rafmacalaba/data-use-mentions-extended huggingface dataset:
47)|
|Observations|678|678|678|678|
|R-squared|0.33|0.28|0.24|0.25|
|Mean dep. var. control (or T4)|0.33|100.38|121.73|98.46|
|SD dep. var. control (or T4)|0.47|151.12|185.92|157.18|
|T1: _βE_+_βI_+_βG_ =0|0.00|0.00|0.00|0.00|
|T2: _βE_+_βG_ =0|0.00|0.00|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.00|0.00|0.00|0.00|

Note: this table reports results willingness to pay for YARA fertilizers. Using the full sample of 678 farmers in our study, we report the point estimates of the following specification: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. In Column 1, the dependent variable is a dummy that takes value 1 if the farmer reported his or her willingnes to pay for any of the 3 YARA fertilizers (Urea, DAP and KCl). In Columns 2-4, the dependent variables are the self-reported willingness to pay for a bag of each of the 3 YARA fertilizers. We imput WTP equal
calls for a thorough assessment of the technology available and the messages that are being disseminated through public technical assistance. Unless these can be adapted more to the prevailing economic environment and the incentive structure facing producers, greater public spending on teclnical assistance may be difficult to justify.

From a methodological point of view, our results suggest that farm-level panel information can provide policy-relevant insights that avoid the difficulties inherent in the interpretation of crosssectional data. While the panel underlying our study was collected more by accident than by design, greater emphasis on collection of panel data, adoption of the necessary protocols to ensure data quality, and inclusion of other household characteristics (e.g. asset endowments) is likely to increase the usefulness of such data to yield policy relevant conclusions.

From a substantive point of view, our analysis supports the notion that without "re-emphasizing" the role of agriculture, it will be difficult for African countries to lay the basis for sustained growth and prevent a reversal of policy that would undo much of the progress and potential from recent liberalization. It is now recognized that, eYen though Africa faces specific challenges in the field of health, most of the stagnation of the rural sector has been policy-induced, that the gap between potential and actual output is greatest in Africa, and that demand is unlikely to become a constraint any time soon (McPherson 1999). This reinforces the importance of providing public goods, in addition to price policies, to bring about a sustained agricultural supply response (Schiff and Montenegro 1997). The fact that use of purchased inputs is way below the optimum, that credit constraints prevent investment in productivity-enhancing technology such as livestock, and that public services are not (yet) effective in delivering the needed technology, suggests that much remains to be done to address non-price related constraints to agricultural production, generated and disseminate technology, and thus help rural producers make better use of the resources at their disposal.
specializing in commodities – agriculture and mining (Hanson, 2017). These countries show low backward GVC participation as they predominantly export upstream GVC tasks with low reliance on imported inputs and fewer opportunities to innovate and upgrade (FernandezStark et al., 2011; Taglioni & Winkler, 2016; World Bank, 2020). They typically exhibit high forward GVC participation, namely a high share of domestic value added embodied in their direct partner countries’ exports (Borin & Mancini, 2015, 2019). As a result, higher forward GVC participation in commodity-exporting countries may be associated with a higher RTI, as upstream tasks in agricultural or small-scale mining GVCs are more likely to be routine-intensive.<sup>2</sup> High forward GVC participation is also a feature of countries specialized in innovative GVC tasks (World Bank, 2020), but its expected relationship with RTI contrasts that of commodity exporters. In innovative countries, high value-added upstream tasks, such as research and design services, make up a larger portion of their domestic value added that is re-exported by their bilateral trading partners. These tasks tend to be non-routine. These country examples illustrate that the relationship between GVC participation and RTI may vary across sectors and countries with different development levels and models. It may also differ between backward and forward GVC participation.

We make three key contributions. First, this study quantifies the relationship between GVC participation and worker task demand, which remains under-researched (Marcolin et al., 2016). Our PIAAC, STEP, and CULS survey data cover 47 countries at different development levels and types of integration into GVCs. We measure RTI at a worker level, applying the method proposed by Lewandowski et al. (2022). In the absence of direct

> 1 For instance, some East Asian countries that initially specialized in blue-collar jobs managed to increase their workforce's skill supply, upgraded in GVCs, and shifted towards more upstream and downstream activities (de Vries et al., 2019). Similarly, some Central Eastern European
statistically significant and positive impact of t <mark>he geographical clustering o</mark> f formal and informal firms in India on the productivity of formal firms, but no significant effect for informal businesses.

The rest of the paper is structured as follows. Section 2 describes the data, provides a descriptive analysis of the informal sector in Zambia, and presents the empirical strategy. Section 3 discusses the baseline results and a series of robustness checks. Section 4 shows the heterogeneous effects of geographic proximity across firms. Section 5 analyzes potential spillover channels and, finally, Section 6 concludes.

# **2 Data and Empirical Strategy**

## **a. Data**

The analysis employs the World Bank Enterprise Survey (WBES), the World Bank Micro Enterprise Survey (WBMES), and the World Bank Informal Sector Enterprise Survey (WBISES) for Zambia conducted in 2019. Even though the surveys were conducted in parallel to each other by the same implementing agency, some differences exist. While all three surveys gather information on the performances of firms and the business environment in which they operate, they differ with respect to the assumptions of formality and the sampling approach.

The WBES differentiates informal from formal firms by using the information on the registration status of the entity. In the context of Zambia, informal businesses are defined as those that are either: (1) registered with Zambia’s Patents and Companies Registration Agency (PACRA) but do not hold a council permit and are not registered with Zambia Revenue Authority (ZRA); (2) are not registered with PACRA but hold a council permit; and (3) not registered with PACRA and do not have a council permit. The WBISES captures informal businesses, while the WBES and WBMES survey formal firms.<sup>7</sup> The WBES is a nationally representative survey where a stratified random sampling approach is applied to formally registered firms that have five or more employees, have at least 1 percent of private ownership, and
. _Land resources (L),_ which refer to the aggregated value of crop land, pasture land and protected areas.  Land is valued in terms of the present value of the income it generates rather than its market value.

The gross national income (GNI) and all inputs mentioned above are measured in per capita values at 2000 prices and are taken at the national level for 208 countries. GNI data are obtained from the World Development Indicators (World Bank, 2005). HE is derived based on the work by Barro and Lee (2000); while the remaining variables, K, HR, E and L are the components of wealth as described in World Bank, 2005a (Chapter 3).

The relationships of the production inputs to income are expressed in nested CES production functions described in the Annex. Three different nested CES approaches are examined – (a) one-level function, with two inputs; (b) two-level function, with three inputs; and (c) three-level function with four inputs. The combinations of the variables in the different CES approaches were varied to further investigate any possible differences among substitution elasticities for pairs of inputs.

The production function approach taken so far neglects an important set of factors that influence differences in national income. These relate to the efficiency with which productive

> 5 Per capita dollar values at nominal 2000 prices.

> 6 For energy it would be inappropriate to take the stock value of the asset, as what is relevant for production is the flow of energy available to the economy.  This is given by production plus net imports.  With the other assets (K, H and L) it is also the flow that matters but it is more reasonable to assume that the flow is proportional to the stock. We do note, however, in the conclusions that even this assumption needs to be changed in future work.
123.2}***||{141.5}***|
|No. Observations|4326|4326|3632|3632|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; standard errors clustered at county-level in square brackets; standard errors clustered at province-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
versus labor content of exports. We also look to see whether exports have become more or less important for wages and jobs in South Africa, relative to total domestic production as well as total employment.

# **a. Labor Value Added**

The LACEX data show that the total labor value added contained in South Africa’s exports has been growing robustly and steadily since 2001 (Figure 1). Growth in all the wages paid to produce exports including those to produce domestic inputs of exports—considering the indirect linkages—was 7.9% annually, reaching $34.7 billion in 2011 (up from $16.3 billion in 2001).

The labor value added supported by the indirect linkages with export activities is relatively more important and grew slightly faster than the labor value added directly supported by exports themselves. Growth in the wages paid to produce exports _directly_ over the last decade was 7.7% annually in nominal terms, reaching $15.4 billion in 2011 (up from $7.4 billion in 2001). Growth in the wages paid to produce exports _indirectly_ was 8.1% annually, reaching $19.3 in 2011 (up from $8.9 billion in 2001).

**Figure 1: Direct and total labor content of exports, South Africa 2001-2011**

<!-- Start of picture text --> 2000 2005 2010<br>Year<br>Direct LVAX Total LVAX<br>40000<br>30000<br>US$ Mil<br>20000<br>10000<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

However, labor value added per unit of exports in South Africa has declined since 2001 (Figure 2 and Table A1), witnessed in both the direct and indirect LVAX shares in spite of the nominal value of both direct and indirect LVAX increasing. In 2001, each $100 of gross exports generated $
# **1. Introduction**

The economic gradient in health status — the extent to which poorer people tend to be less healthy — has been of long-standing interest in health policy discussions and the social sciences more generally.<sup>2</sup> An important strand of the literature has used data on self-assessed health ( _SAH_ ) to study the socioeconomic covariates of health status, including its economic gradient. Respondents assess their overall health on (typically) a five-point scale from “very good” to “very bad” and the responses are combined with data on incomes or expenditures for the same individuals to estimate the gradient, possibly with controls. Using _SAH_ data for the US, Smith (1999) found income and wealth gradients, though dampened at high incomes. Also using data for the US, Case et al. (2002) found an income gradient in children’s _SAH_ (as rated by a parent). Currie et al. (2004) also report an economic gradient in adult assessments of children’s _SAH_ in England, although the gradient appears to be smaller than in the US. Kunst et al., (1995) report economic gradients in _SAH_ for eight developed countries.

How reliable are _SAH_ data? A number of studies have reported that people with specific ailments tend to report lower _SAH_ than people in good health. Idler and Benyamini (1997) review research indicating that answers to the _SAH_ question help predict subsequent mortality.<sup>3</sup> On the basis of such evidence, Franzini et al., (2005, 789) claim that: “Twenty years of empirical evidence indicates that _SAH_ is a powerful and reliable predictor of clinical outcomes and mortality.” Others have taken a more critical view of _SAH_ data, arguing that responses on _SAH_ are influenced by various emotional, psychological and knowledge-dependent factors.

Amartya Sen’s critique of self-reported morbidity data has been influential:

> 2 Using micro data, economic gradients in health status have been
<mark>intervention and the beneficiaries’ uncertainty about future public works opportunities. Accompanying these modest gains in food expenditure, there are—at best—modest gains in</mark> food diversity for young children at midline (Annex A Table 5).

<mark>It is possible that some of the income gains from the public works program are used to smooth consumption over time in the months following program completion. Households offered public works are more likely to have savings or insurance. And by endline, after a program has ended, they are more likely to make mortgage payments and owe less to grocers as well as less likely to buy groceries on credit, though these effects are not statistically significant.</mark><sup>21</sup>

# **5.5. Intra-household transfers and women’s empowerment**

In a setting with restrictive gender norms and limited income opportunities for women, the following question arises: Do women keep the income they earn from public works? To answer this question, we administered an innovative module which collected weekly data on intra-household money transfers. We find that the women offered the opportunity to engage in public works are 6.2 percentage points more likely to report giving money to their husbands (see Table 6). This rate, however, is far lower than the rate of program take-up. The aforementioned result is consistent with Islamic social norms whereby women control their own assets and income (Tucker, 2008). On the other hand, based on the program effect (when the income is given), the implied amount of income that women give to their husbands seems much larger than their income gains: 305 DFJ is the transfer effect, which, when applied to the 6.2 percent of women, suggests a transfer of nearly 5,000 FDJ, when the income gains are only about 3,000 FDJ.

The program has no effect on husbands’ income transfers to their wives. Three-quarters of husbands participating in the
**TABLE 1: Geographic distribution of IFC equity investment compared to foreign direct investment.** This table presents IFC equity investment and global FDI inflows by the IFC regional classification. Emerging market and developing economies (EMDEs) and advanced economies are as classified by the IMF as of 2019. Brazil, China, Russian Federation, Nigeria, India and Saudi Arabia are the largest FDI destinations in their regions. The United States; United Kingdom, and Hong Kong SAR (China) are the world’s three largest FDI destinations among advanced economies. The British Virgin Islands, Cayman Islands and United Arab Emirates are the three largest EMDE FDI destinations without IFC cash deployed. Advanced economies with IFC equity investments are, in descending order of real cash deployed: the Republic of Korea, Greece, Czech Republic, Slovak Republic, Latvia, Singapore, Estonia, Slovenia, Lithuania and Taiwan (China). Regional IFC projects are grouped under their region. FDI inflows from UNCTAD up to 2018. Values are 2018 dollars, converted from nominal dollars using the US GDP deflator.

||IFC equity i|nvestment since 1956||FDI infows|since 1970||
|---|---|---|---|---|---|---|
||Billions ($)|Share<br>of<br>SUB-<br>TOTAL|Trillions ($)|Share<br>of<br>TOTAL|SUB-<br>Share of TOTAL|IFC / FDI|
|**A) Countries with IFC cash deployed**|||||||
|**Emerging Market and Developing Economies**|33.77|97.7%|11.33|85.9%|29.1%|0.30%|
|Latin America and the Caribbean|8.01|23.2%|3.45|26.2%|
in _Green_ zones.<sup>16</sup> Throughout the paper, we use this color scheme as the treatment across Indian districts. In particular, each treated firm is located in a _Red_ or _Orange_ district, while each control firm is located in a _Green_ district.

**Physical and cultural distance.** We use different measures of _distance_ , which we include as controls in our empirical results. The measures of geographic distance between districts calculate the length of the shortest distance between district centroids. The measure of linguistic distance between Indian districts is from Kone et al. (2018) using the commonly used ethnolinguistic fractionalization (EFL) index (Mira, 1964). This index measures the probability of two randomly chosen individuals from different districts speaking the same language.

**Other controls.** We control for different firm and district-level time-varying variables such as data on the monthly number of cases, deaths, and recoveries from Covid-19 for all of India at the district level from Covid India. For each firm, we construct two variables that measure the firm’s exposure to global demand and supply shocks that vary at the product and country level, following Hummels et al. (2014). The construction of these exposure variables is described in detail in online data Appendix C.

**Summary statistics.** We present some key summary statistics from our firm-to-firm trade dataset in Table A1 and Figure A1. They report the unique numbers of sellers and buyers, total sales (in million rupees), and total number of transactions during the months of January-March, April-June, and July-September for both 2019 and 2020. The most noticeable pattern is the large drop in all variables in 2020 compared to 2019, particularly during the April-June period, which coincided with the lockdown policies. The total value of sales and the number of transactions fell by almost 60% during April-June of 2020 compared to 2019. For reference, the fall in the value of sales was only 25% after the strict centralized lockdown
0_._021)|(0_._020)|(0_._028)|
|Country FE|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|_Y es_|
|Demographic|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Education|_No_|_Y es_|_Y es_|_Y es_|_No_|_Y es_|_Y es_|_Y es_|
|Employment|_No_|_No_|_Y es_|_No_|_No_|_No_|_Y es_|_No_|
|Adjusted R2|0_._619|0_._713|0_._721|0_._767|0_._626|0_._716|0_._723|0_._770|
|Nr. of countries|19|19|19|19|19|19|19|19|
|Nr. of hh|419519|419519|419519|267397|418633|418633|418633|267397|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DB method. The dependent variable is the log of per capita consumption expenditures, expressed in PPP and not spatially deflated. Only urban households (“Core” or “Suburb”) are included in specifications (4) and (8). The baseline category is “Other rural” in all specifications, except (4) and (8), where it is “Suburb”. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

Table C5: Estimation results of regressions on household poverty
Secretaria de Salud de Mexico, 2021, COVID-19 MÉXICO: Panorama en población que se reconoce como indígena available at https://www.gob.mx/cms/uploads/attachment/file/636501/panoramacovid19-poblacion-indigena-29-abril-2021.pdf

- Turkington R, Sangster K. 2006. From housing to social mix: housing’s contribution to social sustainability. _Town and Country Planning_ 75: 184–185.

- UN. 2015. UN SDG Declaration: Transforming Our World: The 2030 Agenda for Sustainable Development. UN: New York.

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- White House, 2021. FACT SHEET: President Biden and G7 Leaders Launch Build Back Better World (B3W) Partnership. JUNE 12, 2021 Statements and Releases, available at:

- https://www.whitehouse.gov/briefing-room/statements-releases/2021/06/12/fact-sheet-presidentbiden-and-g7-leaders-launch-build-back-better-world-b3w-partnership/

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World Bank 2021 Global Economic Prospects. June 2021. Washington DC: World Bank.

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World Health Organization, WHO. 2021. WHO Coronavirus (COVID-19) Dashboard. Available at: https://covid19.who.int/table

- Yiftachel O, Hedgcock D. 1993. Urban social sustainability: the planning of an Australian city. _Cities_ 10
conducts annual surveys of large and medium scale establishments engaged in manufacturing. In the surveys, economic activities are categorized as manufacturing based on United Nations ISIC-Rev.3 classification, and encompass industries 15-37 at the two-digit level. The survey covers all establishments with at least 10 employees and which use power-driven machinery.

The data set contains detailed information on key variables including plants’ geographical location, business ownership form, output production, sales (local and export) and other revenue receipts, material inputs (local and imported), industrial and non-industrial costs, employee composition, and fixed asset holdings. Gross output is revenue generated from local and export sales after adjusting for stock of goods at the beginning and end of the year. Value-added is sales revenue after deducting material cost. Capital is measured by the size of fixed assets holdings at the beginning of the year. Employment size is the number of persons engaged in production in full-time equivalents and comprises permanent as well as temporary and seasonal workers. Labor cost includes wages and fringe benefits. Nominal values are converted from local currency to U.S. dollars using exchange rate obtained from IMF’s International Financial Statistics, and finally converted to 2010 U.S. dollars.

After merging manufacturing census data with the geospatial variables based on town names, the establishmentlevel data has complete information on 3,465 establishments engaged in 18 industries located across 91 towns. Similarly, the establishment-product level data contains 2363 establishments, 17 industries, and 116 products.

# _Indonesia_

The economic and manufacturing census of Indonesia contains data on the universe of all manufacturing plants with at least 20 employees. The dataset provides information on primary activity of the plant (UN ISIC Rev.3 <u>https://www.dane.gov.co/files/sen/nomenclatura/tablasCorrelativas/CIIU3ACvsCIIU3Internal.pdf</u> version. For a detailed crosswalk and comparison, see 21