rafmacalaba/data-use-mentions-extended huggingface dataset:
# **4. Robustness checks**

# **4.1. Sensitivity to lining up methods**

The results reported above are based on the selected line-up method (Method 1), which adjusts the population using the UNDESA projections and redraws the poverty line in each survey to match the World Bank’s published poverty estimates for 2013, as described in section 2.2 above. This method aims to reduce biases that arise from pooling data collected in different years and ensures consistency with the existing published estimates. However, the method relies on a line-up procedure that imposes strong assumptions. These assumptions have not to our knowledge been systematically validated across several countries, and there is no hard evidence that applying the procedure provides a more accurate poverty profile for 2013 than simply pooling data for multiple years. Therefore, as an initial robustness check, we report selected key results under two other lineup methods: one that adjusts both survey weights and the welfare aggregate as described in section 2.2 above, and uses the international poverty lines $1.90 and $3.10 (Method 2); and another that adjusts only the sample survey weights to match the UNDESA population projections (Method 3).

In general, the choice of method makes little difference to the estimates. The key results, with respect to the share of the extreme poor belonging to different groups, changes by at most one to two a percentage points (Table 8). The results that are mildly sensitive to the choice of lineup method are the share of the poor working in agriculture and the share of the poor with no education, but even in these cases the changes barely exceed 2 percentage points and do not alter the qualitative nature of the results. This comparison does, however, illustrate that replicating the lineup procedure used to generate the published estimates is not necessarily straightforward, and that the nature of the line-up method usually has small but noticeable impacts on the profile.

**Table 8: Sensitivity to line‐
|1.9|2,036|
|.. in management|2.4|3.8|1,660|
|.. in banking|1.1|2.3|1,280|
|.. other|0.7|2.4|488/213**|
|Memo: Committees with no member reporting<br>professional expertise|16.5%|||

Note: “Full sample” results assume that missing values mean that there is nobody with that skill or education on the committee; “Restricted sample” shows the means for cases that reported, with the sample size in the final column. Source: Village Fund Survey, 2010.

**<mark>Table 6.  Frequency and Length of Village Fund Meetings</mark>**

|Number of VF committee<br>meetingsperyear|% breakdown|Length of a typical VF<br>committee meeting,minutes|% breakdown|
|---|---|---|---|
|1|7.6|About 30 *≤ 30+|10.0|
|2|19.8|About 45 *>30, ≤45+|3.6|
|3|16.2|About 60 [> 45, ≤60+|25.2|
|4|12.4|About 90 *>60, ≤90+|12.6|
|5|6.4|About 120 *>90, ≤120+|24.8|
|6|8.0|About 180 *>120, ≤180+|10.9|
|7 – 11|7.7|About 240 [>180]|2.5|
|12|18.1|||
|> 12|3.7|||
|Memo: mean|5.8|Memo: mean|91.8
# **_<u>The micro module: linking household surveys to the CGE model</u>_**

Poverty effects of trade reforms are estimated using a top-down approach. Initially the CGE model calculates the new equilibrium (i.e., new relative prices and quantities for factors and commodities) following a trade shock. Then prices are transferred to the micro module to estimate a new income distribution and poverty effects are calculated. No feedback from the micro module to the macro model is explicitly accounted for in this version. The following equation<sup>9</sup> represents the core of the micro module: where the relative gains or losses ( _W_ represents welfare) for each household ( _h_ ) depend on: 1) changes in prices for purchased goods ( _pg_ , where a dot represents percentage change) and the initial share of expenditure on each good ( θ _hc_ , _g_ ); 2) changes in factor returns ( _w_ stands for returns to skilled and unskilled labor, and _π_ is returns to capital) and the shares of total initial income by source ( θ _h_ l and θ _hkap_ ); 3) remittances and other transfers which depend on the wage rate and the government revenues. Income by source is calculated for each member of the household, and the above equation, to keep notation simple, shows results after aggregating incomes for each individual in the same household. Once the changes in welfare are calculated, a new distribution of income is generated and this counterfactual distribution is then compared to the initial distribution.

The main advantage of this approach is that it takes into account important sources of heterogeneity across households given that the structure of income by type and the composition of consumption by commodity, the various _θ_ ’s in the above equation, are household specific. A large literature on trade and poverty<sup>10</sup> has shown that changes in the distribution of income (or consumption) might differ considerably across different

> 9 The formal derivation
|
|Trade elasticity (θ)|6.5|Eaton & Kortum (2002);<br>Simonovska & Waugh (2014)|
|Nonland share of production|0.8|Greenwood et al. (1997);<br>Desmet & Rappaport (2017)|
|Parameter (ξ)|0.125|Desmet & Rossi-Hansberg<br>(2015)|
|Modes of travel parameters: Rail|0.1434|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Rail|0.4302|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Major Road|0.5636|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Other Road|1.1272|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: No Road|1.9726|<sup>Allen and Arkolakis (2014)</sup>|
|Modes of travel parameters: Water/No Water|0.0779|<sup>Allen and Arkolakis (2014)</sup>|

The main source for these data is the G-Econ data set (Nordhaus et al., 2006), which provides gridded GDP and population data covering the entire globe. Desmet et al. (2018) use the Gallup World Poll 10
logistics indicators described in the previous section. Most have coefficients with the expected negative sign and are statistically significant; three are not statistically significant. Thus it can be concluded that most of the global logistics indicators have a significant effect on bilateral trade, in the expected direction.

> 10 The data for GDP are from the World Bank’s World Development Indicators database. The data for distance are from Gleditsch and Ward (2001), except for Hong Kong (China), for which the source is <u>http://www.wcrl.ars.usda.gov/cec/java/capitals.htm.</u>

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)|(0.207)|
|p-value UCT vs. CCT|0.061|0.343|0.424|0.379|
|p-value Treatment|0.170|0.477|0.393|0.566|
|Mean in Control Group|0.000|0.000|0.000|3.352|
|Sample Size|2,049|2,049|2,049|2,049|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Detail on the construction of the indices can be found in Appendix A and Appendix B. Aspirations asks the respondent where she sees herself on a 10-step ladder comparing today to five years from now, where zero represents the worst possible life she could have and 10 represents the best possible life she could have. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
insurgency which occurred between 1981 and 1993 as a case study. In districts affected by terroristic attacks, the long-term yearly investments in agricultural technology is reduced by around 17 percent. This contraction turns in to an average reduction of farmers’ income of 4 percent.

Besley and Mueller (2012) study the effect of violence on house prices in Northern Ireland. They show that the peace process and its corresponding decline in violence led to an increase of house prices in the most affected regions compared to other regions. Their paper offers a way to understand the heterogeneity of these changes across regions which directly link to the role of expectations. They find that house prices in Belfast increased by 6 to 16 percent due to changes in expected violence in the peace process.

# **6.2 Political Risk in Enterprise Surveys**

Political risk consistently features as a crucial constraint to economic activity in enterprise surveys. The Multilateral Investment Guarantee Agency (MIGA), for example, provides surveys for a number of years each of which show that multinational enterprises report political risks as a key factor for investment in developing countries. Especially for economic activity which requires a mid-term perspective of three years and more political risk is crucial. However, the term political risk captures “the probability of disruption of the operations of multinational enterprises by political forces or events” which is clearly related to political violence but not necessarily larger in conflict countries. In this regard MIGA (2011) is particularly useful as it collected data particularly for risk in Conflict-Affected and Fragile (CAF) economies. The report stresses that these countries are ranked as particularly risky. The three main channels of transmission of political risk to foreign investors in CAF countries are:

- the possible destruction of assets resulting from conflict itself;

- the unavailability of inputs and adequate human resources;

- abrupt declines in domestic demand, leading to lasting impoverishment that persists beyond the end of hostilities.

For investors in CAF states, past losses appear directly related to divestment: 22 percent of MNEs surveyed say that
not have panel data, our data fully include pre-crisis and crisis price levels, unlike Jensen and Miller (2008). We estimate the relationship between household-level food security and increasing food prices using a unique cross-sectional, temporally stratified, nationallyrepresentative survey. The data are from the National Risk and Vulnerability Assessment (NRVA) 2007/08, a sample of over 20,000 households from all 34 provinces of Afghanistan, conducted over a 13-month period. It is the first nationally-representative household survey in Afghanistan<sup>8</sup> designed to account for seasonal variations in consumption and other measures of wellbeing.<sup>9</sup> The most important feature of the design for this analysis is that the NRVA provides a comprehensive and representative portrayal of consumption patterns prior to and after the onset of the 2007/08 food price shock, providing substantial variation in prices and comparable sub-samples for our analysis.

A particular strength of the NRVA survey is its detailed information on the frequency and quantity of food consumption of 91 different food items, which allows us to observe how households change the composition of their diets in response to price changes and also allows us to create multiple measures of household food security. Food security broadly consists of four main dimensions: availability, access, utilization, and stability.<sup>10</sup> In this analysis, we concentrate on aspects of access and utilization. The former refers to a household’s ability to obtain food, which depends on income, prices, and market access; the latter refers to an individual’s ability to process nutrients and energy from food, which depends on many factors, including dietary diversity and nutrient absorption, intrahousehold allocation of food, and hygienic preparation. In our analysis we examine two aspects of utilization, namely measures of dietary diversity and consumption of a key macronutrient – protein.

Several salient findings emerge from the household-level food security analysis. Prior to and during the large increase in food prices from August 2007 to September 2008, the
Nonetheless, the ES include rich information about the business environment, firms’ characteristics and owner’s characteristics. The data were gathered from interviews with top managers and CEOs in the formally registered non-agricultural private sector; firms that are fully state-owned are excluded from eligibility for the study.<sup>8</sup> As such they notably omit informal businesses, a sector known to comprise a predominant share of employment in Ghana. Another notable point in the case of Ghana is that the global Enterprise Survey methodology excludes extractive industries. Firms eligible for interviews operate in the manufacturing, construction, retail, wholesale, hospitality, and transport sectors (as well as IT firms).<sup>9</sup> Additionally, in the case of Ghana, firms with between one and four full-time workers (so-called ‘micro’ firms) were interviewed, and the data used here include this supplementary source of data.

The study was conducted using a stratified survey design, along dimensions of firm size (micro [1–4 employees], small [5–19], medium [20–99], and large [100+]), sector of activity within industry and services (food, chemicals/plastics/rubber, metals, other manufacturing, construction; retail, other services<sup>10</sup> ), and in four of the most active economic cities in Ghana: Accra Metropolitan Area and Tema (Accra), Kumasi (Ashanti), Tamale (Northern) and Takoradi (Western). In all, interviews were completed with 1,324 firms. Table 1 provides basic descriptive statistics of the sample.

The sample consisted of 720 small, medium and large registered establishments and 604 registered microenterprises with the Ghana Registrar’s General Department. These establishments were located from lists compiled from the following: firm registry, list of firms paying value added tax (VAT), list of large taxpayers, Ghana Chamber of Commerce and Industry, business associations; General Department of
problem with using the 2007 round as a base year is that maize and wheat prices were already increasing in 2007, so it is difficult to regard 2007 as a pure precrisis period. Thus, we might underestimate the food insecurity impacts of the crisis if the 2005–06 round is shown to be unreliable. However, we note that there is no analogous problem with the 2008 data. The vast majority of the GWP surveys in 30
16 income so defined _net_ of out-of-pocket payments. For reasons indicated in section V, prepayment and postpayment income are both defined to be gross of food consumption-this enables us to see in the case of postpayment income what households have available to spend on food after paying for health services. Both prepayment and postpayment income are defined on a per capita basis. Out-of-pocket payments are derived in both years from two questions on health spending over the last 12 months, one specifically on hospital care, the other on all other goods and services associated with the treatment and diagnosis of illness and injury.

The WHO FFC index is computed on the same data to provide the comparison with the Aronson et al. approach. The FFC index is straightforward to compute. The Aronson et al. decomposition is more involved. _RE_ can be computed simply as the difference between _Gpre_ and _Gp,st._ In each case, the convenient covariance approach was used on household-level data [121. The out-of-pocket share g is computed simply as mean out-of-pocket payments divided by mean prepayment income. To compute K (or more precisely the concentration index for out-of-pocket payments, Cp) and _Cx p_ one has to decide on appropriate groups of prepayment equals.<sup>5</sup> In this illustration, prepayment equals were defined by expressing prepayment income as a multiple of the poverty lines derived by Glewwe, Gragnolati and Zaman [131. Households below the poverty line were divided into eight groups, the first comprising households with a prepayment income between 0% and 12.5% of the poverty line, the second comprising households with a prepayment income between 12.5% and 25% of the poverty line, and so on. Households with a prepayment income of between 100% and 200% of the poverty line were divided into just four groups, along similar lines, while those with prepayment incomes in excess of 200% of the poverty line were divided into just three groups. To put this into perspective, nearly 60% of households fell below
ban areas play an important role in providing service delivery, which in turn influences poverty. Different measures of urbanity have been proposed in the literature (e.g Dijkstra and Poelman, 2014; Aubrecht et al., 2016; Roberts et al., 2017). We take advantage of geospatial subnational gridded global data to measure travel time to major cities, the agglomeration index, population count, and built-up area.

We construct population density and totals from two main sources of global model population distribution data: Landscan (e.g. Bright et al., 2013) and GHS-Pop (Freire et al., 2016).<sup>21</sup> Landscan is a model that uses dasymetric and spatial modeling methods to estimate population. The model takes advantage of multiple sources of spatial microdata including land cover, road, slopes and urban areas data, and population census data. Figure A2.6 illustrates the spatial distribution of the total population across the enumeration area.

GHSpop data are derived from a raster-based dasymetric model estimating global population. Using newly processed LandSat data to provide multiple periods in time (1975, 1990, 2000 and 2014), this model leverages newly available built-up data (GHSBUILT) to define the locations where the GHSpop allocates population. The estimates of population are from the highest number of spatial units available to account for the total population known across subnational areas. We construct the following variables of total population within the administrative unit: _GHS_ _~~p~~ optot_ _~~1~~ 975_ , _GHS_ _~~p~~ optot_ _~~1~~ 990_ , _GHS_ _~~p~~ optot_ _~~2~~ 000_ , and _GHS_ _~~p~~ optot_ _~~2~~ 014_ . GHS-BUILT at 300m spatial resolution is the source of built-up area within each administrative unit from which we derive the variables: _GHSL_ _~~b~~ uilt_ _~~k~~ m2_ _~~
protected area-year. This approach enables us to understand eBird data in terms of visits to specific protected areas over time.

There are 3,625 observations in the regression because the data span the years 1998 to 2022 and there are 145 protected areas. The mean value of the dependent variable in control protected areas is 0.510.

We also repeat our regression with log(number of eBird visits per year) as the dependent variable (Figure S11b). Because the dependent variable is a natural logarithm, we restrict the data to area-years with positive visits. The number of observations in the regression is therefore 1,767. We obtain an ATT of 0.259 with a standard error of 0.096.

# **A.5 Economic Development (Figure S12)**

We measure economic development using data on “asset wealth”. Atlas AI provided asset wealth data to us at an annual frequency and delineated by second-level administrative divisions, spanning the years from 2003 to 2021 [63, 106]. We filter the data to include only those administrative divisions that are located within a 25 km radius of our treatment and control protected areas. When multiple administrative divisions intersect a protected area, we weight asset wealth across administrative divisions by their area of overlap with the protected area. The resulting panel data set consists of 2,755 observations, because we observe 145 protected areas over 19 years. Given that the asset wealth index is unitless, we standardize asset wealth across all protected area-years. This involves subtracting the mean asset wealth and then dividing by the standard deviation of asset wealth. Figure S12a illustrates the mean standardized asset wealth for each protected area included in our data set, and Figure S12b displays the dynamic difference-in-differences estimates.

# **A.6 Conflict (Figures S13 to S18)**

The Armed Conflict Location and Event Database Project (ACLED) uses reports from local, national, and international sources to generate geocoded event data on conflict around the world. Data from ACLED document numerous features
financing available to Indian SMEs is of a short maturity (less than one year) and is relatively costly compared with their counterparts in other countries.<sup>9</sup> In large part, the financing constraint faced by SMEs may be attributed to credit market imperfections, resulting in high transactions costs and default risk associated with bank lending to SMEs. Specific problems include: (a) insufficient credit information on SMEs; (b) poor SME credit-assessment practices and poor lending technologies, such as inadequate use of credit scoring/rating tools; and (c) problems in using land as collateral and nonrecognition by lenders of other types of collateral, difficulty in collateral enforcement and loan recovery, and a bankruptcy framework that prevents easy exits for troubled firms. A fourth possible contributor is the degree of confidence lenders have in courts contract enforcement mechanisms.

# B. <u>Agglomeration Economies</u>

Agglomeration economies are production related externalities that directly affect the firm’s microeconomic decision making. Most fundamentally, clusters of firms that are predominantly in the same sector take advantage of so-called localization economies. They include sharing of sector specific inputs, skilled labor and knowledge, intra-industry linkages, and opportunities for efficient subcontracting. Marshall-Arrow-Romer [Marshall 1890, Romer 1986] externalities suggest that cost-saving externalities are maximized when a local industry is specialized. These models predict that these

> 9 An alternative indicator of the ease of access to formal sector external finance is the proportion of small businesses that have active bank credit lines or overdraft facilities -- approximately 54 percent of small businesses in the India ICS sample belong to this group. This is much higher relative to China but lower than Brazil’s by about 50 percent.

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