rafmacalaba/data-use-mentions-extended huggingface dataset:
-|---|
|Rural|29.2|43.9|46.2|64|73.6|
|Urban|55.1|90.6|89.9|92|97.0|
|Rural|25.9|24.9|24.0|26.2|28.9|
|Urban|79.5|77.1|78.3|—|—|

ß _Source_ : WHO Joint Monitoring Program 2010a and 2010b, from the Demographic and Health Surveys for 1986, 1993, 1997, and 2005. _Note_ : Improved water defined as piped water, stand posts, and safe wells/boreholes; improved sanitation defined as flush toilets, septic tanks, and improved latrines, accordingly to the JMP methodology. — = Not available.

In terms of sanitation, 25 percent of the population still practices open defecation. This figure, while much better than that of other African low-income countries, is twice as high as what is seen in middleincome countries and a source of concern in itself.

In general, progress on the sanitation front has been much more modest than in the water sector, due to the enormous and widening gap in access to improved water supply between rural and urban areas. As of 2008 access to sanitation in urban areas was estimated at 63.6 percent; in rural areas it was as low as 28.3 percent.

25
,000<br>1,400,000<br>40<br>1,200,000<br>1,000,000 30<br>800,000<br>600,000 20<br>400,000<br>10<br>200,000<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Domestic Total International Intercontinental Domestic<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source_ : Bofinger 2009. Derived from AICD national database downloadable from http://www.infrastructureafrica.org/aicd/tools/data _Note_ : As reported to international reservation systems.

21
long-run GPA (Appendix Table A4.2). This is an interesting finding in itself, pointing towards either an inconsistent use of official records in the country, true dropout behavior, migration, or a combination of these. To further look into how attrition affects our findings, we re-estimate Table 5 using a balanced sample, including only students who are observable in official records during the entire period from school year 2015/2016 to school year 2016/2017 (Appendix Table A4.3). We find that our results (especially those for Roma students) remain robust using this specification, suggesting that attrition and resulting changes in sample composition, although a real phenomenon, are unlikely to drive the materializing impacts on GPAs over time.

In sum, for Roma students, we find a consistent pattern: large impacts on socio-emotional skills are mirrored by very small impacts on GPAs immediately after the intervention, which turn into medium impacts half a year later and medium to large impacts one year after. Such a pattern of emerging impacts over time might point towards sustained behavior change and that students reap the benefits of such behavior change over time only, at least when it comes to academic achievement as reflected in school GPAs.

Which Roma students benefited the most? To answer this question exploratorily (noting that sample sizes are small at times), we dig deeper into our finding on Roma students and extend our analysis on GPAs over time by looking at heterogeneous treatment effects (Appendix 3 Table A3.1). There is some evidence that, while both male and female Roma students benefited, female students benefited more in the long-run. The difference in GPAs between male and female students, however, cannot be rejected at a conventional level of statistical significance. When it comes heterogeneous treatment effects by pre-treatment GPAs, we find that students in higher terciles clearly benefited more; for students in the upper tercile, the intervention had a positive effect on GPAs at the 1% level, increasing GPAs by about 8% SD in the short-run
MSE of _Y_<sup>_r_</sup> _/Y_<sup>_∗_</sup> conditional on deciles of _Y_<sup>_∗_</sup> at left.

benchmark is less pronounced.

# **5 Errors in Survey Reports**

## **5.1 Diary and Recall Measurements**

The presumption that diaries outperform recall data for the measurement of well-being finds little empirical support. This can be seen in Figure 1, where diary and recall histograms are compared with the distribution of usual consumption, _fY ∗_ [ _y_ ] (the continuous line). The lower tail of the recall distribution is closer to that of usual consumption, and mismeasurement from diaries is more substantial at the bottom end. As diary measurements are centered at _Y_<sup>_∗_</sup> (because of Assumption 1), the difference in the modes of _fY ∗_ [ _y_ ] and _fY d_ [ _y_ ] suggests that diary errors must have a thick lower tail. Figure 1 also illustrates how the upper tail of the distribution of recall data is substantially thicker than the distribution of usual consumption, which is better approximated using data from diaries. These conclusions are confirmed by the first three columns of Table 3, where different percentiles of empirical and estimated distributions are compared. The bottom three deciles of the recall distribution are closer to the deciles of _Y_<sup>_∗_</sup> than are those of the diaries. The opposite conclusion applies for the remaining deciles. Consistent with predictions from the model in Section 2, the Gini coefficient from diaries overstates the real value of this index; the distance from the real value is minimized using recall data.

These findings have important implications for the computation of aggregate poverty statistics. This 19
the most novel and influential green technologies. Section 4 presents evidence about the diffusion of green technologies among firms, sectors, and countries using textual analysis of earnings calls transcripts. Section 5 reports evidence of patterns of diffusion of green technologies through firm-to-firm relationships. Section 6 concludes the paper with the main takeaways.

# **2 Data sources and textual analysis methodology**

We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. Using these keywords and textual analysis of patents and earnings conference calls, we build a multi-country firm-year panel data set of exposure to green construction technologies spanning the period 2012-2022 and covering 10,000 firms in 79 countries. This section describes the data sources and our approach in more detail.

3
WPS3698

# Searching for the Economic Gradient in Self-Assessed Health

## Michael Lokshin and Martin Ravallion<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433 USA_

**Abstract:** Can self-assessments of health reveal the true health differentials between “rich” and “poor”?  The potential sources of bias include psychological adaptation to ill-health, socioeconomic covariates of health reporting errors and income measurement errors.  We propose an estimation method to reduce the bias, by isolating the component of self-assessed health that is explicable in terms of objective health indicators and allowing for broader dimensions of economic welfare than captured by current incomes.  On applying our method to survey data for Russia we find a pronounced (nonlinear) economic gradient in health status that is not evident in the raw data.  This is largely attributable to the health effects of age, education and location.

**JEL:** D63, I12, I31

**Keywords:** Self-assessed health, subjective welfare, gradient, measurement errors, Russia **Correspondence:** M. Ravallion, World Bank, 1818 H Street NW, Washington DC, 20433 USA. mravallion@worldbank.org

World Bank Policy Research Working Paper 3698, September 2005; Revised July 2006 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 The authors are grateful to Mingshan Lu, Vassilis Hajivassilou and participants at the Fifth World Congress of the
Rajan, R. and Zingales, L., 1998. Financial Dependence and Growth, American Economic Review.</mark> 88 (1998): 559-586 Ru, H., 2018. Government Credit, a Double‐Edged Sword: Evidence from the China Development Bank. _The Journal of Finance_ , 73(1), pp.275-316.

<mark>Sapienza, P., 2004. The effects of government ownership on bank lending.</mark> _<mark>Journal of financial economics</mark>_ <mark>,</mark> _<mark>72</mark>_ <mark>(2), pp.357-384.</mark> <mark>Sharpe, S.A., 1990. Asymmetric information, bank lending, and implicit contracts: A stylized model of customer relationships.</mark> _<mark>The journal of finance</mark>_ <mark>, 45(4), pp.1069-1087.</mark> <mark>Stiglitz, J.E. and Weiss, A., 1981. Credit rationing in markets with imperfect information.</mark> _<mark>The American Economic Review</mark>_ <mark>, 71(3), pp.393-410.</mark> <mark>Von Thadden, E.L., 2004. Asymmetric information, bank lending and implicit contracts: the winner's curse.</mark> _<mark>Finance Research Letters</mark>_ <mark>, 1(1), pp.11-23.</mark> <mark>Yasuda, A., 2005. Do bank relationships affect the firm’s underwriter choice in the corporate-bond underwriting market?</mark> _<mark>Journal of Finance</mark>_ <mark>60, 1259-1292.</mark> 18
private sector as a percent of GDP (World Bank FinStats, end-2019), and iv) adoption of the capital requirements of the Basel III framework (which include the creation of new buffers and the need for more and higher-quality capital) (World Bank Bank Regulation and Supervision Survey, 2016). However, the country sample size decreases significantly, so we report regression results in Appendix C as they need to be interpreted with more care.

# **4 Determinants of the Financial Sector Policy Response to COVID-19: Empirical Results**

# _4.1 Time until the first policy response is taken: Kaplan-Meier survival estimates_

Table 2 reports the descriptive statistics on the time taken (measured in days) to implement the first policy measure by category.<sup>14</sup> With a median of 50 days, _Banking Sector_ and _Liquidity and Funding_ measures were implemented faster compared to other categories. By the end of March 2020, around 21 percent of countries had taken their first measure in the _Banking Sector_ and _Liquidity and Funding_ categories. In contrast, by that time, around 10 percent of countries had implemented measures in the _Financial Markets and NBFIs_ and _Payments Systems_ categories.

**Table 2. Descriptive Statistics: Time Until First Policy Measure by Category (in days)**

|**Category**|**Countries**|**Mean**|**Std. Dev.**|**Min**|**Median**|**Max**|
|---|---|---|---|---|---|---|
|Banking Sector|154|56.5|31.8|2|50|216|
|Financial Markets and NBFIs|154|128.6|80.7|20|97|216|
|Liquidity and Funding|154|63.6|46.7|6|50|216|
according to the number of persons employed in the business (both part-time and full-time), and sector of economic activity was defined as the activity that is the main source of income for the business. Due to the difficulties of identifying and interviewing larger firms in the much smaller Machala and Tulcán, only firms with up to 10 employees were sampled in these cities. The survey was complemented by 24 focus groups and 10 interviews with entrepreneurs as well as workers in all four cities to allow for a greater variety of answers and a more in-depth discussion of reasons behind the observed behavior.

The sampling framework of the EMES was based on the 2004 national survey of urban micro-enterprises but the results also correspond closely to the recently completed economic census. The survey design was based on a 2004 survey sponsored by USAID under the project “Proyecto SALTO” (USAID, 2005), which collected information on close to 18,000 micro-entrepreneurs in low- and middle-income urban areas of Ecuador. Using this structure, the EMES sample was designed in such a way that sufficient observations to permit statistical analysis and hypothesis testing would be available along any of the three dimensions of firm size, sector, and city.<sup>2</sup>

# **2.2 Owner characteristics**

A large majority of entrepreneurs value the independence and flexibility of having their own business. The survey asked entrepreneurs to establish a ranking among seven different reasons for having their own business rather than being a wage employee. Figure 1 displays the percentage of firms that cite each of the reasons among the top three in their ranking. Consistent with other evidence from Latin America (see Perry et al., 2007), over 70 percent of firm owners cite the advantage of being one’s “own boss” as a top reason. Only around 30 percent of entrepreneurs mention the lack of wage employment, and only around 20 percent go into business mainly to avoid paying mandatory social security contributions (IESS). Similar sentiments were expressed in the focus
share of working‐age population)|512|0.04|0.03|
||Tertiary education (share of working‐age population)|512|0.01|0.01|
||Access to finance (share of total households)|512|0.33|0.14|
|Sil ili|Gender gap in secondary education (percentage point difference)|512|0.03|0.02|
|oca ncuson|Share of scheduled castes (share of total households)|512|0.13|0.23|
||Share of scheduled tribes (share of total households)|512|0.19|0.11|
||Crime rate (incidence per million people)|512|47.0|34.0|
|Governance|Labor regulation index (index)|512|0.03|1.20|
||Land reform index (index)|512|4.51|3.03|

_Source:_ Authors, based on Li et al. (2015).

_Note:_ The 21 largest states or union territories are Andhra Pradesh, Assam, Bihar, Chhattisgarh, Delhi, Gujarat, Haryana, Himachal Pradesh, Jammu & Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Orissa, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttaranchal and West Bengal. km<sup>2</sup> = square kilometer; m = meter; mm = millimeter; Rs = Indian rupees 16
market governance reforms

<!-- Start of picture text --> A. Equity market governance reforms B. Bankruptcy rights protection<br>Number of EMDEs Distance to frontier score, best=100<br>30<br>80 Advanced economy average<br>25<br>2018 2007<br>20 60<br>15<br>40<br>10<br>5 20<br>0<br>0<br>ECA EAP LAC SAR MNA SSA<br>Source: World Bank Doing Business<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. Number of EMDEs with an improvement in the score for minority investor rights protection from the previous year. B. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms that improve the business climate. EAP, ECA, LAC, MNA, SAR, and SSA include 19, 20, 30, 18, 8, and 46 economies, respectively. Advanced economies include 34 economies. Based on World Bank Doing Business report.
Cc. i <!-- Start of picture text --> B<br><!-- End of picture text --> B <!-- Start of picture text --> Bp<br><!-- End of picture text --> Bp Ki = exp(—fic,,) <!-- Start of picture text --> K,=(+e, y?<br><!-- End of picture text --> K,=(+e, y? o <!-- Start of picture text --> C5 log” +c,) log” +c,) +c,)<br><!-- End of picture text --> <!-- Start of picture text --> Beo<br><!-- End of picture text --> C5 log” +c,) log” +c,) +c,) Beo <!-- Start of picture text --> Log(X,,) = 4,Log(GDP) + a,Log(GDP) = 4,Log(GDP) + a,Log(GDP) 4,Log(GDP) + a,Log(GDP) + a,Log(GDP) a,Log(GDP)<br>SS _ i i<br>I<br>+ bLog(GDPpercap,) + b,Log(Log(GDPpercap,) + b,Log( + b,Log( b,Log( GDPpercap ,,) + c,domestic efficiency variables for i + c,domestic efficiency variables for i c,domestic efficiency variables for i efficiency variables for i variables for i for i i<br>+iO fj<br>tt Sos ro’<br>Ht<br>+d, Log(distance,,) + d,other separation variables +
Table 7: IV estimates of infrastructure provision (second-stage)

|||_Dependent var_|_iable:_|
|---|---|---|---|
||GVA per<br>capita (log)|GVA (log)|Population (log)|
||(1)|(2)|(3)|
|Electricity Access|_−_2.341<sup>_∗∗∗_</sup>|_−_1.366<sup>_∗∗∗_</sup>|0.975<sup>_∗∗∗_</sup>|
||(0.386)|(0.462)|(0.214)|
|Highway Access|_−_0.554<br>(0.496)|_−_0.406<br>(0.576)|0.148<br>(0.276)|
|Electricity Access X|3.372<sup>_∗∗∗_</sup>|2.898<sup>_∗∗∗_</sup>|_−_0.475|
|Highway Access|(0.951)|(1.110)|(0.532)|
|Wald Test _χ_<sup>2</sup>|3844.2<sup>_∗∗∗_</sup>|2577.3<sup>_∗∗∗_</sup>|2501.3<sup>_∗∗∗_</sup>|
|Observations|1,536|1,536|1,536|

_Note: Robust standard errors clustered at the state level in parentheses. ∗p<0.1,∗∗p<0.05,∗∗∗p<0.01._ However, these negative effects associated with the provision of electricity
services from the Global Findex database with individual-level demographic characteristics from the Gallup World Poll. We also utilize country-level indicators on laws and gender norms that may discourage or hinder women’s use of financial services. Because the later country-level variables show no variation across high income economies, we focus our regression analysis on a sample of up to 98 developing countries.

# **_3.1 Individual-level Characteristics_**

We control for several individual characteristics in our regressions, including dummy variables for gender, income quintiles, completed level of education, rural or urban residence, marital status, being head of household, and employment status. We also include a continuous variable for age and its squared term, and the household dependency ratio (children under age 15 as a fraction of total household size). Table 1 shows univariate summary statistics for all individual characteristics (disaggregated by gender) for the sample of developing countries used in our regression analysis. As in the descriptive statistics discussed above, we find that women are less likely to be financially included: they are less likely to have an account, exhibit lower rates of informal and formal savings and informal and formal credit. We also find that women are more likely to be poor, possess lower levels of education, head a single adult household, be divorced, separated, or widowed and out of the workforce. At the same time, women are less likely to be self-employed, formal business owners or employed by an employer. T-tests indicate that these differences between men and women are statistically significant at the 1 percent confidence level.

14
# **2. ANATOMY OF LATVIA’S LABOR MARKET**

Significant changes to the Latvian labor force have occurred since the transition to a market economy in the early 1990s, and subsequent stabilization. The Latvian population has declined substantially since 1990 and so has the labor force. At the same time, structural changes in the economy have led to labor reallocations between sectors of the economy. As in all transition countries unemployment increased during the first part of the 1990s. However, the economic recovery in the following years has allowed unemployment to stabilize.

This section is devoted to trends and issues in Latvia’s labor market, with a specific focus on demographics, employment, and unemployment. Using data from the labor force survey (LFS) we provide an overview of the evolution of labor force participation rates, employment/population ratios, employment composition, and unemployment rates by age and gender for 1997-2002. Latvia’s labor market indicators are compared with the ones found in other EU accession countries and selected OECD countries, as well as with the EU-15 average figures.

## **A. POPULATION TRENDS**

Effective policy making in Latvia is confounded by demographic trends and uncertainty about the extent of potential labor migration after EU accession which occurred in May 2004. While unemployment will remain a problem in the short to medium term, in the longer term labor shortage is a very likely scenario. The 2003 OECD report warns Baltic countries that “insofar as a possibly emerging scarcity of labor in the future would be unlikely to be offset by a steep rise in immigration or fertility, it will be all the more important to enhance the existing human capital and to ensure that it is productively employed” (OECD, 2003a).

Since the beginning of the transition to a market economy, Latvia has experienced a steep and sustained decline in total population. The population fell from about 2.67 million in 1990 to 2.33 million at the end of 2002 (a drop of about 12 percent or about 1
Yielding Insights: Machine Learning-Driven Imputations to Filling Agricultural Data Gaps<sup>∗</sup> Isma ̈el Yacoubou Djima<sup>†</sup> Marco Tiberti<sup>_†_</sup> Talip Kilic<sup>_†_</sup>

**JEL Codes:** C53; C55; C83; Q12.

**Key words:** Smallholder farming, Agricultural Crop Yields Measurements, Machine learning, Missing Data, Multiple Imputation, Household Surveys

> ∗The authors would like to thank (i) Ksenia Abanokova, the participants of The Ninth International Conference on Agricultural Statistics (ICAS IX) for their comments, (ii) the Statistics Unit of the Ministry of Agriculture in Mali (CPS/SDR) for the successful implementation of the agricultural surveys that this study leverages, (iii) Giulia Ponzini who co-lead the technical support to CPS/SDR for the implementation and the data curation of the surveys data used for this study. We are grateful for the funding from the Mali Mission of the United States Agency for International Development (USAID) for the implementation of the LSMS–Integrated Surveys on Agriculture (LSMS-ISA)-supported surveys in Mali. This paper was produced with the financial support from the World Bank LSMS Program (worldbank.org/lsms) and the 50x2030 Initiative to Close the Agricultural Data Gap (50x2030.org), a multi-partner program that seeks to bridge the global agricultural data gap by transforming data systems in 50 countries in Africa, Asia, the Middle East and Latin America by 2030.

> †Living Standards Measurement Study (LSMS), Development Data Group, World Bank.
to address scarce water supplies. During seasonal shortages, higher marginal cost prices should be used to ration all of the water and to recover fixed costs during peak demand (Seagraves and Easter, 1983). Many informal allocation systems have developed in the absence of prices or formal markets to address scarcity. These traditional, communal arrangemerts have often operated successfully for many years, but may not be efficient or equitable: _warabandi_ system in Pakistani (Easter and Welsch, 1986) and India (Perry and Narayanamurthy, 1998), _subaki_ system in Bali (Sutawan, 1989), and the _entornador-entornador_ system in Cape Verde (Langworthy and Finan, 1996). When flows are uncertain, shares rather than volumes of water can be allocated to individual farms. When these shares are tradable, efficient allocations can be achieved by equating marginal values across users (Seagraves and Easter, 1983).

Another mechanism to cover scarcity ccosts is the introduction of a fixed charge to balance the budget of the CWA. In this manner the short-run efficiency of marginal cost pricing can be extended (using a two-part tariff method) to account for long-run fixed cost considerations. Similarly an annual _Pigouvian_ tax can be used to manage scarcity. This avoids distortionary affects of other taxing forms and is therefore capable of achieving long-run efficiency (Laffont and Tirole, 1993; Tsur and Dinar, 1995). Examples of endogenously determined water prices under uncertain supply can be found in rsur (1990, 1997), Tsur and Graham-Tomasi (1991), and

> 15 Interesting to note are the advances in satelliti, remote sensing methods to collect irrigation data in a cost effective manner. An example for India can be found in Thiruvengadachari and Sakthivadivel (1997). These may further reduce costs associated with asymmetric infonriation.

13