rafmacalaba/datause-encoder-data huggingface dataset:
survey collects data from over 150,000 Indian establishments. In this respect, the surveys are comparable to the Annual Survey of Manufacturing conducted in the United States, with the Indian sampling frame being about three times larger.

Establishments are surveyed with state and four-digit National Industry Classification (NIC) stratification. The surveys provide sample weights that we use to construct populationlevel estimates of total establishments, employment, and output by district. Districts are administrative subdivisions of Indian states or territories that provide meaningful local economic conditions. The average district size is around 5,500 square kilometers—roughly twice the size of a U.S. county—and there is substantial variability in district size (standard deviation of ~5,500 square kilometers). Indian districts can be effectively considered as self-contained labor markets and, to some degree, economic units.

Our surveys record economic characteristics of plants like employment, output, and raw materials. When we estimate plant-level production functions, we use the reported traits directly. Most of our analysis considers aggregated measures of manufacturing activity in locations. For this purpose, we sum the activity of plants up to the district or district-industry level, combining the organized and unorganized sectors and using sample weights. We use the two-digit level of the NIC system for calculating industrial specialization and diversity for districts. This level of aggregation contains 22 manufacturing industries.<sup>1</sup> Our core sample contains 429 districts. This sample is smaller than the total number of districts in India of 630, but it accounts for almost all of the plants, employment, and output in the manufacturing sector throughout the period of study. The reductions from the 630 baseline occur due to requirements that manufacturing employment be observed in every period for the district (i.e., we have balanced panels of districts from 1989 to 2010). Even with these requirements, some districts have a small number of observations, and this could be worrisome given that our data do not constitute a complete census of Indian businesses and have state-level survey stratification. We consider several checks below (e.g., excluding smaller districts) to verify that the results discussed are robust to these considerations.

> 1 We also considered three-digit level metrics. Using finer industry categories has the challenge of larger numbers of extreme values, especially for the specialization index. The two-digit level balances meaningful industry groups with better measurement.

5
population in the Dominican Republic increased at a faster rate, 5.5 percentage points, than in Latin America and the Caribbean as a whole, standing at 2 percentage points.<sup>6</sup> Other approaches show a more dramatic figure. According to the 2015 Global Assessment Report (GAR), 96.2 percent of total losses from natural disasters between 1900 and 2014 are associated with hydrometeorological events. Similarly, the 2015 Germanwatch Global Climate Risk Index scores Dominican Republic as the 8<sup>th</sup> most vulnerable country in the world to climate change impacts. Importantly, because of incomplete or inadequate risk management strategies, those vulnerable households may be a disaster away from falling below the poverty line or sliding further back into poverty. Shocks created by disasters have regressive distributional effects as vulnerability to climate shocks is higher for the poorest households.<sup>7</sup> The Dominican Republic is one of the more hurricane prone countries within the Atlantic Basin, averaging a damaging storm about every 3 years.<sup>8</sup> A recent telling example of how damaging these can be is Hurricane Georges (1998), a category 4 storm which is estimated to have induced damages in the Dominican Republic of up to USD 1.2 billion (1998 USD). Nevertheless, despite such potentially large losses, it is a priori not clear by how much these translate into actual reductions in economic activity and for how long such an effect might persist. Indeed, although there are now a number of studies that have empirically examined this issue, these have tended to pool data across countries, thus estimating only an average country effect. In this regard, generally the impact of tropical cyclones has been found to have on average a significant, particularly for large events, but

> 6 See Ferreira et al (2013).

> 7 See Báez et al (2017).

> 8 See Bertinelli et al (2016).

3
## **Analytical methods used**

This paper is utilizing several relatively simple and straight-forward statistical methods.

1. T-tests for independent samples were used when analyses of continuous data were performed. T-test scores are quoted together with significance levels.

2. Chi-tests were used when analyses of categorical data had to be performed. Chi-test results are also quoted together with their significance levels. Chi-tests were performed only in cases when visual inspection of cross tabulation confirmed that the potential statistical significance could be interpreted, e.g. when the causality is clear and unambiguous.

3. Analysis of variance was performed to analyze relations of continuous and categorical variables. F-statistics were quoted with their significance levels. In addition Scheffe tests were also performed to determine the exact categories between which the significant difference exists.

4. Ordinary Least Square Regression models were used to test whether behavior of variables of interest affects the dependent variable (cost of compliance).

# **2.  Outsourcing patterns**

It is given that all companies that function as officially registered entities must register with the tax authorities and file tax returns on a regular basis. Tax compliance work can be done either entirely in-house, fully outsourced to outside specialists, or be a mixture of in-house efforts and fee-for-service outsourcing (referred to in this paper as “partial outsourcing”). The South Africa Survey of Formal SMMEs allows an analysis of these differences from various angles.<sup>13</sup> Figure 1 shows that different legal forms of businesses have somewhat different approaches to their tax compliance work. The level of outsourcing is somewhat higher among sole proprietors and Close Corporations. Analysis of variance confirms that Close Corporations and sole proprietors have a significantly higher outsourcing index<sup>14</sup> than companies (PTY Ltd, which are usually somewhat larger firms) and differences also exist between Partnerships and Close Corporations (see Table 1 below).

> 13 The full questionnaire is presented in Annex 1. 14 See Annex 2 for a discussion of how the Outsourcing index was constructed.

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− − robust with respect to outliers compared to using ( _yi y_ )( _y j y_ ) as the dependent variable.

8See Mengistae and Honorati (2009) for details on the survey methodology. For a thorough report on the survey, see the World Bank (2009).

9See H&H Consultancy (2008) for details on the survey methodology.

10Maps of the survey locations are shown in section 3 in the online appendix.

11For some of our outcome variables, there are missing values in the baseline sample. Therefore, some of our regressions will be estimated on a smaller sample than the baseline sample.

12Fafchamps and Söderbom (2006) argue that the ratio of nonproduction workers to total employment indicates the ease with which firms manage their labor force. They show that many African firms have a high ratio of nonproduction workers to total employment despite the relative simplicity of their production processes.

> 13Because the majority of firms (approximately 70 percent) list three names, there is truncation in the observed network because some existing links are not recorded. This problem may cause a downward bias in the estimated network effects.

14Here, the three levels of usage are combined. The results for the alternative specifications that model email and website use are shown separately in tables S1.E and S1.S, columns [1]–[2], in the online appendix. The results are similar to those shown in table 3.

15Columns [3]–[8] in tables S.1E and S1.S in the online appendix show the results for alternative specifications in which the reputation variables are defined as binary variables in the following way: yes = 1 and maybe or no = 0. The results are similar to those in table 5.

16Firms are defined as having a similar size if the absolute log of the difference in employment is less than 0.2.

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<!-- Start of picture text --> i<br>i”<br>a?oS<br>P<br>0 100,000,000 200,000,000 300,000,000 400,000,0¢<br>Per capita expenditure (LCU/person/year)<br><!-- End of picture text --> <!-- Start of picture text --> i<br>=<br>fa)<br>8<br>g<br>°<br>0 5,000,000 10,000,000 15,000,000 20,000,001<br>Per capita expenditure (LCU/person/year)<br><!-- End of picture text --> <!-- Start of picture text --> ai<br>&<br>i<br>Hy=<br>;<br>agi<br>6<br>°<br>C) 5,000,000 10,000,000 15,000,00<br>Per capita expenditure (LCU/person/year)<br><!-- End of picture text --> <!-- Start of picture text --> i5<br>5i<br>P35<br>ae28<br>iai<br>°<br>° 5,000,000 10,000,000 15,000,00<br>Per capita expenditure (LCU/person/year)<br><!-- End of picture text --> <!-- Start of picture text --> ao i<br>i<br>2aeiaeii 242422<br>5<br>6<br>° °<br>C) 5,000,000 10,000,000 15,000,00 0 20,000,000 40,000,000 60,000,000 80,000,001<br>Per capita expenditure (LCU/person/year) Per capita expenditure (LCU/person/year)<br>i 8<br>ai<br>2<br>8;; 38888<br>rs<br>° 2 ie ta it eunacarte SE ° C) 5,000,000 10,000,000 15,000,001<br>Per capita expenditure (LCU/person/year) capita expenditure (LCU/person/year) expenditure (LCU/person/year) Per capitaexpenditure (LCU.Ipersonyear) capitaexpenditure (LCU.Ipersonyear)expenditure (LCU.Ipersonyear) (LCU.Ipersonyear)Ipersonyear)sonyear)year) se<br><!-- End of picture text --> <!-- Start of picture text --> ao i<br>i<br>2aeiaeii 242422<br>5<br>6<br>° °<br>C) 5,000,000 10,000,000 15,000,00 0 20,000,000 40,000,000 60,000,000 80,000,001<br>Per capita expenditure (LCU/person/year) Per capita expenditure (LCU/person/year)<br>i 8<br>ai<br>2<br>8;; 38888<br>rs<br>° 2 ie ta it eunacarte SE ° C) 5,000,000 10,000,000 15,000,001<br>Per capita expenditure (LCU/person/year) capita expenditure (LCU/person/year) expenditure (LCU/person/year) Per capitaexpenditure (LCU.Ipersonyear) capitaexpenditure (LCU.Ipersonyear)expenditure (LCU.Ipersonyear) (LCU.Ipersonyear)Ipersonyear)sonyear)year) se<br><!-- End of picture text -->
marginal. Nonetheless, the efficiency term is positive in all countries, indicating no allocative inefficiencies in any of the eight countries over the period 2001 to 2004.

Figure 10 graphically presents the relative percentage contribution of changes in each business environment indicator to log TFP growth over the period 2001 to 2004 calculated according to equation (8) by country. That is, each bar in Figure 10 shows the relative weight of the average change in each business environment indicator with respect to the total impact of the changes in all six business environment indicators for the respective country sample. Because all coefficient estimates from the OLS regression of equation (4) are positive (see column 7 of Table 9), a positive (negative) relative percentage indicates an improvement (worsening), on average, in the respective business environment indicator. For example, infrastructure quality improved, on average, in all countries over the period 2001 to 2003, while labor quality, on average, increased in the Czech Republic, Estonia, Poland, Romania, and Ukraine, but decreased in Bulgaria, Croatia, and Serbia.

For the sample as a whole (first column in Figure 10), all aspects of the business environment, on average, improve over the period 2001 to 2003. Improvements in the infrastructure quality and governance indicators have relative contributions of 27.8 and 22.7 percent. Changes in the labor quality (7.5 percent), financial development (5.6 percent), labor market flexibility (2.6 percent), and competition (2.2 percent) indicators account for the remaining positive business environment impacts on log TFP growth. These results from the Olley Pakes decomposition of log TFP growth by country are consistent with the OLS regression results for the full sample presented in Table 9.

In Bulgaria, only two aspects of the business environment improve over the period 2001 to 2003. Relative to the total change in all six business environment indicators, increases in the infrastructure quality and financial development indicators contribute 14.7 and 6.0 percent, respectively, to log TFP growth over the period 2002 to 2004. Conversely, negative changes in the governance, labor market flexibility, labor quality, and competition indicators dominate the positive contributions of increases in infrastructure quality and financial development. A worsening in the competition indicator accounts for a third (33.3 percent) of the total impact of business environment changes on log TFP growth over the period. Negative changes in labor quality (−24.8 percent), labor market flexibility (−14.7 percent), and governance (−6.5 percent) account for the remaining impacts on log TFP growth.

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 Second-stage regression (3) uses _P j_ predicted value of the actual PAF treatment that is obtained through a first-stage regression in (2). The coefficient  _iv_ from the second-stage IV regression is the TOT impact estimate on the outcome of interest. Cluster-corrected standard error associated with this coefficient indicates statistical significance. We also correct for the standard errors to account for the fact that predicted values, and not the actual values of the P, are used in the second-stage regression. To generate consistent estimates of the parameter coefficients and their standard errors, we implement the empirical strategy using “ivreg2” command in STATA (Baum et al. 2007), with “cluster” option and appropriate specification to account for the instrumentation of the interaction term in the model when applicable.

The validity of IV estimation strategy requires that two conditions are met: the instrument is _exogenous and relevant_ . Exogeneity means that the instrument should not be correlated with outcomes (except through actual intervention) or with unobserved variables. Relevancy implies that the instrument should be correlated with the actual treatment status. The first condition is met by construction in our evaluation, since the instrument A is randomly assigned. The second condition is met if there is a strong correlation between random assignment A and actual treatment status P and this can be tested using the results from the first-stage regression.

# **4. Results**

We start with comparison of baseline outcome variables as well other characteristics between the two randomly assigned treatment and control groups. As seen in annex table 1, the mean differences between the two types of villages are not statistically different, suggesting that the randomization was generally successful.

As described above in section 2 and table 1, actual PAF treatment status for a village did not follow random assignment perfectly. However, random assignment strongly predicted actual treatment status (72% PAF intervention from the treatment assignment, compared to only 39% from the control-assignment, thus a 33% differential take-up). We note that the actual treatment status may have additionally been driven by implementation readiness, pressure from a particular village, geography, socio-economic conditions and other factors. The first stage regression estimates, as presented in annex table 2, confirm that this is indeed the case. Probability of actual PAF intervention is positively associated with random treatment assignment (instrument is relevant), “poorest” third of villages as ranked by the NGOs, higher elevation, further distance from district headquarter, higher proportion of uneducated households heads, higher proportion of 8
# **4 Empirical Framework**

To draw a comparison with night lights, we use its core empirical framework. After introducing it, we explain its key identification problem, and our new data-based strategy to overcome it.

## **4.1 Chen and Nordhaus (2011)-Henderson et al. (2012) Model**

Here we review the error-measurement framework introduced by Chen and Nordhaus (2011) and Henderson et al. (2012), which revolves around the fact that there is classical measurement error in GDP.<sup>10</sup> The variance of this error varies by-country data quality grade. Suppressing subscripts for now, for countries with data quality grade _g_ =A, _. . ._ , E we write: where _z_ is log GDP measured in national accounts, _y_ is log unobservable true output or income, and the variance of _εz,g_ is denoted _σz,g_<sup>2.A log signal of economic activity</sup><sup>_x_(e.g.NO2or lights)</sup> is related to _y_ via: Measurement error in the signal is assumed uncorrelated with the measurement error in GDP, which is defensible for satellite signals. _β_ is the _growth elasticity_ of the signal. Combining the last two equations: where _ψ_ = 1 _/β_ the inverse growth elasticity. Due to errors-in-variables (correlation between _x_ and _εx_ ), the OLS estimator _ψ_<sup>�</sup> is biased. Nonetheless, this is still a best-fit relationship for � producing the best-fit values, called a _proxy_ , _z_ = _ψx_<sup>�</sup> . In all regressions, the model we estimate when constructing proxies is the following, with all subscripts now explicit for completeness: Above, _eijt_ is an error, for time _t_ , place _j_ , and sensor _i_ . _dt_ is a time fixed effect and _cj_ is a place fixed effect, both of which are systematically included in Chen and Nordhaus (2011) and Henderson et al. (2012). Our controls are distinct in two respects. First, our newer night lights sample contains observations from both the historical DMSP-OLS sensor from 2005-2013, and the more modern VIIRS sensor from 2014-2020. Thus, we include a sensor fixed effect, _bi_ . Second, we systematically include a country-specific time trend _aj · t_ unless otherwise noted. Thus, the specifications of primary interest to us involve off-trend fluctuations, unlike previous

> 10 We use the notation of Henderson et al. (2012) with minor changes. Chen and Nordhaus (2011) named the model (p. 8,590).

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<!-- Start of picture text -->
1.5% mlnterquartile range Median<br>1.0%<br>te)<br>0.5%<br>0.0%<br>-0.5%<br>Oil exporters Oil importers<br><!-- End of picture text -->

<!-- Start of picture text --> 0.8% mlnterquartile range Median<br>0.6%<br>0.4%<br>OD aoe0<br>ve-U.4/0<br>-0.6%<br>Coal exporters Coal importers<br><!-- End of picture text --> <!-- Start of picture text -->
1.0% mlnterquartile range Median<br>0.50%07/0<br>0.0%0<br>-0.5%<br>Large Small<br><!-- End of picture text -->

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1.5% mlnterquartile range Median<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>Surplus Deficit<br><!-- End of picture text -->

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1.0% mlnterquartile range Median<br>0.5%<br>0.0%<br>0.5%<br>-1.0%<br>More open Less open<br><!-- End of picture text -->
Our findings demonstrated the value of data for targeting users and tracking them to measure ad effectiveness, contributing to the ongoing debate on privacy and the use of third-party data in digital marketing. We proposed a series of micro-targeting techniques to maximize impacts in subpopulations of interest. By integrating privacy considerations into our experimental methodology, our approach highlighted how first-party data, directly collected from users on social media, can inform targeting and help reach the most valuable segments of the population—in our context, those most at risk of malaria. The methodology used in this paper is sector-agnostic and can be applied to both social and commercial campaigns.

## **Funding and Competing Interests**

This research has received funds from the World Bank’s Development Impact Department (DIME) and the Facebook’s Campaigns for a Healthier World initiative. Three authors do not have any competing interests to this work. One author has ownership interests in Virtual Lab LLC, a company that uses the open-source methodology described in the paper.

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