rafmacalaba/data-use-mentions-extended huggingface dataset:
_Social Capital, Household Welfare and Posverty in Indonesia_ _9_ First, we consider six social capital dimensions: the density of associations, their internal heterogeneity, the frequency of meeting attendance, members' effective participation in decision making, payment of dues (in cash and in kind), and the community orientation of associations (section 3). These can be combined in an index or each dimension can be considered in the model separately. Since the conceptual literature on social capital does not provicle guidance to prefer one approach over the other, we test both approaches empirically (sections 4 and 5).

Second, in addition to estimating the effects on household welfare, we model the impact of ownership of social capital on the incidence of poverty. We also attempt to compare the returns to social capital between poor and non-poor households (section 5).

Third, the impact of social capital on household welfare is usually indirect: it operates through access to credit, asset accumulation, collective action, etc. We will attempt to measure some of these links directly (sections 6.1 to 6.3).

Fourth, we revisit the question of whLether social capital operates at the household level or at the village level. While Narayan and Pritchett relied on village averages of household-level indicators, the analysis be:low uses independent and historical village information (section 6.4).

Fifth, the Tanzania study did not distinguish between different types of organizations and assumed in fact that each association has the same effect, regardless of whether it is, e.g., a parent/teacher association, a church group, or a local political party committee. In the analysis below, we differentiate four types of institutions and pay
The Sri Lankan household survey of 2001/2002 indicates that about 28 percent of remittance recipient households stand within the top income quintile and around 27 percent stand within the lowest two income quintiles. These ratios, however, change considerably in the pre-remittance scenario. When remittances are deducted from total household expenditure, nearly 45 percent of recipient households stand within the lowest two income quintiles. Although information on how Sri Lankan recipient households use remittances is not yet conclusive, the above findings suggest that remittances can have an important impact on recipient households. A broader study by the International Monetary Fund (IMF) on remittances concluded that “remittances can help improve a country’s development prospects, maintain macroeconomic stability, mitigate the impact of adverse shocks, and reduce poverty”<sup>6</sup> . Remittances allow families to maintain or increase expenditure on basic consumption, housing, education, and small-business formation; they can also promote financial development in cash-based developing economies (IMF 2005). Speaking to the role of the financial sector in facilitating remittances, the IMF study also concluded that long-run output growth resulting from additional investments in physical and human capital financed by remittances might be especially likely “where a well-developed financial system and institutions allow remittances to be effectively intermediated and efficiently used” (IMF 2005).

The Sri Lankan government<sup>7</sup> like other developing country governments has recognized the importance of remittances and introduced incentives to facilitate foreign employment—including pre-migration facilities and services, identification of employment opportunities, training, and pre-departure loan schemes (Annex 1). It is also debating the capacity of formal financial institutions to facilitate migration policies and the challenge of enhancing the quality and outreach of the remittance infrastructure, especially in the face of an active informal remittance sector. To the extent remittances are transferred through formal financial institutions, the government’s balance of payments benefits from the increased foreign-exchange reserves. In addition, the anonymity that is possible through informal remittance systems renders remittance systems highly vulnerable to abuse and
receive the SCTS. However, not all village groups within the four TAs had been reached by the SCTS in March 2007, given that the scheme was scaling up through the district as time and human resources allowed. In February 2007 the District Assembly identified the next 8 village groups eligible for the SCT according to the scale-up plan. At that time, the impact evaluation team randomly assigned 4 village groups to an “intervention” group and 4 to a “control” group. While the choice of the village groups to be assigned to treatment and control was random, the selection of beneficiaries within each village group was based on a multi participatory community based targeting, which enables local community volunteers to determine which households among them are the worst off and most vulnerable<sup>6</sup> .

The evaluation team followed both groups for one year until the comparison group of eligible households began to receive the transfer. Baseline data were collected in March 2007, before households received the cash transfer according to the government's schedule for rolling out the scheme. The second follow-up was in August 2007 and the final round in March 2008. The study included a longitudinal household survey, consisting of three rounds of collection with a panel of intervention and control households in cash transfer recipient and non-recipient village groups.

The household survey, used in the three data collection rounds, was adapted from existing household surveys used with similar populations throughout Malawi, such as the Integrated Household Survey (IHS), Demographic and Health Survey (DHS) and the Multiple Indicator Cluster Survey (MICS). The household questionnaire documents basic health, demographic and economic conditions, including anthropometric measurements; food security (including the quality and quantity of meals, food diversity, and satisfaction with meals based); educational data; health, hygiene and health seeking behaviors; economic data; time use and adult and child care patterns; emotional wellbeing; mobility and migration; coping behaviors; and characteristics of the house and inhabitants. The household questionnaire was administered to
module must be chosen, the 7-day full list (module 1) presents the fewest complications. Nevertheless, researchers need be aware of the underlying fact that this module is still likely subject to recall and telescoping errors of varying degrees as well as presumably the inability to capture personal out-of-household consumption. For example, the 7-day recall reports significantly less consumption for households with more adult members than does the benchmark, and has a slight tendency to identify larger households as poor. Like other recall modules, it appears subject to either net telescoping or deliberate misreporting that increases in magnitude with the asset wealth of the respondent (Table 6). Nevertheless, this module, as virtually all other modules, yields household consumption rankings that are remarkably stable with respect to key household characteristics, indicating that analysis that focuses on the determinants of household ranking within the consumption distribution will be largely consistent regardless of which module is used. There are long-held perceptions of the inadequacy of using a recall module compared with a household diary to measure consumption expenditures, despite the
empirical model, and how these variables differ on average between periods of sustained poverty reduction and periods without sustained poverty reduction. Further details on construction of the variables and additional summary statistics are in the Online Supplement.

All variables are predetermined in each of our data windows, as _t_ refers to the first year of the five-year period. Our sample of 377 country-five-year periods includes 94 countries, observed in 5-year periods from 1981-2015, where five year periods range between 1981 to 2015: 81-85, 8690, 91-95, 96-00, 01-05, 06-10, 11-15. This sample excludes advanced economies, or those with a poverty headcount below 3 percent for all years in the data. Advanced economies cannot provide any information about sustained poverty reduction, because poverty has been eliminated (following the World Bank definition) for all years they are observed.

**Sustained poverty reduction** The first key variable is our outcome, _Dit_ , a binary variable indicating whether a country experienced sustained extreme poverty reduction over a five-year period. The data used to construct our outcome variable are from the World Bank (2020), which reports the national extreme poverty headcount, or the percent of population living below $1.90 PPP in 2011 US dollars, in years where household survey data are available. The indicator variable has the advantage of a clearer link between the empirics and the theoretical model, which makes qualitative statements about industrialization/development, but is silent on magnitudes. It also allows us to identify periods of “ _sustained_ poverty reduction” as 5-year windows during which the poverty count is consistently reduced abstracting from cases where poverty reduction is transitory due for example to a short-lived commodity price boom.<sup>8</sup> Figure 1 shows this variable for select countries.

To summarize the main patterns in the poverty reduction data, Figure 2 displays the share of five-year periods which achieved sustained extreme poverty reduction, over time across the world and five continents. Notably, the share is above 50% for most years in most continents. This
both coefficients are large and negative.

The strategy of importing higher value shipments less frequently should impact plants’ inventories of raw inputs, a hypothesis supported by the results in Table 8. The coefficient in column (1) suggests that PSI on inputs increases inventories.<sup>28</sup> This is consistent with the notion that higher inventories help buffering against the greater uncertainty of input supply generated by PSI.<sup>29</sup> Inventory costs increase with the length of time the input lies in stock. Therefore, plants can lower markup to increase sales and reduce inventories. Consistent with this view, the coefficient of input PSI on sales in

> 26We express the shares as a fraction of the total value of imports in the first available observation. We then drop the first observation to avoid issues of endogeneity.

> 27Due to such data limitations we cannot link customs and SI data.

> 28The lower number of observations relative to Table 5 is due to missing data on inventories.

> 29Inventories are expressed as (log) averages of beginning- and end-of-the-year values of raw inputs, deflated by the plant-level input price index.

26
**Table A.22:** Robustness Checks on Public Goods Provision (Table 7)

||**Public Goods Index**|**Citizen**|**survey**|**Village head survey**|
|---|---|---|---|---|
||_Podes_2021|Access|Quality|Take Action|
||(1)|(2)|(3)|(4)|
|1. Baseline|0.503<sup>_∗_</sup><br>(0.263)|0.073<sup>_∗∗_</sup><br>(0.043)|0.208<sup>_∗∗_</sup><br>(0.102)|0.165<sup>_∗∗_</sup><br>(0.086)|
|2. No controls|0.682<sup>_∗∗∗_</sup><br>(0.276)|0.061<sup>_∗_</sup><br>(0.039)|0.202<sup>_∗∗_</sup><br>(0.098)|0.126<br>(0.098)|
|3. Region fxed effects|0.452<sup>_∗∗_</sup><br>(0.238)|0.054<sup>_∗_</sup><br>(0.040)|0.172<sup>_∗∗_</sup><br>(0.098)|0.117<br>(0.087)|
|4. 3rd-degree polynomial|0.282<br>(0.
describes some instruments governments use for industrial policy, while still operating within the legal constraints of the international trading system; for example, governments can provide targeted industries or sectors with improved access to new customers or input suppliers, training, physical and regulatory infrastructure, or product quality certification. Some interventions along these lines have been evaluated in recent literature on firms, trade, and development (for discussion, see the surveys by Verhoogen 2023; McKenzie 2024). Potentially important institutional details of export promotion agencies are highlighted, drawing on the case of Costa Rica, a small country that has grown rapidly through exports and foreign investment, while reducing poverty and protecting the environment. Though the discussion focuses on export promotion, I expect these institutional details have external validity for industrial policy targeting production for the domestic market, especially in countries with a large middle class.

The approach to export-led industrial policy described here—choosing sectors to target based on trade data and choosing instruments that do not violate existing trade rules—is similar in spirit to what countries around the world have already adopted. As one example, it is somewhat like the Enterprise Map Project of John Sutton, which detailed the capabilities and ambitions of leading firms in several African economies, yielding messages about what government inputs could help them grow. Of course, if any industrial policy is to succeed, it requires a high-bandwidth government agency capable of doing the analysis and putting the public good ahead of rent-seeking behavior by large firms.

# **The What: Growth, Competition, and Productivity Potential as Guides for Industrial Policy**

Within the broad category of exports, there is no shortage of theories that certain sectors are more beneficial for development than others. Manufacturing has long received focus, though that has given way to an alternative focus on tradable services. Other theories emphasize products exported by countries with a high GDP per capita (Hausmann, Hwang, and Rodrik 2007), products exported by countries with a similar GDP per capita (Lin 2011), complex products (
# **Are Regional Fiscal Multipliers on EU Structural and Investment Fund Spending Large? A Reassessment of the Evidence**

Federico Fiuratti, Desislava Nikolova, Steven Pennings and Marc Schiffbauer<sup>1</sup> 1 All World Bank. Corresponding Author: Steven Pennings (spennings@worldbank.org).

JEL: E62; F45. Keywords: Fiscal Multiplier, European Union, Monetary Union.

We thank several World Bank staff for comments (especially Reena Badiani-Magnusson); Luigi Durand and Raphael Espinoza for sharing links to EU fund data and assistance replicating their results; Guido Damonte for helping us building a neighbors dataset for each NUTS2 region; and helpful comments from European Commission seminar participants and our discussant Philippe Monfort. The views expressed here are those of the authors and not necessarily those of the World Bank, its executive directors or the countries they represent. The authors are grateful for financial support from the Knowledge for Change (KPC) Program, administered by the World Bank, and funded by The Swedish International Development Cooperation Agency (SIDA), Agence Française de Développement (AFD) - French Development Agency, the Government of Japan, and the European Union.

An online appendix is available at: https://sites.google.com/site/stevenpennings/ or <u>https://drive.google.com/file/d/1BT-mTW-J26Pt42Jn6FIqgI8F2WhE8Cmu/view?usp=sharing</u>
mark> _<mark>Personality and Social Psychology Review</mark>_ <mark>, 11(4):328-363.</mark> Lindqvist, E. and Vestman, R. (2009). The Labor Market Returns to Cognitive and Noncognitive Ability: Evidence from the Swedish Enlistment. _<mark>IFN Working Paper</mark>_ <mark>No. 794.</mark> <u><mark>https://ssrn.com/abstract=1367603 or http://dx.doi.org/10.2139/ssrn.1367603</mark></u> Lippman, L., Ryberg, R., Carney, R., and Moore, K. (2015). Workforce Connections: Key “Soft Skills” that Foster Youth Workforce Success: Toward a Consensus Across Fields. _Child Trends._ <mark>Loeb, S., Christian, M. S., Hough, H. J., Meyer, R. H., Rice, A. B., & West, M. R. (2018). School Effects on Social-Emotional Learning: Findings from the First Large-Scale Panel Survey of Students. Working Paper.</mark> _<mark>Policy Analysis for California Education, PACE</mark>_ <mark>.</mark> 36
While Wesolowski, Erbach-Schoenberg, et al. (2017) look at seasonality of movement patterns across Kenya, Pakistan and Namibia, they only connect this theoretically to impact on disease and do not study the relationship with incidence data. Papers that have combined seasonal mobility data from mobile phones with seasonal disease incidence data, such as Wesolowski, C. Metcalf, et al. (2015) for rubella and Wesolowski, Qureshi, et al. (2015) for dengue, have not done so in a causal framework. This paper contributes to the existing work by aiming to measure the causal relationship and size of the eect of imported malaria using a linear dynamic panel-data model and controlling for time xed eects. Therefore, in addition to the two areas already identied by the economics literature as necessary for malaria reductionpricing and adoption of preventative and treatment interventionsthis paper identies targeting of higher risk mobile populations as a third.

While this paper focuses on malaria elimination, it has implications for other diseases whose spread has been associated with travel (Adda 2016, Oster 2012,Prothero 1977, Balcan et al. 2009, Stuckler et al. 2011, Tam, Khan, and Legido-Quigley 2016). Since travel patterns studied using cell phone data could lead to the transmission of any communicable disease, if these data are obtained for other countries or for dierent diseases, it is possible to replicate the analysis using the methods developed in this paper. I demonstrate how new sources of big data can be used to measure externalities associated with travel to develop more eective targeting strategies that can be combined with pricing and adoption policies. This further expands the use of big data for development in areas such as risk-sharing (Blumenstock, Eagle, and Fafchamps 2016), measuring poverty (Blumenstock, Cadamuro, and On 2015, Blumenstock 2016) and providing credit to the poor (Bjorkegren and Grissen 2018).

The paper begins by providing some background and describing the data. It then goes on
# **3.2 Weather Data**

We match our daily arrest counts with daily weather data from the PRISM Climate Group’s gridded re-analysis product. The PRISM product provides daily information on minimum and maximum temperature, minimum and maximum vapor pressure deficit, dew point, and precipitation on a 4km by 4km grid for the continental United States. We aggregate these measures to the county level by taking the average across the grid points within the county. We assign daily maximum temperature to one of 12 5<sup>_◦_</sup> F temperature bins from 40<sup>_◦_</sup> F up to 100<sup>_◦_</sup> F. Days below 40<sup>_◦_</sup> F and above 100<sup>_◦_</sup> F are included in separate bins. We also bin daily precipitation to control for the impacts of particularly rainy days. We assign days to four exclusive precipitation bins: no precipitation, less than half an inch, one half to one inch, and more than one inch.

# **3.3 Socio-economic and Neighborhood Data**

We collect socio-economic data at the census block group level from the 5-year American Community Survey (ACS) for every year in our sample. We use the geocoded addresses from the arrest data to assign individuals to census block groups and categorize arrests based on the characteristics of the block group in which the defendant lives at the time of the arrest. We focus in particular on median income, the poverty rate, and the median housing age. These allow us to classify arrests as occurring in block groups based on income or poverty rates and by the age of the housing stock, which we take as a proxy for the presence of air conditioning. We also use the share of residents in a block group living in an urban environment to classify block groups as urban or rural. We define block groups as urban if at
) would accelerate if we had better empirical estimates of the mark-ups firms impose and the extent of economies of scale in different industries. These modifications are especially crucial for the services sector, as is the incorporation of foreign direct investment flows. Hence the more these models are going to be used to analyse services policy reforms, the more important are those inclusions. Also needed are better data on services trade and better specifications of services policy measures, particularly if the GTAP database (which many other non-GTAP global modellers also depend on) is to be disaggregated beyond the 15 services sectors in the current Version 6.

# _d. Asset values and dynamics of trade liberalization_

Including capital accumulation and thereby making global CGE models dynamic is a tall order (see, e.g., Grossman and Helpman 1991), but it would open up opportunities to address additional issues. One is the intergenerational transfers that could result from tariff reforms affecting asset values. Those effects would depend on any terms of trade changes and hence are affected by whether a small economy’s liberalization is unilateral or part of a multilateral reform package. Another issue such a model could handle better than present ones is selective temporary protection (of which anti-dumping is perhaps the most notorious). A third issue, and one that is important for poorer countries, is the greater scope there would be to assess ways of accommodating the revenue consequences of tariff changes, including via debt financing (Keuschnigg and Kohler 1997). And forth, estimates of the costs of adjustment over time could be more-easily incorporated.

21
Our main results can be summarized as follows: Gender differences arose both because of sorting and differential treatment and these worked in opposite directions for blue-collar workers but not for white-collar workers. Overall, sorting dominated – and was favorable to women‘s employment. Even though on average 46% of all manufacturing jobs were held by women over the period studied, women‘s net job losses accounted for only a third of all the jobs lost during the crisis. However, we find heterogeneity across occupations and types of firms. Blue-collar women suffered from unfavorable differential treatment in hiring and firing. By contrast, white collar women were more likely to retain their jobs than their male counterparts in such firms. While, the results show a large gender gap in wages, as well as substantial adjustment of wages during the crisis, there was little change in the wage gender gap during the crisis. These patterns are also mirrored in the labor force data analyzed in the appendix.

# **2 Previous Literature and Context**

# **2.1  Related Literature**

This study brings together and builds on two strands of literature that have evolved quite separately up until this point. While a large body of literature has examined gender differences in the labor market outcomes of crises and shocks, focusing especially on the supply side by concentrating on households and individuals,<sup>5</sup> few such studies have analyzed the role of employers. Conversely, the relatively small literature on firms‘ adjustment during crises has not explored whether firms‘ responses result in gender differences in wage and employment outcomes.

The literature on differences in men‘s and women‘s employment outcomes due to macroeconomic shocks has typically focused on changes brought on by either large-scale structural policy regime shifts (i.e., trade, privatization or financial liberalization) or by macroeconomic crises.<sup>6</sup>

> 5 A broader literature has explored the question of whether women bear the brunt of coping with economic shocks (see