rafmacalaba/data-use-mentions-extended huggingface dataset:
the RAIS micro data, provided by the Labor Ministry.<sup>1</sup> We study the effects of the CF’s on entry by firms in 18 sectors (11 manufacturing, 7 service) into 265 “urban agglomerations” (see description below) over the years 1993-2001.<sup>2</sup> The RAIS database contains a vast range of information for all economic establishments in the formal sector in Brazil from 1986 onward. For each establishment in this database, we have annual information on the number of employees at the beginning and end of each year; total salaries and wages; in which municipality the company is located; as well as the establishment’s economic activity according to several industry classifications. To avoid problems caused by a large number of establishments with very few employees, we limit our analysis to establishments with no fewer than 10 employees. This paper is the first research project to use the RAIS data to study firm geography and the effectiveness of fiscal incentives.

As discussed above, one important piece of information in the estimated models is the composition and location of the relatives for each establishment. To uncover the composition of each family of establishments (parent and sibling establishments), which constitutes a firm, we make use of the establishment identification number, hereafter CNPJ. The CNPJ has 14 digits and is the official identification number for all productive units in the formal sector. The first 8 digits of the CNPJ indicate the company (or family) of each establishment. The next 4 digits indicate the position of the establishment in the family. For example, “0001” indicates the first establishment in the company, which is assumed in this paper to be the company headquarters. The other establishments in the company are given sequential codes, so that the second establishment receives “0002”, the

> 1 The RAIS data was used under a cooperation agreement between the Labor Ministry and the Institute of Applied Economic Research (IPEA).

9
the informal economy. We explore information on individual characteristics (age, level of education, race, marital status), household structure (number of children younger than 13 years old, number of household members 12 years old or more, presence of mother or mother-inlaw), whether female is the household head, whether the spouse works, type of employment of other household members, dwelling characteristics, location, and sectoral structure of municipal employment.

Next, we assess the importance of a wide set of drivers of FLFP within Guatemala in 2018, relying on the variables defined above and adding characteristics that are not available in the 2002 census. These include the use of internet, availability of home appliances (washing machine), remittances,

> 6 In Section I of the supplementary materials, we present a brief conceptual framework and a review of the literature on the factors driving FLFP.

> 7 We follow Berlinski and Galiani (2007) and others and use 49 years old as an upper- bound. They use this age threshold to analyze the relationship between FLFP or other labor market outcomes and having children of young age and public policies such as the construction of preprimary schools. 8 The Census data can identify mothers and children only in households where women are household heads or when their spouses are.

7
institutional details of SNAP payments, and introduces the instrument. Section 3 formally lays out the empirical specifications. Section 4 reports the main results. Section 5 derives pass-through formulas to shed light on the factors that determine the size of the price response, calibrates a theoretical partial-equilibrium framework to compare with the empirical findings, and calculates the incidence of changes in SNAP benefits. Section 6 concludes.

# **2 Data and Institutional Details**

This section gives an overview of the data used for analysis, including data on SNAP benefits, retail prices, quantities sold, household expenditures, and other information. We also explain the formula used by the government to determine SNAP benefits for a given household, which motivates the construction of the IV.

## **2.1 SNAP Benefits**

We use publicly available state-by-month data on SNAP benefits and SNAP participation released by the Department of Agriculture (USDA) Food and Nutrition Service (FNS). We use state-by-month data for our main results; the results are robust to using county-level benefits for the set of available states.<sup>13</sup> Figure 1 plots out the SNAP benefits per population over our sample period from 2006 to 2015, which we use as our variable of interest to capture the amount of SNAP spending retailers receive. We divide total SNAP benefits by population as we are interested in changes in the scale of SNAP that are not driven by changes in population or market size. Substantial variation exists across states, with benefits per population ranging from $ 5 to $ 30 per month. Large discrete jumps in SNAP benefits per population mainly come from two events, the 2008 Farm Bill and the 2009 ARRA, in which maximum benefits for SNAP households were raised by around 8.5% and 13.6%, respectively. Both of these events increased SNAP benefits at the federal level in October 2008 and April 2009, respectively, through the intensive margin (SNAP benefits per recipient) as shown in Figure 2a, with benefits per recipient
. The imputation process involves identifying donor households in the 2022 survey and assigning their shares and domains to the corresponding 2016 (2010) households. This additional step introduces an element of uncertainty in the imputation and may affect the accuracy of poverty estimates.

Additionally, the imputed consumption data for 2016 (2010) are based on predictions and statistical matching techniques. While efforts have been made to improve the accuracy of the imputed values, there is inherent uncertainty associated with imputation methods. The imputed data may not perfectly capture the actual consumption patterns in 2016 (2010), and errors or biases in the imputed values may be introduced. This could be especially concerning if the assumption that the share of additional consumption relative to common items remained stable over time for comparable households does not hold. However, analysis of consumption patterns suggests they have not drastically changed, particularly for the last 6 years, as indicated in Table A.1 in Annex 1.

In summary, although the imputation approach provides a valuable tool for reconstructing poverty and inequality trends, it is essential to consider the limitations of the methodology. The inclusion of data 8
an EROI greater than 1–i.e., their cumulative electricity generated will be less than their embodied primary energy. The energy costs of re...ning and purifying crystalline silicon and the complex process of turning silicon wafers into PV cells are mainly responsible for the high fraction of gross production expended for the PVs’initial emplacement. Such studies have raised doubts about the sustainability of PV systems (Odum, 1996). More recent studies, however, re‡ecting the signi...cant technological change of the last few years, claim that PV systems are able to reimburse their initial energy requirements within a few years of operation (Bankier and Gale, 2006). Thus, there is a huge variance across studies in their estimates of EROI and the payback time �1 for PV systems–re‡ecting both methodological di¤erences (choice of system boundaries) as well as di¤erences in cell manufacturing technology (silicon puri...cation and crystallization process), insolation, type of encapsulation, frame and array of support, and the type of system application (autonomous vs grid-based).

A recent report based on data from 26 OECD countries presented the following estimates: (i) for roof-mounted PV systems (taking into account not only the panels but also the wires and electronic devices) the payback time �1 is in the range of 19 to 40 months and for PVfacades the corresponding range is 32 to 56 months; (ii) EROI is between 8 to 18 for roofmounted systems and between 5.4 to 10 for facades (IEA, 2006). Based on these ranges, the implied values for the growth parameter � are between .03 and .08 for roof-mounted systems and between .01 and .04 for facades; the doubling time �2 is: between 8.3 and 27.5 years for roof-mounted systems and between 17.8 and 75.7 years for facades. A study by Fthenakis and Alsema (2006), based on
the likelihood of being employed in farm activities, but reduces (increases) the likelihood of being engaged in informal (farm) non-farm activities. The positive impact of literacy however is mostly in small business, business and being self-employed. While literacy does have a positive impact on white collar activities, the coefficient is an eighth in magnitude to that of secondary education. In conjunction with the limited impact of literacy on being engaged in any non-farm work, this points to a higher demand for skills (measured using educational attainment) in non-farm activities over time, even amongst rural workers.

Considering the effect of being a gender or social minority, we note that consistent with the historical data from the NSS, women have a significantly higher likelihood of being employed in the farm sector. Thus, conditional on participating in the labor force, women have a 7 percentage point higher likelihood of being engaged in farm activities than men. In a departure from the findings using the NSS data, women however have a higher likelihood of being employed in formal non-farm activities, driven in particular by white collar occupations.

This is contrary to the situation for religious or social minorities (Muslims and SC/ST), who have a significantly lower likelihood of being employed in farm activities, and a significantly higher likelihood of being engaged in informal non-farm activities. For SC/STs, this – takes the form of wage labor thus, hailing from the SC/ST community increases a rural worker’s likelihood of working as a non-farm wage laborer by over 30 percentage points. Rural Muslim workers, in addition to having a higher likelihood of being employed as wage laborers, also have a higher likelihood of operating a small business. Workers from both groups continue to be excluded from more remunerative occupations such as business ownership, qualified self-employment, and white collar jobs. In conjunction with our findings in Section 3.1, this indicates significant limitations on upward mobility for individuals hailing from gender and social minorities.

# **3.5.2 Heterogeneity
output declines with uncertainty innovations explaining about 3 percent of variation in GDP growth after 8 quarters based on a VAR model.

Most recently, adapting the WU index methodology to include trade-related keywords, Ahir et al. (2019) construct a world trade uncertainty (WTU) index based on which they show that trade uncertainty explains more than 70 percent of the increase in uncertainty in the first quarter of 2019 and also that the increase in trade uncertainty observed in the first quarter of 2019 could be enough to reduce global growth by up to 0.75 percentage points in 2019.

A number of recent studies focus specifically on the relationship between trade policy uncertainty and trade (Handley, 2014; Handley and Limao, 2015 and 2017; and Crowley et al., 2018). They find that trade policy uncertainty has a negative impact on international trade flows through different channels. Handley (2014) and Handley and Limao (2015) find that trade policy uncertainty delays firms’ entry into foreign markets. In particular, Handley and Limao (2015) structurally estimate the effect of policy uncertainty on firm entry following Portugal's accession to the European Community in 1986 and find that (i) the trade policy reform accounted for a large fraction of Portuguese exporting firms' entry and sales, (ii) the accession removed uncertainty about future EC trade policies, and (iii) this uncertainty channel accounted for a large fraction of the observed growth.

Handley and Limao (2017) have looked at China's accession to the WTO. This event was characterized by little or no change in the level of US import tariffs on goods from China, but involved a substantial reduction in uncertainty about what the US tariff rate on Chinese goods would be in the future. The study uses the elimination of pre-existing cross-sectional variation in tariff uncertainty that came with China's accession to identify the impact on trade flows. They find that this reduction in trade policy uncertainty can explain 22-30 percent of China's subsequent export growth to the US.
|**Table 3: **<br>|**Change in Inters**<br>Change in X [201<br>|**ectoral Link**<br>3–2004] in th|**ages, 2004–2013**<br>e south–change i<br>|n X [2013–2004] in the north<br>|
|---|---|---|---|---|
|X =|Value-added<br>share|Domar<br>weight|Total factor<br>productivity|Intermediate use<br>share<br>Input<br>downstreamness<br>index|
||(1)|(2)|(3)|(4)<br>(5)|
|AGR|0.181|0.162|–0.021|0.005<br>0.005|
|MIN|**0.067**|**0.070**|**0.004**|**0.019**<br>**0.033**|
|O-IND|–0.118|–0.092|0.355|–0.007<br>–0.025|
|WRT|0.026|–0.058|0.067|–0.248<br>–0.308|
|O-SER|–0.155|–0.253|–0.065|–0.023<br>–0.116|

_<mark>Source</mark>_ <mark>: Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).</mark> _Note:_ TFP = total factor productivity. AGR = agriculture. MIN = mining; O-IND
the role of contextual complementary factors and firms’ capacity to enhance the returns of investment in innovation. We organize this body of literature into three groups: those studies that discuss contextual aspects that enhance firms’ technological learning capabilities (that is, knowledge complementary factors); studies that discuss the role of contextual factors affecting firms’ capacity of doing business or fully appropriating the rewards of their assets (that is, market complementary factors); and studies discussing the role of micro heterogeneity in taking advantage of contextual opportunities (that is, firms’ innovative capacity).

7 Jefferson et al. (2006) used panel data from China to estimate the impact of R&D in terms of productivity and profitability. The paper establishes a lag structure to offset simultaneity biases and correct for endogeneity. It finds that R&D has positive effect on both profitability and productivity. Antoncic et al. (2007) used a structural equation framework on cross-sectional data for Slovenia and Romania to test a hypothesis about the positive impact of organizational support and alliances on innovativeness and, in turn, a positive impact of innovation on firms’ performance (measured by growth, profitability, and wealth). They find empirical support for their hypotheses. Benavente (2006) applied an adapted version of the CDM framework using Chilean cross-section data. They find that neither R&D nor innovation results (share of sales of new products—so-called innovative sales) have an effect on productivity (measured as value added per worker). Crespi and Zuniga (2012) applied the CDM framework on micro data for six Latin American countries. They find that greater investment in R&D leads to a higher probability of having at least one process or product innovation. In addition, results show a positive impact of technological innovation on productivity (log of sales per employee) for all countries except Costa Rica. Moreover, they find that the magnitude of the results is very heterogeneous. Crespi, Tacsir, and Vargas (2016) used 2010 World Bank Enterprise Survey
households in the bottom of the income distribution disproportionally benefit from an increase in the number of workers in their families, as they move towards fertility transition.

The realization of the first demographic dividend, led by reductions in child dependency ratios, could facilitate the eradication of global poverty (figure 4). In 1990, East Asia had a higher average poverty headcount than South Asia. However, poverty headcount rates in East Asia decreased from 61 percent to only 7.2 percent between 1990 and 2012. This poverty reduction was paralleled by sharp reductions in child dependency ratios in the region. For a country perspective, evidence from Bangladesh suggests that demographic factors, including age-structure, gender, and regional distributions of populations, accounted for a quarter of the rapid reductions in poverty between 2000 and 2010 (World Bank 2013). Bangladesh halved its fertility rate between 1971 and 2004, going from more than 6 children per woman to about 3, and is on track to reach replacement rates in the coming decades.

**Figure 4 Lower child dependency ratios are associated with lower poverty rates**

Poverty rate and child dependency ratio, percent <!-- Start of picture text --> 100<br>1990 2012<br>80<br>60<br>40<br>20<br>0<br>EAP SAR SSA EAP SAR SSA<br>Child dependency Poverty headcount<br><!-- End of picture text --> _Source:_ Authors’ estimates _Note_ : Data are from United Nations (2015) and PovcalNet. The poverty headcount rate is based on the $1.90 a day poverty line.

Because of the association between fertility and education, income, and life expectancy, households in the top 60 percent of the income distribution tend to have lower child dependency ratios and to pass through the demographic transition before households in the bottom 40 percent in almost all countries for which data are available (figure 5). As fertility rates fall, the demographic
Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. (2) Importers are firms with an imported input share of at least 10% of total inputs. (3) GVC participants are firms that are classified as both exporters and importers. (4) FDI refers to firms with a foreign ownership share of at least 10%. The trade variables are dummies taking a value of 1 if the firm trades, and 0 if it does not. The model also controls for country‐sector, subnational region and year fixed effects.

# **2.2 Data**

Our dataset draws on two underlying datasets, published by the World Bank Enterprise Analysis Unit, namely the Enterprise Surveys Global Database and the Firm‐level TFP Estimates and Factor Ratios. The Enterprise Surveys Global Database covers 242 surveys in 140 countries over the period 2006 to 2017.

The Enterprise Surveys represent a comprehensive source of firm‐level data in emerging markets and developing economies. One major advantage of the Enterprise Surveys is that the survey questions are the same across all countries. Moreover, the Enterprise Surveys represent a stratified random sample of firms using three levels of stratification: sector, firm size, and region. Sectors are determined based on the ISIC Rev. 3.1 classification of the main product or service.

The Enterprise Surveys Global Database covers a wide range of indicators on firm characteristics, the business environment, innovation and technology, and workforce and skills among others. We merged this dataset with data on firm‐level output, value added, and capital stock obtained from the Firm‐level TFP Estimates and Factor Ratios dataset. All local currencies have been converted into US dollars and deflated using a GDP deflator in USD (base year 2009). Exchange rates and GDP deflators have been obtained from the World Development Indicators.

We apply the following rules to the dataset: (i) We include only the most recent Enterprise Surveys for each country. (ii) We
|100%|85%|
|Senegal|88%|77%|
|Sierra Leone|100%|42%|
|Tanzania|96%|46%|
|Uganda|100%|54%|

_Source: Rystad Energy’s Ucube database; authors’ calculations_ In each of the 12 countries reviewed, we identified a trigger event—the announcement of a specific discovery, sequence of discoveries or field reevaluation—that led to a significant shift in expectations regarding oil or gas prospects in the country.<sup>7</sup> As we will discuss in later sections, shortly after these trigger events, companies, governments and international experts showed increased interest in further exploration, started planning for the quick ramping up of petroleum production and began preparing for a transition to a petroleum-producing economy. Table 2 sets out some of the details of these events.

> 6 These criteria are based on resources found rather than proven reserves.

> 7 This list of events builds on the discoveries presented in the World Bank Africa’s Resource Future dataset.

3
**Table 10: Bangladesh’s Increased Exports** **<u>to India – Three Scenarios</u>**

|||_Bangladesh has duty-_<br>_free market access in_<br>_India with no sensitive_<br>_list_|**Scenarios**<br>_Bangladesh has duty-_<br>_free market access in_<br>_India keeping India’s_<br>_sensitive list of 480_<br>_productsfor LDCs_|_Bangladesh has duty-_<br>_free market access in_<br>_India keeping India’s_<br>_sensitive list of 46_<br>_productsfor LDCs_|
|---|---|---|---|---|
|**Rise in**<br>**exports to**<br>**base (%)**|**Bangladesh**<br>**India, from**|133.53|19.12|85.09|

_Source_ : GTAP simulation results. The increase in exports is calculated with reference to the baseline.

The static export potential could be considered as an increase in exports of 134 percent (the first scenario above), i.e., with no sensitive list (Table 10). Other simulation results show that India‘s maintaining of the sensitive list of 480 products would only see an export increase of about 19 percent, much less than the first scenario. It should be noted, however, that Bangladesh‘s major export items––readymade garments (RMGs, HS codes 61 and 62)––have a very limited export base in India, so the GTAP model would always show a small rise in these product exports, even under a ―no sensitive list‖ scenario. Initially, India was wary of allowing Bangladesh‘s garments into its market. But after the September 2011 prime ministerial agreement, India has allowed Bangladesh‘s RMGs into its
# **6.5 The Role of Expectations: Evidence**

From the model presented in the previous section we expect changes in investments to be largest if _pi_ and _qi_ are large. The simplest possible way to calculate these two values is to calculate and for each country _i_ . We then interact the peace dummy in (9) with these values to see whether there is any heterogeneity with regard to the persistence of peace and war. Table 9 shows the result in columns (1) and (2). Indeed, transitions to peace are particularly beneficial in terms of investment flows if peace in the country is persistent, i.e. if _qi_ is high. Somewhat surprisingly, _pi_ is not associated with any heterogeneous effects. This pattern is fairly robust across the other FDI data sources (except for data from the UN) and exclusion of outliers like Israel. It is also economically meaningful. If we take the coefficient in column (1) we get that a change of persistence by 10 percentage points, from say 0.9 to close to 1 raises investment flows in peacetime by about 15 percent.

What drives this heterogeneity? Up until now we assumed _qi_ to be constant over time and both forward and backward looking. If investors have an understanding of the conditions in the respective country then this might not be the best model of expectations. Instead, it could be that investors have a model to forecast instability which is taking factors like the quality of a cease-fire agreement into account. Investors could know, for example, that the stability of peace ( _qit_ ) rises sharply with the duration of peace - from only 72 percent in the first year after conflict to about 90 percent five years later. This implies that the first five years after conflict feature a particularly high risk of relapsing, i.e. low _qit_ . Only about 40 percent of all recoveries last longer than five years. To explore this, we construct a dummy that takes a value of 1 in the first five