rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. What drives the skill content of jobs across countries?**

# **a. Extending the sample to other countries**

This section explores the drivers of the changing skill content of jobs by assigning the task content of each occupation estimated using the STEP surveys and O*NET, to the corresponding occupations in the I2D2 data set. We exclude the agricultural sector and agricultural workers from the I2D2 data set since they are under-represented in the STEP surveys.

This exercise relies on the assumption that the task content of occupations of developing countries not covered by STEP surveys is similar to that of those with a STEP survey. While the data do not allow to test this assumption, we proceed by first checking if the basic correlations between the skill indexes and the level of GDP per capita still hold for the extended sample. Figure 4 and Figure 5 show the correlation of the skill content of jobs with the level of economic development, using the O*NET- and STEP-based methodology, respectively. The patterns are very similar to those of Figure 1 and Figure 2, respectively. The non-routine cognitive and interpersonal task intensity tends to increase with the level of GDP per capita, while routine manual task intensity tends to decline. Accordingly, the non-routine manual content tends to increase with GDP per capita according to the STEP-based indexes, while the opposite is true for O*NET-based ones. While according to the O*NET-based index the routine cognitive intensity increases with the level of development, it declines when using the STEP-based indexes, which is more consistent with the findings for developed countries and the prediction that new technologies are more likely to displace this type of occupations. Since poorer countries are more likely to have a skill content of jobs more similar to that of other poorer countries than to that of the US, these correlations suggest that using STEP-based indexes provides more accurate estimates of the skill content of jobs for developing countries than US-based ones.

14
diaries provide an error-free benchmark has little statistical justification. Diaries arguably minimize various types of reporting errors, including recall errors, telescoping, rule of thumb, and errors of omission (Lyberg and Kasprzyk, 2004). However, diaries are far from perfect. Most diary surveys are characterized by differences in reporting within the interview period, usually with a declining pattern over time (Silberstein and Scott, 2004). Possible explanations are not only declining cooperation due to fatigue but also the tendency of participants to deviate from the usual purchasing behavior as an effect of the diary or social pressure (Peterson Zwane et al., 2011). The proxy reporting of events experienced by persons other than the respondent yields figures different from those obtained from individual diaries (Beegle et al., 2012). The ratio between food totals computed from household surveys using diaries and national accounts have been found to be considerably low (Gieseman, 1987, and Bee et al., 2013). Moreover, diaries are expensive, as completion involves a reasonable standard of literacy and commitment, and regular visits from interviewers to ensure quality standards boost costs.

Faced with high costs of implementation, respondent fatigue, the need for enumerator effort, and the risks of enumerator shirking, many developing countries have moved to collecting recall data.<sup>1</sup> In recall interviews, households are typically asked to report on food _consumption_ during the specified period and

> 1The World Bank’s Living Standards Measurement Study (LSMS) survey finder includes a list of household consumption and expenditure surveys: of almost 90 surveys from more than 25 countries, more than 75 use recall. See also the guidelines on the use of recall interviews endorsed by the United Nations Statistical Commission (FAO and The World Bank, 2018).

1
foreign direct investment in business services with Dixit Stiglitz effects is as important for assessing household impacts.

We describe the model, algorithm and data in sections 4.2 and 4.3. Results are presented in section 4.4. We show the results of piecemeal and systematic sensitivity analysis in section 4.5. Also in section 4.5, we assess the estimation error in the sequential approach and suggest conditions under which the sequential approach may be a good approximation for our integrated approach.

## **4.2. The Model**

Since we have described the structure of that single representative agent model in section 3, we only briefly describe the structure of the representative model here. Rather we focus on the features of the model that are necessary to generalize the model to 55,098 households.

# **Household Consumer Demand**<sup>42</sup>

Based on the data work described below, we aggregate individuals within each of the 55,098 households to obtain household factor income shares, expenditure shares on the commodities in our model and transfers between the household and the government and savings. We assume each household maximizes a Cobb Douglas utility function of the aggregate 35 goods in our model subject to its budget constraint (which is factor income net of transfers). Each of the 35 aggregate commodities is a CES (―Armington‖) aggregate of imported goods or services and goods or services produced in Russia. In imperfectly competitive goods sectors, imported and Russian produced goods are Dixit Stiglitz aggregates of the outputs of foreign or Russian firms. (Since consumer demand is analogous to firm level demand, the structure is depicted in figure 3.1 under ―composite intermediate

> 42 For a mathematical treatment of the algorithm we have developed see the appendices to Rutherford, Tarr and Shepotylo (2005). We also provide a graphical interpretation there to intuitively explain the algorithm, and why it allows us to integrate all households from any household budget survey into a general equilibrium model
and governance (Klapper et al., 2010; Klapper and Love, 2011a and 2011b). The literature also considers the relationship of the legal and regulatory environment on firm entry and growth. For instance, Klapper et al. (2006) provides a comprehensive picture of entry rates across high- and middle-income countries in Europe (using a firm-level database of registered firms) showing that onerous entry regulations hamper the creation of new firms, especially in industries that naturally should have high entry. Furthermore,

> 5 See Caves (1998) for a thorough survey of the literature on job turnover and firm mobility.

> 6 There is also a substantial literature on entry into an industry (possibly by a firm from another industry) as distinguished from firm creation or entrepreneurship; see Gilbert (1989) for a comprehensive survey.

4
yellow (2.5 square meters between students) and orange (4 square meters between persons); and finally, municipalities classified in red were only allowed to conduct virtual schooling activities.

We employ a difference-in-differences strategy given that we have administrative records for all registered students in the education system over time. Therefore, we construct a panel that tracks the same students over 2018-2022, providing two pre-COVID-19 and two post-COVID-19 years. These data contain information on students, their schools, and their municipality of attendance. We believe these records are thus well-suited to estimate the effects of differential municipal exposure to COVID-19 infections on student-level educational outcomes in the shortterm.

Results show that that locations with greater exposure to COVID-19 infections had greater educational losses. Higher values of the COVID-19 warning index increased dropout rates between 0.7-0.8 percentage points across the Guatemalan education system, an increase of about 8.1% compared to the pre-pandemic average. We also see a reduction of 2.4% in effective promotion rates<sup>6</sup> and an increase in movers from private to public schools of 10% compared to pre-pandemic levels, with a corresponding reduction in the other direction (public to private schools) of 6.3%.

We explore further heterogeneity in two dimensions: over time and by educational level. Effects were stronger during the first year of the pandemic: dropout increased by 10.5% in 2020 and 5.8% in 2021; promotion fell by 3.4% and 0.7%; switching from private to public schools increased by 0.2% in 2020 and was unchanged in 2021, while the reverse direction shows a decrease of 0.2% in both years. We reject the null hypothesis that estimated effects are equal in 2020 and 2021 across outcomes and specifications. We also find greater losses for younger children from higher COVID-19 exposure. Effects for most outcomes are significant for initial

> 6 Effective promotion is a binary indicator that is equal to one if a student
literature that this may be due to poor indicators of school price (Grootaert and Patrinos, 1998).

We follow a different route. We examine how parents' choices between sending their kids to school versus work in rural Bangladesh are affected by the Food-for-Education (FFE) program. The program aims to keep the children of poor rural families in school. In 1995-96, 2.2 million children participated (13% of total enrolment). Participating households receive monthly food rations as long as they send their children to primary school. Targeting is done in two stages. First economically backward areas are chosen by the center. Second, community groups -exploiting idiosyncratic local information - select participants within those areas. From the 1995-96 Household Expenditure Survey (HES) done by the Bangladesh Bureau of Statistics (BBS), the mean amount of rice received under the FFE program was 114 kg per year per participating household. Based on the same survey, we estimate that the average price of rice paid by the poor in 1996 was 12.5 Tk per kilo in rural areas. That translates in an average monetary value for the FFE stipend of 119 Tk per month. A separate BBS survey in 1996 found that the average monthly income of boys in paid work was 464 Tk while it was 291 Tk for girls (BBS, 1996, Table 5.11, p.53). Given that there are on average about two children of primaryschool age in participating households, the value of the FFE stipend is about 13% of the average monthly earnings of boys and 20% of that for girls.' To receive the stipend, children must attend at least 85 percent of all classes each month. The headmaster of the school monitors school attendance and the food distribution is made within the school each week. The schools submit estimates of their grain needs to the local district headquarters, which then takes charge of transport, distribution, and handling.

7 These are probably underestimates, since there is anecdotal evidence that FFE rations are
br>Outside Group 1; and<br> The minimum and maximum values do not fall within the beginning and<br>ending decades; and<br>The maximum value is more than double the size of the minimum value.<br><!-- End of picture text --> <!-- Start of picture text --> Group 3: Stagnant (Plain)<br>Outside Group 1 and 2; and<br> The maximum value is less than double the size of minimum value<br>throughout the six decades.<br><!-- End of picture text --> _Note:_ The value(s) in the diagram refer to real GDP per capita (at chained PPPs in 2017 US$) from Penn World Table version 10.01 (Feenstra, Inklaar and Timmer 2015).
, Peter Lanjouw, and Berk Özler. 2008. “Local Inequality and Project Choice : Theory and Evidence from Ecuador.” _Journal of Public Economics_ , 92(5-6), pp. 1022-46.

- Baird, Sarah, Craig McIntosh, and Berk Özler. 2009. “The Squeaky Wheels Get the Grease: Applications and Targeting in Tanzania’s Social Action Fund,” Working Paper, IRPS UC San Diego.

- Banerjee, Abhijit and Esther Duflo. 2003. “Inequality and Growth: What Can the Data Say?” _Journal of Economic Growth_ , 8: 267-99.

- Banerjee, Abhijit, Paul Gertler, and Maitreesh Ghatak. 2002. “Empowerment and Efficiency: Tenancy Reform in West Bengal.” _Journal of Political Economy_ , 110(2): 239-80.

- Banerjee, Abhijit and Lakshmi Iyer. 2005. “History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India.” _American Economic Review_ , 95(4): 1190-213.

- Banerjee, Abhijit, Dilip Mookherjee, Kaivan Munshi, and Debraj Ray. 2001. “Inequality, Control Rights, and Rent Seeking: Sugar Cooperatives in Maharashtra.” _Journal of Political Economy_ , 109(1): 138-90.

- Bardhan, Pranab, Maitreesh Ghatak, and Alexander Karaivanov. 2007. “Wealth Inequality and Collective Action.” _Journal of Public Economics_ , 91(9): 1843-74.

- Bardhan, Pranab, Sandip Mitra, Dilip Mookherjee, and Abhirup Sarkar. 2008. “Political Participation, Clientelism and Targeting of Local Government Programs: Analysis of Survey Results from Rural West Bengal, India.” Boston: Department of Economics, Boston University.

- Benabou, Roland. 1996. “Heterogeneity, Stratification and Growth: Macroeconomic Implications of Community Structure and School Finance.” _American Economic Review_ , 86(3): 584-609.

- Benjamin, Dwayne and Loren Brandt. 2002. “Property Rights, Labour Markets, and Efficiency in a

_Inequality and Growth in Rural China_ , Page 23
# I. Introduction

Understanding the rationale behind direct state participation in markets through state-owned enterprises (SOEs) is critical to the design of appropriate SOE reform policies. Not all SOEs are equal as they can participate in a diverse and wide range of economic activities in the economy. As evidenced in the novel WB Global Business of the State (BOS) database, there is a large heterogeneity in the sectors where the state intervenes as a market player. The economic costs and benefits from this intervention can also vary substantially depending on the type of economic activity where the SOEs operate.

This paper proposes how to classify economic activities where the state can be present as a market player based on their intrinsic characteristics and associated market failures.<sup>1</sup> Economic theory provides an efficiency-based rationale to identify instances (market failures) in which a non-profit-maximizing SOE would be able to pursue optimal policies (bringing benefits that improve overall welfare), provided that state capture<sup>2</sup> and other inefficiencies or distortions generated by public ownership (the costs) would not outweigh the benefits of SOE presence. Following the Industrial Organization (IO) literature that analyzes the exercise and control of market power (Tirole, 2014), this paper proposes how to classify the economic activities where the state can be present as a market player based on the intrinsic economic characteristics and market failures. In doing so, the paper contributes to filling the gap on how to connect IO research on the differences across markets and industry dynamics with the policy agenda related to the role of the state in markets.<sup>3</sup> The comprehensive taxonomy presented in this paper rationalizes the potential presence of SOEs at the level of disaggregated economic sectors and by doing so avoids a “one-size-fits-all” approach to analyzing the economic footprint of the state in markets. An efficiency-based rationale for SOE presence suggests heterogeneity of the companies with state participation across sectors, as they may be characterized by different technological and market features. Economic theory
collects just 47 non-food non-durable items, while Bangladesh and India include over 190 items, and Maldives 401 items. Maldives, Sri Lanka and Pakistan do not add durable goods in the consumption aggregate, but the rest of the countries do. Nepal and Afghanistan do not include health care expenditures, and India and Maldives do not include imputed rent of owner-occupied housing. Moreover, the methodology to collect consumption data also varies from country to country. For instance, Maldives does not include home production of goods in the national consumption aggregate, and Bangladesh, Pakistan and Sri Lanka do not include food/meals bought from outside the household as part of the national consumption aggregate.

Still, by far, the most important source of differences in the construction of consumption aggregates is the treatment of imputed rents. Except for Maldives, all the other countries in South Asia collect imputed rent of owner-occupied housing as part of the consumption aggregate. While India does not include imputed rent as part of its national consumption aggregate, it does collect that information for urban dwellers. We find that the absence of imputed rent of owner-occupied housing from the national consumption aggregates of India is the main factor that makes its proportion of international extreme poor the highest among South Asia.

Using the standardized consumption aggregate reduces the number of extreme poor in South Asia, and it also makes India appear equally poor as Bangladesh. While India has the largest proportion of international extreme poor when the national consumption aggregates are used to measure poverty, Bangladesh becomes the home to the largest proportion of international extreme poor in South Asia when standardized data sets are used. This is being driven by the most part by housing expenditures. The standardized data set includes imputed rent of owner-occupied housing in urban regions of India.

The choice of CPI also plays an important role in determining the number of poor. Each country deflates the international poverty line to align it with the year the survey was undertaken. Except for Bangladesh and Afghanistan, all the other countries use CPI to deflate the prices. The evidence
model 50 times, yielding 50 complete data sets, the distribution of which is intended to reflect the sampling variability. The average of these 50 predictions is taken to be the estimated value of a specific asset whose value was not reported by its owner.

Overall, the LSMS+ supported surveys have a rich set of individual, household and community characteristics — along with data on features of asset ownership — that were used as explanatory variables in our multiple imputation model that relies on predictive mean matching with 5 neighbors. The covariates that are used in the multiple imputation model are detailed in Appendix Table A2. These variables are strongly associated with asset valuation and factors related to the missing data generation process, and include survey design features in accordance with the guidance provided by Rubin (1996) and van Buuren et al. (1999). We provide a set of estimates that are obtained with no imputation and another set that is based on multiple imputation to recover missing asset values.

Related to the second source of measurement error in the creation of individual-level wealth aggregates, within each of the no-imputation and multiple imputation approaches, we assigned values to jointly owned assets in two different ways by either (a) retaining joint owners’ self-reports (hence ignoring the intrahousehold discrepancy) or (b) assigning the maximum asset value reported among any of the joint owners.

In total, then, we have four different versions of the individual wealth variable that allows us to understand how the distribution of wealth and trends in wealth inequality change depending on differences in sources of measurement error. There are namely: 18
Policy Research Working Paper 6331

# **Abstract**

The paper proposes a modification of Baulch’s parity bounds model to measure the market integration of food markets in developing countries. Instead of extrapolating a single observation of transaction costs, it estimates transaction costs. Predicted transaction costs compare well with survey data of traders. Probabilities of market regimes, computed on the basis of predicted transaction costs, fluctuate significantly and do not support fixed regime probabilities over time. The probability of market integration with trade decreases consistently during food shortages, increasing either the probability of no trade or loss-making trade or the probability of profitable but unexploited trade opportunities. The data support a negative trend in market integration with trade.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The author may be contacted at wouter.zant@vu.nl.

_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._ Produced by the Research Support Team
**<u>Table 9: Estimates of program effect on agricultural productivity using implementation data</u>**

||**All s**|**tates**||**8 st**|**ates with imp**|**lementation**|**data**||
|---|---|---|---|---|---|---|---|---|
||**MNREG**|**S dummy**|**Labor **|**days spent on **|**all MNREG**|**S works**|**MNREGs **|**water only**|
|MNREGS|0.021||0.071***||0.103**||0.127|0.242*|
|* post|(0.043)||(0.025)||(0.044)||(0.179)|(0.142)|
|<br>MNREGS *||0.121*||0.071***||0.104**|||
|post* small||(0.071)||(0.024)||(0.042)|||
|<br>MNREGS *||-0.017||0.066**||0.088*|||
|post* medium||(0.070)||(0.026)||(0.051)|||
|<br>MNREGS *||<br>-0.025||<br>0.079***||<br>0.124**|||
|
Policy Research Working Paper 10892 Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data _Ifeanyi Edochie David Newhouse Nikos Tzavidis Timo Schmid Elizabeth Foster Angela Luna Hernandez Aissatou Ouedraogo Aly Sanoh Aboudrahyme Savadogo_ Development Data Group & Poverty and Equity Global Practice September 2024
Sri Lanka’s Household Income and Ex-<br>penditure Survey (HIES), from DCS|
|**Wages,**<br>**land**<br>**ownership,**<br>**occupation,**<br>**household**<br>**characteristics**|**[23]** Household size (number of members), income, expenditure<br>patterns (food prices and quantities, fraction of income spent on<br>food), “income by chance or_ad hoc_gains” (including lottery and<br>disaster/relief payments)|2019|HIES|
||**[24]**Land ownershipand land distribution|2016|HIES|
|**Night Lights**|**[25]**Night light intensity at district-month level|January<br>2016-<br>December 2022|Visible and Infrared Imaging Suite (VI-<br>IRS) Day Night Band (DNB), from Col-<br>orado School of Mines|