rafmacalaba/data-use-mentions-extended huggingface dataset:
environmental hygiene and domestic health management practices, so that children are less exposed to disease and its consequent toll on child growth.<sup>14</sup>

# **3. Data and Definitions**

This paper uses the data from the two rounds of the National Family Health Surveys (NFHS) conducted in India during 1992/93 and 1998/99. The surveys cover all the states of India (with the exception of Sikkim in 1992/93, a total of 26 states at the time of survey), and the survey samples are designed to ensure that the data is representative at the state level.<sup>15</sup> Both surveys target about 90,000 households each, and approximately the same number of ever-married women was interviewed. NFHS-1 (92/93) and NFHS-2 (98/99) use three types of questionnaire: the Village, Household, and the Woman’s Questionnaire.

The Village Questionnaire collected information on the availability of various facilities in the village and amenities such as electricity and telephone connections, and type of the drainage system. Respondents to the Village Questionnaire were also asked about development and welfare programs operating in the village. In particular, the Village Questionnaire identifies the villages that received the Integrated Child Development Services (ICDS) Program.

The Household Questionnaire includes information on age, sex, education, employment status, occupation, marital status, and relationship to the head of the household for each household member. It inquires about household dwelling conditions and the ownership of various assets. Information is also obtained on religion and caste/tribe of the household head. At the same time, the Household Questionnaire does not include any direct measures of household income or consumption expenditure.

The Woman’s Questionnaire gathers information from all ever-married women age 15-49 who were usual residents of the sample households or visitors who stayed in the sample households the night before the interview. The questionnaire collects information about woman’s education, age at marriage, reproductive behavior,
WPS3681

# **Productivity, Ownership and the Investment Climate: International Lessons for Priorities in Serbia**

## **Itzhak Goldberg, Branko Radulovic and Mark Schaffer**<sup>**1**</sup>

### _Abstract_

_This paper uses data on 27,000 firms from 50 countries, half of which are transition economies, together with the specific case of Serbia to examine the relationship between productivity, the investment climate and private ownership of firms. As government capacity to address the investment climate constraints is limited, the prioritization of the constraints is critical. Identification of the relative effects of various investment climate constraints and ownership on productivity should serve as a guide for such prioritization._ _Although ownership has recently received less attention in policy decisions than previously, according to the econometric analysis of productivity reported in the paper, private ownership is an equally or more important determinant of productivity than other components of the investment climate. The importance of ownership shows that an unfinished privatization and restructuring agenda might have negative effects on productivity, in parallel to poor investment climate. Another important finding is that countries in which firms complain more about infrastructure tend to have less productive firms._ World Bank Policy Research Working Paper 3681, August 2005

> _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
gaps across workers can become smaller over time because different departments in firms start paying more similar wages. The importance of intra-firm wage differences and their contribution to changes in wage inequality are not directly disentangled in the AKM models. To tackle this question, this paper examines the role played by the variability in earnings between and within firms in each country, by considering that in year _t_ , the wage of worker _i_ employed by firm _j_ equals the economywide average wage plus the deviations between the average wage of the worker’s firm and the economy (employer deviation), and the deviation between the worker’s wage and the average wage of the worker’s firm (worker deviation). More formally, as in Song et al. (2018) and Alvarez et al. (2018) for the United States and manufacturing firms in Brazil, respectively, this paper identifies and take variances on both sides of the equation, obtaining The results are summarized in figure 10.<sup>27</sup> The figure plots the evolution of the between and within components of the variance of log earnings for Brazil (panel a), Costa Rica (panel b), and Ecuador (panel c). The between-firm component fell sharply in Brazil and Ecuador and increased in Costa Rica. The variance of wages between firms explains a large share of the changes in the total variance of wages. Specifically, 75 percent of the decrease in wage inequality was associated with a reduction in inter-firm wage differentials in Brazil, and 99 percent in Ecuador. In Costa Rica, 80 percent of the increase in the variance of wages is explained by inter-firm differences in pay.<sup>28</sup>

> 27Table S2.13 in supplementary online appendix S2 presents the detailed results.

> 28These results also hold within sector-occupations in Brazil, the only administrative data set used in the section that contains information on occupations. Decomposing the variance within-sector-occupations into between- and within-firm components (as in Helpman et al. 2017,
the Luhansk oblast. These estimates are driven by the (ex ante) perceived welfare shocks by economic agents, which trigger migration decisions, and they should be interpreted as the lower bounds of the welfare impact. When conflicts affect welfare in non-conflict areas significantly or boost mortality significantly,<sup>2</sup> thereby distorting migration numbers, the actual impact can be larger.

To our knowledge, this paper is the first to estimate the economic impact of war by using preference-based approaches inferred from migration patterns. There is a large and growing body of research focusing on the economic and social impact of wars.<sup>3</sup> This includes a strand that uses synthetic control methods to estimate the GDP impact of war, starting with Abadie and Gardeazabal, 2003.<sup>4</sup> There is also a diverse body of research that uses various methods of inference to measure conflict-driven impact on health and experienced well-being (Clark et al., 2020 and Bendavid et al., 2021), on macroeconomic indicators like GDP, investments, and fiscal flows (Edwards, 2014, and Auray and Eyquem, 2019), and on international trade flows (Glick and Taylor, 2010). Among the latter group, Korovkin and Makarin, 2019 estimate the impact of the conflict in Eastern Ukraine on Ukrainian firms’ trade with the Russian Federation between 2013 and 2016 by exploiting the spatial variation of pre-conflict Russian-speaking population as a proxy in a difference-in-difference setting. Unlike our approach, however, these studies limit attention to a subset of welfare components – often pecuniary ones only. In the international trade literature, Artu ̧c et al., 2010 estimate the welfare generated by mobility using labor flows, and Arkolakis et al., 2012 calculate income gains from trade using trade flows. Both studies use inversion equations similar to Hotz and Miller, 1993, that are related to the inversion equation in our approach. Different from them, our method is implemented using partially observed outflow data, instead
rabbits. Therefore, livestock sales do not appear to be a coping mechanism that is widely available to Sahelian households either.

## Section 3.3. Social assistance in the Sahel

Few households receive food or cash transfers, although in-kind benefits are more widespread. Distribution of food, cash, or in-kind benefits may further help to smooth consumption between the lean and non-lean season. However, for the survey period, only around 10.5 percent of Sahelians live in a household that received food during the previous 12 months, and 3.6 percent live in a household receiving cash transfers (Figure 5). Yet a much larger share of the population receives in-kind support in the form of care for children under 5 and bed nets. These in-kind benefits may help boost longer-term efforts to build human capital, but they may be less likely to support households in coping with short-term shocks and seasonality.

**Figure 5. Share of the population receiving support from different social safety nets, by country**

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Food Non-food<br>Burkina Faso Mali Niger Senegal Chad All countries<br>Percent<br>Cereals Food for work Any food Public works women Any non-food<br>Flour from cereals government<br>Supplements for<br>Food for school students malnourished children Cash transfers from the Program for pregnant Care for children under 5 Distribution of bed nets<br><!-- End of picture text --> Note: Individual-level sampling weights applied. Statistics capture whether any household member received that type of support during the previous 12 months. Source: EHCVM and World Bank estimates.

These descriptive statistics show that Sahelian households’ mechanisms for managing and coping with the effects of seasonality may be limited. All other things equal, this could leave households exposed to large seasonal swings in welfare.
>Reference for preindustrial level (1850-1900)<br>0.0<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br>Temperature anomaly (°C)<br><!-- End of picture text --> _Source: Copernicus Climate Change Services (C3S) Data Store._ This study examines the uneven effects of exposure to temperature anomalies on firm-level productivity.<sup>2</sup> We use data from the World Bank Enterprise Survey (WBES), which has more than 190,000 observations covering 154 countries

> 1Some estimates put the increase in temperatures between 40 _C_ , and 60 _C_ (Houghton, 2004; World Bank, 2013)

> 2The US National Aeronautics and Space Administration defines temperature anomaly as “the difference in temperature from an average or baseline.” The baseline temperature is typically computed by averaging 30 or more years of temperature data.
# **1. Introduction**

HIV/AIDS is the leading cause of adult mortality in Africa. In Mozambique, 12.5% of adults were HIV positive in 2008 (UNAIDS/WHO, 2008). The proportion of HIV positive patients with an advanced infection receiving HAART (Highly Active Antiretroviral Treatment) in Mozambique was 24% in 2007 (UNAIDS/WHO, 2008). Adherence to treatment is a key determinant of treatment success and is crucial in avoiding the development of resistances. Antiretroviral therapies need to be taken at least once a day and need to be taken after a meal to avoid side effects. Good nutrition is therefore an essential element in ensuring adherence and treatment success among HIV/AIDS patients. The food crisis that culminated in 2008 constitutes a major risk by reducing access to food for HIV/AIDS patients who generally are already among the most vulnerable groups in the communities. In addition, even if the antiretroviral medicines are generally subsidized, access to treatment is costly in terms of travel to the health facility and opportunity costs for patients and their households.

The aim of this paper is twofold. First, we try to evaluate the impact of the food crisis on households living with HIV/AIDS. In particular, our estimates will give an indication of whether these households are more vulnerable than others in times of economic crisis. Second, we assess whether the change in welfare of households, potentially caused by the crisis, affects their health status and adherence. We use data from a longitudinal survey carried out in Mozambique in 2007 and 2008, which was designed to include households with HIV positive individuals as well as comparison households with no identified HIV positive members.

At the global level, food grain prices have risen by 150% between January 2006 and June 2008, with about 40% of that rise that occurred in just the first half of 2008 (World Bank, 2008) _._ Ivanic and Martin (2008) have estimated that, as a consequence of higher global food
. The considerable increase in the R-squared measure in all regressions indicates the predictive power of commune size independently of nightlights and confirms that commune boundaries are endogenous.

# **3.2.2 Landsat Spectral Indices**

The focus is now on the question whether Landsat band values have similar predictive power for ground truth data of economic activity as nightlights. Since Landsat bands measure the reflectance of light of a certain wavelength and are difficult to interpret numerically, the 12
forced to balance by making arbitrary adjustments on both sides. This can give rise to misleading inferences about the incidence of commodity price changes on the poor, in particular, for the same reasons mentioned above.

Data reconciliation between household surveys and national accounts is also required on the income side (Robilliard and Robinson, 2003). Indeed, due to under-sampling of wealthy households, and under-reporting of income, it is not uncommon for total income reported in a household survey to be less than half that reported in the national accounts. In addition, the under sampling/reporting problem tends to be more severe for capital, as opposed to labor, and for non-farm activities as opposed to agriculture (Ivanic, 2004). Thus a failure to correct for this problem can result in two very different looking economies, depending on whether one uses the survey or national accounts data. Consequently, estimates based on the household survey data will tend to be excessively intensive in

> 13 Chen and Ravallion take an exogenous margin into account when computing their consumer prices.

29
including expenditure amounts matters.

The final rows of Tables 3 and 4 report the population-weighted mean rainfall, across districts, in terms of deviation from historical district means. Monsoons in India typically occur between June and October, making the third quarter rainfall important for agricultural production. Table 4 indicates that in rural areas, rainfall was below average in the second half of 2004, the third quarter of 2009 and the first two

> 24While the recall periods are the same and most of the categories of transportation expenditure are similar across surveys, in the NSSO72 the surveyors were asked to exclude expenditure on fuel for one’s own transport. The consumption expenditure surveys do include expenditure on petrol and diesel for vehicles. Also, in the NSSO72 survey questions regarding transportation expenditure were asked separately for overnight and non-overnight journeys. We include the expenses for both types of journeys as well as expenditure on services incidental to transport to make it comparable to the consumption expenditures surveys where the questions do not make any distinction on the type of journey and also include expenditures on services incidental to transport.

9
<!-- Start of picture text --> RS/I<br>> N " 6.5<br>aod N Ww<br>y ry ~ p<<br>Mato Grosso<br>=<br>Goias<br>Mato Gross...<br>Sao Paulo cA<br>Parana<br>- © Microsoft,Powered by OpenStreetMap Bing<br><!-- End of picture text -->
Ceola, Serena, Francesco Laio, and Alberto Montanari. 2014. “Satellite Nighttime Lights Reveal Increasing Human Exposure to Floods Worldwide.” _Geophysical Research Letters_ 41 (20): 7184– 90. doi:10.1002/2014GL061859.

- Chinh, DV, Li JianCheng, and Thi Kien Trinh Bui. 2014. “Estimating the Design Values of Sea Level Heights at Some Tidal Stations along the Coast of Vietnam.” International conference on GIS and Resource Management.

- De Lay, Suzanne. 2011. “Slums in Ho Chi Minh City, Vietnam.” Global Cities 2011.

- http://hochiminhcity2011.jimdo.com/slums/.

- DHI. 2003. “MIKE 11 – A Modelling System for Rivers and Channels. Short Introduction Tutorial.” Water and Environment.

- Fay, M. 2005. _The Urban Poor in Latin America_ . Directions in Development ‐ General. The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/0‐8213‐6069‐8.

- FIM. 2013. “Final Report, Volume 2, Integrated Flood Management Strategy.” Ho Chi Minh City Flood and Inundation Management.

- Geographic Information Science and Technology. 2015. “Landscan Population Data.” Oak Ridge National Laboratory.

- GFDRR. 2015. “Country Profile ‐ Vietnam.” https://www.gfdrr.org/sites/gfdrr/files/region/VN.pdf.

- Habitat for Humanity. 2008. “Mapping Urban Poverty in Ho Chi Minh City.”

- http://content.yudu.com/Library/A1gwji/MappingUrbanPovertyi/resources/8.htm.

- Hallegatte, S., A. Shah, C. Brown, R. Lempert, and S. Gill. 2012. “Investment Decision Making under Deep Uncertainty–application to Climate Change.” _World Bank Policy Research Working Paper_ , no. 6193. http://papers.ssrn.com
consumption.<sup>22</sup>

# **6. Conclusions**

Using a new, large, and representative household finance data set for China, we examine how access to informal finance affects household consumption. We pay particular attention to how participation in two distinct social groups, the family and the Communist Party, affects access to finance and consumption. We first document that use of informal credit is more prevalent among economically disadvantaged households in general, but especially among households with more extensive private social networks. Use of credit from formal sources is not strongly linked to the extent of private social networks, though it is positively linked to affiliation with the Communist Party for rural households.

We then present evidence that access to informal finance significantly increases households’ consumption, especially their long-term consumption. For rural households, only long-term consumption increases with access to informal finance; for urban households, daily consumption drops while long-term consumption increases with access to informal finance. We address the potential endogeneity of informal finance using the number of siblings of the household head (and his/her spouse) as an instrument. We confirm that having a more extensive family network enables greater access to informal credit. Consistent with our priors, the instrumental variable estimates of the effect of informal finance on consumption are positive, significant and substantially larger than the OLS estimates.

We also find that affiliation with the Communist Party is positively associated with consumption, though the result is more pronounced for daily consumption in rural areas and long-term consumption in urban areas. The effects of party affiliation on consumption appear to work independently of its effects on access to formal credit, however. In that sense, the social capital associated with party affiliation does not seem to influence formal credit markets to promote household welfare. Our results also indicate that a more extensive family network boosts access to informal finance and

> 22 In the models in both Tables 9 and 10, controlling for the log of formal loan amounts produces results qualitatively very similar to those presented.

21
farm wage employment. Second, when looking at the local and temporary migrant employment separately, cadre households are more likely to have a family member employed locally but _less_ likely to be employed as migrants.

**_To What Extent do Cadre Subsidies Drive the Off-Farm Income Result?_** The income return to cadre households might be driven by the possibility that cadres receive wages or compensation from their administrative position while non-cadre households do not. Then the higher incomes of cadre households are mechanically related to the additional job that is performed. To address whether the income return reflects the contract that the cadre may have with the village, the most direct solution would be to further break down employers and sources of income. The design of the RCRE survey makes it difficult to separately observe the cadre wage. Specifically, additional wage income earned in a household with a cadre comes from two non-overlapping sections of the survey. The first is found in the local wage income category which includes compensation and subsidies from serving as a cadre. The second section with relevant information is in the ―transfer from government category‖, which includes compensation and subsidies from government treasury for being a cadre.<sup>25</sup> As the compensation and subsidies from village coffers and the government treasury are lumped together in the RCRE survey with _other incomes_ in the local wage income category and the transfer from the government treasury

> 25 Essentially these two components distinguish regular wages from cash and in-kind subsidies. The incomes for being a cadre are commonly called compensation and subsidies rather than wage because cadres do not work a fixed number of hours, but take time for village affairs besides managing their own family economic activities.

24
Policy Research Working Paper 10964

# **Abstract**

This paper addresses the challenge of missing crop yield data in large-scale agricultural surveys, where crop-cutting, the most accurate method for yield measurement, is often limited due to cost constraints. Multiple imputation techniques, supported by machine learning models are used to predict missing yield data. This method is validated using survey data from Mali, which includes both crop-cut and self-reported yield information. The analysis covers several crops, providing insights into the importance of different predictors, including farmer-reported yields and geo-spatial variables, and the conditions under which the approach is valid. The findings show that machine learning-based imputations can provide accurate yield estimates, especially for crops with low intercropping rates and higher commercialization. However, survey-to-survey imputations are less accurate than within-survey imputations, suggesting limitations in extrapolating data across different survey rounds. The study contributes valuable insights into improving cost-efficiency in agricultural surveys and the potential of imputation methods.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at iyacouboudjima@worldbank.org; mtiberti@orldbank.org; and tkilic@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent