rafmacalaba/data-use-mentions-extended huggingface dataset:
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# ANNEX: SOCLAL SURVEY BACKGROUND

To study the issues raised in this work, we used the findings of surveys conducted in Russian cities in 1992-1996. They were all conducted by a group of sociologists headed by the author of this paper who works at the Institute of Economic Forecasting, Russian Academy of Sciences.

Uniform sampling and survey techniques were used. In all cities, the sample was chosen from the existing housing units (i.e., units were selected and their occupants were interviewed). In all cases, the sample units were randomly drawn. For each household, one adult member was interviewed. The questionnaires were developed by the staff of the Institute of Economic Forecasting, Russian Academy of Sciences, Moscow and the Urban Institute, Washington. The findings of sociological surveys based on Russian data and presented below were obtained by primary data processing of the relevant studies.

- _USAID-World Bank Survey in Seven Russian Cities_ (1993-1994) was conducted as part of the Housing Sector Reform Project (World Bank, 1994; Struyk, 1994). A research team of the Institute of Economic Forecasting, Russian Academy of Sciences (head, Oleg S. Pchelintsev) was Involved in this work. In December, 1993-January, 1994, a group of sociologists headed by the author of this study conducted household surveys in Moscow, Nizhny Novgorod, and Barnaul, in which 2,150, 1,002 and 806 households respectively were polled. The Moscow survey used the sample developed for the Moscow Longitudinal Household Survey, 1993, second wave (see below). The main purpose of the surveys was to obtain information about the actual housing situation in the cities and to assess the structure of demand for housing. The main findings are presented in Guzanova et al., 1994a; Guzanova et al., 1994b; Guzanova et al., 1994c; Guzanova, 1994; Pchelintsev, 1994; Abankina and Zyev, 1994. The technique for the study of the households' needs, plans and preferences as regards housing, as
referring only those in the bottom 18 percent nationally (the national poverty rate according to SWIFT 2017/18 survey

> 4 Throughout the paper, Social safety net (SSN), Social Protection Network (SPN), national program, MoLSA’s cash transfer program etc. are used loosely and interchangeably to refer to the government’s cash transfer program delivered by the Ministry of Social and Labour Affairs (MoLSA).

> 5 Due to the CLCI’s CT program coverage, the database is limited to the Northern or ‘conflict-affected’ governorates: Anbar, Diyala, Kirkuk, Ninewa, Salah al-Din.

3
sample and global funds invest in only 22 firms. In other words, a considerably large proportion of the number holdings of specialized funds in developing countries are not shared by the global funds.

_[Insert Table 1 about here]_ The other type of data collected is firm characteristics on a yearly basis. We use three types of data: information on foreign activity (i.e. whether and when a firm issues ADRs), balance sheet data, and trading data. On foreign activity, we have data from the Bank of New York, which covers the three major stock exchanges in the U.S.: NYSE, NASDAQ, and AMEX.

The Complete DR Directory contains the list of current DR programs and the effective date of each program. However, to account for DR programs that were terminated during the 1997-2003 period, we use an additional database, also provided by the Bank of New York, which lists all terminated DR programs. These two databases allow us to determine whether a firm has issued (or have outstanding) ADRs on any year in our sample.

We collect firm-level balance sheet data from Worldscope (Thomson Financial Company). This constitutes our “universe” of firms covering the period 1997-2003. Based on an extensive literature characterizing portfolio choice with balance sheet data, we have grouped the variables into 4 categories according to their effect on portfolio choice: firm size measures, performance variables, a prudence variable, and financial health variables.<sup>18</sup> In particular, the following attributes are considered:

> 18 See Falkenstein (1996), Kang and Stulz (1997), Dahlquist and Robertsson (2001), Gompers and Metrick (2001), Edison and Warnock (2004), among many others.

18
.008<sup>***</sup>|
|<br>_(initial level, logs)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|ICRG Political Risk Index|0.00475<sup>***</sup>|0.009<sup>***</sup>|0.004<sup>***</sup>|0.00381<sup>***</sup>|0.008<sup>***</sup>|0.003<sup>***</sup>|
|_(principal components)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Credit to private sector|-0.0125<sup>***</sup>|-0.015<sup>***</sup>|-0.017<sup>***</sup>|-0.0148<sup>***</sup>|-0.015<sup>***</sup>|-0.019<sup>***</sup>|
|<br>_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Government consumption|0.00572<sup>***</sup>|0.006<sup>**</sup>|0.011<sup>***</sup>
this basis can be misleading. In two countries producing a similar value added, the one with the most specialised plants will have a higher gross output because there will be more interplant shipments for intermediate processing. In order to eliminate this type of duplication and other differences in the degree to which plants use external inputs, the concept of value added was developed, and has now become quite familiar to the general public, because tax systems, particularly in EC countries, use this concept to measure economic activity. With the value added concept, the intermediate inputs used by a manufacturing establishment are deducted before arriving at the measure of output. All the manufacturing censuses we used show value added as well as gross output.

One major problem which arises in reconciling the census information with the national accounts, is that industrial census definitions of value added are less sophisticated and less standardised.
# **1. Introduction**

A striking feature of the limited literature that exists on country size is that country size does not seem to matter much for various social and economic variables. For example, a recent study by Rose (2006) uses panel data for 200 countries and for over 40 years for a large number of socioeconomic variables. However, the study fails to find any significant impact of country size on for example, income level, inflation, material wellbeing, health, education, quality of a country’s institutions and a number of different indices and rankings. The only exception is the volume of international trade (exports plus imports as a percentage of GDP) with small countries showing higher trade volumes than large countries. The present paper takes a novel approach towards uncovering the relevance of country size by using micro or firm-level data on firms’ experience with tax administration, an important but neglected aspect of the business environment. The results show that country size matters and in a robust way for the quality of tax administration. Implications of our findings for the broader literature are discussed.

Empirical work on the effect of country size on various social and economic variables is rather limited. However, theoretical studies suggest a number of channels through which country size may matter. For example Alesina and Spolaore (2003) list a number of country size related benefits that include lower per capita costs of public goods (monetary and financial institutions, judicial system, communication infrastructure, police and crime prevention, public health, etc.) and more efficient tax systems; cheaper per capita defense and military costs; greater productivity due to specialization (though access to international markets may reduce this effect); greater ability to provide regional insurance; and greater ability to redistribute income within the country. Against these potential benefits, there are some disadvantages of being large. For example, it is argued that larger countries have more diverse preferences, cultures and languages 2
hypothetical full size of total enrollment. This would be the full size of enrollment if there were no dropouts or repetitions in subsequent years. Table 5.5 compares the hypothetical number of students per academic classroom in high schools in school years 2002/03 and 2007/08 by our sample divisions. It is important to note that large numbers of new classrooms were added in Neuva Vizcaya (non-TEEP) and Antique and Leyte (both TEEP). Interestingly, we observe that this measure of school quality has converged between adjacent TEEP and non-TEEP divisions.

# **6. Results**

In this section, we discuss the empirical results.

# **_Results on Schooling Attainment_**

To provide appropriate weights for the analysis, we estimate a logit selection function of TEEP schools in which we control for initial school quality and the poverty level of the community of the school location. School quality is proxied by pupil–teacher ratio, total student enrollment in grade 6, and total classes provided by the school. These variables are based on the 2002/03 Basic Education Information System (BEIS) data. Community-level poverty is proxied by the mean of the logarithm of the value of the consumer durables of the sampled households with students at the school. We also include school location dummies and their interactions with pupil–teacher ratio and community poverty. The results are reported in Table A.1. Although only three variables are statistically significant, the overall explanatory power is favorable (the pseudo R-squared is 0.144).

Tables 6.1, 6.2, and 6.3 report the regression results of the three schooling outcomes: years of schooling, number of repetitions in high school, and college entry, respectively.<sup>24</sup> In each table, we report the result for the untrimmed and unweighted sample (full sample) and the trimmed and weighted sample. In both samples, we estimate two specifications: with and without cohort-specific 24In all the estimations below, we restrict
Table 11: Placebo Test: Gorontalo (Restaurants)

|**Control vs. POS reci**|**pient resta**|**urants**||
|---|---|---|---|
|**Dependent Variable:**|**(1)**|**(2)**|**(3)**|
|**Log (restaurant tax receipt)**|**RPOS**|**RPOS-**<br>**Grab**|**TMD**|
|ATET (given RPOS vs. not)|0.149**<br>(0.059)|||
|ATET (given RPOS-Grab vs. not)||0.343***<br>(0.105)||
|ATET (given TMD vs. not)|||-0.055<br>(0.098)|
|Constant|0.913***|0.832***|0.939***|
||(0.025)|(0.026)|(0.026)|
|Observations|5,969|5,086|5,201|
|Adj. R<sup>2</sup>|0.001|0.005|0.001|

Robust standard errors in parentheses *** ** * _p <_ 0 _._ 01, _p <_ 0 _._ 05, _p <_ 0 _._ 1 Table 12: Placebo Test: Gorontalo (Hotels)

|**Control vs. POS recipient **|**hotels**||
|---|---|---|
|**Dependent Variable:**|**(1)**|**(
this backdrop, this paper contributes to the existing body of research by focusing on the impact of country-specific energy price fluctuations during 2019-2023 on firms in emerging economies using the World Bank Business Pulse Survey—a dataset not used yet for this type of analysis in the literature— together with the CPRS classification of Battiston et al. (2022) to gauge the risk exposure of sectors to (the intensity of treatment by) the energy price fluctuations. Furthermore, the paper contributes to the literature by shedding light on the specific context of emerging economies, employing rapid survey data to grasp the nuances of business cycles, using the quarterly country-specific electricity price tariffs for businesses, matching the latter with the month in which a firm was surveyed, and highlighting the differentiated effects of sectoral energy intensity in conjunction with firm-level energy efficiency measures as indicators of a firm's exposure to energy price fluctuations and preparedness to manage energy price shocks.

Cali et al. (2022, 2023) that electricity price increases, on average, lead to higher productivity and sales for firms. However, the magnitude of this positive effect is significantly reduced for firms that have not implemented energy efficiency measures, suggesting that such initiatives are critical for firms to adapt to evolving energy market trends. In addition, when the baseline results are subjected to a battery of robustness tests the results do not survive in all alternative specifications. By contrast, we find that rising electricity prices exert a negative effect on employment, particularly in energy-intensive industries where energy efficiency measures are absent. This result highlights the vulnerability of labor markets in sectors heavily reliant on energy (electricity) inputs, especially in developing economies where the adoption of efficiency-enhancing technologies often lags.

3
be located after multiple attempts spanning at least two days, if they refused to participate, or if they were already part of the RMG or WL sample.<sup>9</sup> This sampling strategy enables comparison of household characteristics between the RMG-eligible and RS groups to establish whether the households granted entry to the lucky draw were truly among the villages’ poorer households. Second, we administered the village heads a targeting survey to probe the key factors influencing their selection of welfare beneficiaries and to understand their underlying perceptions regarding what constitutes poverty.

> 8 Although the RMG program was implemented in 86 villages, one village was dropped from the analysis because recruitment of beneficiaries there deviated from the model described above.

> 9 There was a total of 274 replacements: 151 households were replaced because they were already in the RMG/WL sample. The remaining replacements were due to: absence of women of eligible age in the household (36); households were absent due to harvesting season (20); households were no longer in the village (33); refusal (14) and other reasons, such as mental illness or temporary absence (20).

6
1 if a smoke plume covered that pixel that day, and value 0 otherwise. We summed the daily images to obtain one final raster for low and high smoke, where each pixel represents the total number of days during the growing season that the pixel was exposed to a low- or high-density plume (Figure 5 panel **B** ).

# **4.1.2 Corn and soybean exposure to smoke**

We use Google Earth Engine<sup>31</sup> to access the USDA’s Cropland Data Layer (CDL),<sup>32</sup> which maps crop types across the coterminous US at 30m spatial resolution (Figure 5 panel **C** ). CDL is available from 2008 to 2020 and allows us to precisely map corn and soybean exposure to smoke plumes. We overlaid CDL with our growing season low- and high-density smoke rasters (Figure 5 panel **D** ), and, for each county and year between 2008 and 2020 (excluding 2009), we computed the average number of days that crops in a given county were under low- and high-density smoke for corn and soybeans from May 1 to September 30. These values are the treatment variables on the right-hand side of Equation (1).

# **4.1.3 Meteorological variables**

To measure temperature and precipitation in each county-year, we collected an updated version of the modified PRISM dataset used in ref.<sup>33</sup> We modified this data to pixel-weight it by crop 16
21 The earlier urban data set presented in Appendix Table 3 of Ingram and Liu (1997) includes 35 cities, each with observations at two points in time. In the current urban crosssection analyses (see Appendix Tables 5 and 7 of the current paper), two cities (Beijing and Santiago), both with only one year observation, were added to the data set. The data for Beijing and Santiago are shown in Table A-2.

Table **A-2.** Data for Beijing **and** Santiago

||Beijing, 1994|Santiago, 1990|
|---|---|---|
|Urban population ('000)|_5,748_|**4,768**|
|Urban land area (kM <sup>2</sup>)|485|467|
|Per capita GNP in 1987 US$|807|1,960|
|Population density (persons/km2)|11,860|10,201|
|Total motor vehicles per 1,000 persons|54|84|
|Passenger cars per 1,000 persons|46|67|
|Road network density (km/kM<sup>2</sup>)|7.47|5.10|
|Per capita road length (m)|0.63|0.50|
|Motor vehicles per km of road|86|168|

Source: Data for Beijing are primarily from Li (1996); and for Santiago, from Kain and Liu (1994).

**Annex B. Deriving the Elasticity from a Quadratic Log-log Equation**

Regression estimated: logY = log a **+** b, log X **+** b2 (log X)<sup>2</sup> (1) Transforming: Taking derivatives of (2): dY/Y = (b,/X) dX + {(2 b2 log X)/X
<!-- Start of picture text --> Figure 3: Governance and Infant Mortality<br><!-- End of picture text --> <!-- Start of picture text --> Voice and Accountability Political Instability and Violence<br>4<br>3 3<br>2 ' 2<br>-2.5 .1.5 -0.5 0.5 1.5 2.S -2.5 .1.5 .0.5 0.5 1.5 2.5<br>Vieu and Accountab8ity PoEca Viobnc *nd Inalabilt<br>Govemment Effectiveness Regulatory Burden<br>4-~~~~~~~~~~~~~~~~~~~~~~~<br>2-<br>0-0<br>-2.5 -1.5 -0.5 0.5 1.5 2.5 -2.5 -15 -0.5 0.5 1.5 2.5<br>Govwmnwint Efecsvenes R"ulay Bun<br>Rule of Law Graft<br>8 8<br>2 2<br>-2.5 -1.5 -0.5 0.5 1.5 2.5 -2.5 -1.5 -0.5 0.5 1.5 2.5<br>Rul of Law Graft<br><!-- End of picture text --> Notes: Each graph plots the indicated governance aggregate (on the horizontal axis) against the logarithm of infant mortality per thousand live births (on the vertical axis) for the sample of countries covered by the govemance aggregate.

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no addition to real investment. Or the funds received may be passed on to another affiliate in the same multinational group. In such cases, inward FDI into a jurisdiction will exceed real investment there. On the other hand, inward FDI will understate foreign affiliates’ real investment to the extent that their tangible investment is financed in ways other than by cross-border transfers or retentions (but instead by, instance, local borrowing or local issuance of shares). FAI data does not suffer from either form of misstatement of tangible investment.

The second and closely related key difference<sup>25</sup> is that FAI data see through to the location of the ultimate parent company, whereas in FDI data the ‘parent’ country typically refers to the location of the immediate investor.<sup>26</sup> This reporting convention for FDI creates additional and serious issues for the measurement of investment: whenever funds pass through an intermediate country the same underlying funds will be recorded twice in the FDI statistics, with the intermediate country as the destination country for the first observation and as the ‘parent’ country for the second. FDI data will thus double count (or more) investments whenever conduit transactions arise between the ultimate parent and the final host country. This double counting, which is rooted in the equity method of accounting, also gives rise to double counting of profits for any company with intermediate affiliates at the micro level (Blouin and Robinson, 2019). At the global level, double counting of multinational investments would imply a misleadingly high level of aggregate FDI, inflated by pass-through funds that are recorded multiple times.

An example illustrating these core differences between FAI and FDI data—which will also prove helpful in understanding some later results—is provided in Figure 1, with implications summarized in Table 1. It takes the case of an ultimate parent in country UP which has a controlled affiliate in a country of intermediate ownership IP, that affiliate in turn being the immediate parent of another affiliate in host
trips to origins ( _ji_ , and _ik_ ), giving us a total sample of 362,436 trips between 491 origin and 589 destination districts. The dependent variable in panel A of Table 4 is the log of value of trade flow and in Panel B, the log of total quantity hauled, both estimated from trip data. All regressions control for the log of distance but the sets of control variables vary across columns.

The first column in Table 3 reports the estimates when the log of the distance between OD pairs and interaction of crude price with average VAT rate between OD pairs are the controls in _Zij_ . The second column adds the log of diesel prices along the route to controls in column 1. The sample for this third specification is smaller as we have daily diesel prices in different districts only for 2019. The third column adds the log of night light luminosity along the route to the set of controls in the first column. It should be noted that both diesel price and night light luminosity are endogenous to trade flows and transport costs and could be argued to be bad controls in our context. These specifications are estimated to check the robustness of IV results later. The OLS estimate of trade elasticity with respect to the freight rate has the expected negative sign and is statistically significant in each of the three regression specifications. The estimates are remarkably consistent across specifications and fall within a narrow bound of -0.24 to -0.27. The estimates imply an average elasticity of trade with respect to the freight rate of around -0.25. The elasticity of volume of trade with respect to the freight rate is larger in magnitude (-0.79) than the average value of trade elasticity.

To take care of the potential endogeneity of freight cost in the trade gravity regression, we start with first stage estimates which are reported in Table 5. The instrument is the average VAT rate for the next trip from destination district _j_ ( _vj_ ) as defined earlier. The controls in each column
|3|
|Afghanistan|71|Mauritius|18|Cayman Islands|2|
|Poland|70|Brunei Darussalam|17|Equatorial Guinea|1|
|Iran, Islamic Rep.|67|Guyana|17|St. Lucia|1|
|Liberia|67|Sri Lanka|16|Monserat|1|
|Malta|63|Marshall Islands|16|St Hellen|1|
|French Polynesia|63|Barbados|15|Azerbaijan|0|
|Peru|57|Cook Islands|15|Central African Republic|0|
|Egypt, Arab Rep.|55|Fiji|15|Federal States of Micronesia|0|
|Djibouti|53|Guatemala|15|Qatar|0|
|Chad|52|Sierra Leone|14|Sudan|0|
|Saudi Arabia|51|Honduras|12|Slovenia|0|
|Papua New Guinea|48|Nicaragua|12|Tuvalu|0|
|Vietnam<br>Note: This table reports the|48<br>number of|Portuguese emigrants in 19|3 countrie|s. The data come from the Global Mig|rant|

<u>Note: This table reports the number of Portuguese emigrants in 193 countries. The data come from the Global Migrant</u> Origin Database and refer to the year 2000.

11
For each group defined by these aggregation methods, we use the mean value of the variables.

# 3.5 Descriptive statistics

The final dataset contains 23,437,477 firm-level observations from 64 countries. In total, there are 3,882 economically meaningful grids where firms from our Orbis dataset are present. Table 1 presents the summary statistics for the key weather and economic variables based on grid-level observations.

The mean temperature uncertainty across all grids during the sample period from 2001 to 2019 is 2.09<sup>◦</sup> C. The average temperature (MeanT) and maximum temperature (MaxT) are 16.20<sup>◦</sup> C and 30.54<sup>◦</sup> C, respectively. The mean fixed asset growth (SizeGrowth) is -1.45%.

Further detailed descriptions can be found in the appendices, where we provide comprehensive country-level summary statistics. Table 7 in Appendix B presents these country-level statistics using country-specific firm-level observations. Table 8 in Appendix B shows the number of grids for each country for each year.

||Mean|SD|Min|Q25|Q50|Q75|Max|
|---|---|---|---|---|---|---|---|
|# of frms|302.89|1253.10|2.00|5.25|22.58|136.08|26070.63|
|Uncertainty|2.09|0.52|0.71|1.72|2.06|2.43|4.55|
|MeanT|16.20|9.94|-9.87|8.05|15.22|24.02|35.46|
|MaxT|30.54|5.97|11.96|26.42|30.51|34.73|49