rafmacalaba/datause-encoder-data huggingface dataset:
are also more likely to have a higher wage or income _growth_ (on top of a higher wage level).<sup>25</sup> If this were the case the difference in difference estimates would overestimate the real effect of the program. The most credible way to solve this problem is to have an instrument that is simultaneously correlated with program participation but that is exogenous to the outcomes of interest. To this end, we will exploit the fact that, at least during the initial implementation of the program, some municipalities were too concerned with the administrative burden and decided not to participate.<sup>26</sup> As such participation at the municipal level represents a valid instrument in that it is (positively) correlated with the individual participation, but affects outcomes only directly through individual participation. We will adopt a FE-IV (fixed effects instrumental variable) method to estimate:

# **4. Data and Descriptive Statistics**

A baseline household survey, carried out under the supervision SIEMPRO, was administered to participants and non-participants in November 2004.<sup>27</sup> The same households were reinterviewed one year later, at the end of 2005. The questionnaire was based on a shorter version of the Argentinean labor force survey (the Permanent Household Survey). It collects information on basic individual and household characteristics including, their education, labor market history and income sources. For MEP beneficiaries it also collects detailed information on project

> 25 The classic references for the bias associated with differences-in-differences estimates of the impact of the program is related to a possibly pre-program drop in earnings (or the Ahenfelter’s dip as in Ashenfelter, 1978). 26 Let Z be a dummy variable that assumes the value one if municipality c participated in the program. We will estimate equation (2) under the assumption that Z is independent of  the error term but not of D. The nonparticipating municipalities include _Ciudad Autonoma de Buenos Aires, Almirante Brown_ and _Escobar_ .

> 27 SIEMPRO is an Argentinean public agency which carries out analyses in the area of poverty and monitoring of social programs.

16
After this, the third step was to construct within Bankscope maturity (purpose of credit) shares by country-year and apply these to the IFS domestic private credit to the nonfinancial sector.

In the case of loan maturity, we aggregated loans to customers (which include loans to public sector and nonbank financial corporations) across banks for a given maturity bracket and divided by the total sum of loans to customers reported by all banks. In the case of purpose of loans, we aggregated loans to customers for commercial purposes (excluding other category) or consumer or mortgage to the sum of total loans to customers (excluding other category) reported by banks.<sup>18</sup> Multiplying these shares by IFS credit to the nonfinancial private sector, we obtained the U.S. dollar credit exposures by maturity and loan purpose. Finally, these were normalized by GDP to get a credit share of credit by maturity or purpose over GDP.

We decided to consider a country aggregate as representative if the sum of credit reported by maturity or loan distribution in Bankscope was at least 30 percent of the IFS total credit value for that year. Figures and Tables

**Figure 1** shows the density plots where each observation is a country-year for both these threshold values (maturity and purpose) as well as the location for the selected cutoff of 0.30. These cumulative density graphs (the first panel of the graph) show that for both maturity and purpose, this is equivalent to the top 55 percent of country-years. The probability distribution graphs show that there are a number of countries with a threshold above 1—which means that these country-years from Bankscope most likely include loans to non-domestic counterparties and also public agencies.

A subsequent criterion is to count the number of years of data that this threshold yields for each country for both maturity and for purpose. Countries with less than three years of data for a country (not necessarily continuous years) are dropped. With the selected threshold and the country count criteria, the loan purpose data set contains 539 country-years and the loan maturity contains 565 country-years. The earlier discussion summarizes the criteria that are imposed to admit a particular country-year from the Bankscope database into the final global sector and maturity loan database. Using this criterion, **Error! Reference source not found.** 3 summarizes

> 18 The implicit assumption with this approach is that the other Bankscope category does not contain a significant portion of loans to the domestic private nonfinancial sector. Further, omitting the other category from the sector threshold calculation is a conservative approach as it reduces the size of the total Bankscope sector universe.

12
# **A The FAT Survey**

This section provides more details on the Firm Adoption of Technologies (FAT) survey and its implementation. We start with a description of the grid of technologies in FAT. Then we describe the sampling frames used and the construction of sampling weights. We finalize describing all the tests conducted to minimize potential biases, including validation exercises ex-post implemented with external data sources.

## **A.1 The Survey**

The FAT survey is a multi-country, multi-sector, representative firm-level survey. It collects information on the technologies used by firms in specific business functions that encompass the key activities that each firm conducts. Compared to existing firm-level surveys, the FAT survey covers a significantly larger number of technologies and business functions (Table A.1), and a wider range of sectors; for example, it covers agriculture distinguishing between crops and livestock.

Table A.1: Coverage of Firm-Level Technology Surveys

|Surveys|# of<br>Technologies|# of<br>Business Functions|Includes Firms<br>in Agriculture|
|---|---|---|---|
|Firm-level Adoption of Technology (FAT) Survey|305|63|Yes|
|Manufacturing Technology Survey (MTS)|17|0|No|
|Survey of Advanced Technology (SAT)|57|3|No|
|Community Survey on ICT Usage and E-Commerce in Enterprises|9|0|No|
|Information & Communication Technology Survey (ICTS)|4|0|No|
|Annual Business Survey (ABS) 2018 Technology module|10|0|No|
|Annual Business Survey (ABS) 2019 Technology module|5|0|No|

Note: The Number of technologies and business functions are computed by authors. MTS, ICTS, and ABS were conducted by the United States Census Bureau. SAT was conducted by the Statistics Canada. ICT Usage in Enterprise is conducted by EUROSTAT.

The FAT survey addresses important knowledge gaps compared to other surveys measuring technology at the firm or establishment level. To start, the number of technologies covered is rather limited when compared to how many technologies are involved in production processes. Second, their focus on the presence of advanced technologies makes it impossible to understand how production takes place in companies without such advanced technologies. This concern is most relevant in developing countries where advanced technologies have diffused less. Third, since their unit of analysis is the firm, existing studies are not designed to analyze what business functions benefit from each technology. This drawback is particularly problematic for general technologies that can be relevant for multiple business functions. Finally, existing surveys largely omit questions about how intensively a 60
**Community Programs and Women's Participation: The Chinese Experience**

# **1. Introduction**

Globally, over the last few decades, women's economic and social well-being has improved substantially. However, gender inequality is still widespread, and particularly so within developing countries. Empirical evidence from a number of countries establishes the fact that women still have substantial disadvantages over men, both within households and in economic and social life (Haddad, Hoddinott and Alderman, 1997; Dreze and Sen, _1995;_ Filmer, King and Pritchett, 1998).

The persistence of female deprivation is generally attributed to a combination of social and cultural norms as well as being a result of various market failures. At the same time, a large body of empirical studies across many developing countries, in particular in Asia and Africa, consistently shows that improvements in women's education, health, employment opportunities and social participation, can generate substantial benefits. Investment in women not only benefits women themselves but also has relatively high social returns reflected in an improvement in their children's welfare and a reduction of fertility, poverty and gender bias (Summners, 1992; Subbarao _et al,_ 1993; Quibria, 1993).' Therefore, gender-focused public policies are desirable not only from the perspective of social justice, but also because of the substantial social and economic benefits which result directly from enhancing women's social and economic status both within the household and in society as a whole. Also, the importance of market failures in determining outcomes suggests a potentially important role for public polices aimed at improving the welfare of women.

Summers (1992, p1)argues that "an extensive body of recent research conducted at the World Bank and elsewhere has convinced me that once its benefits are recognized, investments in girls' education may well be the highest return investment available in the developing world".
Alcohol and drugs are also prevalent, though less so for young women. Being a victim of alcohol or drug‐ related activity is reported by 24.6% of the sample overall, and by 30.8% of young men and 13.5% of young women. This gender imbalance is slightly greater for those that abuse alcohol and drugs, as the incidence is 28.1% for males, and only 5.6% for females (see Panel C, Figure 2). As one might expect, this type of activity is closely related to other types of criminal activities. Of all the youth reporting to have been engaged in alcohol‐related activity, 50.7% also report engaging in stealing, and 61.5% report engaging in assault.

Compared with assault, Involvement in trespassing is less common. In the baseline sample, only 11.6% of youth report ever being a victim of trespassing, with 14.1% and 7.1% for men and women, respectively. When it comes to (self‐reported) incidence of involvement in such an activity, the numbers are 13.9% and 1.9% for males and females, respectively (see Panel D, Figure 2).

In summary, these results confirm that the sample is capturing the high prevalence of self‐reported anti‐ social behavior and criminal activity among youth in Port Moresby. Strong gender differences also emerge, with men much more likely to abuse alcohol, and engage in robbery or assault. In this context, we next provide evidence on whether the program succeeded in reducing participants’ exposure to and involvement in anti‐social behavior and crime.

# _5.2 Impacts on social behavior, violence and crime_

This section reports the estimated impacts of the intervention on a variety of outcomes of interest related to social behavior, violence and crime. Each of these outcomes of interest is observed for treatment and control groups both at baseline, prior to the intervention, and in the follow‐up survey (FUS) after the intervention. For both treatment and control groups, we evaluate the changes over time, test the statistical significance of these changes, and calculate the difference‐in‐differences (DD) estimates.

# 5.2.1 Impacts on social behavior/environment

Here we report the results from looking at the social behavior and environment indicators. The program may have improved outcomes for participants by altering the people they interact with. One indicator of this is whether youth report hanging out with friends late at night, which is a strong correlate of anti‐social behavior and crime. We find that for the treatment group, the reported incidence declined from 36.8% at baseline to 29.6% post‐intervention), or 18.7 percentage points (significant at a 5% level). For the control group, the incidence rose slightly from 42.2% to 44.2%, which is not statistically significant. Hence, the Difference in Difference estimate is 8.8 percentage points, significant at the 5% level, which is a decline of 24.2 percentage points relative to the baseline value for the treatment group. These results are reported in Table 5. They are also presented graphically in Figure 4 (Panel A). At baseline, those in treatment group 20
around the world obtain most of their financing to fund fixed assets from banks, regardless of their size. Although firms in high-income countries raise more long-term financing and debt from capital markets than firms in developing countries, over 60 percent of their investments are financed by bank loans.<sup>3</sup> Households’ main long-term investment, housing, is also overwhelmingly financed by banks. Capital markets, however, may be an important indirect source of long-term financing to the extent that they fund banks’ provision of long-term credit. This is done either through purchases of long-term bank bonds and equity or through investments in longterm deposits in the case of pension funds and insurance companies.

Despite the importance of long-term finance, data on bank loan maturity are scarce, and particularly so for LAC. Bankscope, a commonly used provider of financial information including bank balance sheets, does not have an adequate coverage of loan maturity for the region.<sup>4</sup> In 2004, only 2 percent of the total loans originated by reporting banks in LAC included information on the maturity of these loans. This Bankscope reporting share has increased to 18 percent (as of September 2015), but LAC is still the region with the lowest data coverage on loan maturity. In contrast, the reporting share ranges between 50 percent and 56 percent in all other regions, except for Middle East and North Africa (MNA) where only 22 percent of loans include maturity data. While the European Central Bank Data Warehouse provides data on loan by maturity for European Union countries, no equivalent data set exits for LAC countries.<sup>5</sup> Orbis, a data set on firm balance sheets, contains information on the maturity of firms’ bank debt, but the coverage is also low for LAC. For example, less than 30 firms report annual bank debt maturity data in countries such as Chile, Peru, and Mexico during 2004–2014 (Demirguc-Kunt, Peria, and Tressel 2015).

Many financial supervisory authorities in LAC do not publicly publish loan maturity data; however, most authorities report loan by purpose/borrower (that is, consumer, mortgage, commercial, financial sector, or government entities). To the extent that consumer lending is mostly short-term (below five years maturity in most countries) and mortgage is long-term, looking at data by purpose of loan provides some useful information about loan maturity. Further,

> 3 The Global Financial Development Report 2015, World Bank.

> 4 This database has historically been provided by Bureau van Dijk using data supplied by Fitch. In December 2016, Bureau van Dijk and Fitch parted ways. The data used in this paper were sourced before the separation.

> 5 European Central Bank (ECB) Data Warehouse website: http://sdw.ecb.europa.eu/.

3
# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. AICD provides a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The AICD’s first phase focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d'Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term ―Africa‖ is used throughout this report as a shorthand for ―Sub-Saharan Africa.‖ The World Bank has implemented the AICD with the guidance of a steering committee that represents the African Union, the New Partnership for Africa’s Development (NEPAD), Africa’s regional economic
Table E4: Monthly Tuition Fees by Grades

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Lower than 1|1 to 3|4 to 5|6 to 8|9 to 12|
|High|14.43|21.22*|19.38|36.87**|142.64**|
||(10.49)|(12.12)|(12.54)|(17.75)|(66.98)|
|Low Treated|-4.85|-3.22|-8.05|-18.75|88.64|
||(5.39)|(6.39)|(8.04)|(12.58)|(78.69)|
|Low Untreated|2.33|4.23|-1.06|-2.44|-68.85|
||(4.59)|(6.21)|(6.54)|(11.24)|(54.93)|
|Baseline|0.83***|0.75***|0.79***|0.67***|0.47***|
||(0.05)|(0.05)|(0.04)|(0.06)|(0.13)|
|R-Squared|0.64|0.60|0.59|0.57|0.48|
|Observations|2,277|2,278|2,240|1,485|360|
|# Schools (Rounds)|789 (3)|789 (3)|773 (3)|542 (3)|144 (3)|
|Mean Depvar|169.89|207.82|237.43|319.88|425.94|
|Test pval (H=0)|0.17|0.08|0.12|0.04|0.04|
|Test pval (_L_<sup>_t_</sup>= 0)|0.37|0.61|0.32|0.14|0.26|
|Test pval (_L_<sup>_t_</sup>=H)|0.08|0.05|0.04|0.00|0.53|

Notes: * p<0.1, ** p<0.05, *** p<0.01 a) This table averages monthly tuition fees by grade bins to assess whether fee changes occur in specific grades. Fees for closed schools or schools that do not offer certain grade levels are coded as missing. b) Regressions are weighted to adjust for sampling and include strata and round fixed effects, with standard errors clustered at village level. We report the number of observations and the unique number of schools and rounds in each regression. Higher grades have fewer school observations because fewer schools offer those grade levels and hence post tuition fees. These observations are subsequently coded as missing. In contrast, in Table E2, enrollment in higher grades is coded as 0 if a school does not offer those grades. The pattern of results in Table E2 stay the same if we restrict its sample to the sample in this table. The mean of the dependent variable in all regressions is its baseline value.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

41
especially that provided by friends and relatives, is positively associated with the sales growth of rural microenterprises that have at least one employee.<sup>7</sup> Similarly, Zhang (2008) finds that proxies for reputation and relationships play important roles in explaining usage of formal finance by small firms in Chengdu (one of China’s largest inland cities), even though those factors tend to be more pivotal for access to informal finance. While those studies have focused on the firm as their unit of observation, we examine reliance on informal sources of finance by Chinese households.

To investigate how social capital affects financial usage and whether it matters for consumption, and how specific components of consumption are affected differently by financial usage, we rely on a comprehensive new national data set, the Chinese Household Finance Survey (CHFS) collected in 2013 by the Chinese Household Finance Research Center of Southwestern University of Finance and Economics. It is representative of Chinese households and contains detailed information on household consumption, structure, income, and access to various types of finance.

For access to finance, we rely on variables describing access to and amounts of formal and informal loans. Another advantage over other studies of finance in China afforded by our data is that we can directly compare the effects of informal finance in rural versus urban settings. And because we have information on household income and wealth, we can directly compare the consumption effects of financial usage in poor versus non-poor households. As noted above, a further advantage is that we are able to use information on household structure and social networks to construct variables to instrument for our informal finance variable, thus enabling us to better identify the causal effects of informal financial arrangements on consumption. A final advantage is that we are able to examine the effect of access to two distinct types of social capital— access to the traditional family-based social network and access to the modern, partybased political network—and contrast how they affect access to finance and the private welfare of households, and how those benefits differ across segments of the society.

Our main finding is robust evidence that access to informal loans significantly

> 7 Those authors rely on the 2009 Rural Finance Survey conducted by the National School of Development at Peking University, which covers 1,950 households in 81 rural villages in three provinces (Heilongjiang, Hunan, and Yunnan).

5