rafmacalaba/data-use-mentions-extended huggingface dataset:
Closely related to our paper is the work by Love (2000) who studies the relationship between financial development and financing constraints by estimating Euler equations on a firm level for a sample of 40 countries. Love (2000) finds a strong negative relationship between the sensitivity of investment to the availability of internal funds and an indicator of financial market development, and concludes that financial development reduces the effect of financing constraints on investment. This result provides evidence for the hypothesis that financial development reduces informational asymmetries in financial markets which leads to an improvement in the allocation of capital and ultimately to a higher level of growth.

Section 2 reviews the literature on financing constraints. Section 3 presents the structural model of firm investment that we use to estimate the impact of financial liberalization on financing constraints of firms. Section 4 describes the econometric techniques we employ to estimate our structural model of firm investment. Section 5 presents the firm-level data used in our empirical work. Section 6 presents the results of our empirical work. Section 7 assesses the robustness of our results. Section 8 concludes.

# **2 Literature Review**

Following the work of Fazzari, Hubbard and Petersen (1988) a large body of literature has emerged to provide evidence of such financing constraints. This literature relies on the assumption that external finance is more costly than internal finance due to asymmetric information and agency problems, and that the "premium" on external finance is an inverse function of a borrower's net worth. It has been found that financial variables such as cash flow are important explanatory variables for investment. These findings are usually attributed to capital market imperfections as described above (see the surveys by Schiantarelli (1995), Blundell, Bond and Meghir (1996) and Hubbard (1998)). Following Fazzari, Hubbard and Petersen (1988) it is usually assumed that there are cross-sectional differences in effects of internal funds on firms' investment, so that the investment equation should hold across adjacent periods for _a priori_ unconstrained firms but be violated for constrained firms
# **6. CONCLUSION**

In spite of their increasing policy-prominence, relatively little is known about gender inequities in rural non-agricultural labor market outcomes. This is unfortunate since non-farm enterprises account for a substantial share of rural income and employment (Haggblade, Hazell and Reardon, 2007). Using novel matched household-enterprise-community datasets from Bangladesh, Ethiopia, Indonesia and Sri Lanka we attempt to redress this lacuna in the literature by documenting and analyzing gender differences in individuals’ activity portfolio choices, as well as differences in the productivity of rural non-farm firms.

Women are much less likely to engage in income earning activities and gender differences in non-farm entrepreneurship are large. Women have lower participation rates in non-farm enterprise activities in Bangladesh, Indonesia and Sri Lanka, but not in Ethiopia. However, working in rural non-farm firms appears to be very important for women who partake in income earning activities and especially so for those who are the heads of their households. Somewhat surprisingly, women’s propensity to be non-farm entrepreneurs is not strongly correlated with their educational attainment or the number of children in the household, whereas the share of children in the households is negatively correlated with women’s engagement in wage employment in Bangladesh and Indonesia. This suggests that running a non-farm firm is more compatible with combining domestic work with income earning activities than wage employment is, which is consistent with most non-farm enterprises managed by women being home-based. Women’s activity choices are also correlated with their husbands’ educational attainment, with women whose partners have either very little, or a lot of education seemingly less likely to run non-farm firms, although this pattern is not very pronounced.

Female firms are much smaller and much less productive on average, yet gender differences in productivity vary dramatically across countries. Mean differences in log output per worker and 21
hand, the degree of non-compliance could be more limited among the D2 respondents in comparison to their D1 counterparts, mainly due to the significantly more valuable set of incentives that they received to be part of CVIP.P25F<sup>26</sup> In comparison to the recall variants, both diary treatments are expected to reduce two main sources of bias that typically lead to underestimation of annual cassava production. First, since the harvest is captured immediately upon harvest, recall decay should be minimized. Second, diaries should reduce the scope for respondent bias that may arise with recall. Recall-based interviews are often conducted in a specific time frame that revolves less around the schedule of the ideal respondents (i.e. the plot managers) resulting in a greater potential for proxy reporting. Lastly, within the recall domain, it is possible that R1 reduces the scope for recall decay to a greater degree compared to R2, since the R1 respondents are asked to recall over 6, as opposed to 12, months.

> 25 A specific example of an unsuccessful diary effort is in fact from Malawi, with 40 percent of households in the First Integrated Household Survey (IHS1) 1997/98 having been judged to have incomplete or unreliable expenditure data due to poor diary-keeping.

> 26 This claim needs to be supported with the available empirical evidence on the impact of gifting on survey data quality.

13
1 uses the normal linear regression model with the theoretical distribution of the error terms and Method 2 uses the empirical distribution of the error terms. Both methods employ cluster random effects. Models 3.1 and 4.1 employ non-standardized variables. Models 3.2 and 4.2 employ variables in TA 3 standardized by those in NPS 2020/21. Food and non-food expenditures, household size, and age in both surveys, are transformed to normality using the Box-Cox method before standardization. The imputed poverty rates for TA 3 use the estimated parameters based on the 2019/20 data. 1000 simulations are implemented. The true poverty rate for TA 1 is the estimate directly obtained using the consumption data in TA1 and the poverty line in NPS 2019/20 deflated to 2022 prices with the annual WB deflator. The estimates shown in bold or with an “a” respectively fall within the 95% CI or one standard error of the true poverty rate. The underlying regression results are provided in Appendix A, Table A.6.

57
employed in the previous section are the same as those displayed in Table 2. These were calculated from sectoral GDP figures, assuming a uniform national population growth rate of 2.9%. This population growth rate corresponds to the change in national population between the 1988 and 2002 censuses. Alternatively, we can use sectoral population figures to calculate the per capita growth rates. Annual population growth averaged 4.6% in urban areas and 2.5% in rural areas.

Appendix Table A5 and Figure 7 below display results from a simulation employing these alternative growth rate figures. This simulation fairs much worse than others in terms of matching the true 2002 poverty rates. It shows the rural headcount rate declining to 0.318, far below the true
(%)<br><!-- End of picture text -->

- **Growth rate of TFP:** �� ��%. A TFP index for the country is calculated using the data and methodology listed in PWT 8.1 and using the labor share of 0.51 obtained from the GTAP database. The growth rate of TFP is generally volatile and it is also true for the case of Bangladesh as seen in the figure below. The TFP was on an upward trend from the 1980s till it suddenly dropped in 2001. After remaining in the negative territory for another two years, the TFP growth started trending upwards again. The average growth rate of TFP during 1991 – 2011 has barely managed to remain in the positive

9
enumerators asked other 8<sup>th</sup> grade students in that school whether they knew the missing girl. If someone knew the girl in question, they were asked whether they knew she was definitely enrolled elsewhere; definitely not enrolled; or whether respondents were not certain about the enrollment status of the missing girl. On the basis of the enrollment registers and the questions asked of 8<sup>th</sup> grade girls during the school visits, we were able to establish the enrollment status of 95.2 percent of all of the girls who had completed scholarship applications. One way to think of the girls whose enrollment status could not be established is as attritors out of the sample. The attrition rate, 4.8 percent, is low—as a point of comparison, attrition in a recent evaluation of school voucher scheme in Colombia was almost 50 percent (Angrist et al. 2002). Nevertheless, we carefully consider the extent to which selective attrition could bias our estimates of program impact.

Table 1 compares enrollment, attendance, and the characteristics of girls who were awarded scholarships and those who were not. The table shows that, on average, scholarship recipients had significantly lower socioeconomic status than non-recipients: Recipients had parents with lower education levels; they were more likely to live in a hut or a house with an earthen floor, and less likely to have houses made of high-grade materials like cement, brick, tiles, metal or fibre; recipients were also less likely to own any one of a number of means of transportation, less likely to own more than a hectare of land, less likely to regularly lend money, and more likely to have debts. These results confirm that the LMCs were successful at targeting scholarships to girls with lower socioeconomic status. Table 1 also shows that 86.7 percent of recipients, but only 64.6 percent of non-recipients were enrolled in a JFPR 5
would be important to assess the long-term impact on debt sustainability. Growth estimates are not dependent on debt burden indicators and therefore they may be considered optimistic, especially for those countries with already high debt ratios. This metric can be further expanded, through the consideration of indicators measuring the quality of the governance and strengths of fiscal institutions and the assessment of the domestic revenue generation capacity, which would further strengthen the discussion of fiscal space and indicate further policy options.

Figure 15. Projected Status of BRI Recipient Countries in 2023 (In percent of GDP) <!-- Start of picture text --> LIDCs EMs<br>45%<br>Tajikistan 18%<br>40%<br>16% Armenia<br>35%<br>14%<br>30%<br>12%<br>25% 10% Albania<br>20%<br>Cambodia 8%<br>15% Mongolia<br>Djibouti Timor- 6% Malaysia<br>10%<br>Leste 4%<br>5% Kenya Maldives 2%<br>Afghanista Pakistan<br>0% n 0%<br>0% 10% 20% 30% 40% 50% 0% 2% 4% 6% 8% 10%<br>Fiscal gap Fiscal gap<br>Low Moderate High Low<br>Sources: WEO, WIND Database and LIC DSF DSAs. Sources: WEO, WIND Database and MAC DSAs.<br>Growth gap<br>Growth gap<br><!-- End of picture text --> Long-Term Projections of debt-to-GDP Ratios To assess the impact of BRI financing and investment on growth and indebtedness in the long term, this section uses the results of SGE estimates from de Soyres et al. (2019). To compare the evolution of public debt-to-GDP ratio from 2016 to 2030 with and without BRI infrastructure investment, a number of assumptions, consistent with the previous
, and inventories and stocks, winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentiles. Excludes the value of land and buildings given the highly skewed distribution (only 16 percent of firms report a value) and that it can be intertwined with household assets. Asked only during the in-person survey. Coded as 0 for closed firms. We use the 2016 capital stock as the short-run outcome, to represent capital accumulated after 2 years.

- <u>Entrepreneurial Self-Efficacy: This measures their self-confidence in their ability to carry out</u> different business tasks, regardless of whether or not they currently operate a business. It is the mean of responses (answered on a five-point Likert scale ranging from 1 = Not at all confident to 5 = totally confident) of the following statements:

- To start a business

- Perceive well business opportunities

- Ensure the marketing of the company properly

- Correctly set the prices of products or services

- Negotiate well with other business owners

- Manage a team of staff well

- Manage a business well

- Write a good business plan

- Find capital financing when starting a business

The internal consistency of this scale is good, with a Cronbach alpha of 0.83. These questions were only collected during the in-person long-run survey, and only during the fourth short-term followup in 2016.

- <u>Personal initiative: This is the mean of responses (answered on a five-point Likert scale ranging</u> from 1 = Strongly disagree to 5 = Strongly agree) of agreement with whether in the past six months the following statements apply to them:

- I normally go beyond what is expected of me

- I take the initiative immediately even when others do not

- I use opportunities quickly in order to attain my goals

- I actively tackle problems

- I have a gift for implementing ideas

25
Figure 1: Rates of Participation in Urban Employee Health Insurance and Pension Programs among China’s Rural Migrants <!-- Start of picture text --> Panel A: Health Insurance for Full Sample Panel B: Pension for Full Sample<br>Total Employed Self-Emp Total Employed Self-Emp<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>year year<br>Panel C: Health Insurance for Employed Sample Panel D: Pension for Employed Sample<br>Total Employed Total Employed<br>Employed with contract Employed with contract<br>Employed w/t contract Employed w/t contract<br>2008 2010 2012 2014 2016 2008 2010 2012 2014 2016<br>year year<br>.8 .8<br>.6 .6<br>.4 .4<br>Proportion Proportion<br>.2 .2<br>0 0<br>.8 .8<br>.6 .6<br>.4 .4<br>Proportion Proportion<br>.2 .2<br>0 0<br><!-- End of picture text -->

**Source** : 2008-2016 waves of the Rural-Urban Migration in China (RUMiC) Survey. **Notes** :The employed sample of migrants excludes self-employed workers. Of the total 2015 RUMiC sample 32 percent, 30 percent and 38 percent were employed with a contract, employed without a contract and self-employed, respectively.

37
, we estimate specifications of patent functions with elements of the cumulative-knowledge and R&D-investments models, and allow for countries’ patenting activity to be influenced by the accumulated domestic and foreign knowledge. In a second set of estimations with firm data we explore the determinants of product-quality upgrading and the introduction of new products, with a focus on the role of within-country and within-industry spillovers across firms. That is, the empirical strategy on patents exploits the time dimension of the data, and the analysis of quality upgrading and new products by firms exploits the industrial sector classification of the data but not a time dimension since we do not have panel data on firms (from throughout the world). We would have liked to use sectoral data on patents however there is no unique correspondence between patent classes and industrial sectors.

The literature on spillovers has theoretical foundations on models of endogenous growth. These models were first introduced in Romer (1990, 1986), and then subsequently studied by Grossman and Helpman (1991a,b) and Aghion and Howitt (1992) among others. In Romer (1990) the aggregate stock of designs for intermediate inputs changes as a function of the existing stock of designs 1
that previously negative effects of e-filing adoption are insignificant when we use placebo treatments (Table B.6, columns VII and VIII, and Table B.7 in the Online Appendix). We thus confirm that the results presented in Section 4.1 are not random, and that e-filing implementation does indeed reduce tax compliance costs. We do not conduct similar tests for public procurement, as the direct effects on e- procurement are not significant in the main analysis.

Our firm-level data for the ECA region allows us to control for firm fixed effects. Therefore, we repeat firm-level analysis on the sub-sample of firms from the ECA region and compare estimates from two different sources of variation: within country and sector and within firm (Tables B.8 and B.9 in the Online Appendix).<sup>15</sup> The estimated coefficients for this sub-sample generally have the same sign and significance level as those for the full sample of developing countries. The magnitudes of the coefficients, however, are often different, which reflects region-specific differences in the impact of e- government implementation. Importantly, for the number of visits by a tax official, the coefficient on ����3 increases in absolute value and becomes highly significant when firm fixed effects are taken into account. Similarly, for the likelihood to pay bribes to secure a government contract, the coefficient on ����3 is not positive or significant when firm fixed effects are taken into account, in contrast to the estimation with year, sector, and country fixed effects that had suggested e-procurement resulted in more corruption. These observations highlight the importance of controlling for firm fixed effects in firmlevel analysis, as they help to remove unobservable firm specific characteristics that can bias estimates. Unfortunately, the paucity of WBES data limits the cases in which firm fixed effects can be applied.

Finally, we confirm that in all cases we get similar results if we use OLS methods instead of tobit and probit. For firm-level analysis, we also confirm that
281|-0.078|
||(2.22)*|(1.76)|(3.32)**|(1.83)|(0.62)|(0.07)|
|Dummy (Southern)|-0.013|-0.871|-6.459|-0.04|2.653|-14.22|
||(0.33)|(3.20)**|(6.12)**|(3.87)**|(3.87)**|(8.14)**|
|Constant|-2.415|18.147|141.886|0.766|53.635|95.873|
||(2.36)*|(2.58)*|(18.10)**|(10.08)*<br>*|(10.54)**|(7.40)**|
|Observations|123|123|123|123|123|123|
|Adjusted R^2|0.05|0.18|0.91|0.46|0.59|0.81|

Absolute value of t statistics in parentheses; * significant at 5%; ** significant at 1%

**Table 12: The OLS results of the Quality of Life: Local Government expenditure in 2000**

||log(expenditure<br>on education),<br>per 1000|log(expenditure<br>on security),<br>per 1000|log(expenditure<br>on health),<br>per 1000|log(expenditure<br>on housing),<br>per 1000|log(expenditure<br>on<br>transportation),<br>per 1000|
|--
Monetary Income of the Unemployed and Inactive (IMDI) for the unemployed and inactive, and Income from other sources (IOF) available to everyone. From 2009 onwards, the survey covers 437 municipalities and annually visits an average of 248,000 households concentrated in 22,548 segments.

There are limitations to the survey data collected in 2020. Due to government-imposed restrictions in the first few months of the pandemic, data collection in cities was done over the phone. From March to April, the survey was restricted to 39 questions from the usual 200 to reduce respondent rejection. Moreover, data collection restrictions were only relaxed from June onwards, making it unclear how these measures affected data collection and comparability. Therefore, we only make comparisons with 2020 when analyzing primary labor indicators such as unemployment, participation, and employment, as we have information for March and April. When including 2020

> 5 Nevertheless, the previous section made a descriptive reference to the main indicators for the years 2021-2022 with the information from the new sample framework of 2018.
**Figure 12: Average height and percentage above 150 cm, women age 15-49, Cambodia Demographic and Health Survey 2000**

<!-- Start of picture text --> 153.8 75<br>153.6 74<br>73<br>153.4<br>72<br>153.2<br>71<br>153<br>70<br>152.8<br>69<br>152.6<br>68<br>152.4<br>67<br>152.2 66<br>152 65<br>20-24 25-29 30-34 35-39 40-44 45-49<br>Age in 2000<br>Average height Percentage above 150cm<br>Height in centimeter Percentage > 150cm<br><!-- End of picture text --> 32
, 0)).

Information on firms’ sector of activity is obtained from the firm registry, which contains the six-digit CIIU industry code for all firms ( _Clasificaci ́on industrial internacional uniforme_ ). In the CIIU, the first two digits of the CIIU code capture the division. Division number 46 designates retail firms and division number 47 designates wholesale firms. The firm registry also documents in which one of Uruguay’s 19 departments the firm is located.

Finally, we have access to the list of firms that received the subsidy for POS rental, with the months during which the firm received the subsidy and the total subsidy amount each month.

> 9Having to offer the free bank accounts became a mandate for banks in October 2015. For wage earners and social benefit recipients who did not exercise the option to create a bank account by June 2016, the employer or social security agency had to choose a financial institution for the beneficiary by September 2016. It became mandatory for wages and pensions to be transferred into bank accounts from May 2017 onwards. In 2014, 43 percent of respondents in the World Bank Global Findex Survey indicated having used a debit or credit card in the previous year.

> 10A variable indicating the type of card transactions (debit or credit card) is available only since August 2014.

> 11We are also in the process of trying to obtain data on the number of credit and debit cards issued around the time of the reform from a large private bank.

> 12These data are also used and described in B ́ergolo et al. (2021) and Foremny et al. (2018).

10
/sup> Firm managers are asked to describe several management practices in the firms over a phone interview and are then scored by a trained enumerator. The instrument produces an overall management score, as well as specific scores for the quality of operations management, monitoring of performance, target setting, and HR practices (including recruitment and retention strategies).

> 2I used exchange rate data from the World Bank, which is available here `https://data.worldbank. org/indicator/PA.NUS.FCRF?locations=ET` . For each country, I use the exchange rate for the year prior to the date when the survey was released to the public (e.g. for Ethiopia, I use the 2014 exchange rate). The CPI data is available here `https://www.measuringworth.com/datasets/uscpi/` .

> 3The World Bank Enterprise Survey offers three types of weights. I use the recommended median weights.

> 4I use the 2004-14 combined manufacturing survey data available here: `https:// worldmanagementsurvey.org`

4