rafmacalaba/data-use-mentions-extended huggingface dataset:
In all three of these cases the relationships we observe are less stark than in the LMICs in our sample. This is consistent with differential patterns of residential sorting in the United States – – in particular, greater suburbanization in the United States leading to lower pollution exposure<sup>8</sup> driving a different relationship between pollution exposure and wealth. It is also consistent with a relatively weak relationship between income and ambient pollution exposure in the United States.<sup>9</sup>

# **SI-1.4 Robustness of Results**

Our results are robust to a variety of alternative specification and data choices. Measuring pollution as the median across months and years, rather than the average, in our sample results in broadly similar results (Table SI-1). The choice of weighting scheme appears to matter more when pollution is measured as the median and the magnitude of the correlation is somewhat smaller. In particular, weighting by grid-cell population eliminates the correlation in the pooled sample when using the median. This suggests there are a few grid-cells with very high populations that do not exhibit any correlation between wealth and median pollution but do exhibit a correlation when pollution exposure is measured using the average level of pollution. We believe that using average pollution is a more appropriate measure than median pollution because the average is sensitive to extremely high values of pollution that may be particularly harmful (e.g. spikes in Delhi during the crop burning season).

Alternatively, we can calculate the average level of pollution by month for each grid-cell in our sample and assign the average value of the month with the highest average level of pollution to each grid-cell. This approach focuses heavily on the spikes that might occur as a result of seasonal crop burning or forest clearing or due to seasonal increases in dust storms. Using this approach yields results that are qualitatively similar to using the average across the full sample but the magnitudes of the correlation are larger in the maximum approach (Table SI-1). Comparing the country specific estimates of
short of the sustained 7 percent mark per year needed to make important progress in reducing poverty.

**Table 1. Economic indicators in Senegal and Sub-Saharan Africa**

||GDP growth<br>(1995–2005)|Inflation rate<br>(1995–2005)|Fiscal primary<br>deficit, % of GDP<br>(2000–04)|Gini Index|Aid ($) per capita<br>(1994–2004)|
|---|---|---|---|---|---|
|Senegal|4.7|2.5|2.2|41.3|56.4|
|Sub-Saharan Africa|3.8|7.0|5.4(*)|47.1|26.6|

_Source:_ World Bank 2007a. _Note:_ (*) West African Economic and Monetary Union (WAEMU) average. GDP = gross domestic product.

The rate of growth of the Senegalese economy, if the growth fundamentals were to be raised to the level of the leader in the region, Mauritania, would be 2.7 percentage points (figure 1b)—a marked increase from the current infrastructure contribution of 1.03 percentage points. Two-thirds of this would potentially come from improvement in the power generation, transmission, and distribution networks.

While in many aspects Senegal compares favorably with its neighbors in West Africa, poor infrastructure services have had a severe impact on the economy. Inadequate supply of key infrastructure services has contributed to low productivity in the private sector, coupled by rising transportation and electricity costs (World Bank 2007a). Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 58 percent of the productivity handicap faced by Senegalese firms 3
# **2.3 Background: Government Responses and Economic Impacts**

Governments around the world have responded to COVID-19 by implementing lockdowns and mobility restrictions to slow the spread of the virus. The pandemic itself and government restriction policies have disrupted normal economic activity in a multitude of ways, impacting virtually all major parts of the economy. Consumption was reduced, investment activities have in some cases been slowed down, import and export relationships have become strained. Both the demand and supply sides of labor markets have been heavily impacted in many countries. Table 2 shows measures of government responses to the pandemic, including the economic support index, the stringency index, and the workplace closing index published by the Oxford COVID-19 Government Response Tracker (OxCGRT).<sup>4</sup> On average, countries in the ECA and LAC regions provided the most economic support, though at the same time the stringency of lockdowns and workplace closings ordered by the government were also quite severe in the LAC region. SSA countries provided the least economic support, but they also had the least stringent lockdowns and workplace closing measures on average.

The overall economic impact of the pandemic has been severe: Columns 9-11 of Table 1 show the International Monetary Fund’s estimate of the economic impact of the pandemic in the countries included in our data. Column 9 shows the October 2019 World Economic Outlook (WEO) projection for 2020 GDP change, Column 10 shows the same projection from the October 2020 WEO, and Column 11 shows the difference. For every country in our data, the projection has worsened by at least 4 percentage points, and in many cases by much more. The Latin America & the Caribbean region saw the biggest negative economic impact according to the IMF, with decreases in projections ranging from 5.5 percentage points in Guatemala to 17.6 percentage points in Peru.

> 4The OxCGRT data for the three indices were averaged for the period January 1 to May 31, 2020. The OxCGRT measures aim to analyze the government
# **III. Implementation**

The primary sampling strategy was very standard. A nominal sample of 2,800 households was to be selected in two stages, with 200 Census sectors -hereafter referred to primary sampling units, or PSUs- in the first stage and 14 households per PSU in the second stage. However, the design departed from the standard in the way it addressed stratification in both phases.

## III.1 First sampling stage

The 200 primary sampling units (PSUs) were stratified into three categories depending on the Venezuelan migrant density , defined as the ratio between the number of Venezuelan cellphones in the PSU (as per Telefonica's prediction) and the total population of the PSU (as per INEC's population census), and allocated into these strata as follows:

- _100 High-density PSUs_ : where the density was more than 0.15.;

- _80 Medium-density PSUs_ : where the density was between 0.05 and 0.15.; and

- _20 Low-density PSUs_ : where the density was less than 0.05.

Within each stratum, the sample was selected with probability proportional to the number of households reported by the 2010 Census.

## III.2 Second sampling stage

All households in each of the selected sectors were listed and stratified into three categories considering nationality and demographic composition. In each stratum, households were selected as follow:

- _Seven_ non-Venezuelan households (i.e. Ecuadoran, Peruvian, Colombian or any other nationality except Venezuelan).

- Seven Venezuelan households without children aged less than 5 years.<sup>13</sup>

- All Venezuelan households with children aged less than 5 years.

Within each stratum, the sample was selected by systematic equal-probability sampling.

The second stage posed some fieldwork management difficulties but is conceptually straightforward. A full listing operation of all households was conducted in the 200 PSUs selected in the first stage. The listing operation consisted of a complete enumeration of all physical structures in the area, with each physical structure
applies PovcalNet’s methodology and interpolates between the 2018/19 NLSS and 2003/04 NLSS using growth rates in GDP per capita, excluding the HNLSS 2009/10 estimate. Imputed series use survey-to-survey imputations, data from 2018/19 NLSS household consumption and GHS non-monetary indicators to impute consumption in each of the GHS survey years. All estimates are available in Table 15 (see the Appendix).

# _<u>7.2. Caveats</u>_

Notwithstanding the robustness of the results to different checks, some standard caveats remain to this analysis.

27 The interpolated trend uses PovcalNet’s methodology to interpolate between two survey-based estimates using GDP per capita growth rates (Prydz et al. 2019). In this exercise, we drop available information from the HNLSS 2009/10 survey (currently available in PovcalNet) and interpolate between the 2003/04 NLSS data and the 2018/19 NLSS. In a nutshell, the interpolation applies the same growth rate as GDP per capita to the household consumption vector and allows to calculate poverty rates in each reference year. The estimate of poverty from these two distributions is the weighted average poverty rate from both distributions where whereby each poverty estimate is weighted by the inverse of the relative distance between the survey year and the reference year (Prydz et al. 2019).

22
understand the differences between the various priors. It displays the log densities of _g_ and _δ_ for all the priors discussed here (using _n_ = 72 and _k_ = 41 as in the first growth dataset in Section 7). Interior modes for _g_ occur for the Zellner-Siow, MaruyamaGeorge, Forte _et al._<sup>2</sup> and benchmark priors. All other priors tend to a positive constant or infinity<sup>3</sup> as _g →_ 0. In order to get the same mean shrinkage factor as the benchmark Beta prior, the Feldkircher-Zeugner priors thus need to decrease very rapidly with _g_ in order to compensate for the mass close to _g_ = 0 and then have a very fat far right tail (almost of the order 1 _/g_ ). As a consequence, the prior probability assigned by the Feldkircher-Zeugner priors to _δ > d_ (with _d_ defined as in the beginning of this subsection) is large: over 0.9 when _a_ = 2 + 2 _/n_ and 0.9986 for _a_ = 2 + 2 _/k_<sup>2</sup> . For the benchmark prior this probability decreases from 0.96 to 0.63 as _c_ ranges from 0.01 to 1. Particularly small prior probabilities for large values of _δ_ are associated with the Bottolo-Richardson prior ( _P_ ( _δ > d_ ) = 0 due to the truncation) and the hyper- _g_ ( _a_ = 3 _,_ 4) and horseshoe priors, which have relatively thin right tails for _g_ . The Zellner-Siow prior for _g_ has the same right tail behaviour as the hyper- _g_ with _a_ = 3 and the horseshoe prior and also leads to relatively small mass on _δ > d_ (0.16). The Maruyama-George and Forte _et al._ priors are intermediate cases in this respect. Remember also that the latter two priors depend on the model _Mj_ through its model size, _kj_ . Thus, denoting model size by _W_ ,
this direct approach are Gibson and Kim (2013a), Gibson and Romeo (2017) and Gibson and Kim (2019). The UM uses the relationship between unit values and actual prices to directly estimate the magnitude of quality substitution. McKelvey (2011) demonstrates that quality substitution can induce biased price elasticities even when prices are actually observed.

Gibson and Kim (2013a) provide an application of the UM for Vietnam, estimating a demand system with eight food groups and data from the VHLSS 2010. They demonstrate that price elasticities estimated with approaches that do not allow for quality substitution are overestimated because quality demand response is attributed to quantity response. This has significant repercussions on food security assessments. Gibson and Kim (2013a) show that, when ignoring quality substitution, a 10% increase in the relative price of rice wrongly results in a 4% drop in household calories consumption, while the actual reduction in calorie consumption is less than 2% when quality substitution is accounted for. This because Vietnamese households have considerable room to switch to lower quality rice before having to reduce the calories intake (assuming that different quality rices have the same nutritional value and that different quality varieties are available on the market). For the same reasons, Gibson and Kim 7
(2010) for greater details and general descriptive statistics on these manufacturing surveys.

Our work mainly considers the portion of the Indian economy surveyed by the National Sample Survey Organisation (NSSO). The NSSO periodically collects information on a

> 7 In all cases, the survey was undertaken over two fiscal years (e.g., the 1994 survey was conducted during 1994-1995), but we will only refer to the initial year for simplicity. We utilize the 1989 version of the NSSO survey to check for pre-trends in women’s employment in manufacturing by state, but this dataset unfortunately lacks the key business ownership variables upon which we build our entrepreneurship metrics.

9
# **c) Women**

<!-- Start of picture text --> 100%<br>5.9 5.0 4.9<br>7.4 9.2<br>9.8 12.4 80%<br>17.4<br>15.1 17.8<br>60%<br>15.0<br>18.4<br>40%<br>58.3<br>48.3<br>39.9 20%<br>0%<br>2009 2014 2019<br>Agriculture and forestry Mining Food, beverage and tobacco<br>Textile and apparel Wood Construction<br>Sales Transport Public administration<br>Others<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data the CSES household survey.

# **3. Trade Policies and Trade Flows**

Cambodia's dedication to trade liberalization is apparent through the implementation of a series of trade-related policies. Besides the measures noted, in 1996, the U.S. granted Cambodia most favored nation (MFN) status, followed by the Generalized System of Preferences (GSP) in 1997, and the European Union's Everything-but-Arms (EBA) agreement in 2001. It is also a signatory of the 2006 Trade and Investment Framework Agreement (TIFA) with the U.S. These agreements facilitated intra-regional and international exchange of garment manufacturing and the entry of new investors from Southeast Asia into Cambodia.

Other measures further promoted private sector development, including the 1994 law on investment, which allowed 100% foreign-owned investment and provided guarantees against nationalization and price regulation. To facilitate foreign investment, the country created the Council for the Development of Cambodia (CDC), which functioned as a one-stop for investors. In recent years, Cambodia has also introduced significant cross-border trade facilitation improvements, resulting in faster export and import
seem to be high in landlocked Chad (and neighboring countries), the measures necessitated to mitigate the spread of the disease, including the closure of land borders and the main airport, within-country movement restrictions, individual’s behavioral changes in fear of contracting the disease, and links to a global economy which is severely affected by the COVID-19 crisis pose adverse socioeconomic consequences in Chad as also evidenced from the July 2020 Chad COVID-19 High-Frequency Phone Survey (Chad HFPS 2020).

We use CGE model-based simulations with inputs from household living standards survey data conducted in 2018/19 (ECOSIT 4) and follow Djiofack et al. (2020) to simulate the possible sectoral and distributional effects of the COVID-19 pandemic on Chad’s economy and analyze the economic and labor market impacts on women. We focus on female labor force participation outcomes in the three main sectors: agriculture, manufacturing, and services; and production in female-owned agricultural farms. Next, we use the first round of the Chad HFPS 2020 to assess actual income and employment impacts on femaleheaded households and the coping strategies adopted by these households. The HFPS is a household-level survey with limited information on individuals. Thus, we are unable to assess the impact on women in general through this survey. It is important to note here that women in female-headed households cannot be assumed to have the same outcomes as women in male-headed households, nor are they directly comparable with male-headed households in terms of structure. Nevertheless, female-headed households comprise about 23 percent of Chad’s households and have distinct characteristics that render them vulnerable to shocks.

In what proceeds, we first describe the methodology related to the CGE simulations and the use of phone survey in section 2, followed in section 3 by a discussion of our findings in three sub-sections: i) gendered impacts in labor markets based on our CGE simulation results, ii) impacts on female-headed households 2
episodes as the unit of observation rather than the individual. The resulting statistics on disease incidence or prevalence may have misleading policy implications to the extent that they fail to reveal how far disease is concentrated in a small part of the population with multiple health problems.

An important initiative in many developing countries is the setting up of PHC reporting networks. These have the potential to provide a continuous source if community-based data on health. In so far as PHC systems succeed in extending the provision of health care beyond clinics and other facilities to the whole of the population, infvrnation can be collected that more closely reflects the health status of the community. However, for the full potential of such systems to be realised, greater efforts are needed to improve and standardise the information gathered and to assess possible selection biases that affect these data.

# 2.2 The use of surveys to investigate Patterns of health

The extent to which an international programme of surveys could complement existing health information systems needs to be assessed in terms of its capacity to improve the setting of priorities and the planning, implementation and evaluation of projects and programmes. Moreover in planning for a WHS it is important to consider the opportunity cost of conducting surveys in terms of the limited capacity of developing country statistical organisations as well as in financial terms. Population surveys should only be considered when they can contribute to the long-term development of surveillance of the population's health status and of the effectiveness of health care. In some countries it would be preferable to concentrate resources on the development of simple, low-cost community-based reporting systems that national institutions can maintain and fund themselves.

The brief review of alternative statistical systems in section 2.1 8
<br>district casualties up to<br>NLSS|||0.0442<br>(0.0423)|||
|=1 if 13-17 in 1996 x||||0.0001|-0.0009|
|Maoist abductions||||(0.0008)|(0.0009)|
|Constant|0.8914***|0.9086***|0.6750***|0.8914***|0.9084***|
||(0.0174)|(0.0154)|(0.0326)|(0.0175)|(0.0152)|
|Panel variable|District|District|District|District|District|
|Region x Year of Birth<br>FE|Yes|Yes|Yes|Yes|Yes|
|Observations|3178|1535|1519|3178|1535|
|No. of Clusters|75|75|72|75|75|
|R-Squared|0.0149|0.1760|0.0457|0.0149|0.1764|

All specifications are estimated using the panel fixed-effects estimator. District casualties are expressed per 1000 inhabitants. The sample only includes women aged 13-17 (treatment group) or 23-27 (control group) years old at the start of conflict in 1996 and surveyed in the Nepal Living Standards Survey 2003/04 (Column 3) or Demographic and Health Survey 2006 (all other columns). Standard errors clustered at the district level are in parentheses. *p<0.10, **p<0.05, ***p<0.01.

44
6 concludes.

# **2. Context, the sample and randomization**

The urban labor force participation rate for women aged 20 to 40 in Sri Lanka was 38 percent in 2009, compared to rates over 90 percent for prime-aged men (Department of Census and Planning, 2010). Twenty-eight percent of women in paid work were self-employed, mostly in small and marginally profitable enterprises. Our own representative survey of 795 female enterprises in 2008 in the 31 largest urban areas found median profits of only 5000 Rs per month

> 2 Berge et al. (2010) also consider the impact of training plus grants. However, their sample is of microfinance clients, who may be less credit constrained in the first place, and the grants they give average only approximately 20 percent of monthly profits (compared to 375% of mean monthly profits in our case), making it unlikely that the grants they give are sufficiently large to have an effect (and they find none).

4
(more formal, frequent and explicit) adoption of all structured management practice, and 0 score reflects the other extreme.

Analysis of this newly collected survey data in Croatia, suggests that, _first,_ an average manufacturing firm in Croatia scores 0.538 in adoption of advanced management practices, a value behind the one in frontier economies such as the United States (0.615, Bloom et al., 2019a). Comparing Croatia’s management practices with countries for which comparable data is available<sup>5</sup> , we find that an average manufacturing firm in Croatia is better managed than its counterparts in Russia (average score, 0.432; Grover and Torre, 2019) and Mexico (average score, 0.42; Bloom et al., 2019b) ( **Tables 1a and 1b** ), although we are concerned that the relatively higher score in Croatia vis-à-vis Mexico, for instance, could be the result of low response rate, leading to a possible selection bias and due to differences in coverage with respect to firm size in the sampling frame.<sup>6</sup> Moreover, the quality of management practices across firms in Croatia is highly heterogeneous, with a large share of poorly managed firms and a much smaller share of better managed firms, relative to advanced countries. For example, only 3.5 percent of the firms in Croatia have a management score of over 0.75 as compared to 18 percent in the US.

_Second,_ weaknesses in management practices, particularly relating to data driven performance monitoring, cripples the overall management score in Croatia. Monitoring includes specific practices that help managers define their key performance indicators (e.g. inventory, sales, absenteeism) and appropriately track them periodically over time. By comparison, Croatia’s average score on practices relating to target setting and incentives is relatively closer to that in the United States firms. Incentives practices relate to how managers and non-managers are awarded bonuses and promotion
are weighed OO ii equally in our analysis, each observation (i.e., household) is weighted by the inverse of the ηη district sample size in the CPHS. This guards against the possibility that our estimates are driven by districts with a larger sample. Note that expenditure data is missing for 16,268 households in the June-August 2021 round of the CPHS. These missing values are spread evenly across income deciles as shown in Table S6, distributed across all states, religions and 14