rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10214

# **Abstract**

This paper uses a newly assembled data set on various types of social protection spending in 154 countries during the COVID-19 pandemic in 2020 and 2021 to analyze the effect of the electoral cycle on the size and composition of the social protection stimulus budget. The analysis shows that the longer is the time since the last election in a country—and thus the sooner the next election date—the larger is the share of the social protection pandemic budget allocated to social assistance and income protection and the lower is the share allocated to job retention schemes. The electoral cycle appears to have impacted the size of social assistance spending only in countries with high political competition. In this sense, countries with higher political competition experience stronger effects of political budget cycles.

This paper is a product of the Office of the Chief Economist, Europe and Central Asia Region. 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 mlokshin@worldbank.org, arodriguezferrar@worldbank.org, and itorre@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
substantial share of the industry.

**_Fortune 500 Companies_** In some of the descriptive analyses, we complement our firmto-firm link data with information on the U.S. Fortune 500 agribusiness companies. We sourced these data from the March 2022 list of U.S. Fortune 500 companies, utilizing fuzzy string matching techniques to identify relevant entities in the FactSet database, based on company names and their headquarters’ state in the U.S.. We cleaned and reviewed the matching results manually. Table A.5 presents these firms along with their primary industry classifications, offering a detailed view of the leading agribusiness companies that shape our analysis.

# **3.2 Methodology and Definitions**

## **3.2.1 Descriptive Statistics of the Agribusinesses Network**

To assess the role and importance of individual nodes in the agribusiness supply chain network, we primarily rely on the measure of degree centrality. This refers to the combined number of incoming (indegree or supplier) and outgoing (outdegree or customer) ties or links of a node (firm). For a more nuanced understanding, we calculate the weighted degree centrality, where the weights are determined by the duration of activity for each link and the frequency of interactions between two nodes.<sup>14</sup> If an agribusiness maintains two customer and five supplier ties that are active for the entire year, its weighted degree centrality is seven. In some graphs, we distinguished between indegree (i.e., supplier) and outdegree (i.e., customer) links.

Since we are unlikely to observe the complete set of links for firms that only appear as counterparts in other firms’ reporting, for more accurate insights, our analysis prefocuses on that are monitored in FactSet. This dominantly firms actively encompasses

> 14Two nodes can form multiple supplier or customer relationships. An alternative measure is given by the unweighted degree centrality which corresponds to a 0 or 1 dummy indicating whether at least one relationship is active in a given year.

12
Policy Research Working Paper 7621

# **Abstract**

This paper contributes to the methodological literature on the estimation of poverty lines for country poverty comparisons in Latin America and the Caribbean. The paper exploits a unique, comprehensive data set of 86 up-to-date urban official extreme and moderate poverty lines across 18 countries in Latin America and the Caribbean, as well as the recent values of the national purchasing power parity conversion factors from the 2011 International Comparison Program and a set of harmonized household surveys that are part of the Socio-Economic Database for Latin America and the Caribbean project. Because of the dispersion of country-specific poverty lines, the paper concludes that the value of a regional poverty line largely depends on the selected aggregation method, which ends up having a direct impact on the estimation of regional extreme and moderate poverty headcounts.

This paper is a product of the Poverty and Equity Global Practice Group. 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 authors may be contacted at acastanedaa@worldbank.org, llucchetti@worldbank.org, dvalderramagonza@worldbank.org, gasparinilc@gmail.com, and santiago.garriga@psestudent.eu.

_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
and innovation, I use these output deflators for material and innovation inputs. For the capital input, I have constructed the deflators from the Gross Fixed Capital Formation (GFCF) of the National Account.

The empirical specification and description of variables can be written as follows: ln Y��� = β�ln K��� + β�ln L��� + β�ln M��� + β�ln I��� + β�Ɵ��� + α� + α� + ɛ��� (3)

- Y��� : The real output of firm i operating in sector j at time t, which is calculated by the sales revenue deflated by subindustry-level deflators from the PPI obtained from INS.

- K��� : The value of fixed assets at the beginning of the year, deflated by GFCF, from the National Account.

- � : Number of workers. ���

- ���� : The value of physical material inputs used directly for production, deflated by deflators based on input-output table.

- ���� : Sum of the innovation investment (all), which is the cost of R&D, cost of consulting services, and cost of royalty payment for firm _i_ in sector _j_ at time _t,_ deflated by PPI deflators. The subindustry of the PPI index was used as its output subindustry.

- Ɵ���: A vector of control variables that are the dummies of export and FDI status.

- �� : Time dummies (1997–2007).

- �� : Industry dummies (NACE 15–36).

To answer the main research question, which source of innovation contributes more to productivity, I divide the innovation variables into internal and external innovation as seen in equation (4), and I simply compare coefficient of β� and β� in equation (4).

ln Y��� = β�ln K��� + β�ln L��� + β�ln M��� + β� ln Internal I��� + β� ln External I��� + ��Ɵ��� + �� + �� + ɛ��� (4) To test whether the effect on productivity is different between those firms that invest in one source of innovation and firms that invest in both sources, I add the interaction term between internal and external innovation as seen in equation (5). This is because the β� in equation (4) actually includes not just those firms investing in internal innovation, but also 22
86 differentials between more and less educated women are narrowed by extension work (Schultz, 1988b). Agricultural and family planning extension appears to be a substitute for formal education. Extension particularly benefits the recipient who is less **wvel** trained to decipher and evaluate new technologies, adopt novel techniques, and obtain combinations of inputs that are likely to be more profitable or cost-effective. It has been argued for nearly two decades (Boserup, 1970) that some agricultural extension agents should be women, and that contacts with female as well **as** male farmers should be monitored in areas of the world where farms are often managed be women. Extension programs in the United States have generally included fenale agents to work with women in nutrition and homo health measures, as well as **farn** management. Still there are no strong evaluation studies that distinguish how the gender of extension staff or of the person contacted at the **farm** level affects the, productivity of farm labor. Farm-management and household-production studies have shown the levels of productivity of female farm managers and how it increases with education (Moock, 1976, 1981). But these approaches have not been used to evaluate extension or credit policies or to provide a basis for replication studies (Spring at I.,a 1983; Ngwira, 1987; Herz, 1988; Birkhaeuser, 1988).

Agricultural extension systems are often criticized as not serving women, particularly in subSaharan Africa, where women predominate in the farm workforce (Boserup, 1965; Lele, 1975, Ber,!e* rt1.A., 1984; MRI, 1985; Ngwira, 1987; Herz, 1988). 28 Women tend to concentrate on food staples eaten by the family and men on cash crops in Africa and in other parts of the developing world. But agricultural research to improve yields initially focused on export crops and consequently faised the productivity of men more rapidly than
covered 7,995 households, accounting for 24% of the total sample. But this module covered approximately 24% of households in each region. Similarly, the Dietary (Individual) module covered even fewer households (4,213), accounting for 13% of total households and 53% of households covered in the Dietary (Household) module. Ultimately, the overall demographic characteristics of those covered in this module were not significantly different from those of the full sample. A visualization of the final sample selection process is presented in Figure 1.

_Figure 1. Final Sample Selection Process_ <!-- Start of picture text --> Full Sample<br>33,400 HHs / 160,888<br>individuals<br>Dietary -<br>Household<br>Module<br>7,995 HHs / 40,922<br>Individuals<br>Dietary -<br>Individual<br>Module<br>4,213 HHs / 18,472<br>individuals<br><!-- End of picture text --> We rely on the Family Income and Expenditure Survey (FIES) 2015 for price data. As only _quantities_ of food group consumption were available in NNS 2013, food expenditures were imputed from the FIES 2015 from the Philippine Statistical Authority (PSA, 2020). These expenditures were used to derive prices (or unit values) of food groups surveyed in NNS 2013. More details on this process are in the subsequent section. However, as prices were in 2015, imputed food expenditures were adjusted to 2013 using the official CPI retrieved from the Philippine Statistical Authority. The CPI for 11 food groups in 17 regions was determined for both June 2013 and June 2015. Then CPI deflators were calculated and imputed food expenditures were determined for each food group in the appropriate region by their corresponding CPI deflators.<sup>1</sup>

> 1 Food groups available for CPIs were not perfectly matched with food groups in NNS 2013. The CPIs of vegetables were assigned to expenditures on starchy roots and tubers, dried beans, nuts and seeds, green leafy and yellow

9
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<u>Table 6: Effect of the Number of Nudges (Dosage) in Fokontany on Long-Term Outcomes with Controls</u>

||Preparing<br>meals|diverse|Child<br>interaction|Parenting<br>Behavior|Language<br>Learning|Social<br>Skills|Fine<br>Motor|Composite<br>MDAT|
|---|---|---|---|---|---|---|---|---|
|Number of nudges|0.03**||0.11**|0.03**|0.03|0.06**|0.01|0.05*|
||(0.01)||(0.04)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|
|HH Size|-0.00||-0.03|-0.02**|-0.02|-0.03**|-0.01|-0.03*|
||(0.01)||(0.02)|(0.01)|(0.02)|(0.01)|(0.01)|(0.02)|
|Child’s age|-0.02||1.75**|0.09**|-0.04|-0.03|-0.02|-0.03|
||(0.01)||(0.08)|(0.02)|(0.04)|(0.04)|(0.04)|(0.04)|
|Distance to school (KM)|0.00||-0.04|-0.02*|-0.04**|-0.02|-0.02|-0.03**|
||(0.01)||(0.03)|(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|
|Education of HH head|0.01||0.04|0.02|0.04|-0.00|0.02|0.02|
||(0.01)||(0.05)|(0.02)|(0.03)|(0.03)|(0.03)|(0.03)|
|HH head female|-0.05*||-0.07|-0.08*|0.16**|-0.01|0.08|0.10|
||(0.03)||(0.11)|(0.04)|(0.07)|(0.07)|(0.07)|(0.07)|
|Age of HH head|0.00||-0.01**|0.00|0.00|0.00|0.00|0.00|
||(0.00)||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Constant|0.39**||0.83**|2.43**|-0.04|-0.15|-0.10|-0.11|
||(0.07)||(0.34)|(0.12)|(0.20)|(0.18)|(0.18)|(0.19)|
|Observations|2,210||2,371|2,020|1,364|1,364|1,364|1,364|
|R-squared|0.01||0.37|0.03|0.02|0.01|0.01|0.02|

Sample including only those who received nudges (either planning or self-affirmation). Robust standard errors in parentheses. _'*' denotes significance at p<.1 level, '**' at p<.05._ 25
significant, column (1) reports the results if we restrict the sample to domestic-owned, micro and SME’s who are least likely to be footloose and where the location tends to be associated with the places of birth or residence of the business owner. Foreign firms and large domestic firms are the most likely to be selective in their choice of location. The results are very robust.

# _7.2. Alternative Definitions of Investment Climate Indicators_

The second set of robustness checks involves using alternative measures of the different investment climate areas. The surveys generally provide various measures to choose from. Of particular interest are measures on access to finance. There are two sets of results, that the nature of the finance being captured matters, and, that the results are somewhat sensitive to whether variation across sectors within a country is included or not.

To examine the different types of finance, two additional measures are included – the share of sales made on credit and access to working capital from external institutions. The measures used so far captures a type of finance that is relatively more difficult to access, namely financing for longer term investment projects from formal financial institutions. The other two alternatives presented in Table 5 measure access to a more informal form of financing and to shorter term financing from institutions. In the case of the share of sales on credit, the results show that micro firms do not share in the benefits of access to this financing, consistent with such credit being offered less by micro firms. Small and medium sized firms do benefit. The impact on large firms is not significant, which again is not so surprising as this type of finance is relatively less important for them. For access to working capital, column (3) the benefits are more widespread. If anything, this type of financing is most beneficial for the micro firms, although the differences across sizes are not significant.

The second point is that these results are sensitive to whether one allows for sectoral variation within countries to be included. Our results so far have included the full set of interacted sector-survey dummies. However, here (Table 5, columns 2-4) the results are more significant when sector and country dummies enter separately. There are two interpretations that can be given to this finding. First, the original rationale to include the broader set of dummies was in part to control for demand shocks that could vary by country and sector. Failing to control for sector-country shocks, endogeneity could be re-entering 24
- 2 -

# **I. Introduction**

Large-scale infrastructure investment can facilitate structural change of an economy. The literature is generally supportive of this: There is a wide range of direct and indirect effects of large infrastructure investment (e.g., Datta, 2012; Donaldson, 2018; Duranton and Venables, 2018; Ferraz and Coutinho, 2019; Banerjee et al., 2020; IMF 2020; Li et al., 2020). However, the long-term impact remains open to debate when causality is considered (e.g., Jiang et al., 2017; Valila, 2020). One of the most important empirical difficulties comes from potential endogeneity between economic outcomes and infrastructure placement (e.g., Datta, 2012; Banerjee et al., 2020). While infrastructure investment is generally believed to raise the productivity of an economy, public infrastructure is often placed where economic productivity is inherently high. Therefore, regardless of its real impact, positive correlation is likely to be observed between them.

Using micro data from Kazakhstan, the current paper attempts to address the endogeneity issue and examine the effects of large-scale transport infrastructure investment. Kazakhstan is a large landlocked country with a low population density. Transport connectivity has long been an important challenge to the country. In the early 2010s, the government embarked upon a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure. In the transport sector, the government doubled road investment in regional corridors connecting major cities to Western Europe and Western China. The paper shows that the improved regional connectivity allowed firms to reduce their inventory, increasing their productivity.

In the literature, there are at least two approaches to deal with the endogeneity problem of infrastructure investment: The first approach is the computational general equilibrium (CGE) model, which captures various demand and supply effects as a system of equations. The CGE approach can analyze causality of different economic activities, including transport infrastructure investment. A number of regional and national CGE models exist (e.g., Kim et al., 2004; Nguyen and Wigle, 2011). Brocker et al. (2010), for instance, examine the welfare impact of the TransEuropean Transport Network on the regional economies. Despite their computational sophistication and capability of multisectoral analysis, one of the disadvantages of the CGE
evaluation to highlight its impacts on key seed system indicators means that DSM’s proponents have had very little to show, apart from achievements in coverage extracted from standard monitoring data.

This study addressed this gap by examining the impact of DSM on seed purchases and yield for two crops that were the program's initial focus. It used a quasi-experimental difference-indifferences approach that leverages the staggered scale-up of DSM to demonstrate how the approach led to increased maize yield, primarily by improving farmers’ purchase of hybrid maize seed. Specifically, the study finds that DSM has resulted in a 15-percentage-point increase in the proportion of farmers purchasing maize seed, a 45 percent increase in the quantity of maize seed purchased per hectare, and an 18 percent increase in maize yield. However, DSM’s effects on seed purchases and yields are not statistically significant for wheat.

These crop-specific differences may be partly attributable to differences in the reproductive biology of maize compared to wheat—a difference that incentivizes private participation in maize seed markets more readily than in wheat seed markets. These implications suggest a need for more nuanced policies, institutional arrangements, and market development strategies to accelerate the adoption of improved varieties across a wider range of crops. Clearly, the hybrid maize market will remain more attractive to private investment as DSM becomes the standard channel for distribution. This market may even expand and improve more rapidly—with greater investment, newer traits, better seed traceability, and lower prices for farmers—if state-owned enterprises withdraw or reallocate their resources to other functions, such as early-generation seed production and seed sector capacity development. The same may be possible for other crops where hybrid vigor plays a role, for example, in certain vegetable crops. For OPV maize, wheat, teff, and other crops, there will still be a need for public investment in seed sector development, given the limited impact of DSM. The same may be the case for many other grain and legume crops, where weak returns to private investment are common.

However, this should not be taken to suggest that continued state governance, management, and operation of the seed market of these crops is the only alternative. Several studies explore this topic with forward-looking scenarios that include specific policy recommendations aimed at integrating public, commercial, and farmer-managed seed systems (Hassena, Hospes, and De Jonge 2016; Sisay, Verhees, and Van Trijp 2017), realigning public and private sector roles and 25
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