rafmacalaba/data-use-mentions-extended huggingface dataset:
outcome, and which survey questions we used. While all outcomes are measured over the rainy season, Hill et al. (2019) also measured outcomes over the dry season. In this paper, we focus exclusively on outcomes over the rainy season because (i) data over the dry season are incomplete, (ii) insurance products only cover the rainy season, and (iii) payouts for the rainy season could influence production decisions over the dry season (in Bangladesh, rainy season payouts coincided with the planting of dry season crops). We standardize all outcomes to deal with slight differences in measurement across studies. For example, some studies collect fertilizer expenditures, while others collect only purchased quantities with no information about prices. We winsorize all outcomes at the 99th percentile to reduce the influence of outliers.

# **3.2 Covariates of interest**

Household-level covariates may be particularly helpful to understand what drives the heterogeneity in treatment effects. In addition, they may provide useful evidence to guide the targeting of insurance products and identify subgroups for which production decisions are more responsive to the provision of index insurance.<sup>23</sup> We examine the following covariates: a wealth index, household size, household head age, household head literacy, and predicted outcomes.<sup>24</sup> For continuous covariates, we divide households in two groups with respect to their median.<sup>25</sup> We derive the wealth index using the methodology developed by the DHS. We predict each of our five outcomes using an OLS regression with the following set of predictors measured at baseline: chemicals usage, cultivated area, land ownership, household head gender, literacy, education, age (and age<sup>2</sup> ), household size, wealth, livestock, bank account, distance to rainfall station, experience of past weather shocks, and risk preferences.<sup>26</sup> Table A2, taken from our pre-analysis plan
<!-- Start of picture text --> Year on year, by month (percent)<br>30 5<br>25 Ml Banks'' mortgage loans<br>20 5 .<br>—— Apartment purchase price index, Seoul<br>15 4<br>10<br>ie] T T T T T T T T | I I | T T T T T T T T T T T T T T T T T T<br>2007.1 7 2008.1 7 2009.1 7 2010.1 7<br>5 |<br><!-- End of picture text -->
and possible solutions to address these data needs that can come from improvements to longitudinal, multi-topic household surveys such as the LSMS-ISA. We here start by outlining the main data gaps and needs separately for migration and weather data.

**Migration data** . Until recently, there was a widespread lack of basic migration data, especially in developing countries which are more vulnerable to climate change (Laczko and Aghazarm, 2009). While the situation has improved, and macro-level and international migration data are now available for a wide range of countries, disaggregated and detailed data on internal migration remain unavailable or incomplete for many low-income countries (Beine and Jeusette, 2021). This is a paradox because we know from the literature discussed above that the migration effects of climate change will primarily concern poor people living in developing countries. The paradox can only be solved by scaling up

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the comprehensive wealth accounts published in World Bank (2011) – land values as a share of total wealth.

The physical indicators are clearly not the explanatory variables we are seeking – protected area per capita in developing countries is about 2/3 of the value in OECD countries, while there is little variation in the protected area share of total land area across the different income classes. The third indicator tells the story. Land values (excluding urban land) make up only 1.4% of the total wealth of OECD countries, while it is 10 to 20 times higher as a proportion of wealth in middle and low income countries. Since the opportunity cost of creating a protected area is based on the rents from agricultural land, this will necessarily be a much larger share of GDP in developing countries compared with OECD countries.

**Table 2.  Protected area extent and land values by income class, 2005**

||Low income|Middle income|OECD|
|---|---|---|---|
|Protected area per capita, ha.|0.22|0.21|0.34|
|Protected area as a percent<br>of total land area|10.8%|10.6%|11.8%|
|Land value (cropland,<br>pastureland, forested land,<br>protected areas) as a percent<br>oftotal wealth|29.5%|12.8%|1.4%|

Source: derived from World Bank (2011) and _World Development Indicators_ _Policy implications, and some caveats_ Protected areas and the biodiversity they harbor provide both local and global benefits. The institution that finances environmental global public goods is the Global Environment Facility (GEF), and it is worth assessing the level of finance that it disburses for biodiversity protection.

According to the GEF’s _Annual Report 2010_ , the total finance it provided for biodiversity conservation from 1991 to
>14</sup> Their informal nature constrained them from accessing credit from

> 13 Using self-employment as a proxy for informality, the OECD (2021) estimates that informal employment increased by 4.6 percentage points from 55 percent in 2019 to 59.6 percent in 2020.

> 14 A 2015 survey of micro and small manufacturing firms by BPS revealed that the median cash buffer of MSMEs in Indonesia could only cover about 11 days of operational costs. The survey also showed that about 80 percent of firms would not be able to survive for more than 30 days without income from operations (Temenggung, et al., 2021).

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coexist in the market. Nonetheless, little is known about what makes a program “good”—what is distinctive of high-quality programs—and, as result, SCP quality remains a “black box”.

This knowledge gap is largely due to a data gap, as researchers often lack information on student outcomes and, more importantly, on program practices (e.g., how programs relate to local employers) and inputs (e.g., facilities for practical training)—henceforth, quality determinants. Thus, in this paper we open the SCP quality “black box” to identify the quality determinants associated with good academic and labor market outcomes for students.<sup>4</sup> We adopt two complementary approaches. The first one is a regional (aggregated) analysis of program-level average academic and labor market outcomes for five countries in Latin America and the Caribbean (LAC). The second is a country-level analysis for two of those countries using individual-level data on graduation and labor market outcomes.<sup>5</sup> Underpinning both approaches is a new and unique survey, the World Bank Short-Cycle Program Survey (WBSCPS), which we designed and implemented in five countries in LAC, namely Brazil (in the states of São Paulo and Ceará), Colombia, the Dominican Republic, Ecuador, and Peru (for licensed programs). Together, these countries account for more than half of the SCP enrollment in LAC. The survey contains data from nationally representative samples of SCP providers for a total of 2,103 programs. It collects rich program-level information about quality determinants that are not reported in administrative datasets but might be associated with student outcomes (Bailey et al, 2015). We group them in six categories: training and curriculum (T&C),

> 4 Throughout this paper, “determinants” refers to practices and inputs that programs can choose. Other program or institutions characteristics (for example, institution’s age or public/private status) are
Policy Research Working Paper 9182

# **Abstract**

This study proposes a novel supply-side mechanism driving economic structural transformation: grid electrification. Increasing electricity availability affects the reallocation of inputs to more productive activities through generating higher returns and lowering entry costs in sectors with greater infrastructure intensity. The results of modeling and econometric analysis based on Brazil’s historical data over the period 1970‒2006 confirm that the manufacturing sector benefits the most in these two dimensions, followed by services and agriculture. The expansion of electricity infrastructure explains about 17 percent of this process and 32 percent of the observed increase in GDP per capita. Simulations of a multisector neoclassical growth model with heterogeneous firms help assessing the effectiveness of different electrification policies.

This paper is a product of the Development Research 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 jsteinbuks@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._ Produced by the Research Support Team
# **Data source and indicators**

This paper uses the Interim Enterprise Survey of South Asian countries by The World Bank (INTERIM ES). The survey was conducted in 2009 and 2010. The Enterprise Surveys of the World Bank generate establishment-level quantitative information. The stratification of the survey considers representativeness across firm size classes (large firms were oversampled), sector (primary, secondary and tertiary), and regions<sup>4</sup> . The survey is not representative at the industry level. The sample covers 744 observations in Afghanistan (2010), 500 in Bangladesh (2010), 250 in Bhutan (2009), 482 in India (2010), 368 in Nepal (2009), 440 in Pakistan (2010), 484 in Sri Lanka (2010).

The data set contains a rich set of variables. These include information on the survey country and region, industry (2-digit ISIC Rev. 3.1; clustered into 15 industry groups) and sector, information on whether a company invested into physical assets, firm age, and the size of the location. In addition, there is information on ownership structures, export activities, financial structures, and the perception of obstacles such as electricity, access to finance or corruption. Furthermore, the questionnaire of the Interim Enterprise Survey considers employment data of the survey year, as well as recall data from three years prior to the survey year.

The survey instrument that was implemented has two drawbacks. First, it does not contain enough information to conduct a productivity analysis at the firm level. Second, the data are cross sectional, i.e. it is not possible to track firms over time, or create a panel variable.

There are various indicators of employment growth, whose characteristics typically affect results and make the choice of indicators key to any analysis. Growth can be defined in terms of employment, sales and net assets. Each of these indicators is geared to a different aspect of firm growth. The choice of
# **Section 4 Conclusions and Policy Implications**

While a number of studies have examined the economic effects of tenure security on agricultural investment and productivity, this paper has followed Besley (1995) and broadened the scope of investigation to, include the transferability, as well as the security, of household residual income and use rights to land. The importance of transfer rights is likely to vary over time. When there is relatively little variation in factor endowment ratios across households (as in rural China following the household responsibility system reform of circa 1980), then trade restrictions should engender few costs. However, with industrialization, increasing complexity and household economic specialization, the potential for trade-and the economic costs of trade barriers-are likely to rise.

Contemporary China offers an interesting and important case to study the economic impacts of transfer rights alongside the more conventional tenure security effects. Industrialization has spread rapidly, if unevenly, across the country. Both over time and space rural China also exhibits notable variation in both tenure security and transfer rights, opening the way to the fuller investigation of the economics of property rights.

Using a theoretical model that presumes that agency costs block equalization of factor productivities through labor market transactions, this paper derived a number of specific hypotheses concerning the factor price equalization and investment regret mitigation effects of land transfer rights.<sup>24</sup> Panel data on a sample of farm households, analyzed with simulated maximum likelihood methods, revealed a number of interesting, occasionally surprising

> 24 It should be stressed that because it presumes an egalitarian economy that is not marked by the sorts of unequal financial market access that create what Carter and Zimmerman call "countervailing market failures" the model here may be a poor guide to the impacts of transfer rights in inegalitarian economies.

33
<!-- Start of picture text --> Figure 2: Occupations, by industry<br>100%<br>90%<br>Agriculture, Fishing, Forestry<br>80%<br>70%<br>Mining and quarrying<br>60%<br>Manufacturing 50%<br>40%<br>Electricity, gas and water 30%<br>20%<br>Financing, insurance, real 10%<br>estate, business<br>0%<br>Community, social, and<br>personal services<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from UNCOMTRADE and Jordan LFS 2017-2019.

Jordan experienced a transformation in the allocation of employment across sectors between 2017 and 2019. Among those workers exposed to international trade, the shares of workers in “ _Community, Social, and Personal Services_ ” increased year after year, reaching 76% in 2019, at the expense of employment in “ _Financing, Insurance, Real Estate, and Business Services_ ”, as well as in “m _anufacturing_ ” (see Figure 3).

<!-- Start of picture text --> Figure 3: Employment Composition, by aggregate sector (2017-2019)<br>100%<br>90%<br>Community, social, and personal<br>services 80%<br>Financing, insurance, real estate, 70%<br>and business services 70%<br>60% 73% 76%<br>Electricity, gas, and water supply<br>50%<br>Manufacturing 40%<br>30%<br>Mining and quarrying 7%<br>5%<br>20% 5%<br>Agriculture, hunting, forestry, and 10% 16% 14% 12%<br>fishing<br>5% 5% 5
through of taxes to informal prices.<sup>30</sup> The results are partially affected but our key findings remain. First, we find that scenario #1 (exemption of informal varieties) remains very progressive, with the richest quintile paying over 70% more in taxes than the poorest quintile on average. Second, the de facto exemption of the informal sector continues to be more progressive than de jure exemption of food (scenario #2), although the difference in progressivity has decreased. Third, the progressivity impact of the de jure exemption, conditional on allowing for informal consumption, remains smaller than that of the de facto exemption.

**Distributional savings rates** Our baseline results use total expenditures to proxy for household income, assuming households do not save. Intuitively, allowing for savings both decreases effective tax rates (as savings are not taxed) and decreases the progressivity of all tax scenarios if saving rates increase with income.<sup>31</sup> The distribution of savings across income levels is hard to obtain from expenditure surveys, especially in developing countries where income is hard to measure. To assess how savings could affect our results, we use data from the US Consumer Finance Survey, in which savings rates range from 0% for the poorest households to 15% for the richest quintile.<sup>32</sup> Results are presented in Table A6: allowing for distributional savings decreases the progressivity of all scenarios, as expected, but our main findings are unchanged.

**Alternative formality assignment** Finally Table A6 presents our progressivity results under the two alternative rules for of as formal or in- categorizing places purchases formal, as described in Section 2.2 (the probabilistic formality assignment based on the 2013 Mexican Census of retailers and the assignment of specialized stores to the informal sector). Our three main take-aways are unchanged.

> 30 The share of formal inputs used by informal firms is likely to be an upper bound. First, the 10
using WDI population data (panel b).

# _<u>5.2. Survey-to-survey imputation</u>_

This section presents the results of the survey-to-survey imputation into the 2010/11, 2012/13, 2015/16 and 2018/19 GHS. As detailed in Section 4.2, we impute consumption using information on 23 nonmonetary indicators available in the 2018/19 NLSS and all GHS rounds.

Before moving to the results, Figure _3_ shows the average value of each of these indicators over the four rounds of data used in the analysis (see also Table _8_ Table _8_ ). Several of these indicators show an improvement in household welfare over the first three periods, but then worsen after 2015/16, suggesting a reaction to Nigeria’s 2016 oil-price-induced recession. For example, ownership of assets (TV set, generator, cars and other vehicles) was increasing up until 2015/16 and but then registered a decrease in the last wave suggesting that households might be selling their assets to offset a negative shock to their welfare. A similar trend can be seen in the consumption of imported rice. This trend in itself is consistent with a decrease in poverty rates in the first half of the decade and a worsening in more recent years.<sup>18</sup>

> 18 In section 7, we provide additional evidence using data from the DHS that supports these trends in non-monetary indicators.

13
Luxembourg Income Study database. As an alternative source of country-level inequality, we exploit the Standardized World Income Inequality Database (SWIID). SWIID provides standardized Gini income inequality measures for market and net outcomes based on the same concept, and thus allows the comparison of income inequality before and after redistribution by taxation and transfers over time.

# **B3. Weather data**

We match our poverty and inequality data with the ERA5 satellite reanalysis data, which is taken from ECMWF. The ERA5 provides hourly estimates of several climate-related variables at a grid of approximately 0.25 longitude by 0.25 latitude degree resolution with data available since 1979 (Dell _et al._ , 2014). We use air temperature and precipitation, both measured as annual averages, and map the grid spacings in ERA5 to the country/region in our poverty datasets. We follow previous studies and aggregate the gridded data to the region level by computing area-weighted averages (i.e., averaging all grid cells that fall into a region) (e.g., Heyes and Saberian, 2022; Kalkuhl and Wenz, 2020). Figure B3 provides a distribution of average temperature in our sample. It shows that most regions in our sample belong to the temperature range of between 24<sup>◦</sup> C and 28<sup>◦</sup> C. Another dataset that we use in the paper is the global gridded CRU data which provides monthly estimates at 0.5<sup>◦</sup> resolution. The CRU data, however, is subject to absence of data in regions with less coverage of weather stations. Therefore, our main analysis exploits the ERA5 data which combines information from ground stations, satellites, weather balloons and other inputs with a climate model, and therefore is less prone to station weather bias (Auffhammer _et al._ , 2013).

To explore the potential impacts of future climate change on poverty and inequality, we use climate data from the Coupled Model
# Figure III: Payment of Tax Liabilities

## A: Income Tax

Average Payment Share of Net Liability, Income Tax <!-- Start of picture text --> 2006 2008 2010 2012 2014<br>Year<br>Self-Employed, cut 04-2015 Corporations, cut 04-2015<br>Self-Employed, cut 04-2013 Corporations, cut 04-2013<br>Self-Employed, cut 04-2011 Corporations, cut 04-2011<br>1<br>.95<br>.9<br>.85<br>Average Payment Share<br>.8<br><!-- End of picture text -->

## B: Sales Tax

<!-- Start of picture text --> Average Payment Share of Net Liability, Sales Tax<br>2009m1 2010m7 2012m1 2013m7 2015m1<br>Month<br>Self-Employed, cut 04-2015 Corporations, cut 04-2015<br>Self-Employed, cut 04-2013 Corporations, cut 04-2013<br>Self-Employed, cut 04-2011 Corporations, cut 04-2011<br>1.05<br>1<br>.95<br>.9<br>.85<br>.8<br>Average Payment Share<br>.75<br><!-- End of picture text --> Notes: This gure shows the average of the payment share, dened as the total payment made by the taxpayer for a specic tax period, divided by the nal tax liability to be paid for that period. The average is an unweighted average across all taxpayers with an positive nal liability for each scal period. The nal tax liability is net of any deduction made for tax withheld, and the payment data does not include tax withheld. The income tax data includes all declarations led and payments made by June 2015. The sales tax data includes all declarations led and payments made by October 2015. The blue series correspond to the self-employed and the red series corresponds to corporations. We show the series for three di
This research also contributes to the literature on the drivers of the skill content of jobs by using the World Bank’s International Income Distribution Dataset (I2D2). I2D2 covers more than 150 countries and several years of household survey data. By applying the skill-intensity measures to each occupation, we estimate cross-country regressions and find that the positive correlation between economic development and the intensity of jobs in non-routine cognitive skills weakens or disappears once other factors are accounted for. ICT adoption is consistently correlated with job de-routinization. The magnitudes of the estimated coefficients are also economically significant: An increase of 50 percentage points in internet penetration – roughly the increase experienced by developing countries since the early 1990s - is associated with a decline in the routine cognitive intensity of jobs equivalent to 42 percent of the decline in this measure experienced by Ghana since the 1990s. Higher levels of exports, in contrast, are accompanied by an increasing routinization of the labor market. These findings are robust to several specifications.

Interaction terms suggest that ICT adoption coupled with high population growth – the _demographic dividend_ - seem to be stronger predictors of the increase in the share of jobs intensive in non-routine cognitive skills. We also find that the change in the demand for skills associated with ICT is linked to labor market disruptions. ICT adoption is followed by lower employment growth in countries with a higher share of routine jobs, which are more susceptible to being replaced by this technology.

The rest of this paper is structured as follows. Section 2 contains a review of the literature and discusses the contribution of this paper. Section 3 describes the data, while Section 4 presents the methodology and the estimated trends in the skill content of jobs. Section 5 investigates the drivers of the skill content of jobs across countries, while Section 6 estimates the impacts of ICT adoption on employment. Finally, Section 7 concludes.

# **2. Literature review**

While the canonical model assumes a one-to-one link between skills and tasks, there is a rising body of literature that emphasizes the distinction between