=GFDD. * p < 0.05, ** p < 0.01, *** p < 0.001. “Account ownership” is the percentage of survey respondents (aged 15 or above) who report having an account (by themselves or together with someone else) at a bank or another type of financial institution or report personally using a mobile money service in the past 12 months. The IMF’s “financial development” index has the following two components: “financial markets” development and “financial institutions” development”. The IMF’s “financial markets” development index captures access to, and depth and efficiency of, a country’s stock and debt markets. The IMF’s “financial institutions” development index measures how developed financial institutions are in terms of their depth (size and liquidity), access (the ability of individuals and companies to access financial services), and efficiency (the ability of institutions to provide financial services at low cost and with sustainable revenues). Data on financial development over the period 2000-18 are available from the World Bank’s Global Financial Development Database, the World Development Indicators, the Global Findex Database, and the IMF’s Financial Development Index Database.

19
that the family counseling activities did not result in higher welfare goal achievement among internally displaced families or among families targeted through the SISBEN (from rural or urban areas), as compared to similarly vulnerable families that did not receive the intervention. This suggests that observed gains were driven by countrywide progress or other social programs rather than by the initial engagement with the program. A companion survey that was designed to evaluate the knowledge and use of the social programs available in the municipalities also showed that beneficiaries of _Red Unidos_ have the same knowledge of existing programs as do those who are not beneficiaries.

The 2011 evaluation also studied how early program activities affected other dimensions of their lives. These included conflict management; the inclination of parents to make decisions together about their children’s education; risk aversion; and patience, the last measured as the preference to receive a small amount of money in the present against a bigger amount in the future. Again, _Red Unidos_ and non-beneficiaries had the same behaviors along these dimensions (Econometría Consultores et al 2012).

The partial intervention appears to have had an impact on beneficiaries’ perceptions and attitudes (Econometría Consultores et al 2012). Beneficiaries recognized having learned from social workers how to improve their nutrition, maintain crops, apply for housing subsidies, and learned about the importance of savings. Beneficiaries of _Red Unidos_ have also developed greater hope in a better future, and have more positive attitudes towards life. Thus, the initial contact with the families and family plan exercise can change beneficiary information sets and some attitudes that inhibit the poor from being protagonists of their own well-being.

However, these results must be taken with extreme caution since the program was not yet doing its core business – supporting families to access services to meet their goals – when the evaluation was undertaken. Further, it was undertaken in the first year of the program, when implementation issues are being sorted out, potential beneficiaries were just getting to know the program, and _cogestores_ were still learning their jobs and creating the tools to carry them out. The program has matured sufficiently that a new impact evaluation should be carried out to assess the degree that current program design is having its intended effects. Since the program is several years into implementation, there is less of a risk that program effectiveness will be confused with start-up challenges.

34
Table 5: Estimates of catch-up <u>growth in height-for-age outcomes (conditional model)</u>

||Between||With|in|Both|
|---|---|---|---|---|---|
|_α_0|_β_0|?|_ραβ_|?|?|
|_Externally standardiz_|_ed difference_|(HAZ):||||
|Ethiopia<br>-1.38|0.00|N|-0.30|Y|N|
|India<br>-1.44|-0.01|N|-0.19|Y|N|
|Peru<br>-1.24|0.05|Y|-0.13|Y|Y|
|Vietnam<br>-1.12|0.02|Y|0.04|N|N|
|_Ratio to reference me_|_dian_(HAR_×_|100):||||
|Ethiopia<br>-5.76|-0.13|N|-0.01|N|N|
|India<br>-6.07|-0.22|N|0.14|N|N|
|Peru<br>-5.36|0.10|Y|0.02|N|N|
|Vietnam<br>-4.71|-0.01|N|0.37|N|N|
|_Difference to referenc_|_e median_(H|AD):||||
|Ethiopia<br>-6.89|-0.52|N|0.53|N|N|
|India<br>-7.29|-0.61|N|0.64|N|N|
|Peru<br>-6.54|-0.18|N|0.39|N|N|
|Vietnam<br>-5.60|-0.28|N|0.76|N|N|
|_Not stunted_(HAZ > -|2):|||||
|Ethiopia<br>-26.75|0.56|Y|0.06|N|N|
|India<br>-27.85|-0.12|N|0.12|N|N|
|Peru<br>-22.22|1.06|Y|-0.30|Y|Y|
|Vietnam<br>-18.45|0.39|Y|0.04|N|N|
|_Not severely stunted_(|HAZ > -3):|||||
|Ethiopia<br>-9.02|0.76|Y|-0.28|Y|Y|
|India<br>-6.59|0.41|Y|-0.11|Y|Y|
|Peru<br>-5.33|0.59|Y|-0.54|Y|Y|
|Vietnam<br>-3.89|0.01|N|0.05|N|N|

Note: columns _α_ 0 _, β_ 0 _, ραβ_ report parameter estimates based on the FEIS estimator; all specifications include a set of time-varying controls; columns denoted ‘?’ report the decision (N = no; Y = yes) of whether we can reject the (composite) null hypotheses regarding the presence of specific forms of catch-up growth. Source: own estimates.

22
**Blackout or Blanked Out?** Monitoring the Quality of Electricity Service in Developing Countries

William Seitz<sup>1</sup> Yuya Kudo<sup>2</sup> Joao Pedro Azevedo<sup>3</sup>

**Keywords:** electricity outages, survey methods, Listening to Tajikistan, survey bias, automated monitoring

**JEL:** C42, L94, Q41

> 1 The World Bank. Email: wseitz@worldbank.org 2 Institute of Developing Economies (IDE-JETRO). Email: Yuya_Kudo@ide.go.jp 3 The World Bank. Email: jazevedo@worldbank.org
# **Box 8**

# **Decision-making questions in the Demographic and Health Surveys (DHS)**

The DHS asks married women aged 15-49 on their decision-making roles (“who usually makes decisions about [X]”) across the following areas:

- (1) using/not using contraception

- (2) how the money you earn will be used

- (3) how your (husband's/partner's) earnings will be used

- (4) health care for respondent

- (5) major household purchases

- (6) visits to respondent’s family or relatives

For each decision, respondents are asked who the main decision-maker was. Typical response options are (a) respondent, (b) spouse, (c) joint decision, (d) someone else, (e) other. For domestic violence and individual health care, the DHS also asks women if they tried to seek care for themselves, and if they faced any constraints in doing so.

This phrasing of asking about the individual who usually has a say (or the final say) is used in most studies that include questions on women’s decision-making, as well as the main compendia of empowerment scales, such as the C-Change Compendium of Gender Scales or the Roadmap for Promoting Women’s Economic Empowerment (Knowles 2015).

The first standard feature of these decision-making questions is that they do not include the discussions or negotiations (particularly, whether opinions were conflicting) in the decision-making process. In many instances, for example, the respondent might have regular discussions or other input with family members over different topics — and feel that their opinion is valued — even if they do not consider themselves the main or usual decision-maker, either solely or jointly. They also do not account for whether the respondent desires to be involved in a particular decision-making domain; for example, women might not want to be involved in decision-making over certain household purchases. As such, _not_ being involved in certain decision-making areas could be a manifestation of women acting on their goals, and thus agency, as discussed in Section 3. Moreover, joint decision-making in a case where all participants agree may reflect a different dynamic than joint decision-making where there is conflict, and “jointness” might reflect cooperation and compromise, or capitulation to the wishes of a dominant household member, with the respondent not feeling that their opinion is valued (Seymour and Peterman 2016).

This point has been raised in the reproductive health literature early on. For example, in a study on rural household economies and the role of women in West Java, Sajogyo et al. (1979) write that questions on “general” patterns of decision-making in the respondent’s household may yield “responses far removed from reality”, and recommended focusing on specific events, asking questions such as “did you and your 31
# **Figure 17. Fiscal and monetary policy space**

_Elevated debt and wide fiscal deficits restrict the use of counter‐cyclical fiscal stimulus in a number of EMDEs. Above‐target inflation, especially in many commodity‐exporting EMDEs, constrains the use of monetary stimulus._

## **A. Government debt and fiscal balance**

## **B. Gap between inflation and inflation target**

<!-- Start of picture text --> Percent of GDP Percent of GDP<br>6 Commodity 60<br>4 exporters 50<br>Commodity<br>2 importers 40<br>0<br>30<br>-2<br>20<br>-4<br>-6 10<br>-8 0<br>2007 2010 2016 2007 2010 2016<br>Balance (LHS) Debt (RHS)<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points<br>25 Range Median Mean<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>Apr-16 Jul-16 Latest Apr-16 Jul-16 Latest<br>Commodity exporters Commodity importers<br>B. Investment gaps in reaching SDG<br>US$, billions<br>1,600 Investment gap, average 2015-30<br>1,200 Investment, 2014<br>800<br>400<br>0<br>Power Transport Telecoms Water and Sanitation Food security Climate change Health Education<br><!-- End of picture text --> Sources: Central Banking News, Haver Analytics, International Monetary Fund. A. “Balance” stands for fiscal balance and reflects the unweighted average of 89 commodity‐exporting and 62 commodity‐ importing EMDEs. “Debt” stands for general government debt and reflects unweighted average gross government debt of 86 commodity‐exporting and 61 commodity‐importing EMDEs.

B. Figure includes 22 commodity‐exporting and 18 commodity‐importing countries with a stated inflation target and for which current inflation data is available. Latest observation is for Nov 2016.

# **Figure 18. Infrastructure, education, and health investment needs**

_Substantial gaps in infrastructure, education, and health investment needs remain across the world._

## **A. Global infrastructure investment gap**

<!-- Start of picture text --> Percent of global GDP 0.58 3.5<br>0.71<br>0.75<br>1.01 0.27 0.04 0.12<br>Roads Rail Ports Airports Power Water Telecom Total<br><!-- End of picture text --> Sources: UNCTAD (2014), World Bank (2016b), World Bank estimates. A. The figure shows global investment in infrastructure (as percent of GDP) required over 2015‐30, as projected by McKinsey Global Institute.

B. Investment refers to capital expenditure. Upper bounds for the estimated investment needs are reported. Red column denotes 2014 or latest available year. SDG refers to the United Nation’s Sustainable Development Goals.

38
Figure 4a. Share of couples' responses (averaged across countries)<sup>27</sup>

|**Member**<br>**of the**<br>**household**<br>**Who usually makes**<br>**decisions about**<br>**making major**<br>**household purchases?**|**Wife**<br>**Husband**|**Joint**|**Wife**|
|---|---|---|---|
|**Husband**<br>**Wife**|4.6%|4.4%|1.4%|
|**Joint**|14.5%|24.3%|4.0%|
|**Husband**|27.6%|16.0%|3.3%|

Figure 4b. Share of couples' responses (averaged across countries)

|**Member**<br>**of the**<br>**household**|<br>**Who usually decides how**<br>**your husband's (your)**<br>**earnings will be used?**|**Wife**<br>**Husband**|**Joint**|**Wife**|
|---|---|---|---|---|
|**Husband**|**Wife**|2.9%|2.5%|0.6%|
||**Joint**|13.9%|25.6%|2.8%|
||**Husband**|34.6%|14.1%|3.1%|

Where couples agree on decision-making (diagonal of the matrix), it is nearly always that both agree that the husband is the sole decision-maker (occurring much more frequently in the case of husbands’ earnings) or that both agree that decision-making is joint. In both matrices, we can observe that the percentage of the sample where spouses agree that the wife is the main decision-maker is very low. Within disagreement, the DHS data shows that the most prevalent combinations of responses are: (1) wife says the

> 27The figures in each matrix are computed using the total sample for which we had information on the decision-making question for both the wife and husband (excluding cases where they report someone else making the decision). In the case of the purchases of large items the number of observations is 63,477 and in the case of husbands’ earnings the total number of observations is 48,960. Along these lines, the number of households where the wife says the main decision maker over large purchases is the husband and the husband says the main decision maker over large purchases is the wife is 2,920 observations.

40
- Francois, J.F. and I. Wooton, 2001, “Trade and Competition in Shipping Services and the GATS,” _Review of International Economics_ , 9(2).

- Francois, J.F. and I. Wooton 2005, “Market Structure in Services and Market Access in Goods,” background paper for the DFID Global Trade Architecture project.

- Grynberg, Roman and Sacha Silva. 2004, “Preference-Dependent Economies and Multilateral Liberalisation: Impacts and Options, Commonwealth Secretariat Report.

- Hoekman, Bernard. 2004. “Dismantling Discrimination Against Developing Countries: Access, Rules and Differential Treatment,” CEPR Discussion Paper 4694.

- Hoekman, Bernard. 2005. “Operationalizing the Concept of Policy Space in the WTO: Beyond Special and Differential Treatment,” _Journal of International Economic Law,_ forthcoming _._

- Hoekman, B. and C. Özden. 2005.  “Trade Preferences and Differential Treatment of Developing Countries: A Selective Survey,” Policy Research Working Paper 3566, World Bank.

- Imbs, Jean and Romain Wacziarg. 2003. “Stages of diversification”, _American Economic Review_ 93: 63-86.

- IMF. 2003. “Financing of Losses from Preference Erosion, Note on Issues raised by Developing Countries in the Doha Round,” Communication to the WTO from the International Monetary Fund, WT/TF/COH/14, 14 February.

- IMF. 2004, “Fund Support for Trade-Related Balance of Payments Adjustment”, April.

- IMF and World Bank, 2004, Aid Effectiveness and Financing Modalities, Development Committee Report and Background Paper, September, DC2004-0012 DC20040012/Add.1

- Inama, Stefano. 2005. “Erosion of trade preferences for Least Developed Countries and other vulnerable countries: Where, when and how much and possible measures to alleviate it,” UNCTAD, mimeo.

- Karacaovali, Baybars and Nuno Limão. 2005. “The Clash of Liberalizations: Preferential vs. Multilateral Trade Liberalization in the European Union,” Policy Research Working Paper 3493, World Bank.

- Kowalski, Przemek. 2005. “The Canadian Preferential Tariff Regime and Potential Economic Impacts of Its Erosion,” OECD, mimeo.

- Langhammer, R. and A. Sapir, 1987, _Economic Impact of Generalized Tariff Preferences_ (London: Trade Policy Research Center).

- Limão, N. 2005. “Preferential Trade Agreements as Stumbling Blocks for Multilateral Trade Liberalization: Evidence for the U.S.,” CEPR Discussion Paper 4884.

- Limão, Nuno and Marcelo Olarreaga. 2005, “Trade preferences to small countries and the welfare costs of lost multilateral liberalization,” Policy Research Working Paper 3565, The World Bank.

- Lippoldt, Douglas. 2005. “The Canadian Preferential Tariff Regime,” OECD, mimeo.

- Olarreaga, M. and C. Özden. 2005, “AGOA and Apparel: Who Captures the Tariff Rent in the Presence of Preferential Market Access?” _World Economy_ 28(1), 63-77.

28
Figure.4: US Ethanol Tariff Lowers the World Sugar.and Ethanol.Prices <!-- Start of picture text --> Brazil sugar demand and Brazil sugar or Brazil demand Rest of the world world Rest of the world<br>ethanol supply for ethanol demand and supply of demand and supply of<br>ethanol<br>sugar<br>on world sugw prko<br>\.Ihanol dmn<br>DROWS 8 ROWS<br>DROWE SROWE<br>PsL SSSE1<br>L7~t2;<br>.P,_ ) - - - - ----- MM '-- 1-A /I<br>CIP /,R~~ 3 OWSI DW<br>'I III. I I I IJ I<br>gA . C I B I C-A-B'<br>I ,.I .I<br>A c B D<br><!-- End of picture text --> <!-- Start of picture text --> cli<br><!-- End of picture text -->
We next check whether our policy change proxy serves as a reasonable predictor of dam allocation over our sample period. To this end, we rerun the specification described above but include our policy change proxy, _Pit_ , and control for basin-level fixed effects and thus for all geographic and nongeographic time-invariant features. As shown, for both small and large dams, the estimated coefficient suggests that the policy changes from 1994 onward increased dam construction in basins that are predominantly based in former homelands.

Finally, we report the estimated coefficients of _RGr_ ( _k_ ) _i_ * _Pit_ from the first step regression of (2) for large dams and those of these interaction terms as well as with ephemeral river length for small dams for the pooled sample over all years in Table 3. As shown, the individual interaction terms are significant in all cases. Most important, the joint F-test statistic for the river gradient interaction variables demonstrates that these variables have substantial predictive power in all samples.

# _Main Results_

When estimating equation (3), one concern is that, as Duflo and Pande (2007) note, agricultural data tend to be strongly autocorrelated. Indeed, tests indicated that our measure of agricultural productivity was autocorrelated to the third order. To address this problem, Duflo and Pande (2007) use a feasible optimal IV estimator. In our context, however, NPP data are also highly spatially correlated. A Moran’s (1950) test indicates that for each of our 20 years, the null hypothesis of no spatial correlation could be 29
but by much less for the top quintile. This, together with the fact that the number of workdays lost to sickness show a marked decline, suggests that the policy change could have had a significant impact.

To provide a background for the subsequent regressions as well as the cost-benefit analysis based on them, table 3 summarizes household characteristics as well as household level spending on health services for both 2002 and 1999. In terms of descriptive statistics, one notes that the large majority of households (83-84%) were from rural areas and point to some improvement in some socio-economic indicators such as education of the head (from 5 to 6 years), value of assets owned (from US $ 1,414 to 1,504) and dwelling characteristics such as cement floors (from 22% to 26%), availability of toilets (65% to 75%) and iron roofs (58% to 65%).

Before looking at the impact of the policy shift econometrically, it is important to assess the extent to which the abolition of user fees was not only decreed by political authorities but actually implemented on the ground. One way of doing so would be to compare spending by sick individuals on health care before and after the policy change. Unfortunately, since neither of the two surveys aimed to look specifically at health issues, the level of health spending is given only at the household rather than the individual level. Comparing the amounts spent on health by households with at least one sick person before and after the abolition of user charges can thus provide a first indication of the extent to which this policy was effective and will also be critical to make inferences on the overall costs and benefits of this measure. We note that, in the aggregate, the amount of monthly resources spent on health by households who had at least one person sick, increased slightly, from the equivalent of US$4.92 to US$5.00 although a t-test does not allow to reject the hypotheses of the two coefficients being equal. However, descriptive evidence points towards a statistically significant (at 1%) decrease in health spending for the bottom two quintiles and (at 5%) for the fourth quintile.

A second way to assess the impact of the policy change at a descriptive level is to draw on data from the 2002/3 community survey which obtained information on the fees charged for a consultation and for different medicines, as well as the availability of these medicines in both periods (i.e. 2002/3 and retrospectively for 1999/2000), for private and public health facilities. Results from doing so, illustrated in table 4, suggest that the elimination of fees for consultation at government health units was indeed implemented quite uniformly: while almost 80% of units had charged for a consultation before the policy change, only 3% did so in the second period. Similarly, only about 4% of public health units charged for antibiotics and malaria drugs although a much lower share of 22% and 27%, respectively, had charged for them in the initial period to begin with. Compared to the marked decrease in the number of units that 13
lagged growth rates in the domestic exchange rate, , and allows studying asymmetric food price transmission: To facilitate a consistent interpretation across MENA countries, we derive estimates of the 6 and 12-month pass-through effects from cumulative impulse response functions.<sup>15</sup> The coefficients show the models‟ predicted adjustment of domestic food prices to changes in world food prices, controlling for the exchange rate and domestic factors. In most countries, the food price pass-through effects fade out after about one year. Our pass-through coefficients are, therefore, identical to those that can be directly obtained from equation (4).

When estimating the models, we use monthly data from December 1998 to mid-2011 for most countries, allowing for lags. The cumulative lag structure is chosen to minimize the Akaike Information Criterion (AIC) and by lag exclusion tests. The optimal lag length is found to be . In some country cases (Lebanon, Djibouti, Yemen, Syria, Iraq, Oman, Qatar, and the UAE) we use less lags because of the short time series. For these countries, the results are sensitive to outliers. Hence, we selectively employ impulse dummies to correct for extreme jumps in domestic food prices (i.e. due to religious holidays, specific national events, or weather effects). We also use time trends when significant to capture other unknown exogenous factors on domestic prices. Because we estimate the model in annual growth rates, we explicitly control for seasonal factors.

# **5. Empirical Results**

# **_Pass-Through Coefficients_**

The dynamics and the magnitude of the food price pass-through largely vary by country (Table 3). International food price shocks are a risk for many Arab countries despite food subsidies that are widely believed to shield consumers from increases in

> 15 Impulse response functions allow us to trace out the time path of the effect of structural food price shocks from the autoregressive model. A <mark>ccumulated impulse response functions compute the cumulative sum of the shock over time.</mark>

-17-