rafmacalaba/data-use-mentions-extended huggingface dataset:
- Molina, I. (2019), Desagregación de datos en encuestas de hogares: Metodologías de estimación en áreas pequenas. CEPAL: Comisión Ecónomica para América Latina y el Caribe.

- Molina, I., Rao, J.N.K. (2010), Small area estimation of poverty indicators. _Canadian Journal of Statistics_ , **38(3)** , pp. 369–385.

Rao J.N.K. (2003), _Small Area Estimation_ . New York: Wiley.

- Tarozzi, A., Deaton, A. (2009), Using census and survey data to estimate poverty and inequality for small areas. _The Review of Economics and Statistics_ , **91(4)** , pp. 773792.

- UNECE (2020), _Poverty Measurement. Guide to Data Disaggregation_ , Geneva: United Nations Economic Commission for Europe.

- Van der Weide, R. (2014), GLS estimation and empirical bayes prediction for linear mixed models with heteroskedasticity and sampling weights: A background study for the povmap project. World Bank Policy Research Working Paper No 7028. Washington DC: The World Bank.

- Verma, V. (1991), _Sampling Methods_ , Statistical Institute for Asia and Pacific, Tokyo. Verma, V., Betti, G. (2011), Taylor linearization sampling errors and design effects for poverty measures and other complex statistics, _Journal of Applied Statistics_ , **38(8)** , pp. 1549-1576.

Wolter, K. (1985), _Introduction to Variance Estimation_ . Springer-Verlag.

23
such, appropriateness is key to preventing data misuse, such as surveillance or exclusion.

Governments’ tracking and contact tracing of infected and at-risk individuals through smartphone apps or phone location data during the COVID-19 pandemic shed light on the relevance of appropriateness. Some of these tracking efforts have been large-scale, data, and technology enabled extensions of manual contact tracing, with the aim to identify and isolate possibly contagious individuals to contain the spread of the virus. In principle, tracking and contact tracing measures thus serve an important policy function (though their effectiveness is still subject to debate (Almagor and Picascia (2020); Fraser et al. 2020)). Yet, the risk of surveillance and other kinds of overreach are high. To minimize the risk of surveillance beyond what is needed to minimize the spread of the virus, it is imperative that only data strictly necessary to carry out contact tracing are collected.

> 5 <u>https://www.firstpost.com/tech/news-analysis/aadhaar-security-breaches-here-are-the-major-untowardincidents-that-have-happened-with-aadhaar-and-what-was-actually-affected-4300349.html.</u>

21
2013 Mexican retail census, which classifies retailers in similar categories as our broad place of purchase taxonomy. The figures show the log median number of employees by place of purchase (Panel C) and the share of firms paying Value-Added-Taxes on sales (Panel C). The data comes from the following firm censuses, keeping only firms which operate in the retail sector: Recensement G ́en ́eral des entreprises 2016 (Cameroun), Censo Econ ́omico 2014 (Mexico), Censo Nacional Econ ́omico 2008 (Peru), Establishment Census 2011 (Rwanda).

38
control for all city-wide factors through city dummies (City fixed effects). Note that city fixed effects absorb country fixed effects. The identification of the main results here comes from variations across firms within a country rather than across countries. Thus, cross-country differences in factors such as overall economic development, quality of institutions, historical and geographical influences on employment growth, culture, and the quality of the business environment that may lead to omitted variable bias problem are accounted for.

The regression exercise involves repeating the baseline regressions (as in Table 4) with country or city dummies included in all the specifications and using cell averages of informal competition at the country-industry or city-industry level.

Our main result for the negative relationship between informal competition and employment growth continues to hold with country-industry and city-industry cell averages. We present the results for the city-industry level as they are on the conservative side. Table 7 contains the results. The results show that the estimated coefficient value of informal competition is large, negative and significant at the 5 percent level in all the specifications. It is roughly equal in magnitude to the ones in the baseline model. For instance, for the final specification with all the baseline controls included, the coefficient value equals -5.8 (column 4, Table 7) compared to -5.7 in the baseline model (column 5, Table 4).

# 4.4 _Two-Stage Estimation_

So far, we used the cell averages of informal competition directly in the regressions. An alternative is two-stage estimation where in the first stage we regress the dummy equal to 1 if the firm competes against informal sector firms and 0 otherwise (henceforth, informal competition dummy) on its country level cell average. Predicted values of the informal competition dummy are obtained 24
iNaturalist visits by 21.5 percentage points, or by 47% relative to the mean among control areas (Figure 3, Row 3 and Figure S8). “Positive visits” refers to the presence of any iNaturalist observations in a given protected area-year; this condition is met slightly less than half of the time in the control group. We obtain similar results when we exclude observations submitted by potential protected area staff (Figure S9).

To supplement this finding, we also use eBird data as a proxy for tourism (SI A.4). On average, AP management increases the probability of positive eBird visits by 19 percentage points, or by 37% relative to the mean among control areas (Figure 3, Row 4).

For both tourism proxies, interpretation of these effects is not complicated because there is no trend in pre-period outcomes (Figures S8 and S10). We also obtain positive effects when we use the log number of iNaturalist or eBird visits as the dependent variable (Figure S11). Considering the two proxies together, it seems likely that AP management increases tourism.

# **2.3 Economic Development**

In addition to its efforts to conserve wildlife and stimulate tourism, AP initiates local economic development projects in communities adjacent to the protected areas they manage. We use data on “asset wealth” from Atlas AI, a private data provider, to test 8
or due to portfolio risk concerns, pass use still peaks at the first quarter of the loan duration.<sup>9</sup> The proportion of extension passes increases over time as clients have less remaining time to repay the skipped balance within the original loan duration.

We report the reasons for pass use given by clients in Panel A of Appendix Table 7.<sup>10</sup> Forty-one percent report using the pass to make an investment in the business and separate qualitative data indicates that these business investments include making use of an opportunity for discounted bulk buying of inputs, financing inputs for a large customer order and covering lost revenue from temporarily closing the business for renovations. Dealing with shocks is another important reason why clients use passes --- 44% of flexible clients in the phone survey sample who used a pass did so to deal with a personal or family calamity while 19% used a pass to deal with business problems.

Appendix Table 7 Panel B reports client satisfaction using data from the lender phone survey. To keep answers comparable across treatment arms, questions about satisfaction were asked _before_ questions about pass use. While most borrowers feel confident about repaying their loan five months after disbursement (p-value of t-test of equality between flexible and standard loan borrowers is 0.51), borrowers of the flexible loan are 7 percentage points more likely to report higher quality of service from FMSD. Among the reasons given for good service, the flexibility of the product was the only one that was statistically significantly different at conventional levels (p-value 0.00).

# **Default, Business, and Stress-Related Treatment Effects**

We estimate the average treatment effect of a flexible contract relative to a standard contract, pooling across initial offers as discussed above. Since the probability of assignment to a flexible credit offer changed during the experiment (see Figure 1 and Appendix A), we adjust the standard estimation equation to avoid potential bias from correlation of client characteristics with the assignment probability. Following Gibbons,
.70 in arm C. In the case of free provision, switching rates are varied from 0.3 to 0.75, consistent with findings from earlier work that evaluated switching after free provision. In the study area, the fraction of unsafe wells varied in the 16-27% range, while the earlier BAMWSP figures in these same villages varied from 40 to 90%. To cover a wide range of possibilities calculations are shown using a fraction of unsafe wells that is either low (20%), medium (40%) or high (80%). The results are summarized in Table 5, assuming that a policy maker is deciding how best to allocate a total and fixed budget of USD10,000.

While free provision maximizes switching opportunities within a given locality, charging a fee allows for a larger coverage—at the cost of reducing uptake among those with low willingness or ability to pay. Given the fixed budget, the total number of tests ranges from a maximum of 33,333 with individual sales, to a minimum of 7,692 with sales of tests supplied with a metal placard, so that the placards make arm C even more expensive (per test) than free provision without placards. Under the simplifying assumption that the probability of uncovering an unsafe well does not depend on the mode of supply, these figures imply that individual sales (A) would be the strategy that maximizes the number of unsafe wells uncovered, followed by group sales (B) and free provision, while sales with placard (C) would be the worst in this respect. However, the relative performance of the strategies changes once the different switching rates are taken into account. In particular, given the high switching rates observed in B and C, and the relatively low cost of group sales (B), it is group sales that maximize

> 20Jamil et al. (2019), using data from blanket free testing in Araihazar, estimates a total cost of _<_ USD1 per person whose exposure
in the energy mix<br>in the production process significantly promotes TFP in<br>the long run, while it is inconclusive in the short run.|
|Cao et al. (2021)|PSM-DID method is applied<br>to Chinese energy firms from<br>2000 to 2007.|The cleaner production regulations increased regulated<br>firms’ TFP, especially small firms. Also, the impact of<br>cleaner production regulations of state-owned firms<br>was greater than that of non-state-owned firms.|
|Peng et al. (2021)|A DID method is used to a<br>panel data of Chinese<br>industrial enterprises for the<br>1998-2007 period.|The market-based environmental regulation exerted<br>significant productivity-enhancing effects across all<br>types of industrial enterprises, with stronger effects<br>associated with privately owned, more productive, and<br>less pollution-intensive enterprises.|
|Aldieri et al. (2020)|The empirical method using<br>panel data on firms’ patents<br>on environmental innovation<br>from 9 European countries,<br>Japan, and the USA for the<br>2002-2007 period.|Green technology innovation and its knowledge<br>spillovers affect firms’ TFP significantly and positively<br>in all the investigated economic areas.|
|Leoncini et al. (2019)|A quantile regression method<br>is used for 5498<br>manufacturing firms in Italy<br>from 2000 to 2008.|There is a positive effect of green technologies on a<br>firm’s growth. A firm’s experience appears important<br>for the growth benefits of green technologies.|
|Stucki (2019)|The ordinary least square<br>model is applied using firm-<br>level data to Austria,|Firms with relatively high energy costs show<br>significantly larger marginal effects of green<
br>theoretical<br>standard<br>energy consumption and<br>actual consumption for<br>space heatingand cooling|Several<sup>20</sup>|Requires<br>consumption<br>data|
|Expenditure-<br>Based<br>Low-Income High<br>Costs<br>(Hills, 2011)|Requires two conditions:<br>Required<br>energy<br>expenditure above the<br>median plus remaining<br>income below the official<br>povertythreshold|Originated in<br>England,<br>possible with<br>HBS under<br>conditions|Possible with<br>HBS when<br>relying on HBS<br>income|
|After-fuel-costs<br>poverty (AFCP)<br>indicator<br>(Hills, 2011)|Household<br>equalized<br>income<br>(possibly<br>subtracting housing and<br>domestic energy costs) is<br>below<br>the<br>standard<br>threshold<br>of<br>60%<br>of<br>equivalized<br>national<br>income.|Originated in<br>England,<br>possible with<br>HBS under<br>conditions|Possible with<br>HBS when<br>relying on HBS<br>income|
|Minimum<br>Income Standard<br>(Moore, 2009)|Fuel costs (EHS) > Net<br>household income (EHS) –<br>housing costs (EHS) –<br>minimum<br>living<br>costs<br>(MIS).|England|Requires a<br>combination<br>of several<br>sources|

> 20 Energy needs of a dwelling, or data on indoor temperature (see Palma and Gouveira (2022)).

9
Being a household head or a household head’s spouse was associated with being less conservative for all categories, except for employment.

Respondents who were from a household owning a house and who were poor or B40 (being in the bottom 40 percent of the income distribution) exhibited more conservative attitude across all categories, but a less conservative attitude towards employment. To the contrary, respondents from a household living in an urban area and owning land exhibited less conservative attitude across all categories, but a more conservative attitude towards employment. Other individual characteristics such as age, financial situation, employment status, and personal habits, and household characteristics such as household size and composition did not clearly define respondents’ attitudes towards gender questions.

**Figure 2: Post-Estimated Distribution of Gender Attitude Composite Indexes by Migrant Type**

<!-- Start of picture text --> Standardized Employment Index Standardized General Index Standardized Decision Making Index<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Standardized GBV Index Standardized Extreme Index<br>-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6<br>Non-migrants Returned migrants from Gulf Countries<br>Returned migrants from Saudi Arabia<br>2.5 2.5<br>2.5<br>2 2<br>2<br>1.5 1.5<br>1.5<br>1 1<br>1<br>.5 .5 .5<br>0 0 0<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br><!-- End of picture text --> Data source: KMS 2003, 2013 The comparison group
raises quite different policy and empirical questions.

Although the size of the sample for each region is sufficiently large to make it representative of the region as a whole (see Figure 2), the above qualifications make it clear that one should take the regional averages shown below only as pointers for further analysis. For example, the extremely low civil servants' compensation in Eastern Europe and the former USSR is, in part, a clue to the many in-kind subsidies and benefits enjoyed by public employees in that region. In a real sense, it is the country-bycountry data in the Appendix which matter, although even these can be quite misleading in the absence of knowledge of the country and the circumstances.

# 2. THE KEY FINDINGS ON GOVERNMENT EMPLOYMENT

Table 1 and Figure 3 show civilian employment in central and local government administration, and teaching and health employment in the various regions - as percentage of population. The key findings are as follows:

- worldwide, total government civilian employment currently averages (on an unweighted basis for all the countries of the sample) about 4.7% of population. It is relatively largest in the developed market economies of the OECD (7.7% of population), and second largest in Eastern Europe and the former Soviet Union -

- (Europe and Central Asia ECA), at 6.9% of population. Government employment is relatively smallest in Africa and Asia (respectively, 2.0% and 2.6% of population). Latin America and the Caribbean (LAC), and the Middle East and North Africa (MENA) are in between, with employment accounting for about 3.0% and 3.9% of population respectively;
to a 20 percent increase in area flooded in Bangladesh. Severity of extreme floods, such as the 20-year flood event, is estimated to increase marginally. Another study conducted in 1998 concluded that an increase in monsoon rainfall by 11 and 20 percent will increase surface runoff by 20 to 45 percent in Bangladesh (Ahmed and Alam, 1998).<sup>13</sup> For Bangladesh alone, the median predictions of 16 General Circulation Models for three emission scenarios considered by the IPCC AR4 point to a warming of 1.55  C and an increase in precipitation of 4% by 2050 (Yu et al. 2010).<sup>14</sup> In sum, the warmer and wetter climate predicted for the GBM basin by most of the climate-related research indicates vulnerability of Bangladesh to severe monsoon floods will increase with climate change.

# 4. Exposure to Inundation Risk

In order to assess potential change in the extent and depth of inundation in a changing climate, this research conducted flood modeling for two scenarios. The 1998 exceptional flood event (see Box 1) with a return period of 90 years has been considered as the baseline scenario. It is the counterfactual against which future climate change risk is measured. For the climate change scenario, changes in the monthly precipitation and temperature predicted by the GCM- MIROC 3.2 under the A2 emission scenario have been considered.<sup>15</sup> Since flooding in Bangladesh depends on the rainfall in the entire GBM basin, predictions of precipitation and temperature between 1998 and 2050 for each 50km x 50 km grid covering GBM basin from the MIROC 3.2 have been applied to the historical monthly averages for 1998 to approximate the monthly precipitation and temperature over the entire GBM basin in 2050.

Inundation risk exposure for each scenario is then determined in four steps.

_First_ , the Ganga Brahmaputra Meghna model in the MIKE BASIN platform, developed at the Flood Forecasting and Warning Centre (FFWC) of Bangladesh Water

> 13 An Earlier study indicates
</sup> we approximate female wage rates at the firm level by assuming the same ratio of female‐to‐ average wages in a given sector as in the gender‐disaggregated wage data from the World Bank Household Surveys. Total female compensation is then obtained by multiplying the female wage rate with the number of female employees from the Enterprise Surveys.

The analysis examines two measures of _trade participation:_

> 10 These are obtained dividing a firm‘s total compensation by its total number of employees.

7
1.84|
|HEI characteristics (PCA Score)|10.48|4.93|2.30|3.32|
|Country fixed effects|7.37|31.57|38.58|71.19|
|Field of study fixed effects|21.08|10.99|6.21|8.13|
|R-squared|0.080|0.087|0.134|0.178|
|Obs.<br>_Source_: Own estimations using WBSCPS data. For varia|1,526<br>ble definitions, see|1,693<br>Section 3 and A|1,270<br>ppendix 1.|1,752|

_Source_ : Own estimations using WBSCPS data. For variable definitions, see Section 3 and Appendix 1. _Notes_ : This table present results from the R-squared Shapley-Owen decomposition for the regressions reported in Tables A11 (dropout rate and extra time-to-graduate) and A12 (formal employment and wages), estimated with WBSCPS program-level data. For each regression, the table shows R-squared (net of the variation explained by survey noise variables) and the percent of the (net) explained variation attributable to each set of variables. “Obs.” indicates number of programs (equal to the number of observations in the underlying regression).

44
advantage of the IFR data is the consistency and comparability across many jurisdictions which is typically lacking with firm level robot data, allowing us to consider 59 exporting countries.<sup>10</sup> We include country-industry controls for employment and labor productivity (real value-added per worker in constant price 2015 USD), which are obtained from the UNIDO Industrial Statistics Database.<sup>11</sup>

# **_ii. Measuring quality_**

Export quality is derived from detailed United States HS 10 digit import data which we match to the robotics data by country and industry over time. We exclude robot products from the trade data. We harmonize the HS import data, in order to consistently measure HS 10 digit products over time, following the procedure of Pierce and Schott (2012).<sup>12</sup> We use US import data, due to its availability at the 10-digit level, and so allowing the measurement of quality for detailed product varieties.<sup>1314</sup> Historically, the trade literature has used unit prices (trade values per unit quantity) as a proxy for quality (Schott, 2004; Hallak, 2010). However, the literature has recently recognized that a range of factors other than quality itself may influence prices such as transport costs, tariffs, competition and productivity. As a result, our main export quality measure for this paper follows the microfounded model of Khandelwal et al (2013) and Breinlich et al. (2016). Demand for each variety is given by: 10 Canada and Mexico have been dropped from the sample due to inconsistent data collection over the sample period. Before 2010, the United States, Canada and Mexico robot sales were classified jointly as North America. 11 Value-added in USD is deflated to constant prices using US CPI-U, consistent with the treatment of trade values. Other controls in the UNIDO Industrial Statistics Database, such as wages, capital investment or number of enterprises, are not widely available for our sample
sup>16</sup> Collective interest representation is sometimes considered a key determinant of basic rights at work and an important source of subjective job satisfaction, and it has been included in some JQMs (ILO 2008; Leschke and Watt 2014). Another view is that labor organizing and bargaining rights are important, but are not job quality outcomes per se. Others argue that, instead of including these indicators in the JQMs, it is useful to analyze the relationship between good jobs and unionization (Brummund, Mann, and Rodríguez-Castelán 2018; Sehnbruch et al. 2020). In Latin American countries, Brummund, Mann, and Rodríguez-Castelán (2018) find that union membership is associated with high levels of job quality, but that a small share of workers (7 percent) in general are unionized. Data limitations prevent a similar analysis from being conducted here.

# **b. The Alkire-Foster method**

The selected method for measurement and aggregation across dimensions adapts the Alkire-Foster theoretical framework originally developed as an approach for measuring multidimensional poverty (Alkire and Foster 2011). This method assigns indicators to each dimension and defines deprivation cutoffs (threshold levels of each indicator). If individuals are below the deprivation cutoff in a dimension, they are considered deprived in that dimension. The deprivation indicators of each of the dimensions are then aggregated for each individual, creating a multidimensional measure of poverty.

> 15 These indicators are likely to be correlated, though, with other indicators included in the measure of job quality. For example, health and safety issues may be correlated with working excessive hours. Also, informal learning may be proxied ex post by years of experience in a given job, included as tenure in the job stability dimension, and a potential proxy for formal learning is whether the employer is a formal firm, also included in the job stability category. Informal firms employ a predominantly low-skill workforce, have lower access to financing and capital markets and services and are thus less likely to invest in the human capital of their