rafmacalaba/datause-encoder-data huggingface dataset:
which is consistent with Deaton’s (2007) observation that income does not explain crosscountry height differences.<sup>4</sup> This is reflected in panel (a) of figure 2, which plots the residuals after regressing height of children under 3 years old on the log of GDP against the residuals after regressing open defecation on the log of GDP. The association remains, and the _R_<sup>2</sup> is similar: sanitation linearly explains 54.2 percent of variation in child height, and the sanitation residual explains 53.9 percent of the variation in the height residual.

Panel (b) of figure 2 adds average height and exposure to open defecation in wealth subsets of India’s 2005 DHS to the basic plot of country mean height and sanitation. Included in published DHS data is a classification of households into wealth quintiles based on asset ownership. Average height of children within these groups is plotted against the rate of open defecation among all households in the primary sampling unit where they live, that is, the local open defecation to which they are exposed. Additionally, I follow Tarozzi (2008) in identifying an elite top 2.5 percent of the Indian population: children who live in urban homes with flush toilets that they do not share with other households; whose mothers are literate and have been to secondary school; and whose families have electricity, a radio, a refrigerator, and a motorcycle or car. Even these relatively rich children are shorter than healthy norms; this is expected, because 7 percent of the households living near even these rich children defecate openly. Indeed, the graph shows that their stunted height is approximately what would be predicted given the open defecation in their environment. More broadly, the association between height and sanitation among these wealth groups is close to the the international trend computed from country means. Exposure to nearby open defecation linearly explains 99.5 percent of the variation in child height across the five asset

> 4GDP per capita statistically significantly interacts with open defecation to predict height: _height_ � _cy_ = _−_ 1 _._ 42 _−_ 1 _._ 18 _open defecationcy_ + 0 _._ 14 ln ( _GDP_ ) _cy −_ 0 _._ 59 _open defecationcy ×_ ln ( _GDP_ ) _cy_ , where open defecation and GDP are demeaned, the coefficients on open defecation and the interaction are statistically significant at the 0.01 level, and GDP is statistically significant at the 0.05 level. Thus, the slope on ln(GDP) would be 0.36 with no open defecation, but only 0.038 at India’s 2005 level of open defecation, consistent with the low apparent effect of recent Indian economic growth on stunting. Although it is difficult to interpret this result causally, one possibility is that private health inputs such as food do less to promote child height in a very threatening disease environment; I thank Angus Deaton for this suggestion.

9
- _High-quality accreditation._ A program has a high-quality accreditation if it has satisfactorily passed through a special accreditation process (beyond the usual licensing and authorization requirements) established by the Ministry of Education, and commonly regarded as a signal of quality.

_Local market conditions._ We define three conditions within each local market (recall that a market is a city-field combination).

- _Institution market power_ consists of the number of programs taught by the institution in each local market divided by the total number of programs in the local market. The index ranges between 0 and 1; a higher index indicates that the institution wields more power in the local market.

- _Market concentration._ We estimate a Herfindahl-Hirschman index (HHI) at the local marketlevel. It is calculated as the sum of squared enrollment shares for all programs taught in each local market. This index ranges between 0, indicating a perfectly competitive local market, and 1, signifying a monopolistic or concentrated local market.

- _City size._ We use data on population from Duranton (2016) to create a measure of city size, which we calculate as the number of inhabitants (in log) in each city. This measure varies across cities but, within a city, it is the same across fields.

_Local SCP supply measures._ To characterize students’ program choice and mitigate horizontal selection concerns (Hoxby, 2020), we construct a rich set of supply-side variables to characterize the local supply facing the student in her high school city. We focus on local SCP supply because more than 90 percent of SCP students in our data enroll in a program offered in the same city as their high school. Specifically, we create four measures of SCP supply in the student’s high school city (see below); we calculate them for the city overall and separately by field, institution governance (public non-SENA, private, or SENA), and program length:

- _Program supply_ is the number of available SCPs in the student’s high school city.

- _Capacity_ is quantified as the total enrollment across programs in the student’s high school city.

- _Selectivity_ is proxied by the average SABER 11 standardized test score across the programs in the student’s high school city. The higher the score, the more selective the program.

51
Figure 4: Comparison of Structural and Transient Poor, SOMD <!-- Start of picture text --> 'i 200 1989<br>a.<br>E<br>.9 150<br>Xs 100 | structural poor<br>LL OM 50 transient poor > i-<br>'- 50<br>O O<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Lines<br>-<br>40 r | 1990<br>E<br>o - 20 z |- - -. . . structural poor<br>O 0 / l transient poor<br>'2 O<br>00 10.25 0.5 0.75 1 125 1.5<br>Alternative Poverty Lines<br>a 150 - 1991<br>04an 100<br>(0 ao ....-. tutrlpo<br>X a 50 transient poor<br>0 ' 10 l.tucua poo<br>0<br>}<br>0.25 0.5 0.75 1 1.25 1.5<br>Alternative Poverty Lines<br><!-- End of picture text -->

Note: Alternative poverty lines represent fractions of J$6198 (in 1991 dollars, which is the third decile of 1989 percapita expenditure).

33
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Côte d’Ivoire has forced Mali to shift trade traffic to other regional ports of the region and to develop alternative corridors. Until 2000, Abidjan captured about 80 percent of all transit traffic to and from Mali. By 2003, that figure had dropped to only 14 percent, as traffic was diverted to Dakar in Senegal, Lomé in Togo, and Tema in Ghana. Dakar became the most important port for Mali, capturing one-third of traffic. The shift from the traditional Abidjan corridor to other corridors has cost Mali an estimated $12 million per year. Mali’s transport costs are now the highest in the region. Furthermore, at 30 percent of import values, Mali’s transport costs surpass those of all other comparator countries or country groups, including its landlocked neighbors Niger (14.5 percent) and Burkina Faso (21.5 percent) and the landlocked country group (18 percent) (World Bank 2006).

Underpinning road performance in Mali is the reform of road sector institutions, in which included the creation of a second-generation road fund in 2000 followed by the inception of a Road Maintenance Executing Agency (AGEROUTE). With these institutional reforms, Mali is setting the grounds for the enforcement and proper implementation of towards multiyear, performance-based maintenance contracts.

# **Challenges**

Road maintenance in Mali remains inadequately funded. Furthermore, adjustments to the fuel levy have not kept pace with changes in inflation, maintenance requirements, and axel loads (figure 6). The government has been systematically covering the funding gap with transfers from the treasury. In 2009, the government sharply increased the per liter fuel levy from $0.6 cents (3 CFA) to $5.5 cents (25 CFA). This increase has yet to result in an increase in maintenance spending. Even if the fuel levy were fully collected, a shortfall of between 42 percent (if only maintenance is considered) and 56 percent (if rehabilitation needs are also considered) would remain. To bridge the maintenance funding gap through the road fund, the fuel levy would need to almost double to $10 cents per liter, which would still be within the average range for Africa (figure 6).

**Figure 6. Mali’s fuel levy is low relative to neighboring countries**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Niger Liberia Sénégal Côte Ghana Nigeria Bénin Mali<br>d'Ivoire<br>Optimal for maintenance and rehabilitation Optimal for Maintenance<br>US$ cents per liter<br><!-- End of picture text --> Source: Gwillliam and others 2009.

Mobilizing adequate financial resources from the government to maintain the existing road network is an important challenge for Mali in the road sector. Based on the experience of other countries in Africa, 13
Based on these alternative approaches we end up with four sets of food insecurity indicators: (i) raw responses to each of the eight questions, (ii) raw score across the eight questions, (iii) a binary indicator of experience of moderate or severe food insecurity based on severity of food insecurity, and (iv) binary indicator of experience of food insecurity (assuming a value of 1 for those households with raw score above zero and 0 otherwise). Table 1 shows the weighted pooled summary statistics. Forty-five percent of households experienced moderate or severe food insecurity. These are comparable with other recent studies from Ethiopia and other African countries (e.g., Josephson et al., 2020). Sixty-four percent of households report experiencing at least one of the eight dimensions of food insecurity that constitute the FIES. Figure 2 (in Section 3) shows the temporal evolution of these outcomes across survey rounds as well as across conflict affected and unaffected regions.

**Table 1: Summary of Food Insecurity Measures and Indicators**

||Number of<br>observations|Mean|Standard deviations|
|---|---|---|---|
|Moderate or severe food insecure|14,523|0.45|0.50|
|Food insecure|14,523|0.64|0.48|
|Raw FIES score|14,523|2.49|2.50|
|Worried|14,524|0.48|0.50|
|Healthy|14,524|0.51|0.50|
|Few Food|14,524|0.48|0.50|
|Skipped|14,524|0.29|0.45|
|AteLess|14,524|0.34|0.47|
|RunOut|17,196|0.21|0.41|
|Hungry|17,196|0.12|0.33|
|WhlDay|17,196|0.10|0.30|

Notes: households’ classification into moderate or severe insecurity follows the parametric analysis and procedure outlined by FAO and using the analytical tool: <u>https://fies.shinyapps.io/ExtendedApp/. This tool categorizes</u> households’ food insecurity status based on severity of food insecurity experience. We construct the second binary indicator, which assumes a value of 1 for those households experiencing one or more form of food insecurity and 0 otherwise, based on the raw FIES score. The third indicator is constructed by adding raw responses to the eight FIES questions. Th remaining eight indicators come from responses to standard FIES questions on whether the respondent or household member (1) was _worried_ about having enough food to eat, (2) ate only a _few kinds_ of foods, (3) unable to eat _healthy_ and nutritious foods, (4) _ate less_ than should have eaten, (5) had _skipped_ a meal, (6) _run out_ of food, (7) was _hungry_ but did not eat, and (8) went without eating for a _whole day_ . Summary statistics are weighted using the sampling weight discussed above.

13
regardless of the choice of weights, we find the treatment impacts are positive, similar in magnitude, and statistically significant.

_Robustness to attrition:_ Appendix 6 examines robustness of our results to attrition of the management practice data. It shows that the firms for which we have endline management practice data have similar baseline management practices to those firms which attrit, and that this also holds separately by treatment status. It provides Lee bounds for the impact on management practices. These bounds are relatively narrow and positive, and statistically significant, even at the lower bound when measuring the impact during the intervention. However, since control group attrition is higher by the endline, the bounds are wider for the post-intervention period, and the lower bound for the treatment effects are positive, but not statistically significant for either treatment. However, for this lower bound to hold, it would need to be the case that the best managed control firms were the ones that attrited. We show that this is not the case in terms of either baseline management practices, nor management practices as measured in the first follow-up. Coupled with our use of a balanced panel and randomization triplet fixed effects as controls (which identifies treatment by comparing firms with similar baseline characteristics), we believe survey attrition is extremely unlikely to be driving the positive impacts found on management.

_Robustness to alternative measurement of management:_ Appendix 7 discusses our efforts to also measure changes in management using the World Management Survey (WMS) and Management and Organizational Practices Survey (MOPS). These measures are at a more general level than the Anexo K measures, and were designed for medium-sized firms of 50 or more employees, whereas our sample includes firms with as low as 10 workers. A combination of budget constraints and attrition mean that we only have this data for 70 of the 159 firms (WMS), and 95 firms (MOPS). We show that our Anexo K measures are correlated with the WMS and MOPS in the cross-section, but not in the panel, and that our WMS and MOPS measures appear to be noisily measured, with less predictive power for business outcomes than Anexo K. Our measured treatment impacts on these two measures are smaller in magnitude and not statistically significant. The improvement in management we obtain is thus not able to be detected using these alternative management instruments.

18
**UNCTAD:** _World Investment Report 2002: Trasnational Coorporations and Export Competitiveness._ New York and Geneva, 2002

**Venables J. Anthony:** Winners and losers from regional integration agreements. Center for Economic Policy Research (CEPR). Discussion Paper Series No. 2528:1-[26], (U.K.); August 2000.

**Willem Gunning, Jan:** _Trade Blocks: Relevant for Africa?._ Journal of African Economies, Volume 10, No. 3. Oxford, 2002.

**Winters, Alan:** _Doha and the World Poverty Targets._ Paper presented at the Annual Bank Conference on Development Economics. The World Bank, Washington April, 2002.

**Wood, Adrian and Jorg Mayer (2001)** _Africa's Export Structure in a Comparative Perspectiv.,_ Cambridge Journal of Economics Vol. 25, No. 3, May, pp. 369-94

**World Bank,** _Can Africa Claim the 21st Century._ Oxford Press. Washington DC, 2001.

**World Bank:** _Regional integration in Southern Africa: Overview, Challenges and Recent Developments._ Background Paper, unpublished. Washington 2001.

**World Bank.** _Trade Blocs._ World Bank Policy Research Report. Washington D.C. 2000.

**Yagci, Fahretfin and Enrique Aldaz-Carroll.** _Trade Perfornance in Eastern and Southern Africa._ As part of Trade Policies and Performance in Eastern and Southern Africa. Mimeo 2003. Washington, DC.

**Yeats, Alexander J..** _What Can Be Expected From African Regional Trade Arrangement? Some Empirical Evidence._ The World Bank. Policy Research Working Paper, No 2004. Washington D.C., 1998.

25
32 between the observed 1996 and the observed 1976 distributions. Along with it, we have also plotted every (cumulative) stage of our simulations. First the immiserizing (but roughly equal) price effects; then these combined with the highly immiserizing occupational choice effects; then the slightly less bleak picture arising from a combination of the latter with the parameters of the family size equations. And finally, the curve plotting the differences between the incomes from the simulation with all parameters changing, and observed 1976. As can be seen, it would not appear that the last line replicates the actual differences badly. Of course, the point of the exercise is not to replicate the actual changes perfectly, but rather to learn the different effects of different parameters, and possibly to infer any policy implications from them. But the success of the last simulation in approximately matching the actual changes does provide some extra confidence in the methodology, and in any lessons we may derive from it.

# **[See Figure 15 in Appendix** 4]

# **7. Conclusions**

In the end, does this exercise help us improve our understanding of the evolution of Brazil's urban income distribution over this turbulent twenty-year period? Whereas many traditional analysts of income distribution dynamics might have inferred, from the small changes in mean income, in various inequality indices, and in poverty incidence<sup>1</sup> ", that there was little - if anything - to investigate, digging a little deeper has unearthed a wealth of economic factors interacting to determine substantial changes in the environment faced by individuals and families, and in their responses.

In particular, we have found that, despite a small fall in measured inequality (although the Lorenz curves cross as expected, see Figure 2b) and a small increase in mean income, extreme poverty has increased, for sufficiently low poverty lines or sufficiently poverty aversion parameters. This seems to have been caused by outcomes related to participation decisions and occupational choices, in combination with declines in the labour market returns to education and experience. These changes are associated with greater unemployment and informality, as one would expect, but more research into them seems necessary. While we seem to have identified the existence a group excluded from both the productive labour markets and any substantive 17 With respect to the already low R$ 60/month poverty line, by historical standards for Brazil.
32

**Table 7:  Distribution of Remittance-Receiving Households by Decile Group, Ranked by Predicted Per Capita Household Expenditure, Excluding Remittances**

|Rank|Households<br>receiving internal<br>remittances (from<br>Ghana)<br>(1)|Internal remittances<br>as percent of total<br>per capita<br>household<br>expenditure<br>(including<br>remittances)<br>(2)|Households receiving<br>international<br>remittances (from<br>African or other<br>countries)<br>(3)|International<br>remittances as a<br>percent of total per<br>capita household<br>expenditure<br>(including<br>remittances)<br>(4)|
|---|---|---|---|---|
|(decile)|(percent)|(percent)|(percent)|(percent)|
|Lowest 10|10.7|13.8|6.3|22.7|
|Second 10|4.9|7.6|2.1|19.4|
|Third 10|5.2|7.8|6.2|8.2|
|Fourth 10|7.8|5.1|5.5|10.3|
|Fifth 10|10.5|6.5|5.6|15.0|
|Sixth 10|13.0|5.5|11.9|13.5|
|Seventh 10|13.6|7.9|14.1|12.0|
|Eighth 10|13.1|8.2|11.2|13.7|
|Ninth 10|13.0|11.3|19.8|17.2|
|Top10|8.2|18.8|17.3|25.6|
||100.0||100.0||

Notes: Households ranked into decile groups on the basis of predicted per capita household expenditure (excluding remittances). For those households receiving internal remittances (from Ghana), column (2) shows the percent of total per capita household expenditure (including remittances) coming from internal remittances. For those households receiving international remittances (from African or other countries), column (4) shows the percent of total per capita household expenditure (including remittances) coming from international remittances. See equation (7) and text for predicted income equation.

Source: Calculated from 1998/1999 Ghana Living Standards Survey (GLSS 4).
# **Financial Inclusion for Financial Stability:**

# **_Access to Bank Deposits and the Growth of Deposits in the Global Financial Crisis_**<sup>**_*_**</sup>

Rui Han Martin Melecky<sup>#</sup> _World Bank World Bank_

**Abstract** In crisis times, depositors get anxious, can run on banks, and withdraw their deposits. Correlated withdrawals of bank deposits could be mitigated if bank deposits are more diversified, that is, held by more individuals. This paper examines the link between the broader access to bank deposits prior to the 2008 crisis and the dynamics of bank deposit growth during the crisis, while controlling for relevant covariates. Employing proxies for access to deposits and the use of bank deposits, the authors find that greater access to bank deposits can make the deposit funding base of banks more resilient in times of financial stress. Policy efforts to enhance financial stability should thus not only focus on macroprudential regulation, but also recognize the positive effect of broader access to bank deposits on financial stability.

**Keywords** : Access to Deposits, Bank Deposit Withdrawals, Banking Sector Resilience, Global Financial Crisis.

**JEL Classification** : G21, G01, G28, G32.

> * We thank Thorsten Beck, Inci Otker-Robe, Johannes Breckenfelder, and the participants of the IFABS 5th Conference on "The Search for Financial Stability: Models, Policies and Prospects", and the 2013 International Risk Management Conference on “Enduring Financial Stability: Contemporary Challenges in Risk Management and Corporate Governance” for helpful comments and suggestions on earlier drafts of the paper. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations.

> # corresponding author, email: mmelecky@worldbank.org.
**<u>Table A4. Within-household Fixed Effect Estimates of Land Tenure Impact on Land Investment</u>**

|||Land impr|ovement||Access to|Irrigation|
|---|---|---|---|---|---|---|
||Improvement<br>past 8years|Family labor<br>used in 2008|Spending in<br>past 8years.|Days spent in<br>2008.|Private|Public|
|||**Su**|**bsample with 50**|**/50 sharing rule**|||
|Barga land|-0.212***|-0.174***|-1,711.630**|-19.883***|-0.056*|0.006|
||(7.07)|(5.94)|(2.29)|(6.25)|(1.94)|(0.46)|
|Plot size (acre)|0.011|0.002|2,071.171**|8.429|-0.015|0.010|
||(0.38)|(0.15)|(2.18)|(1.21)|(0.56)|(0.81)|
|Distance to|0.002|-0.004|12.630|-0.907|-0.061**|0.003|
|homestead (km)|(0.11)|(0.30)|(0.07)|(0.72)|(2.49)|(0.47)|
|Observations|2555|2555|2555|2542|2555|2555|
|No. of households|512|512|||512|512|
|R-squared|0.12|0.11|||0.05|0.02|
|||**Su**|**bsample with 75**|**/25 sharing rule**|||
|Barga land|-0.213***|-0.185***|-1,025.248***|-15.191***|-0.055**|-0.002|
||(5.33)|(5.54)|(3.30)|(5.54)|(2.11)|(0.10)|
|Plot size (acre)|0.002|0.008|313.166|28.356***|0.002|0.000|
||(0.55)|(0.98)|(0.24)|(5.53)|(0.78)|(0.07)|
|Distance to|0.014|0.001|191.388|0.134|-0.007|0.019|
|homestead (km)|(1.11)|(0.05)|(0.89)|(0.08)|(0.46)|(1.04)|
|Observations|2292|2292|2292|2283|2292|2292|
|No. of households|451|451|||451|451|
|R-squared|0.16|0.15|||0.03|0.02|

_Notes:_ All regressions include household level fixed effects. Col. 1, 2,5, 6 use a fixed effect linear probability model while col. 3 and 4 report results from fixed-effect tobit models. Plot and soil characteristics are included throughout. Robust t statistics in parentheses. *, **, and *** denote significance at 10%, 5%, and 1%.

27
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