rafmacalaba/datause-encoder-data huggingface dataset:
financing constraints. Analyzing data from the Enterprise surveys, Chavis, Klapper, and Love (2011) show that across countries, younger firms rely less on bank financing and more on informal financing and as firms mature they switch to using more bank finance. However, financing from informal sources which includes financing from family and friends is often “unreliable, untimely, and bearing significant non-financial costs” (Djankov, McLiesh, and Shleifer 2007). For instance, Ayyagari, Demirgüç-Kunt and Maksimovic (2010) show that while there is a large amount of informal financing in China, only bank financing is associated with higher growth rates.

Other empirical studies try to estimate financing constraints in young firms from the study of firm size distributions. Under perfect financial markets, firm growth should be independent of firm size (Gibrat’s law) and firm size distributions are expected to be lognormal. However, Cabral and Mata (2003) and Angelini and Generale (2008) show that the firm size distribution for young firms is heavily skewed to the right and only becomes symmetric over time as firms age. They argue that underinvestment due to financing constraints explains the right skewness in FSDs of young firms especially in developing countries.<sup>9</sup> While institutions such as venture capital and angel investors in developed countries are focused on promoting start-ups (Samila and Sorenson (2011), Kerr, Lerner, and Schoar (2011)), the absence of these institutions in developing countries further exacerbates financing constraints in those countries.<sup>10</sup>

> 9 See Quadrini (2000) and Cooley and Quadrini (2001) for structural models of firm dynamics using financing constraints.

> 10 Several reasons explain why venture capital funding is not common in developing countries, including the lack of an IPO market that provides exit options to investors (e.g Hall and Lerner

29
economic growth when there is a savings motive for having children. As economies develop, children become less important as a savings mechanism, and in that process adult survival gradually becomes a key to increased capital stocks—equivalent to the finding in the existing literature.

In sum, by targeting health conditions for all generations, but especially for children, we find that there is a potential for stimulating the demographic transition with lower fertility and higher productivity growth.

# **References**

Arrow, K. J. (1962), "The Economic Implications of Learning By Doing," Review of Economic Studies, 29, 155-173.

Barro, R. J. and G. Becker (1989), "Fertility Choice in a Model of Economic Growth," Econometrica, 57, 481-502.

Barro, R. J. and J. Lee (1994), "Sources of Economic Growth," Carnegie Rochester Conferences Series on Public Policy, 40, 1-46.

Blackburn, K. and G. P. Cipriani (1998), "Endogenous Fertility, Mortality and Growth," Journal of Population Economics, 11, 517-534.

__________ (2002), "A Model of longevity, Fertility and Growth," Journal of Economic Dynamics & Control, 26, 187-204.

__________ (2005), "Intergenerational Transfers and Demographic Transition," Journal of Development Economics, 78, 191-214.

Bloom, D. E. and J. Williamson (1998), "Demographic Transitions and Economic Miracles in Emerging Asia," World Bank Economic Review, 12(3), 419-455.

Bloom, D. E., D. Canning and J. Sevilla (2003), "The Demographic Dividend: A New Perspective on the Economic Consequences of Population Change," Population Matters, Monograph, MR-1274, RAND, Santa Monica.

__________ (2004), "The Effect of Health on Economic Growth: A Production Function Approach," World Development, 32(1), 1-13.

Bloom, D. E., D. Canning and M. Moore (2005), "The Effect of Improvements in Health and Longevity on Optimal Retirement and Savings," PGDA working paper, 2.

Bloom, D. E., D. Canning, R. Mansfield and M. Moore (2007), "Demographic Change, Social Security Systems and Savings," Journal of Monetary Economics, 54(1), 92-114.

Bohn, H. (2001), "Social Security and Demographic Uncertainty: The Risk-Sharing Properties of Alternative Policies," in J. Campbell and M. Feldstein (eds.), Risk Aspects of Investment Based Social Security Reform, 203-241, University of Chicago Press.

Boldrin, M., M. De Nardi and L. E. Jones (2005), "Fertility and Social Security," NBER Working Paper 11146, Cambridge.

Bongaarts, J. (1990), "The Measurement of Wanted Fertility," Population and Development Review, 16(3), 487-506.

19
8 In table 2-5, where yearly flows are given, the increased share going to Asian countries is apparent. By 1985-86 one-third of all FDI flows to developing countries was going to Asian countries. Among Latin American countries, Colombia has been an exception to the general decline in FDI during the 1980s. After several years of declining investor interest in the early 1980s, FDI in Mexico increased substantially in the late 1980s. Overall, however, by 1986 the Latin American countries' share of FDI flows had declined to 24.6 percent.

Table 2-5. Foreign Direct Investment Flows Region of Host Developing Countries, 1981-87 (percent of all developing countries)

|Region|1981|1982|1983|1984|1985|1986|<br>1987|
|---|---|---|---|---|---|---|---|
|Latin America|35.5|24.5|21.2|20.4|31.6|24.6|<br>46.4|
|Africa|7.0|6.9|7.2|6.7|6.4|5.6|5.6|
|Asia|22.1|17.6|31.0|28.8|37.6|32.8|<br>51.6|
|Middle East|31.3|48.1|35.8|38.5|17.3|30.8|<br>-13.8|
|Europe|4.4|3.0|5.5|5.7|6.8|6.2|<br>10.3|

Source: IMF.

Uritil 1989, FDI in China had been increasing substantially--from SDR389 million in 1982 to SDR1,598 million in 1986 (Pfeffermann 1988). Elsewhere in Asi4, FDI flows have also increased--in recent years the Republic of Korea, and in earlier years to Malaysia, Singapore, and Indonesia have received increased shares. As noted in the section entitled "Sectoral Shares," the geographic distribution trends for FDI are affected by the large amounts of FDI in off-shore banking centers and in flags-of-convenience shipping.

# Source Countries

There has been a high degree of concentration in FDI flows by source country (table 2-6). Five countries--the United States, United Kingdom, Federal Republic of Germany, France, and Japan--have accounted for 80 percent or more of the total FDI flows to developing countries. The U.S. share had been around 4060 percent in the late 1970s, but declined in the 1980s. In contrast, Japan's share rose from 10.9 percent in 1975 to 37.2 percent in 1987. The three European countries' individual and collective shares have fluctuated from year to year, but exhibited no strong trends over the 1975-87 period.
|Table <br>Dependent variable:|1: Effect of Digital Public En<br>N.Complaints|gagement on Electoral Competitio<br>V Incumbent<br>M.Victory|n<br>Turnout|
|---|---|---|---|
|GMM Stage:|I|II<br>I|I|
||PANEL A: 3-6 months|before the election||
|Px|-1.11**<br>|||
||(0.53)|||
|Biv|8.22***|||
||(1.99)|||
|p||0.44<br>0.33|1.82|
|||(0.97)<br>(0.94)|(2.25)|
|B||30.75***<br>18.81***|-0.001|
|||(7.75)<br>(6.16)|(0.007)|
|Waldx?(p)|16.97(0.00)|||
|<br>P<br>|<br>A<br>B: 2-5 months <br>|NE<br> before the election<br>=|L<br>s—‘i—sS|
|oe|-1.12**<br>|||
||(0.53)|||
|Biv|7.78***<br>|||
||(2.09)|||
|p||1.02<br>0.92|1.51|
|||(0.83)<br>(0.80)|(1.99)|
|B||28.99***<br>18.16***|-0.001|
|||(7.46)<br>(5.90)|(0.007)|
|Wald x2 (p)|13.73 (0.00)<br>|||
|P|A<br>C:1-4months|NE<br>beforetheelection<br>—ss|L<br>—‘—~s~s~TMS|
||<br>(0.50)|<br>||
|Biv|4.15%|||
||(1.48)|||
|0||1.04<br>1.03|1.39|
|||(0.70)<br>(0.68)|(1.77)|
|B||40.66***<br>26.677**<br><br>|-0.008<br>|
|||(10.66)<br>(8.40)|(0.01)|
|Waldy?(p)|7.80(0.00)|||
|<br>P|<br>A<br>D: 0-3 months|NE<br> before the election|L|
|px|-0.38<br>|||
||(0.37)|||
|Biv|3.45"|||
||(1.21)|||
|p||0.77<br>0.82|1.76|
|||(0.71)<br>(0.68)|(1.78)|
|B||43.70***<br>27.23**|-0.004|
|||(13.28)<br>(10.46)|(0.01)|
|Wald x2 (p)|8.06 (0.00)|||
|Observations|250|250<br>250|250|
|Year & Spatial Fixed <br>Note. Each column repre<br>[M-K-3,M-K]beforeelec|Effects<br>Yes<br>sents a separate regression.<br>Panel<br>tions.<br>N.Complaintsisthenumbe|Yes<br>Yes<br>s A, B, C, and D report 3-month windo<br>rofcumulativedigitalpotholescomplai|Yes<br>ws over the period<br>ntsoverthethree|

Note. Each column represents a separate regression. Panels A, B, C, and D report 3-month windows over the period [M-K-3, M-K] before elections. N.Complaints is the number of cumulative digital potholes complaints over the three months window before elections. V.Incumbent, M.Victory, and Turnout are, respectively, total number of votes for the incumbent, the margin of victory of the incumbent, and the voter turnout. px indicates the spatial lag of complaints in the regression first stage. Gry is the marginal effect of the instrumental variable defined in equation (2). p is the spatial lag of the dependent variable in the regression second-stage. 6 measures the marginal effect of digital public engagement (proxied by digital pothole complaints). The Wald test y? (p-value in parentheses) measures the joint significance of the excluded instruments. Robust Standard errors in parentheses. * p < 0.1, ** p < 0.05, *** p < 0.01.
These measures of macroeconomic effective taxation capture the overall tax burden on labor and capital, building on the work of Mendoza, Razin, and Tesar (1994) and Carey and Rabesona (2004).7 These ETRs have a number of advantages. First, they capture the economically relevant tax wedges on each factor of production (i.e., the wedges that matter for production decisions), such as the difference between the costs to employ a worker and what the worker receives.8 Second, because national account statistics are compiled following harmonized concepts and methods, they are conceptually comparable over time and across countries. Third, since they rely on the amount of tax effectively collected by governments, they incorporate the net effects of all tax rules— such as base reductions, exemptions, and tax credits—and of tax avoidance and evasion.9

# **3.2 Data sources**

## **3.2.1 National income components**

To estimate factor shares for 156 countries since the 1960s, we create a harmonized panel of national accounts which combines data from the UN System of National Accounts, the World Inequality Database, and other sources.

From the World Inequality Database (WID, 2020), we retrieve United Nations (UN) System of National Accounts (SNA) data that covers 4,000 country-years. These data come from the production and income accounts of the online UN SNA (2008), “Main Aggregates and Detailed Tables.” In addition, the UN Statistics Division provided us access to their archival data on the components of GDP, with over 2,000 country-year observations from the 1960s and 1970s, presented following the 1968 System of National Accounts (UN

> 7Compared to pre-existing work, our ETRs are global, cover over a half-century, systematically integrate mixed income, and account for heterogeneity in the capital component of PIT revenue using new data on the location of the PIT exemption threshold and on the relative taxation of dividends vs. labor income.

> 8The computation of these economically relevant wedges does not require one to make assumptions about behavioral responses (and hence about the incidence of taxes). See Mendoza, Razin, and Tesar (1994) and Saez and Zucman (2019a) for a conceptual discussion.

> 9The ETRs computed in (5) are called backward-looking ETRs in the literature. A separate literature tries to model all statutory features of the tax system at a point in time in order to measure forward-looking ETRs (see, e.g., King and Fullerton, 1984; Devereux, 2004).

11
**Table 6: Regressions in Changes in Urbanization**

|Regression|1|2|3|4|
|---|---|---|---|---|
|dates available|1965-95|1965-95|1970-95|1970-95|
|ln(u60)|-0.012**|-0.012**|-0.013**|-0.012**|
|year|-0.00028**|-0.00019**|-0.00015**|-0.00014**|
|zy|0.016|00.015|||
|Africa|0.0011||||
|Africa*pre80||0.0043**|0.0036*|0.0035|
|Africa*post80||-0.0026*|-0.0052**|-0.0051_**_|
|zYags|||-0.023*|-0.023*|
|zYinds|||0.016**|0.016**|
|lagruw||||-0.0014*|
|Adj. R<sup>2</sup>|.549|.562|.603|.585|
|N|630|630|443|437|

Dependent variable is change in % urban population. Notes: see Table 2.

Since urbanization is thought to be associated with structural changes in the economy, we include terms for the share of growth derived from agriculture and from industry: ZYAGS and ZYINDS.<sup>17</sup> The share of growth in services was never significant, so we omitted it because it introduced collinearity problems. As expected, we find that growth in agricultural value added is associated with slower urbanization rates, while increases in industrial value added are positively correlated with urbanization. The explanatory power of structural change variables is small, however, as they only increase the R2 by about 1 percentage point. However, their explanatory power increases substantially when we exclude Africa from the sample (then they add about 4 percentage points to the R2.) Neither ZYAGS nor ZYINDS is significant in the African sample.

We also test whether differences in rural and urban wages affect the speed of the urbanization process. Since we want to establish the direction of causality, we used lagged values of RUW. The negative coefficient on LAGRUW confirms what most micro studies have found, namely that low rural (relative to urban) wages contribute to rural-urban migration. In addition, the AFRICA*PRE80 dummy is generally not significant once LAGRUW is included in the regression. In other words, the speed of Africa's urbanization prior to 1980 is no longer extraordinary if we take into account the very large disparities between urban and rural wages. In Africa in 1970, the average product of labor in agriculture was only 15% of that in industry and services. In South Asia and East Asia it was about 26%.

> 17 These are calculated as annual average growth in value added in agriculture and in industry, weighted by their shares of GDP. They are not deflated by growth in population.

23
43

**-** Togo 1 PFP Projections and Actuals

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**Togo 4 - PFP Pro_ectlons and Actual_**

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**Informal Financing** which is a dummy variable that takes the value 1 if the firm reported that the sum of Family, Informal (e.g. moneylender), and Other financing of new investments or working capital is 50% or greater. Informal Financing takes the value 0 if the sum of family, informal and other financing of new investments and working capital is equal to 0 %.

As a measure of firm performance we use the firm’s average **Capacity Utilization** which is defined as the amount of output actually produced relative to the maximum amount that could be produced with the firm’s existing machinery and equipment and regular shifts. As a check we also present results with **Labor Productivity** , which is the ratio of labor productivity of the firm to the mean labor productivity in its country where labor productivity is defined as (Total SalesRaw Material Costs)/Total Number of Workers in the previous year. Scaling by the country mean allows us to account for the wide heterogeneity in firm performances. Using a ratio also allows us to avoid dealing with exchange rate fluctuations in the time period. We also use **Sales Growth** over the past year as an alternate indicator of firm performance. We prefer capacity utilization as the main performance measure since labor productivity is a direct function of firm sales and hence may be mis-reported as well and we prefer capacity utilization to sales growth since the latter is available for a much smaller sample of firms. The ES surveys also contain detailed information on firm size, age, legal status, industry sector, and ownership, all of which are used as controls in our study. The survey defines firms of different sizes on the basis of the number of full time workers<sup>15</sup> - small firms have less than 20 employees, medium firms employ 20 to 99 employees, and large firms employ 100 or more employees.

For a smaller sample of 27 transition countries, the ES surveys were implemented in 2002 and 2005 as a joint initiative of the European Bank for Reconstruction and Development (EBRD) and the World Bank Group and are called the Business Environment and Enterprise Performance Surveys (BEEPS). The BEEPS surveys have more detailed data on profit margins and alternate measures of corruption and tax evasion, which we use as robustness checks in section 6. In addition, the BEEPS data contains a panel component, where 1,443 firms that were surveyed in 2002 were surveyed again in 2005. While we rely on the pooled 2002 and 2005 data for our main robustness checks we use the panel data for additional robustness checks to verify

> 15 Employment is typically the most reliable figure in developing countries. Hence, number of full time workers is used as a measure of firm size by the World Bank Group and other international survey teams including RPED and the Oxford Centre for the Study of African Economies.

12
|Honduras|HND|0.168<br>(0.01)|0.087<br>(0.01)|1.1|2001|2011<br>|100,016<br>|1,789|
|---|---|---|---|---|---|---|---|---|
|Chile|CHL|0.116<br>(0.01)|0.076<br>(0.01)|1.9|1990|2013<br>|458,846<br>|10,664|
|Guatemala|GTM|0.14<br>(0.03)|0.071<br>(0.03)|1.0|2000|2003<br>|16,448<br>|2,573|
|Nicaragua|NIC|‐0.059<br>(0.05)|‐0.074<br>(0.05)|‐5.2|2005|2005<br>|9,910<br>|1,409|
|Bangladesh|BGD|‐0.465<br>(0.03)|‐0.431<br>(0.03)|12.7|2010|2015<br>|22,904<br>|783|
|Year Fixed Effec|ts|YES|YES||||||
|Occupation Fixe|d Effects|YES|YES||||||
|Industry Fixed E|ffects|YES|YES||||||
|Mincer controls||NO|YES||||||

_Notes:_ Wage is hourly, in real US dollars. Large indicates that the employer has more than 100 employees. Mincer controls include age, age squared, and dummies for completion of primary, secondary and tertiary education. Heteroskedasticity robust standard errors in parenthesis 10
Figure B1 reports simulation results of a tari cut in textiles. To facilitate the comparison we reproduce simulation results for the dynamics of capital, wages and employment from Figure 1 in the left panel. Simulations following the Cobb-Douglas specication are in the right panel. The qualitative results of both models are very similar. Quantitatively, responses are higher in the baseline case, especially for employment, but of similar orders of magnitude. Results are consistent across both specications and do not hinge on a particular specication for the production function.

56
which social protection and safety nets benefit recipients are ambiguous (Banerjee et al., 2020).<sup>1</sup> Third, we assess the impact of the pandemic on the diets of women and pre-school children. As is well understood, the impact of shocks on households can have unequal effects on individual household members (Alderman et al., 1995; Hoddinott 2006) and our data allow us to assess some of the intra-household impacts of COVID-19.

In March and August 2019, we conducted face-to-face surveys with mothers of children under the age of 24 months to assess how access to Ethiopia’s flagship social protection program, the Productive Safety Net Program (PSNP) had affected their food security and nutritional status. In June 2020, we re-interviewed these mothers by phone. Our data cover drought-prone areas of Ethiopia, spanning the four main highland regions of the country. Having access to pre-pandemic data allows us to assess the extent to which household food security and diets of individual members have changed following the start of the pandemic in Ethiopia. The longitudinal nature of our data, together with a sample that included both PSNP and non-PSNP households allows us to combine a difference-in-difference approach with a household fixed effect estimator, allowing us to control for a wide range of confounding factors.

We find that the percentage of households reporting a food gap, a widely used measure of food insecurity in Ethiopia, increased by 11.7 percentage points and the size of the food gap by 0.47 month. Participation in the PSNP offsets virtually all of this adverse change; the likelihood of becoming food insecure increased by only 2.4 percentage points for PSNP households and the duration of the food gap increased by only 0.13 month. The protective role of PSNP in food security is higher for poorer households and those living in remote areas. Results are robust to definitions of PSNP participation, different estimators and how we account for the nonrandomness of mobile phone ownership. PSNP households were less likely to reduce expenditures on health and education by 7.7 percentage points and were less likely to reduce expenditures on agricultural inputs by 13 percentage points. By contrast, mothers’ and children’s diets changed little, despite some changes in the composition of diets as consumption of animal source foods declined significantly. Our findings highlight the value of having a well-functioning social

> 1 While safety nets and transfers enable beneficiaries smooth consumption during income losses, some forms of transfers can increase beneficiaries’ exposure to the virus if these transfers trigger new economic activities and businesses (Banerjee et al., 2020).

3
# **Bibliography**

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De Carvalho, D. (2003) _Migrants and Identity in Japan and Brazil: The Nikkeijin_ , London and New York: Routledge Curzon.

Kikumura-Yano, A. (2002) _Encyclopedia of Japanese Descendants in the Americas: An Illustrated History of the Nikkeijin_ , Tokyo: Japanese American National Museum.

Takahashi, Y. (1993) _Nikkeijin Burajiru Iminshi_ (A History of _Nikkeijin_ in Brazil), Tokyo: San-Ichi Shobo (in Japanese).

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Goto, J. (1993) _Gaikokujin Rodosha to Nihon Keizai_ (Migrant Workers and Japanese Economy), Tokyo: Yuhikaku (in Japanese).

The Japan Institute of Labor (1995) “Nikkeijin Rodosha no Jukyu Sistem to Shuro Keiken” (Demand-Supply System and Work Experiences of Nikkeijin Workers), Tokyo (in Japanese).

Japanese Ministry of Labor (1991) “Gaikokujin Rodosha ga Rodomentou ni oyobosu eikyoutou ni kannsuru kennkyuukai senmon bukai houkokusho” (A Report of Committee on the Impact of Migrant Workers on Employment and Labor in Japan), Tokyo (in Japanese).

McKenzie, D. and J. Mistiaen (2007) “Surveying Migrant Households: A Comparison of Census-Based, Snowball, and Intercept Surveys”, mimeo, Washington, D.C.: World Bank.

25