rafmacalaba/data-use-mentions-extended huggingface dataset:
consumed items, and a one-year recall for clothing, footwear, health, education, and durable goods.

The monthly per capita consumption estimates are put in spatially and temporally real terms by using India’s official poverty lines as deflators.<sup>7</sup> We use two poverty measures. The first is the headcount index, given by the percentage of the population living in households with consumption per capita less than the poverty line. The second is the poverty gap index, given by the mean distance below the poverty line expressed as a proportion of that line, where the mean is formed over the entire population (counting the non-poor as having zero poverty gap). In keeping with our focus on how urban economic growth has impacted rural poverty we confine attention to the rural poverty measures.<sup>8</sup> Much of the research on poverty in India is based on a disaggregation into the urban and rural sectors of each state or union territory (UT). Here we use instead more finely grained data, based on “NSS Regions”. These regions have been used by the NSSO since the 4<sup>th</sup> round, where disaggregation below the state level was first available. Initially, 52 regions were formed, where a region was defined as a group of contiguous districts from within the same state that had similar geographical features and had similar population densities in the 1951 population census. With the subsequent splitting of states and districts, the number of NSS regions grew to 88 by the 68<sup>th</sup> round survey in 2011/12. We only use data for 19 major states, and we also control for state- and district-level splitting by anchoring our panel to the administrative geography of India as of the 55<sup>th</sup> round survey in 1999/00. Consequently, the number of regions we have poverty estimates for is 59, with these available for four years, giving a balanced
# **Supplementary materials**

# **Appendix A - Data referred to in Section 2**

## **A1. General business functions in the FAT survey**

Figure A1 lists the general business functions included in FAT and the technologies associated with them. The General Business Support Functions (GBFs) are commonly available across all firms and are therefore comparable across firms, sectors, and countries. The GBFs are: 1) Business Administration; 2) Production Planning; 3) Sourcing and Procurement; 4) Marketing and Customer Information; 5) Sales; 6) Methods of Payment; and 7) Quality Control. The technologies associated with each business function follow a ladder of sophistication that goes from the most basic to the most sophisticated level. This ranking was developed in consultation with several sector experts.<sup>19</sup> <!-- Start of picture text -->
1. Administration (HR  2. Production or  4. Marketing/<br>processes, Finance,  service operations  3. Sourcing and Procurement Costumer  5.  Sales 6. Payment methods 7. Quality control<br>Accounting) planning information<br>Handwritten processes Handwritten processes suppliers, without Manual search of centralized database. (face-to-face)Informal chat  establishmentDirect sales at the  Cash Manual, visual or written processes<br>without the<br>Computers with software (e.g.standard Excel) with standard Computers software standard softwareComputers with  WhatsApp or Online chat (e.g.,  Direct sales by phone or e-mail voucher or bank wireCheque,  technologiessupport of digital<br>Internet)<br>Manual, visual<br>Mobile Apps platformsor Digital  Mobile Apps platformsor digital  Online social  Structured costumer  Sales through social media platforms or  Prepaid, credit or debit card processes with the support of or written<br>media, specialized  surveys apps digital<br>Apps or digital<br>Computers with  platforms Online or  technologies<br>specialized installed  Costumer  Online sales using  electronic<
cross-country analysis on how fiscal outlays and recovery relate. The main paper to date is Honohan and Klingebiel (2003). They find that accommodative policy measures, such as substantial liquidity support, explicit government guarantee on financial institutions’ liabilities and forbearance from prudential regulations, tend to be fiscally costly and that these particular policies do not accelerate the speed of recovery. However, the authors focus less on the institutional frameworks within which these policies take place. It might well be that the effectiveness of fiscal outlays depends on the institutional environment, or even that the importance of the institutional environment dominates the effectiveness of any fiscal outlays.

We are interested in examining what combination of fiscal outlays, policy choices and institutional frameworks have proven to be the most effective in terms of resolving a systemic crisis. Specifically, we investigate how fiscal outlays associated with resolving a systemic crisis relate to economic output losses and how this relationship depends on a country’s policy choices and institutional characteristics. We try to answer these questions using a country-level database on fiscal outlays and output losses for 29 countries with systemic banking crises.

We confirm that the size of fiscal cost is related to the extent to which countries adopt accommodative policies, in particular explicit government guarantees on financial institutions’ liabilities and forbearance from prudential regulations. When examining whether fiscal outlays produce faster recovery, we find that output losses are reduced by fiscal outlays and that none of the specific policy measures we study positively affects this relationship. Rather the opposite, we find that some of the policy measures in fact appear to worsen the effects of fiscal outlays on economic recovery. On the other hand, 4
Ebenstein et al. (2009), but to our knowledge no other papers have applied this idea to study intra-national diffusion of trade shocks.

Our analysis draws upon a unique matched worker-firm dataset that allows us to control for individual worker characteristics as they move between firms. In the presence of heterogeneous firms and assortative matching, matched worker-firm data are required to control for worker, firm, and match heterogeneity that has been shown to have significant effects on wages, employment, and inequality (Davidson et al. 2010, and Helpman et al. 2010). Our unique dataset of quarterly observations is also particularly well suited to capture leads and lags in response to the sudden reduction in U.S. demand after September 2008. It also allows us to examine the changes in the employment composition (the mix of skilled and less skilled workers) that is featured in this literature.

The rest of the paper is organized as follows. Section 2 describes the trade shock and the general trends in wages and employment in Northern Mexico. Section 3 describes the labor market data. Section 4 discusses our strategies for identifying each of the four channels through which shocks emanating from the U.S. were transmitted to and amplified in Mexico’s offshoring industries. Section 5 presents the econometric results, and Section 6 concludes by summarizing the main findings and lays out suggestions for future research.

# **2. The Great Trade Collapse and U.S.-Mexican Trade**

The Great Trade Collapse has received a great deal of attention both in the academic literature and popular press. The collection of essays published in Baldwin (2009) suggest the “Great Trade Collapse” between the third quarter of 2008 and the second quarter of 2009 was primarily a demand-side shock induced in large part by European Union and U.S. firms and 5
00]|1.28|(1.13)|1,422|
|Boys (13-17)|0.04|(0.09)|[1.00]|0.35|(0.63)|1,422|
|Girls|-0.17|(0.19)|[1.00]|1.60|(1.45)|1,422|
|Girls Below 13|-0.10|(0.17)|[1.00]|1.16|(1.23)|1,422|
|Girls (13-17)|-0.02|(0.09)|[1.00]|0.36|(0.61)|1,422|

**_Notes_** :

- The table shows the regression results on housing quality and housing-related finances that were not pre-specified and on individual items of the indices reported in Table E2 using the 2021 in-person data. Each row is its own dependent variable.

- The outcomes that require definitions are: Quality floor is defined as an indicator variables that equals unity if floors are made of tiles and zero otherwise. Quality roof is defined as an indicator variables that equals unity if the roof is made of finished concrete or tiles/standard bricks on roof and zero otherwise. Quality walls is defined as an indicator variables that equals unity if walls are made of cement or tiles and zero otherwise.

- The independent variable of interest is the TOT treatment indicator, which is the predicted value from a first-stage regression of treatment implementation on treatment assignment.

- Monetary values are in USD PPP. Eviction and monetary values are winsorized at the top 1% of values in order to limit the influence of outliers.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program,
Most fertilizer is applied in solid granule form but liquid forms of ammonia-based fertilizers are also available. Fertilizers are sold either as single nutrient, “straight” fertilizers or multi-nutrient fertilizers in the form of compounds or blends. Urea fertilizer, for example, is a single nutrient fertilizer, 46% nitrogen by weight. Compound fertilizers contain multiple nutrients in each granule with all granules manufactured to have the same nutrient composition. Fertilizer blends are made by mixing granules of different straight fertilizers to achieve a desired nutrient composition.

Sheahan and Barrett (2017) review Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) data to characterize input use among households cultivating at least one agricultural plot in the primary growing season in Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda.<sup>5</sup> They find that approximately 35% of farm households use some amount of mineral fertilizer in the primary growing season. Variation in the use of fertilizers both across and within countries can also be seen using data from the Rural Livelihoods Information System (RuLIS),<sup>6</sup> which has constructed standardized variables across a range of household and farm surveys. Figure 1 presents mineral fertilizer use among crop-farming households for 12 countries in SSA (with data collected from 2011 or later). Statistics are presented separately for small farmers, also referred to as small-scale food producers, and larger household farms.<sup>7</sup> Small farmers have a lower incidence of fertilizer use in nearly all represented countries, with the gap between the rate of use among small and larger household farms as large as 28% (Burkina Faso and Ethiopia).

Variation in use of mineral fertilizer across and within countries is compounded by variation in quantity of fertilizer applied. Sheahan and Barrett (2017) in their review of LSMS-ISA countries also document that the average application of mineral fertilizer per unit of cultivated area across the six countries is 57 kilograms per hectare (26 kilograms per hectare in nutrients) but emphasize considerable variation
Table B1: Aggregates of corporate profits from tax records

||Number|Aggregate profts|Share with|Profts net|Profts matched|Profts matched|
|---|---|---|---|---|---|---|
|o|f Corporations|(L billion)|negative profts|of dividends<br>(L billion)|to individuals<br>(L billion)|to individuals w/ valid ID<br>(L billion)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|2003|10,972|8,037|0.30|8,037|5,758|4,433|
|2004|11,774|11,909|0.30|11,909|8,497|5,465|
|2005|12,399|17,359|0.29|17,359|13,338|7,369|
|2006|13,247|23,431|0.29|23,431|19,192|8,548|
|2007|14,628|26,171|0.27|26,171|23,322|8,507|
|2008|15,674|21,221|0.29|21,221|18,479|4,485|
|2009|16,304|18,831|0.34|18,831|13,991|2,941|
|2010|17,370|22,988|0.27|22,976|15,486|5,064|
|2011|18,293|24,991|0.25|20,059|19,979|6,048|
|2012|19,147|24,
0.4060|0.4111|
||(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|(0.018)|
|2018/19 GHS|0.4188|0.4195|0.4175|0.4182|0.4195|0.4142|
||(0.018)|(0.018)|(0.018)|(0.019)|(0.018)|(0.017)|
|2018/19 NLSS|0.3909|0.3909|0.3909|0.3909|0.3909|0.3909|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|

Note: SE in parentheses. The table shows different imputed poverty estimates from survey-to-survey imputation exercise. Each coefficient is from a separate imputation. Different models are used to test the robustness of the estimates to different specifications of the consumption model, which exclude different sets of covariates as reported in the table. Data are from the 2018/19 NLSS (baseline survey) and 2010/11, 2012/13, 2015/16, 2018/19 GHS.

39
*|
||(0.428)|(0.118)|(0.118)|(0.118)|(0.113)|(0.112)|
|Distance to the nearest town||-0.001|||||
|(km)||(0.001)|||||
|Village accessible by car|||0.087**<br>(0.043)||||
|Kindergarten in village||||0.073***<br>|||
|||||(0.022)|||
|Log of district mean wage|||||0.019<br>(0.035)||
|Log of district per capita||||||0.177***|
|income||||||(0.027)|
|Observations|3,863|2,853|2,853|2,853|3,821|3,863|
|R-squared|0.105|0.071|0.065|0.067|0.101|0.123|

Note: This table reports the coefficients of child care attendance and interactions between child care attendance and other explanatory variables in probit regressions of the probability of women working in a wage-earning job. We first model child care attendance on the instrument and other explanatory variables using probit, and estimate the generalized residuals (Wooldridge, 2015). Then we estimate a probit model of maternal employment using the child care variable, the generalized residuals, interactions, and other explanatory variables. Standard errors are estimated using bootstrap with 200 replications. Standard errors are corrected for sampling weights and cluster correlation at the commune level. Village variables (kindergarten, distance to the nearest town, accessible by car) are available only for the rural sample. Thus, the number of observations
Generally, EPL in NMS-4 is not as strict as in the other group―the average EPL index was significantly lower (1.9 in period 2000-2007). Old European countries recorded average EPL index at 2.4 with a decreasing trend in given period. However, if we extend our comparison to other NMS countries not covered in OECD data as well, the situation looks much different. The rest of the NMS group has generally much tougher legislation compared to NMS being OECD members. Taken altogether, NMS10 group has in average comparably rigid employment protection legislation as old European countries (reaching 2.3 for all three years with data available). The toughest legislation was recorded in Lithuania and Slovenia, but the latter mentioned country relaxed its hiring and firing conditions in given period substantially (similarly to Bulgaria). In contrast, Romania exhibited certain tightening of its employment protection during the examined period.

# _<u>Minimum wages</u>_

Economic theorists have not reached a broad consensus regarding the consequences of the minimum wage so far. Nevertheless, on the microeconomic level it is usually generally accepted that although it might have some positive impact on the motivation to increase productivity among low-paid workers (Stigler, 1946; Acemoglu and Pischke, 1998; Cahuc and Michell, 1996), on shifting the employment composition toward high-wage jobs (Acemoglu, 2001), as a motivational device in the efficient wages framework (Rebitzer and Taylor, 1995; Manning, 1995), or in the case of a monopsony (Card and Krueger, 1995), there

> 21 The OECD developed a system of indicators, including a single overall composite indicator. As many as twenty two measures describing various aspects of EPL, covering regular and temporary contracts and collective dismissals, were aggregated into a summary indicator using a set of weights. The resulting EPL index 2 covers conditions of regular and temporary contracts, and terms of collective dismissals. Indices reach the values from 1 to 6, low index indicates flexible legislation and liberal hiring
Differences by socioeconomic status (i.e., consumption quintiles) in community coping mechanisms are not statistically significant except for evacuation routes by the end of 2021 and early 2023. The proportion of households with access to evacuation routes in the poorest quintiles was 6 percentage points lower than those in the middle- and high-consumption quintiles in 2023 (Figure 7c). By the end of 2022, a higher proportion of households had access to evacuation routes regardless of the consumption quintiles of households (Figure 7b). Following evacuation routes, shelters are the mechanism most available to households in the community. By early 2023, 45 percent of households reported an identified shelter to go to in the event of a natural disaster. This proportion was higher for households in the two bottom consumption quintiles than for those in the middle or higher quintiles (47 percent vs. 43 percent). These gaps were not statistically significant.

**Figure 7: Community disaster mechanisms by consumption quintiles (%) – (2021-2023)** a) By consumption quintiles (2021)                               b) By consumption quintiles (2022)

<!-- Start of picture text --> 63.460.2 63.967.766<br>56.3<br>45.347.546.5 42.444.443.5<br>24.922.823.8 21.718.920.1 28.624.8 26.5 24.221.422.6<br>9.18.9 9 9.49.59.5<br>Evacuation Shelter Trained Committee Emergency Evacuation Shelter Trained Committee Emergency<br>routes community plan routes community plan<br>Low consumption quintiles (Bottom 40 percent) Low consumption quintiles (Bottom 40 percent)<br>Middle & High consumption quintiles (Top 60 percent) Middle & High consumption quintiles (Top 60 percent)<br>Total Total<br>c) By consumption quintiles (2023)<br>65
not adapt effectively in the face of environmental variability. For instance, cooperatives may need to ensure members some minimum level of well-being in order to retain members; this may hinder a cooperative’s ability to dramatically cut fishing effort when environmental conditions negatively impact fish populations.

In this paper, we develop a dynamic model of cooperative decision-making and use rich data from Mexico’s Gulf of California fisheries to examine how fishing cooperatives exercise property rights, with a focus on three issues: 1) Whether cooperatives with property rights manage a resource differently from those without; 2) How such differences depend on the scale of the resource; and 3) How such differences respond to predictable environmental fluctuations associated with ENSO (El Niño/Southern Oscillation) events. Mexico is a natural setting for the analysis. The country’s marine ecosystems are rich in biodiversity, which provides an opportunity to examine how fishing cooperatives shift behavior when fishing on species that vary in key traits, such as mobility. Moreover, ENSO events have important impacts on fisheries, with the direction and magnitude of these impacts differing among species.

We begin our analysis with a dynamic model of cooperative decision-making. Cooperative leadership can engage in more aggressive management by lowering the price that is paid to cooperative members for a specific species and, therefore, disincentivizing fishing of that species. The model yields three testable implications for how cooperative price and resulting catch change in response to external factors. Specifically, compared to other cooperatives, a cooperative with stronger property rights manages effort more aggressively, decreases the relative aggressiveness of management if a species is highly mobile, and restricts effort more when environmental forcing (e.g., ENSO events) limit the population growth rate of a species. Throughout, by “strong property rights” we mean the ability to exclude noncooperative fishers from the cooperative’s established fishing grounds.

We test these implications using daily logbook data from three cooperatives in the Gulf of California region, in northwest Mexico. One cooperative, operating on the Pacific coast
each year for which a household survey is available. There is wide variation across opportunities as measured in terms of average coverage rates. Access to clean water for children under 15 years is widespread, but declining, during this decade; access to electric light is, in contrast, more limited, but it has increased steadily since 1985. Access to sanitation is somewhere in the middle, with a coverage rate between that of electricity and that of water, but hardly any increase in the last two decades. More worrisome, educational opportunities are more limited across Ivorian children, with relatively unchanged attendance to school; despite the increasing timely primary enrollment, there has been a substantive decline in the percentage of teenagers finishing sixth grade. These changes have taken place in a context of changing circumstances: increasing female household heads, declining presence of children in the household, and declining urbanization, as indicated in table 3.

To compute HOI for each individual’s opportunity, the method outlined in section 2 (and box 3) is followed. Access to a particular opportunity (a dummy variable is assigned the value 1 if the child has the 12
the scarcity of good jobs, which are concentrated in the city core, poor accessibility due to limited connective infrastructure and transport system further reduces job opportunities for people living in the outskirts. In fact, those people with inadequate job accessibility in Kinshasa are poorer, female, and/or young workers. This suggests that a mismatch in the labor market could be effectively reduced by complementing labor policies that support disadvantaged workers (such as female and young workers) with spatial interventions to enhance their mobility and access to economic opportunities.

This paper is structured as follows. Section 2 explains the data used in this paper and describes the labor force and employment conditions in Kinshasa. Section 3 assesses the employment conditions by examining who engages in better/worse jobs and how jobs are spatially distributed. Section 4 concludes.

# **2. Data and background**

## **2.1 Data**

The analysis of this paper relies primarily on a recently collected household survey in Kinshasa. Conducted by the INS of the DRC under the World Bank’s statistical operation in the country, it is an integrated survey, consisting of two surveys, targeting individuals and households. The first part of the survey includes sociodemographic characteristics, education, health, living conditions, employment, urban agriculture and non-agricultural household entrepreneurship. The second component is the household consumption survey.

The Kinshasa Household Survey was collected in two 18-day phases between mid-October and the end of November 2018 on a sample of 2,592 households. Given the outdated last population census—which was collected in 1984—the sampling method for the Kinshasa Household Survey was based on high-resolution satellite imagery. A two-stage stratified sampling design with equal allocation of first-degree enumeration area (EAs) was used for the survey. Kinshasa had been divided into two clusters: urban and rural. The 23 communes/districts of the city were considered as urban and some areas of Maluku, as part of the survey, were considered rural.

Two criteria were used to stratify the city (a
the firm was formally registered and the year the firm<br>was surveyed.|
|**Percentage of domestic ownership **|Percentage of ownershipassociated to domestic entities/individuals.|

Note: The source of data is World Bank Enterprise Surveys.

The descriptive statistics appear in Table 2. The median firm in our sample reports that has undertaken two of the four elements we consider in the innovation score (Table 2, Panel A). Moreover, 78 percent of the enterprises undertake at least one of the four elements used to categorize a firm as being 8