rafmacalaba/datause-encoder-data huggingface dataset:
have a relationship with a state-owned bank or government-owned bank are more likely to be credit constrained, to innovate, or to engage in environmentally friendly activities. While we do find that credit constrained firms are more likely to borrow from government-owned banks, and that this is especially the case in countries with good institutions, we do not find any evidence that government owned banks target innovative firms or “green” firms.

Although WBES does not contain detailed balance sheet and income statement data, it does include information on firm characteristics, such as ownership, size, and employment. In the first part of our analysis, we use some of these characteristics as control variables, in the second part we use employment growth as the dependent variable. We start by showing that, in terms of employment growth, firms that borrow from government-owned banks do not differ from firms that borrow from private banks. Next, we show that in firms that borrow from government-owned banks employment reacts less to business cycle conditions than in firms that borrow from private banks. This result is in line with bank-level evidence that lending by government-owned banks is less procyclical than lending by private banks (see, among others, Micco and Panizza, 2006). Next, we study the role of credit constraints and show that having a relationship with a government-owned bank is particularly useful for credit constrained firms. Specifically, we find that employment is much more stable in credit constrained firms that have a relationship with a governmentowned bank with respect to credit constrained firms that borrow from a private bank. These results are robust to controlling for country-year fixed effects that absorb all possible country specific shocks and are also robust to controlling for firm fixed effects.

The rest of the paper proceeds as follows. Section 2 describes our data with special focus on the matching of WBES data with bank-level data and on the construction of three latent variables aimed at measuring the presence of credit constraints, innovation, and green activities. Sections 3 and 4 describe our empirical strategy and present regression results. Section 5 concludes.

# 2. Data

This paper uses 3 types of data: (i) bank-level data from Fitch Connect; (ii) firm survey data from the World Bank Enterprise Survey, and (iii) macroeconomic data from the IMF-WEO database and from the World Bank World Development Indicators. We start by describing the bank-level dataset, we then move to WBES and describe how we matched these two datasets, and conclude by describing the construction of three latent variables aimed at measuring credit constraints, innovation, and green activities.

5
- (i) What were the most prominent characteristics of poverty in the city?  How was poverty  distributed spatially?  Did the prolonged crisis produce hunger?  What were the main characteristics of unemployment?

- (ii) How were municipal expenditures in key areas (infrastructure, social sectors) distributed geographically?  Did they benefit the poor (and poor areas) over- or under-proportionally?

- (iii) How did the poor (and non-poor alike) rate the quality of municipal services which are key to reducing poverty (health, education, water, sanitation, garbage collection, electricity)?

- (iv) What were the main priorities of the poor for municipal programs?  Did these priorities differ from non-poor groups?  Specifically, did the poor also view the large planned outlays for public transport (the metro system) as a major investment priority for the city?

These questions were to be answered by a specially designed and fielded city survey.

# 3. The Instrument: A Rapid City Survey for Cali

## _3.1. City Surveys_

In urban planning, a rich tradition exists to employ random sample, representative city surveys to examine research and policy questions in sectors such as health, environment, housing, water, electricity, transport, and urban livelihoods. We can classify city surveys in three broad groups: those seeking to quantify specific variables of interest; those analyzing qualitative perceptions and priorities of the population; and those monitoring welfare developments over time.

Probably the most common use of city surveys is to quantify specific variables of interest. For example, Gupta and Baghel (1999) quantify the levels and differentials of infant mortality in Calcutta and Raipur City. Swait and Eskeland (1995) examine travel modes based on an urban travel survey in Sao Paulo. Or energy use patterns are examined by Alam et al (1998) in Hyderabad, India. Similarly, city surveys have been extensively applied to examine urban housing market characteristics in both developing country cities (Benjamin et al. 1987, Gouh 1998, Kaitilla and Sarpong-Oti 1993) as well as developed country 6
|Value of machinery<sup>1/</sup>|0.022|0.029|0.039|0.045|
|---|---|---|---|---|
||(0.64)|(0.88)|(1.21)|(1.40)|
|Cost per worker<sup>1/</sup>|-0.033|0.003|-0.049|-0.019|
||(-0.28)|(0.02)|(-0.42)|(-0.16)|
|Firms' age<sup>1/</sup>|0.660***|0.678***|0.584**|0.641**|
||(2.68)|(2.76)|(2.34)|(2.57)|
|Foreign ownership (yes=1)|0.578|0.533|0.338|0.402|
||(1.35)|(1.24)|(0.77)|(0.90)|
|Registered at the beginning<br>(yes=1)|0.025|-0.071|0.015|-0.035|
||(0.04)|(-0.12)|(0.03)|(-0.06)|
|Foreign technology (yes=1)|1.176***|1.244***|0.987**|1.059**|
||(2.76)|(2.88)|(2.33)|(2.48)|
|Imported inputs (yes=1)|0.690|0.397|0.190|0.045|
||(1.60)|(1.02)|(0.48)|(0.10)|
|Female labor intensity (yes=1)|-0.828**|-1.038**|-0.908**|-1.049**|
||(-2.08)|(-2.59)|(-2.30)|(-2.56)|
|Year dummies|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|
|Region dummies|Yes|Yes|Yes|Yes|
|Constant|1.357|0.789|1.449|1.334|
||(0.86)|(0.50)|(0.92)|(0.84)|
|Observations|252|252|252|252|
|R-squared|0.472|0.473|0.474|0.463|
|Number of panelid|126|126|126|126|
|t-statistics in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1|||||
|1/Measured in logarithms.|||||

Table 5.6 considers the _change in total employment_ as the dependent variable and estimates the correlation between it and firms’ characteristics and other independent variables. Results show that employment decreased among firms that stopped exporting. For the other types of firms, results were not statistically significant. A change in employment is positively associated with equipment investment (measured by the change in the value of machinery assets), while it is negatively associated with an increase in costs.

|**Table 5.6 Estimated Relationship Between Change in Employment and Exp**|**orting Status**|
|---|---|
|**Dependent Variable: Change in Total Employment**<sup>**1/**</sup>||
|1<br>2<br>3|4|
||34 |P a g e|
Policy Research Working Paper 6811

# **Abstract**

Using a global panel on research and development (R&D) expenditures, this paper documents that on average poor countries do far less R&D than rich as a share of GDP. This is arguably counter intuitive since the gains from doing the R&D required for technological catch up are thought to be very high and Griffith et al (2004) have documented that in the OECD returns increase dramatically with distance from the frontier. Exploiting recent advances in instrumental variables in a varying coefficient context we find that the rates of return follow an inverted U: they rise with distance to the frontier and then fall thereafter, potentially turning negative for the poorest countries. The findings are consistent with the importance of factors complementary to R&D, such as education, the quality of scientific infrastructure and the overall functioning of the national innovation system, and the quality of the private sector, which become increasingly weak with distance from the frontier and the absence of which can offset the catch up effect. China’s and India’s explosive growth in R&D investment trajectories in spite of expected low returns may be justified by their importing the complementary factors in the form of multinational corporations who do most of the patentable research.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
23 _Fiscal Crisis in Central Europe_

# **IV.2. Tax Revenues from Private Enterprises: Evidence from Poland.**

A valid concern expressed regarding the future evolution of tax revenues is that the combination of (i) the rapidly increasing share of GDP produced by the private sector and (ii) the severe problems faced by the goverrunent in order to properly tax it might lead to future problems.

Poland and Hungary have the most developed private sectors in Eastern Europe. They accounted, respectively, for approximately 50 percent and 35 percent of GDP in 1992. The former Czechoslovakia follows behind, with about 15 percent of its GDP being produced by private enterprises in that year.<sup>20</sup> The history of the private sector is of course also quite different across countries, but the numbter and importance of private enterprises has been rapidly growing in all the countries considered. This calls into question the problem of the proper taxation of the private sector (see Tanzi, 1991, and McKinnon, 1991).

The extent of this problem (if any) has not yet, to our knowledge, been sufficiently documented. We present here some data on tax compliance by the private sector that are now available for Poland (Table 4.3).2 They span 1991, 1992 and the first half of 1993, and are disaggregated by sector.

The evidence is mixed. The share of total turnover tax and profit tax payments accounted for by the private sector is clearly lower than the corresponding share of total revenues - roughly by a half. This pattern is consistent in the whole period covered by the data. In the first half of 1993, private enterprises - all sectors - accounted for about 30 percent of total revenues, but only about 14 percent of profit tax payments and 15 percent of turnover tax payments. In industry - from which originates most of the total turnover taxes and profit taxes paid in the economy - private

> **21** Data refer to medium and large enterprises, defined as those with more than 50 employees in industry and construction, and more than 20 in the other sectors.

**20** World Bank staff estimates.
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educational attainment, such as years of schooling, may emerge more gradually as the benefits of increased income permeate societal structures.

To explore these dynamics, we associate the change in multidimensional poverty with current and lagged GDP changes. For instance, the change in multidimensional poverty between 2012 and 2015 is linked to the change in GDP per capita during the same period, referred to as the current change in GDP. Additionally, we test for lagged impacts, where the change in GDP is defined as the change in the five years before the first survey or before the spell starts. In our example above, this would mean that the lagged effect is defined between 2007 and 2011. The lagged changes in GDP are consistently measured over the five years preceding the first survey. In contrast, the period for the current change in GDP varies depending on the interval between the surveys. Finally, we combine both current and lagged changes to assess the total (current + lagged) impact.

By testing different specifications, we aim to discern whether the effects of economic growth on multidimensional poverty are immediate or if they occur with a delay. This will enhance our understanding of the intricate ways economic changes influence various aspects of poverty.

# _Sample_

Our sample consists of 84 countries distributed across six world regions following OPHI’s region classification. Table 2 lists the six regions, details the number of countries and spells available for each region, and the first and last years for which we have MPI data.<sup>2</sup> Sub-Saharan Africa has the most representation, with 36 countries and 60 spells, whereas South Asia has the least representation, with five countries and seven spells. The number of observations per country ranges from two to four, and the length of each spell varies from one to twelve years, with an average spell length of five years. For further insights, Table A 2 in the appendix provides descriptive statistics for both the dependent and independent variables used in our analysis.

Table 2: Regions of the sample

|**Regions**|**Nr. of**<br>**countries**|**Nr. of spells**|**Firstyear**|**Lastyear**|
|---|---|---|---|---|
|Arab States|9|10|2006|2020|
|East Asia and the Pacific|9|12|2010|2022|
|Europe and Central Asia|12|16|2005|2019|
|Latin America and the Caribbean|13|22|2001|2021|
|South Asia|5|7|2006|2021|
|Sub-Saharan Africa|36|60|2000|2021|
|**Total**|**84**|**127**|**2000**|**2022**|

> 2 For a list of all countries by region refer to Table A 1 in the appendix.

8
21

# Table 3: ILLUSTRATIVE WELFARE CALCULATIONS

|Elasticities\<br>Column||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|---|
|Price Elasticity of<br>Restricted Demand|(CA)|1.0|0.3|0.5|1.0|2.0|
|Price Elasticity of<br>Unrestricted Demand|(CB)|1.0|0.6|1.0|2.0|4.0|
|Elasticity of|||||||
|Transformation|(p)|1.0|0.5|1.5|1.5|3.0|
|Elasticity of<br>Factor Supply|(ES)|1.0|0.5|2.0|2.0|3.0|
|Simulation Results|cl<br>Z||||||
|output a/||-5.0|-3.9|-4.3|-4.5|-4.1|
|Sales Revenue a/||0.0|11.6|4.6|0.0|-2.1|
|Factor Wage a/||-4.0|-7.7|-2.2|-2.3|-1.4|
|Welfare b/|1|6.2|19.7|10.2|5.5|2.7|
||5|-0.5|6.9|6.5|1.7|0.4|
||10|-8.9|-9.1|1.9|-3.0|-2.5|

Notes: Notation is given in appendix B.2. Unrestricted equilibrium: **XA** - 100; XB - 140; Pi - 1.00; Z = 100.

a/ Percent change.

b/ Change in level value.

c/ Size of market for Z in relation to initial allocation of 2 in industry subject to VER.
<!-- Start of picture text --> NO2: Data Lights: Data<br>Black Monday Pandemic Black Monday Pandemic<br>15 16 17 18 19 20 21 15 16 17 18 19 20 21<br>NO2: Cyclical Component Lights: Cyclical Component<br>Black Monday Pandemic Black Monday Pandemic<br>15 16 17 18 19 20 21 15 16 17 18 19 20 21<br>2.4 2.4 2 2<br>2.3 2.3<br>1.8 1.8<br>2.2 2.2<br>2.1 2.1<br>1.6 1.6<br>Radiance: W/(cm2 -sr)<br>2 2<br>Vertical Column Density: 1e15 mol/cm2<br>1.9 1.9 1.4 1.4<br>5 5 10 10<br>0 0<br>5 5<br>-5 -5<br>0 0<br>-10 -10<br>-5 -5<br>NO2: % Difference from Trend -15 -15 Lights: % Difference from Trend<br>-20 -20 -10 -10<br><!-- End of picture text --> Figure 1: China: NO2 and Night Lights, Data and Cyclical Component. _Notes:_ Underlying NO2 data is daily OMI VCD readings and night lights are monthly VIIRS radiance (stable lights, stray-light corrected). For NO2, 365 day daily rolling average is used while for lights, 12-month rolling average is used. For both, trend is calculated using Hamilton (2018) filter.

9
data disaggregated by gender from all destinations in the same subregion as the destination country for which data are lacking. Using the same notation as in the previous section, assume that _Mo,d,t_ is the aggregate migrant stock from origin country _o_ to destination country _d_ in year _t_ and that _Wo,d,t_ is the female migrant stock for the same origin-destination pair in the same year _t._ The ratio of female migrants to male migrants is denoted as  _o,d,t_ , which is given by  _o,d,t = Wo,d,t /Mo,d,t ._ For example, imagine that in a given decade, the gender splits of emigrants from Uruguay in Scandinavian countries are known, except for Sweden. In this situation, it is assumed that the ratio of female migrants to male migrants from Uruguay to Sweden is the ratio of female migrants to male for all of the other Scandinavian countries (Denmark, Finland, Norway) in that decade. Formally, this can be stated as where _G_ is the destination region (the Scandinavian countries except Sweden), _o_ is the origin country (Uruguay), and _d_ is the destination country (Sweden). Once this proportion  _o,d,t_ is calculated, it can be multiplied by the total number of migrants _Mo,d,t_ to Sweden to calculate the number of female migrants. There is considerable variation in the balance between male and female migration from Uruguay to Scandinavian countries other than Sweden (Denmark, Finland, Norway) during the 1990 census round (table A4). On average, however, 47 percent of Uruguayan migrants are men and 53 percent are women. In the 1990 census, Sweden records 2,640 migrants as originating from Uruguay. Then 1,390 (0.53*2,640) of these migrants are women and 1,250 (0.47*2,640) are men.

# **{Table A4 here}**

These calculations based on concurrent shares can be calculated only if data disaggregated by gender exist for all other countries in the destination subregion. If not, the world is divided into destination subregions, and gender splits are calculated based on regional shares over time. Continuing from the previous example, assume the data for Denmark, Finland, and Norway are unavailable in 1990, so that the gender split for Uruguayan migrants in Sweden cannot be calculated based on Scandinavian data for 1990. In this case, the data for Scandinavia across all other decades are used to calculate the average ratios of female migrants to total migrants over time. This can be written formally as: 31
<!-- Start of picture text --> Figure 9. Covariogram of built-up cells - Small tiles<br>S<br>2<br>oO<br>[=i<br>a8<br>Zo<br>><br>Be<br>o<br>s<br>Se<br>So<br>&<br>) 5 10 15 20 25<br>Distance in Km<br>——_ 1975 ——— 1990<br>—— 2000 =————. 2015<br><!-- End of picture text --> <!-- Start of picture text --> Figure 10. Covariogram of built-up cells near a road - Small tiles<br>re] ;<br>oo:<br>>a Na WASLMAR on saraliall . 1<br>2 sii |<br>—o U<br>g<br>e h<br>Qin<br>So<br>fo) 5 10 15 20 25<br>Distance in Km<br>——-_ 1975 ——_ 1990<br>———__ 2000 ——_ 2015<br>————— cl 1975 ———._ cl 1975<br>——— C2015 Cl 2015<br>95%. confidence interval shown for 1975 and 2015<br><!-- End of picture text -->
team greeted deportees after they had been processed and as they were leaving the airport. Enumerators were instructed to randomly select individuals to approach with information about the study and not to select based on any observable characteristics. In practice, this meant selecting every fifth deportee or so, given that so many left the airport at the same time. This methodology gives us confidence that we obtained a reasonably representative sample, as do demographic similarities regarding gender between our data and official ICE data tracked by Syracuse University’s TRAC Immigration. More precisely, 11 percent of Guatemalans deported from 2004 to 2020 were women, and 8 percent of our sample were women. However, we are not able to more thoroughly compare our sample’s demographic characteristics to official records because many other characteristics of deportees are withheld by ICE.

Respondents were offered 50 Quetzales, equivalent to about 6 _._ 50 USD, to participate in the survey. Upon survey completion, respondents were offered an additional 50 Quetzales to provide information for a follow-up survey. We interviewed 1,357 deportees upon their arrival to the country. Respondents who chose to provide contact information during the first round of the survey were contacted for a 1-month follow up as well as a 6 month follow up. The follow-up surveys were conducted over the phone, and respondents who completed these surveys received a phone balance credit of at least 50 Quetzales for each survey. Phone surveys continued through October of 2020, and we collected a total of 645 follow-up surveys across the two waves, with 210 respondents interviewed in both follow-up waves. Questions relevant to our analysis here were primarily asked in rounds 2 (1-month followup) and 3 (6-months followup). We pool round 2 and round 3 results, and all regressions utilize robust standard errors clustered by respondent. The multi-wave survey contains a wide variety of questions covering topics ranging from demographics to experiences in the United States and Guatemala; specific wording for relevant survey questions is included in the online appendix.

Additionally, we conducted 18 semi-structured interviews with deported migrants from our original sample. To recruit interview participants, we divided all baseline survey respondents who reported using a coyote to migrate into four different groups depending on what we theorize to be two important dimensions of variation: (a) their intention to remigrate and (b) whether they experienced extortion. From each of these four groups, we selected a random sample of respondents and interviewed between 4 and 5 respondents per group. Interviews lasted on average 30 minutes, and participants were compensated with 50 Quetzales of phone credit. For more information about the final number of interviews from each group of migrants, see Table A1.

Before proceeding to a discussion of the specific variables used in the analyses below, it is important to briefly discuss a few ethical considerations, especially given the vulner11