rafmacalaba/datause-encoder-data huggingface dataset:
where _Kic_ is the share of instructional time lost in country _c_ for student _i_ , and  is a function of the parental factor of substitution, which takes into account that parents may compensate to a certain degree for the educational loss (see below). The instructional time loss is estimated as: where _t_ is the number of days lost due to school closures in a given country and _T_ is the number of school days in a regular year of schooling. The term in parenthesis is included to consider the compensation of schooling from public actions in home learning tools: _f_ and n are indices that capture the extensiveness of offline and online education tools during the pandemic. _f_ (n) equals one if all the offline (online) educational tools were used by the country’s education system during the school closure, and zero if none of them was used. The parameter δ is a weight between the two set of resources that defines their relative effectivity. Following NLT, we initially set the weight δ equal to 0.5, meaning that both offline and online learning resources are equally capable to transmit learning material and may together be able to replace a regular day in class. Alternative values are used in the robustness analysis.

In NLT, _A_ is defined as the likelihood to have access to the internet (a key factor to be able to receive on-line education, as discussed in Marchionni _et al_ ., 2022) of students in households with a given educational background. In contrast to NLT, where this likelihood is predicted for each individual based on the distribution of access to the internet by education groups estimated for each country from other data sources, in our specification _A_ is the actual availability of internet in the household, a piece of information available in most national household surveys.<sup>8</sup>

> 8 Argentina and Ecuador are the only countries in our sample that do not have information on internet access. In these cases, we randomly imputed internet access based on the percentage of internet access by income deciles of similar countries (Uruguay and Peru, respectively).

12
# Private Cities:

# Implications for Urban Policy in Developing Countries

Yue Li and Martin Rama

## **JEL codes: R5, H4, L1**

___________________ Yue Li and Martin Rama are with the World Bank. Inputs from country- and city-level case studies by Tamer Alshayal, Mulya Amri, Sohaib Athar, Balakrishnan Balachandran, Kun Cheng, Virgilio Galdo, Kareem Ibrahim, Arjun Joshi, Nadia Qureshi and Tony Hartanto Widjarnarso are gratefully acknowledged. The paper benefitted from comments and suggestions by Gilles Duranton, Thomas Farole, Edward Glaeser, Somik Lall, Barjor Mehta, Megha Mukim, Pierre Picard, Mark Roberts, Harris Selod, Hans Timmer, Anthony Venables, Sameh Wahba, Ming Zhang and Siqi Zheng. The corresponding author is Martin Rama, at mrama@worldbank.org.
# Joint contributions

This long-term effect of child-care on women's pension entitlements in a funded system can be ameliorated by some system of contribution-sharing within marriage. To determine the effectiveness of this, we have imposed a contribution-sharing rule on LIFEMOD married couples. The income of married couples is pooled, and pension contributions paid from this joint income are split equally and paid into the individual pension funds of husband and wife during each year of marriage. If the couple divorce, they each carry their individual pension fund with them, and continue to make contributions from their individual earnings while single. If they remarry, contributions from pooled current income of the new couple are again split equally, but the pension funds of husband and wife remain separate. This rule means that, for married couples, individual pension contributions are a function of current joint income, but individual pension funds are a function of individual contribution histories.

Table 14 shows the overall effect of joint contributions in a funded scheme in which all earners make pension contributions of 15% per annum, with i = 3% and dY = 1.5%. The table reports the percentage of the LIFEMOD population surviving to age 65 who accumulate enough capital to purchase an annuity equal to either 50% or 33% of average male full-time LIFEMOD earnings. On the basis of their own contributions, over three-quarters of men reach the 33% replacement rate, and ovcr one-third reach the _50%_ rate. For women, however, fewer than one-quarter meet the lower replacement rate, and a trivial 6% accumulate enough capital to meet the 50% replacement target. Moving from an own contribution to a joint contribution basis fundamentally changes the outcomes. The proportion of the total population meeting the lower 33% replacement target rises from 48% to 53%, but the proportion meeting the higher 50% _targetfalls_ from 21% to 16%. This is because women's earnings (and therefore contributions) are so much lower than men's that the pooling and equal division of contributions drags many men below the higher target which they reached on the basis of their own contributions. The joint contribution rule significantly benefits women, bringing almost half of them up to trie 33%° replacement rate, but it does so at a clear cost to their husbands.

53
the data sets and methodology. Section 4 describes the results in detail. Section 5 is a discussion of the results and section 6 concludes.

# **Section 3: Methodology**

The analysis is done using data from nationally representative and comparable surveys from 13 African countries: Burkina Faso, Cameroon, Côte d’Ivoire, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Malawi, Rwanda, Senegal, Tanzania, and Zimbabwe. Data from all the countries, except Côte d’Ivoire and Tanzania, are from the most recent Demographic Health Surveys (DHS), which include HIV testing for a sub-sample of the population and have similar questions (Burkina Faso 2003; Cameroon 2004; Ethiopia 2005; Ghana 2003; Guinea 2005; Kenya 2003; Lesotho 2004; Malawi 2004; Rwanda 2005; Senegal 2005 and Zimbabwe 2005/06). The data from Côte d’Ivoire and Tanzania (Côte d’Ivoire 2005 and Tanzania 2004) are from the HIV/AIDS Indicator Survey (AIS), which also includes HIV testing and socio-demographic variables (but these are more limited than in the standard DHS). For Ethiopia, the results are for females only because males were not asked the relevant questions about previous marriages in the DHS for Ethiopia. The data is weighted using the sample weights suggested by the data provider and the standard errors are clustered at the enumeration area level.

The samples of the surveys include women ages 15 to 49. There is more variation in the ages of the men; in Burkina Faso, Cameroon, Ghana, Guinea, Lesotho, Rwanda, and Senegal, men are ages 15-59, in Kenya, Malawi, and Zimbabwe, men are ages 15-54 and in Côte d’Ivoire and Tanzania, the men are ages 14-49. Table 1 compares the proportions of remarried and formerly married among all individuals and analyzes their HIV prevalence rates. The p-values from T-tests indicate the statistical significance of the difference between these prevalence rates. All individuals includes single, currently married (first marriage or remarried), and formerly married individuals. Single is defined as those who have never been married. Currently married 6
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# **PART I:  IDENTIFYING PUBLIC LAND APPROPRIATE FOR UNLOCKING VALUE**

Two initial hurdles confront any strategy to unlock public land values:

- i. How do we know what land parcels the different agencies of government, and related parastatals, actually own?

- ii. How should it be decided which of these parcels are “surplus” or more appropriate for an alternative use?

The first question is answered by an inventory of publicly owned land. All of the successful examples examined in this report start with a land inventory.

The second question is perhaps more vexing. Rarely does a government agency volunteer that its landholdings are “surplus” or “excess”. Even more rarely does it take steps on its own, without some type of prodding, to ready land it holds for sale or other type of disposition. This is as true of developed countries as it is of developing nations. Nonetheless, there is considerable international experience to be drawn upon in answering the query: How do we go about identifying publicly held land that is appropriate for disposition?

# **PUBLIC LAND INVENTORIES**

The elements of a good land inventory are straightforward. An inventory of public lands should record:

- The location and dimensions of each land parcel

- Legal title, and any restrictions on development

- Current use and future planned use, if part of a public development plan

- Valuation--for parcels of economic significance

More public institutions are moving to compile inventories of land they own, although progress has been patchy. Indicative of this progress is Indonesia’s Government Regulation Number 6, which requires that every government-owned asset be recorded and inventoried. At the local level, public asset owners are supposed to report this information to the recently created municipal asset manager. The asset manager is designed to be the common point of all asset management for the local government. This arrangement broadly follows asset management models established by Urban Development Investment Companies in China and public asset managers in Korea. The regulation is reported to have improved municipal performance in land inventorying, such that basic information on agency ownership, location, and parcel dimensions is now recorded.

The next step is to make land inventories more accessible and more useful for decisionmaking. As matters now stand, different agencies tend to maintain records in non-compatible 8
Table S1.7: EFFECTS ON LABOR SUPPLY IN ULTRA-POOR HOUSEHOLDS

||_Either r_|_espondent_|_Wome_|_n’s labor_|_Men’_|_s labor_|
|---|---|---|---|---|---|---|
||works for|hours in|works for|hours in|works for|hours in|
||a wage|wage-labor|a wage|wage-labor|a wage|wage-labor|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treatment|-0.082***|-433.971***|-0.101***|-235.044***|-0.017|-67.471**|
||(0.015)|(44.818)|(0.013)|(24.954)|(0.013)|(30.639)|
|Mean in control|0.702|1614.405|0.589|764.414|0.430|692.373|
|Adjusted R-squared|0.170|0.195|0.262|0.257|0.140|0.135|
|Observations|13464|13464|13464|13464|7778|7778|

**Source:** Author’s analysis based on original survey data.

**Notes:** The table reports ITT estimates based on specification (1) estimated with OLS. *** (**) (*) indicates significance at the 1% (5%) (10%) level. The dependent variable in column 1 is a dummy variable (DV) equal to 1 if either the main female respondent, or the male head of the household was engaged in income generating activity where s/he was employed by others during the 12 months preceding the survey. ‘Hours in wage-labor’ gives the total annual hours the respondent spent in wage employment, where annual hours are computed by multiplying the number of hours worked in a typical day by the number of days worked in a year for each wage labor activity and then summing across all wage labor activities. The dependent variable in column 2 is the sum of the hours spent by the main female and the male head of the household in wage-labor. Outcome variables in columns 3-4 (5-6) refer to the main female (male head of the household).

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bureaus. In the asset volatility (book) regressions of Panel A, the triple interactions of a financial market variable, Maturity and Getting credit receive positive coefficients that are statistically significant in regressions 3-5. The double interactions of Maturity and Getting credit obtain negative and significant coefficients in these regressions. These results suggest that with easier access to credit financial market development – implying greater availability of long-term debt - reduces asset growth volatility relatively less for firms with a preference for long-term debt. This reflects that being able to get credit easily is particularly important for firms with a preference for short-term credit.

In the asset volatility (stock) regressions Panel B of Table 8, triple interactions involving Getting credit obtain positive and significant coefficients, while the corresponding double interactions receive negative and significant coefficients. This is further evidence that the ability of get credit easily reduces the negative volatility consequences of a lack of financial market development and long-term finance.

Next, in Table 9 we consider regressions that include the Contract enforcement variable, which is an index of the time and cost of resolving a commercial dispute through a first-instance court. In the asset volatility (book) regressions of Panel A, the triple interactions of a financial development variable, Maturity, and Contract enforcement are estimated with insignificant coefficients. In the asset volatility (stock) regressions in Panel B, these triple interactions instead receive positive coefficients that are statistically significant, while the corresponding double interactions are estimated with negative and significant coefficients. Easier contract enforcement thus appears to reduce the volatility benefits of financial development that facilitates long-term credit, as easier contract enforcement reduces volatility especially for firms with a preference for short-term credit. This may reflect that short-term creditors may be more patient and more likely to refinance their credits in countries with 19
<!-- Start of picture text --> Costa Rica<br>"precios"<br>100<br>80<br>60<br>40<br>20<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> Figure 1: Results for 10 days during the study period for the “precios” keyword in Costa Rica. The dark line is the average. The gray bands are minimum and maximum observed values for that month over the study period.

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# **References**

Boyce, J. K., and M. Ravallion, 1991, A Dynamic Econometric Model of Agricultural Wage Determination in Bangladesh, Oxford Bulletin of Economics and Statistics, 53(4): 361-76 Budd, J. W., 1993, Changing Food Prices and Rural Welfare: A Non-Parametric Examination of the Cote d’Ivoire, Economic Development and Cultural Change, 41(3): 587-603.

Coudouel, A., J. Hentschel, and Q. Wodon, 2002, Poverty Measurement and Analysis, in J. Klugman, editor, A Sourcebook for Poverty Reduction Strategies, Volume 1: Core Techniques and Cross-Cutting Issues, World Bank, Washington, DC.

Barrett, C. D. and P. A. Dorosh, 1996, Farmers' Welfare and Changing Food Prices: Nonparametric Evidence from Rice in Madagascar, <u>American Journal of Agricultural Economics, 78(3): 656-69.</u> Christiaensen, L. and L. Demery, 2007, <u>Down to Earth: Agriculture and Poverty Reduction in Africa, Directions in Development, World Bank, Washington, D.C.</u> Coudouel, A., J. Hentschel, and Q. Wodon, 2002, Poverty Measurement and Analysis, in J. Klugman, editor, A Sourcebook for Poverty Reduction Strategies, Volume 1: Core Techniques and Cross-Cutting Issues, World Bank, Washington.

Coulombe, H., and Q. Wodon, 2007, Poverty, Livelihoods and Access to Basic Services in Ghana, in World Bank, <u>Ghana: Meeting the Challenge of Accelerated and Shared Growth (Country Economic Memorandum), Report No. 40934-GH, Volume III: Background Papers,</u> Washington, DC.

Deaton, A., 1989, Rice Prices and Income Distribution in Thailand: A Non-Parametric Analysis, <u>The Economic Journal, 99(395):1-37.</u> Foster, J. E., J. Greer, E. Thorbecke, 1984, A Class of Decomposable Poverty Indices, <u>Econometrica</u> 52:761-766.

International Monetary Fund (2008) Food and Fuel Prices: Recent Developments, Macroeconomic Impact, and Policy Responses, mimeo, Washington, DC: IMF.

Ivanic, M., and W. Martin, 2007, Implications of Higher Global food Prices for Poverty in LowIncome Countries, Policy Research Working paper 4594, World Bank, Washington, DC.

Loening, J., and G. Oseni, 2007, Approximating Urban and Rural Welfare Effects of Food Price Inflation in Ethiopia, mimeo, World Bank, Washington, DC.

Minot, N, and F. Goletti, 1998, Export Liberalization and Household Welfare: The Case of Rice in Vietnam, American Journal of Agricultural Economics, 80(4): 738-49.

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- 29 _-_

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The optimal amount of labor in the rural sector is determined using the average product pricii ; rule for labor: <!-- Start of picture text --> aj<br>r ~a-' aj 1 aj-1<br>I eua Nj q aj-l1<br>Pj iyj aj K + (I-caj)( E J) I<br>[ q-1<br>(8) WJ , j = 1<br>Nj<br><!-- End of picture text --> where wi is the return to labor in the rural sector.

The return to capital in the urban sector is derived by residual: (9) r= <!-- Start of picture text --> f aj-1 e j-11 a<br>I ____I-1<br>I ej Nj q aj Nj q<br>Pj -yjlajK + (l-aj)( E ej) I -Wi E EJ<br>I q=1 J=<br>j = 2<br>K<br><!-- End of picture text -->

# (b) Consumption

Consumers are differentiated according to their sector of residence, although their utility functions defined over goods and effort (leisure) are the same. We assume an augmented CES fcrm:
estimates of the effect of governance broadly construed on per capita income in the very long run. We return to this point in more detail below.

|**Table4.RegressionResultsUs**|**ingOtherDimensio**|**nsofGovernancea**||
|---|---|---|---|
|_Regressors_|_OLS_|_IV_|_No.observations_|
|Voiceandaccountability|0.806<br>(0.074)|1.495<br>(0.151)|158|
|Political stability|0.951<br>(0.068)|1.546<br>(0.156)|146|
|Government effectiveness|0.978<br>(0.058)|1.389<br>(0.121)|144|
|Regulatoryquality|0.966<br>(0.084)|2.242<br>(0.301)|152|
|Controlofcorruption|0.920<br>(0.063)|1.412<br>(0.139)|145|

a. This table summarizes the results of estimating equation 4 by OLS and IV for five other dimensions of governance. For reasons of space, only the estimated slope coefficients and the number of observations are reported. The dependent variable is ln(per capita GDP). Standard errors are in parentheses.

> Figure 3 plots the estimated effects of governance on per capita income (in the top panel) and the first-stage regression (in the bottom panel), with countries in Latin America and the Caribbean highlighted and labeled. When considering this causal channel from governance to per capita income, the striking feature among the Latin American and Caribbean countries is that the majority of them lie above the instrumented regression line. In fact, we find only six out of twenty-six countries below the regression line (namely, Chile, Uruguay, Belize, Costa Rica, Guyana, and the Dominican Republic). One interpretation of this is that most countries in Latin America and the Caribbean have been surprisingly successful in raising living standards without the benefit of good governance. A less optimistic interpretation is that the (relatively) high income levels observed in many countries in Latin America and the Caribbean are inherently fragile because they are unsupported by sound institutions.<sup>25</sup>

The bottom panel of figure 3 illustrates where countries in Latin America and the Caribbean fall in the first-stage regression of govemance on history. Countries in the region are clustered surprisingly tightly around the middle of the distribution of (in some cases imputed) settler mortality. Only four countries stand out as having particularly inhospitable climates for settlers-Jamaica, Haiti, Panama, and Nicaragua. Moreover, countries in Latin America and the Caribbean are scattered quite evenly above and below the first-stage regression line, suggesting that the effect of history on current institutions is not

> 25. This interpretation is consistent with both anecdotal and more systematic evidence that countries that are hit by large adverse shocks suffer disproportionately more if the quality of their institutions is poor. See, for example, Boone and others (2000), who argue that declines in asset prices during the Asian crisis were larger in countries with weak corporate governance. More systematically, Rodrik (1999) shows that countries lacking institutions to manage social conflict suffered deeper declines in output in response to the oil shocks of the 1970s.

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and the extensive margin, exports are more reactive to appreciations than to depreciations. Proportionately, the difference in the reaction is similar for the intensive margin and the extensive margin of exports.

Table 4: Asymmetric export responses to BRER appreciations and depreciations - 2003-2017

||Total E<br>|xports<br>|Intensive<br>|Margin<br>|Extensiv<br>|e Margin<br>|
|---|---|---|---|---|---|---|
|VARIABLES<br><br>|(1)|(2)|(3)|(4)|(5)|(6)|
|lll<br>it<br><br>|-0.061***|-0.068***|-0.088***|-0.095***|-0.009***|-0.009***|
||(0.001)|(0.002)|(0.004)|(0.004)|(0.000)|(0.000)|
|lll<br>it<sup>× d</sup>it<br>||0.017***<br>(0.003)||0.017***<br>(0.005)||0.002***<br>(0.000)|
|<br>llit<br>∗|0.340***|0.340***|0.507***|0.506***|0.036***|0.036***|
||(0.004)|(0.004)|(0.006)|(0.006)|(0.000)|(0.000)|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Product FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,527,012|1,527,012|367,229|367,229|1,527,012|1,527,012|
|R<sup>2</sup>|0.286|0.286|0.441|0.441|0.253|0.253|

***, **, * imply significance at the 1%, 5% and 10% respectively. SE, adjusted for clustering at the HS6-year level, are reported in brackets.

We then estimate equation (3) for two sub-samples: differentiated products and homogenous products. If part of the reason why exports react less to depreciations than to appreciations of the RER is because increasing shipments or finding new clients takes more time than shrinking or losing clients, since information needs to be gathered in terms of finding clients, building relationships, identifying pricing strategies, and tailoring the product to the needs of the foreign buyer, then it is likely that this asymmetric effect is more marked for differentiated products than homogenous, since these types of information frictions are more binding for them. Results are reported in Table 6.

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