rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 4: Regressions Including the Interaction of Competition and Concentration with Financial Development**

The regressions below are estimated using country fixed effects and robust standard errors clustered at the country-year level. The dependent variable _Access to finance_ is a dummy variable that indicates whether the firm has access to a loan, overdraft, or a line of credit. The _Lerner index_ is a measure of competition (higher values imply lower levels of competition). The _Herfindahl index (HI)_ and _Concentration 3_ are measures of concentration _. Log firm size_ is the logarithm the number of permanent employees. _Log firm age_ is the logarithm of the firm’s age in years. _Government-owned_ and _Foreign-owned_ are dummy variables that equal one if the firm has government or foreign ownership, respectively. _Exporter_ is a dummy variable that indicates if the firm is an exporting firm. _Manufacturing_ is a dummy variable that takes value 1 if the firm is in the manufacturing industry. _Financial development is_ measured as domestic credit to the private sector as a fraction of GDP. The _inflation rate_ is measured as the growth rate of the GDP deflator (annual). Results in column (b) are weighted by the inverse of the standard deviation of the Lerner Index. *** p<0.01, ** p<0.05, * p<0.1

|Variables||Ac|cess to finance||
|---|---|---|---|---|
||(a)|(b)|(c)|(d)|
|Lerner index|-1.354***|-1.386***|||
||[0.368]|[0.429]|||
|Concentration 3|||-0.521***||
||||[0.166]||
|Herfindahl index (HI)||||-0.677***|
|||||[0.158]|
|Financial development|-0.160|-0.047|-0.223|0.130|
||[0.226]|[0.171]|[0.151]|[0.088]|
|LernerFinancial development|1.943**|1.779**|||
||[0.777]|[0.696]|||
|Concentration 3Financial development|||0.898***<br>[0.177]||
|HI IndexFinancial development||||0.993***<br>[0.197]|
|Log firm size|0.086***|0.088***|0.086***|0.086***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Manufacturing|0.027**|0.027**|0.029***|0.029***|
||[0.010]|[0.011]|[0.011]|[0.011]|
|Exporter|0.033***|0.030***|0.032***|0.031***|
||[0.008]|[0.010]|[0.008]|[0.008]|
|Foreign-owned|-0.078***|-0.090***|-0.080***|-0.080***|
||[0.011]|[0.012]|[0.011]|[0.011]|
|Government-owned|-0.136***|-0.164***|-0.128***|-0.129***|
||[0.030]|[0.035]|[0.030]|[0.030]|
|Log firm age|0.012***|0.016***|0.012***|0.011***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Inflation rate|-0.317**|-0.416**|-0.325**|-0.350**|
||[0.145]|[0.160]|[0.135]|[0.134]|
|Constant|0.692***|0.696***|0.816***|0.513***|
||[0.167]|[0.176]|[0.203]|[0.142]|
|Observations|68,353|68,353|67,270|68,353|
|R-squared|0.213|0.189|0.217|0.214|

23
in IPV, but could not rule out that this was driven by increased IPV in the control group. This finding has broader implications for those evaluating programs targeting culturally sensitive norms, where community-level responses may dominate individual treatment effects.<sup>5</sup> Beyond IPV, this study advances our understanding of how entrenched gender norms resist change. We contribute to a growing literature examining interventions aimed at shifting persistent gender gaps (Banerjee et al., 2019; Beaman et al., 2012; Dhar et al., 2022; Field et al., 2021), highlighting how attempts to alter deeply held attitudes and identities can trigger unintended consequences. Our findings suggest that while programs may successfully shift individual attitudes in the short term, community-level backlash can overwhelm these gains when interventions challenge traditional power structures. This speaks to fundamental questions in economics about how preferences and norms evolve, and when policy interventions may accelerate or impede social change (Becker, 2009; Benabou and Tirole, 2011; Giuliano and Nunn, 2021).

Couples-based IPV prevention programs similar to the one studied in this paper are currently being tested or scaled up in multiple countries around the world. Yet there is limited evidence of their effectiveness or potential spillover effects. Our results therefore have important policy implications for governments and other actors seeking to shift sensitive gender norms. They suggest caution should be exercised to mitigate the risks of backlash, and that alternative approaches to changing sensitive outcomes should be considered.

# **2 Study Context and Design**

## **2.1 Country Context**

Rwanda has experienced dramatic social change over the past 30 years, including a radical shift focused on women’s status and empowerment. While modern national policy strongly promotes gender equality, this stands in tension with historically conservative gender norms that persist in many communities. Colonial rule and religious institutions reinforced patriarchal norms that limited women’s autonomy and political power (Asiedu et al., 2018). These attitudes remain influential

> on non-treated community members, with suggestive increases in sexual (p _<_ 0.06) and physical (p _<_ 0.13) IPV.

> 5The only other group-based IPV prevention study with both identical blind recruitment for treatment and control groups and geographic buffers was by Vaillant et al. (2020). Their 16-session program in the Democratic Republic of Congo worked with men’s groups rather than couples and found no impact on IPV.

5
# On Measuring Scientific Influence

## Martin Ravallion and Adam Wagstaff<sup>1</sup>

_Development Research Group, World Bank 1818 H Street NW, Washington DC, 20433, USA_

> 1 Our thanks to Qinghua Zhao for his programming work to help retrieve Google Scholar citation data, and to Imran Hafiz for help retrieving the bibliographic metadata from SCOPUS. The views expressed here are those of the authors and need not reflect those of the World Bank, its Executive Directors, or the countries they represent. Correspondence: mravallion@worldbank.org and <u>awagstaff@worldbank.org.</u>
median urban area would equate to households spending more than half of their current total budget, on average.

**Figure 10: Budget Share Allocated to Rent vs. Simulated Share using Average Urban Rent, the**

**25**<sup>**th**</sup> **percentile of Urban Rent in Tashkent City, and the Median Rent in Tashkent City**

<!-- Start of picture text --> 180%<br>160%<br>140%<br>120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Rental Share Assuming Urban Average Assuming 25th Pctl Tashkent Assuming Median Tashkent<br><!-- End of picture text -->

# _Source: the L2CU Baseline Survey, Author’s Calculations._

_Notes: The bars present the cost of housing at different reference points with respect to the current household budget in each province. Values above 100% indicate that the cost of housing referenced is greater than the total current household budget in that province, on average._ But the city of Tashkent is much more expensive than the median urban area. If instead one looks at scenarios in which the cost of housing in Tashkent city is considered as the reference, there is no other location in the country for which even the 25<sup>th</sup> percentile of housing costs in Tashkent is affordable on average (Figure 10). If one instead assumes the median cost of housing in Tashkent, housing costs are equivalent to more than the average total budget in every other region of the country.

30
Table A1 Intermediate Input Requirements

||Firm data|Input-output data|
|---|---|---|
|Average intermediate input requirement|0.41|0.42|
|Intermediate inputs sourced from own sector|||
|Food and beverages||0.38|
|Textiles||0.31|
|Minerals||0.04|
|Metals||0.40|
|Other manufactures||0.36|

Source: INDEC. Input-output table for year 1997.

also compute the unit input requirements from an input-output table and we average them across sectors. The resulting coecient is 0.42. The input-output coecients used in the estimation of the production function parameters are presented in Table A1. The data come from the IO Tables for 1997 published by INDEC (Instituto Nacional de Estadística y Censos.

In the model of section 2 we further assume that rms source intermediate inputs from their own sector of production only. This assumption is key for the viability of the estimation procedure as it allows us to treat rms in all sectors symmetrically. It is based on the notion that sectors are aggregate enough so that products are suciently dierent within a same sector, but related through the production chain. For example, the main material in the production of shirts is fabric, and both shirts and fabrics are products within the textile sector. As a back-of-the-envelope test of the assumption we compute coecients for own-sector sourcing from the input-output table. These are 0.38, 0.31, 0.40, 0.04, and 0.36 for food and beverages, textiles, metals, minerals and other manufacturing sectors. The own-sector coecients are very close to the calibrated intermediate input requirement of 0.41 in 4 out of the 5 manufacturing sectors, which implies that the assumption agrees with the data relatively well, with the exception of the small minerals sector. See Table A1.

# Stochastic process for sector and rm-level shocks

Firms face sector-level prot shocks _bjt_ , that include shocks to aggregate productivity and to prices, and rm-level shocks _Aijt_ . We assume that _b_ and _A_ follow AR(1) stochastic processes given by 42
only summary statistics are reported here, while the rest can be found online in Annex 2<sup>1</sup> ). This is lower by about one percentage point relative to our (Psacharopoulos and Patrinos 2004a) compilation. It should be noted that the decline over time of the returns to education is very gradual and statistically insignificant (see Figure 1). This could be interpreted as a “race between education and technology” as the price of education fails to decline proportionately in the face of rapid supply increases (Tinbergen 1975). Thus, demonstrating that the demand for skill is outpacing the growth in supply of skills.

**Figure 1: Rate of Return to Schooling over Time**

<!-- Start of picture text --> (%)<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> Note: Regressing the overall Mincerian rate of return on the year of the estimate, gives: _Return_ = 49.611 – 0.020 _Year;_ R<sup>2</sup> =0.003 (t = 1.4) Regressing the Mincerian overall return on the country’s years of schooling ( _S_ ) in the survey year, yields a very small but statistically significant decline of the returns of about 0.2 percentage points for every additional year of schooling (see Figure 2).

> 1 <u>http://datatopics.worldbank.org/education/files/GlobalAchievement/ReturnsEdAnnex2.xlsx.</u>

6
Table 5: OECD Statistics

|Observations=346|**Mean**|**Std. Dev.**|**Min**|**Max**|**% of zeros**|
|---|---|---|---|---|---|
|**Patents**|1975|4560|1|31104|0|
|**ln R&D**|22.3|1.58|18.88|25.8||
|**ln US trade**|15.7|1.53|12.73|19.0||
|**Natural Resources**|0.28|2.08|-3.69|14.9||
|**Quality of Academic Institutions**|5.53|0.62|4.10|6.3||
|**Collaboration with the Private Sector**|4.53|0.75|2.92|5.8||
|**Education**|8.32|1.71|3.27|11.8||
|**IPR**|3.53|0.56|1.98|4.6||
|**ln (Mean Patents)**|5.21|2.15|1.26|8.4||

# Table 6: non-OECD

|Observations=290|**Mean**|**Std. Dev.**|**Min**|**Max**|**% of zeros**|
|---|---|---|---|---|---|
|**Patents**|34|91|0|754|14.40%|
|**ln R&D**|19.23|1.95|11.25|22.80||
|**ln US trade**|14.64|1.67|7.82|18.46||
|**Natural Resources**|0.13|0.91|-4.74|5.07||
|**Quality of Academic Institutions**|4.23|0.82|2.80|6.20||
|**Collaboration with the Private Sector**|3.30|0.71|2.54|5.30||
|**Education**|5.53|1.79|2.21|9.82||
|**IPR**|2.22|0.80|0.33|3.90||
|**ln (Mean Patents)**|1.41|1.60|-2.56|4.13||

29
**Table 6: Exports by Product and by Regional Market** (in billions of 2001 rubles)

|Good|Central F|ar East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|MAR|4.9|1.7|1.7|7.1|4.5|13.7|4.3|2.2|6.9|1.2|
|AGR|3.1|0.5|0.2|2.4|3.5|4.4|0.4|0.3|0.2|0.4|
|AIR|5.6|2.4|1.8|7.6|6.8|13.2|18.9|3.0|4.4|1.2|
|CHM|27.5|0.5|2.0|33.2|18.2|92.0|13.7|5.0|0.3|15.2|
|CNM|1.8|0.2|0.1|1.7|1.2|3.4|1.6|0.6|0.2|0.2|
|COL|0.1|4.2|2.6|26.4|1.3|0.9|||||
|CON|4.0|0.4|0.3|2.9|5.6|8.0|7.3|1.6|1.3|0.6|
|CRU||7.1|27.0|15.8|60.3|156.1|||452.8|1.6|
|ELE|1.1|0.3|0.3|1.4|1.0|2.6|1.8|0.4|0.8|0.2|
|FIN|0.7|0.3|0.2|0.9|0.7|1.6|1.8|0.3|0.7|0.1|
|FME|50.5|1.1|3.7|22.4|11.1|83.8|7.1|4.8||28.3|
|FOO|8.7|9.7|2.5|12.6|23.1|10.1|27.0|9.2|0.1|3.6|
|GAS||1.4|3.1|3.6|11.5|21.8|||410.7|0.0|
|HEA|0.3|0.1|0.1|0.4|0.3|0.6|0.8|0.1|0.2|0.1|
|HOU|0.3|0.1|0.1|0.4|0.3|0.6|0.8|0.1|0.2|0.1|
|MWO|22.8|14.2|1.9|43.0|23.3|97.0|95.5|30.4|1.9|5.0|
|NFM|4.6|15.1|18.4|203.1|11.1|101.6|17.2|11.3||0.6|
|OIL|35.5|13.7|4.5|50.9|29.5|114.4|12.8|21.1|11.5||
|OTH|1.7|0.4|0.5|1.9|1.1|4.0|1.4|0.7|1.6|0.4|
|OTI|7.2|0.3|0.4|1.3|2.1|4.9|5.3|1.4|0.0|0.5|
|PST|0.3|0.1|0.1|0.5|0.4|0.8|0.9|0.2|0.4|0.1|
|RLW|0.9|0.3|0.2|1.1|0.8|2.0|1.3|0.3|0.6|0.2|
|SCI|0.6|0.2|0.2|0.7|0.5|1.6|1.1|0.3|0.7|0.1|
|TMS|0.9|0.3|0.3|1.2|1.0|2.1|2.4|0.4|0.9|0.2|
|TPP|3.1|17.1|29.2|35.1|1.7|13.3|2.1|12.7|0.4|8.1|
|TRD|2.0|0.8|0.7|3.0|2.2|5.3|4.5|1.0|3.9|0.5|
|TRK|0.4|0.1|0.1|0.5|0.4|0.9|0.9|0.2|0.5|0.1|
|TRO|0.5|0.2|0.2|0.7|0.6|1.3|1.2|0.2|0.7|0.1|
|CLI|2.6|1.1|0.8|3.4|3.1|6.0|9.0|1.4|1.3|0.5|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

33
Notes: The dependent variable is the log of labor productivity at firm level. FDI is at sector level. GVC is at firm level. GVC is defined as the average of intermediates import share and the exporting intensity. The latter defines as the ratio of exports over sales. Ln (# skilled workers at t-1) is the log of the number of skilled workers of a firm in the previous year. Ln (years of top management) is the log of years of experience of the top managers. We divide firms into two sectors: manufacturing and services. The standard errors are clustered at the country-sector level. City-year, sector-year and year fixed effects are included. *** p<0.01, ** p<0.05, * p<0.1.

Another way of measuring the productivity of a firm could be its likelihood of engaging in research and development. In Table 13, we test to see if a firm in a sector with higher FDI is more likely to engage in R&D activities if its GVC participation is higher or lower. The results show that a firm with a higher exporting intensity or a higher import share is more likely to engage in R&D. However, its R&D propensity will be less affected by sectoral FDI. In fact, higher FDI in the sector conditional on high exporting intensity can lead to less R&D spending. The results remain qualitatively the same whether we use OLS, Logit and Probit models.

**Table 13: Sectoral FDI has a positive impact on firm R&D propensity**

||OLS|Logit|Probit|
|---|---|---|---|
|Binary Dependent variable: R&D (=1)|(1)|(2)|(3)|
|_ln (FDI)_|0.010***|0.056***|0.034***|
||(0.00)|(0.01)|(0.01)|
|_ln (FDI) × exporting intensity_|-0.012**|-0.068***|-0.040***|
||(0.01)|(0.02)|(0.01)|
|_Exporting intensity_|0.174***|0.973***|0.574***|
||(0.02)|(0.08)|(0.05)|
|_ln (FDI) × foreign import share_|-0.007|-0.044*|-0.027*|
||(0.01)|(0.03)|(0.02)|
|_Import share_|0.078***|0.477***|0.286***|
||(0.01)|(0.05)|(0.03)|
|_Foreign ownership share_|0.013|0.047|0.032|
||(0.01)|(0.05)|(0.03)|
|_Foreign tech adoption (=1)_|0.146***|0.743***|0.445***|
||(0.01)|(0.03)|(0.02)|
|_ln(years of experiences of top_|0.023***|0.136***|0.078***|
||(0.00)|(0.02)|(0.01)|
|_Services dummy_|-0.222***|0.48|0.302|
||(0.02)|(0.95)|(0.54)|
|Year FE|Yes|Yes|Yes|
|City-year FE|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|
|Observations|42,616|42,541|42,541|
|R-squared|0.144|||
retrieve household income with a high degree of precision. However, as with any restricted sample, these surveys are usually representative only nationwide or across provinces or states.

The universal coverage of the population can be approximated through population censuses, which typically provide relevant inputs to measure several dimensions of well-being at high levels of disaggregation (for example, in Mexico, CONEVAL’s social backwardness index or the marginalization index of the National Population Council).<sup>3</sup> This greater geographical detail, however, comes at the cost of a lack of robustness in the information on household incomes. Censuses are not designed to collect comprehensive data on income. They provide an incomplete picture of household monetary circumstances, which, at least for the purposes of this analysis, represents a main weakness.

To address the dilemma between precision and geographical detail and, hence, to make the relevant dataset available for empirical analysis, the Elbers et al. (2003) small area estimation technique is used to impute household per capita income from surveys to corresponding households in censuses. This is accomplished by predicting, from an income model in the survey, the parameters and distribution of errors, which are then used to simulate the income distribution in the census dataset from which to compute poverty and inequality indicators.

Two critical steps are necessary to make this model work properly. The first step consists of considering the household survey as a random sample of the total population found in the sample frame of the census. The second step is to identify a set of potential explanatory variables that are common between the survey and the census and that satisfy a conceptual and statistical equality criterion. This means, respectively, that these variables should measure the same phenomenon in both datasets and that the respective distributions of the variables are statistically indistinguishable, that is, the sample mean is statistically equal to the population mean. The variables that satisfy this criterion are then candidate regressors in the modeling of household per capita income in the survey dataset.

> 3These indexes, computed across villages, municipalities, or states, summarize the degree of deprivation in 12 and 9 indicators, respectively, and focus on the dimensions of education, access to basic services in the dwelling, the quality and size of dwellings, health (the social backwardness index), and labor income (the marginalization index). The two indexes are computed through the principal component analysis technique and are then stratified into five groups according to the degree of backwardness or marginalization: very low, low, medium, high, and very high.

7
to the countries’ policymakers. Second, we obtain estimates for thousands of elasticities of substitution using highly disaggregated import data, which may be useful for other studies. For example, different elasticities may imply different responsiveness of imported products to demand shocks or exchange rate movements suggested by Chen and Ma (2012).

The rest of the paper is organized as follows. Section 2 describes the data and reviews import growth from 1995 to 2021. Section 3 reviews Broda and Weinstein’s (2006) model. Section 4 explains the estimation strategy and gives a brief overview of the importance of elasticities of substitution. Section 5 reports the results of the analysis and presents the welfare gain. Section 6 concludes the study.

# **2 Data and Descriptive Analysis**

We used trade data from BACI (Gaulier and Zignago, 2010).<sup>2</sup> We used the import data of the selected countries from 1995 to 2021, covering 27 continuous years. The data contains information on the total values, quantities, and exporters of registered products to the countries. Unspecified country data and products with data on their quantities are dropped. Furthermore, due to the insufficient numbers of observations, HS-6 products with fewer than 26 observations are dropped in the sample. This is due to the problem that many products were not imported to the countries constantly throughout the period. This left us with more than 40 million observations of 5,383 products.<sup>3</sup> Gross domestic product (GDP) data were taken from the World Bank’s World Development Indicators (WDI) Database.

To study the welfare implications of the drastic increase in imports of the countries, we should consider the increase in value of each product (i.e., the intensive margin) and the increase in the number of products and varieties for each product (i.e., the extensive margin).

Figure 1 shows average annual imports as shares of GDP between 1995 and 2021. The imports share of GDP greatly varies among the countries. For instance, Japan has an import

> 2BACI relies on data from the United Nations Comtrade - International Trade Statistics database

> 3Note that not all 5,383 products were imported to each country. On average, 4,537 products were imported by one country.

4
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37