rafmacalaba/datause-encoder-data huggingface dataset:
rural elderly co-reside with adult children and live in proximity to them. We next provide descriptive evidence on the extent to which private net transfers from adult children respond to low income levels of their elderly parents. To do this, we first introduce a framework for examining how net-transfers respond to elderly income, and then make use of a unique data source with information on the age and residence location of all non-resident children of the household. These features of the data source allow us to reduce one form of bias common in many studies of intergenerational transfers: we control for the size and quality of the transfer network. Given specific concerns over abandonment by adult children we examine whether transfer responsiveness differs when an elderly person has migrant children, and moreover, whether the variance of expected transfers varies between elderly with migrant children and those who only have children living nearby.

The descriptive evidence presented shows somewhat higher variance in the predicted transfers to the elderly who have migrant children, and this is consistent with concerns that the current and future elderly may face more uncertainty in their support from adult children.<sup>1</sup> Given that migrants employed in non-agricultural work are frequently in the informal sector, they often have more risky incomes, and so it is not surprising that this risk is reflected in transfers to their parents.<sup>2</sup> One potential policy response to increasing risk to private transfer income is the introduction of a rural pension capable of reducing vulnerability to poverty in old age. China’s government is currently supporting new rural pension programs which are designed to reduce these perceived risks of elderly poverty and to complement existing motives for private transfers.

> 1For reasons that we will describe in detail in the paper, this should not be interpreted as a causal effect of migration per se, but as a purely descriptive outcome after all welfare maximizing joint migration and transfer decisions have been made.

> 2The vast majority of rural migrants have risky income themselves: they often lack employment contracts, may have seasonal employment of short duration, and lack the workplace protections of formal sector workers.

3
||**Table 2: C**<br>**% Change i**<br>**intensiv**|**ompetitive**<br>**n Imports o**<br>**e Manufact**|**ness Effects**<br>**f Energy-**<br>**uring**|**in Industrial C**<br>**% Change**<br>**intens**|**ountries of U**<br>**in Exports o**<br>**ive Manufact**|**nilateral Em**<br>**f Energy-**<br>**uring**|**issions Reducti**<br>**% Change**<br>**intensiv**|**ons**<br>**in Output o**<br>**e Manufact**|**f Energy-**<br>**uring**|
|---|---|---|---|---|---|---|---|---|---|
|**Scenario**|**High Income**|**US**|**EU**|**High Income**|**US**|**EU**|**High Income**|**US**|**EU**|
|NBTA17|1.3|3.5|3.1|-6.4|-11.6|-5.2|-2.3|-4.4|-1.9|
|BTAFU|-16.8|-10.1|-38.7|-15.7|-15.9|-21.5|-0.3|-2.5|1.8|
|BTADU|-6.2|-4.6|-11.3|-8.8|-14.1|-7.8|-1.5|-3.6|-0.5|
|BTADE|-3.2|-1.1|-7.8|1.4|0.7|4.1|0.0|-0.8|1.0|
|BTAFR|-20.1|-12.4|-46.7|-4.3|-7.0|-3.2|2.2|-0.5|4.5|
|BTADR|-7.5|-6.0|-14.9|-6.1|-10.6|-3.9|-0.8|-2.6|0.4|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 25
7

# **II.   DATA AND EMPIRICAL STRATEGY**

# **A.   Data**

The sample includes 19 emerging market economies and 21 advanced economies.<sup>12</sup> Our classification is based on the core lists of countries that the International Monetary Fund and other organizations such as the World Bank define as AEs and EMs. The main results presented in this paper are robust to using different classifications.<sup>13</sup> The sample period runs from 1991Q1 to 2015Q4. The data are at the quarterly frequency. The economic growth series for the two economic blocks namely EM and AE blocks are based on purchasing power parity (PPP) weighted average of local currency real GDP growth from IMF’s World Economic Outlook live database. For the purpose of checking whether our main results are robust to different country grouping we use smaller country groupings including the Group of Seven represent the world’s largest industrialized economies (G-7), a group composed of Brazil, Russia, India, China (BRIC), and China alone.

The data also include control variables that capture the main channels of transmission (trade, finance, and commodities). To control for trade, we use imports over total trade constructed using bilateral data from IMF’s Direction of Trade Statistics. To control for the financial channel, we construct a spread measure based on the MSCI Emerging Index over the MSCI

> 12 The EM block is comprised of: Argentina, Brazil, Chile, China, Czech, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Peru, the Philippines, Poland, Russia, Taiwan Province of China, Turkey, Thailand and South Africa. The AE block is comprised of: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, Luxembosurg, Netherland, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom and United States.

> 13 Due to the lack of quarterly frequency GDP data, we omit controlling for growth in the residual group of countries composed of developing countries.
technologies and hence to different growth convergence clubs (Howitt & Mayer-Foulkes, 2005), the empirical work on innovative capacity proxied by engineering as a determinant of growth (see, for example, Maloney & Valencia, 2022), as well as the broader literature on national learning (see, for example, Stiglitz & Greenwald, 2014).

# **2 Historical Evidence on the Importance of Entrepreneurial Capital**

In the absence of clear measures of entrepreneurial capital, we explore three features of entrepreneurship at the time of the second industrial revolution that are consistent with our framework.

## **2.1 The dominance of foreign entrepreneurs in the industrialization process**

The fact that immigrants and foreign entrepreneurs play a disproportionate role in the industrialization process in Latin America despite facing an identical institutional and business climate and without enjoying especially favorable access to credit suggests that they embodied a cultural or human capital that local entrepreneurs lacked. Table 1 draws on collected data on firm ownership from detailed census data for Argentina, Mexico and the US, as well as other industrial source data from Brazil (Bresser Pereira, 1994; Birchal, 1999), Chile (Ortega, 1990), Colombia (Rinc ́on et al., 2005; EAFIT, 2013; Becerra & Restrepo, 1987), and the US, at the turn of the 20th century (see Annex 1). Column 2 presents the share of firms owned or started by locals and immigrants for select countries and regions around 1900. Column 3 presents the share of immigrants in the population. Columns 4 and 5 standardize immigrant ownership by share in the population and can be interpreted as a measure of “comparative advantage in entrepreneurship” both relative to the total population and to the male population. Since in this period, most firms were started by men and most immigrants were men, the latter, while a lower bound for immigrant CA, is probably closer to the truth.

Looking first at the US, we find that roughly 30% of firm owners were immigrants which 6
1999; Barua 2011). At the other end of the spectrum are the cases – such as Belgium, Canada, Luxembourg, Singapore, and Japan – where the predicted rating is lower than the actual rating (Table 6).

**Table** **<u>6: Prediction lower than actual rating</u>**

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|Belgium|AA o-|A+ stable|_AAo-_|
|Bulgaria|BBB os|BB+positive|_BBB os_|
|Canada|AAA os|AApositive|_AAAos_|
|El Salvador|BB- o-|B stable|_BB- o-_|
|Guatemala|BB os|B+positive|_BBos_|
|Thailand|BBB+ os|BBB-positive|_BBB+os_|
|Kazakhstan|BBB+ os|BBB-positive|_BBB+ os_|
|Luxembourg|AAA o-|AA negative|_AAAos_|
|Fiji|B os|CCC+ stable|_B os_|
|Singapore|AAA os|AA stable|_AAAos_|
|United Kingdom|AAA o-|AA-positive|_AAA o-_|
|Kenya|B+ os|CCC+positive|_B+os_|
|Trinidad&Tob.|A os|BBBpositive|_Aos_|
|Japan|AA- o-|BBB+ negative|_AA- o-_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

**<u>Table 7: Shadow ratings for unrated countries*</u>**

|Algeria|A- negative|
|---|---|
|Mauritius|BBB negative|
|St. Vincent & the Grenadines|B+positive|
|Dominica|B+positive|
|Swaziland|B+ stable|
|Moldova|B+ negative|
|Seychelles|Bpositive|
|Armenia|Bpositive|
|Bhutan|B stable|
|Tanzania|B negative|
|Mali|B negative|
|Solomon Islands|B-positive|
|Maldives|B- stable|
|Malawi|B- negative|
|Guyana|B- negative|
|Nepal|CCC+positive|
|Ethiopia|CCC+positive|
|Belize*|CCC+ stable*|
|Lao PDR|CCC+ stable|
|Togo|CCC+ stable|
|Nicaragua|CCC+ negative|
|Yemen,Rep.|CCC+ negative|
|Haiti|CCC+ negative|
|Gambia,The|CCC-positive|
|Kyrgyz Rep|CCC- negative|
|Cote d'Ivoire|CC positive|
|Sierra Leone|CC stable|

*Unrated refers to countries not rated by S&P. Belize was in selective default in December 2012 and came back with a rating of B- stable, that is a notch above our prediction in March 2013.

15
Table A.4: Average treatment effects using alternative measures of foreign ownership, excluding exits

|Dep. var.:|(1)<br>Employment (log)||(2)<br>Employment (log)|
|---|---|---|---|
|Post*German ownership (binary)|0.156***<br>(0.041)|Post*German shareholders (num)|0.088***<br>(0.020)|
|Post*French ownership (Binary)|0.154**<br>(0.066)|Post*French shareholders (num)|0.080**<br>(0.037)|
|Post*British ownership (binary)|0.098<br>(0.064)|Post*British shareholders (nun)|0.083<br>(0.056)|
|Year FE|Yes||Yes|
|Firm FE|Yes||Yes|
|Observations|270919||270919|
|Firms|61073||61073|

Notes: Authors’ calculations based on Orbis data. These regressions exclude firms that exit after accession. Each cell is obtained from a different specification where the number of employees is regressed on a time-varying indicator of foreign ownership indicated on the row. All regressions include firm and year fixed effects. Coefficients show the average treatment effects of foreign ownership. Results refer to firms that were observed both before and after accession. Data exclude the year 1999 due to anomalies in the data. Foreign ownership in column (1) is measured by a binary indicator taking value one when a firm had a foreign shareholder. Column (2) uses the number of foreign shareholders as an alternative measure of foreign ownership. Standard errors in parentheses are clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

Table A.5: Difference in differences results: the effect of German influence on German acquisitions of Polish firms

|Dep.var: German acquisition (Yes = 1)|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Post*Germany’s export share (log)|0.004***<br>(0.001)||||||
|Post*Germany’s output share (log)||0.002**<br>(0.001)|||||
|Post*Germany’s initial average wages (log)|||0.003***<br>(0.001)||||
|Post*Germany’s initial capital per worker (log)||||0.002***<br>(0.001)|||
|Post*RCA relative to Poland|||||0.011***<br>(0.002)||
|Post*RCA relative to world||||||0.008***<br>(0.002)|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1010349|1010349|1010349|1010349|1010349|1010349|
|Firms|63971|63971|63971|63971|63971|63971|

Notes: Authors’ calculations based on Orbis dataset. All regressions include firm and year fixed effects. RCA is a standard measure of comparative advantages of Germany versus Poland and the world. All measures of German influence refer to the period 1994-2004. Regressions consider only firms established before 2004. Standard errors in parentheses clustered at 2-digit industry level. *10% level, **5% level, and ***1% level.

34
|<br>**Appendix Tabl**|**e 4.Chan**|**ge in Welf**|**are by reg **|**ion(perce**|**nt)**||
|---|---|---|---|---|---|---|
|**Scenarios**<br>|||Unilateral R|eductions|||
|**Countries/Regions**|NBTA17|BTAFU|BTADU|BTADE|BTAFR|BTADR|
|EU27 with EFTA|-0.7|-0.4|-0.5|-0.5|-0.6|-0.6|
|United States|-0.6|-0.4|-0.5|-0.6|-0.6|-0.6|
|Japan|-0.2|-0.1|-0.1|-0.2|-0.2|-0.2|
|Rest of high income Annex 1|-0.9|-1.0|-0.8|-1.1|-0.9|-0.9|
|Rest of high income|-0.1|-0.5|-0.5|-0.1|-0.3|-0.2|
|Brazil|-0.1|-0.5|-0.4|-0.3|-0.3|-0.2|
|China|-0.2|-3.7|-0.6|-0.3|-0.7|-0.3|
|India|0.0|-1.4|-0.3|0.1|-0.3|-0.1|
|Russia|-1.1|-3.6|-1.7|-1.7|-1.8|-1.4|
|Rest of East Asia|-0.4|-1.3|-0.9|-0.6|-0.8|-0.6|
|Rest of South Asia|-0.3|-0.6|-0.4|-0.3|-0.4|-0.3|
|Rest of ECA|-0.3|-2.3|-0.9|-0.8|-1.5|-0.7|
|Middle East and North Africa|-0.1|-2.4|-0.7|0.0|-0.6|-0.3|
|Sub-Saharan Africa|-0.5|-1.2|-1.0|-0.7|-0.9|-0.7|
|Rest of LAC|-0.7|-1.8|-1.4|-0.8|-1.1|-0.9|
|High income countries|-0.6|-0.4|-0.5|-0.5|-0.5|-0.5|
|Low and middle income countries|-0.3|-2.4|-0.8|-0.5|-0.8|-0.5|
|World total|-0.5|-1.0|-0.6|-0.5|-0.6|-0.5|

_Notes:_ **_NBTA17:_** _Industrial countries alone reduce emissions by 17% and take no trade policy action;_ **_BTAFU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in imports;_ **_BTADU:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on all merchandise imports based on carbon content in domestic production;_ **_BTADE:_** _Industrial countries alone reduce emissions by 17% with import tariffs on all merchandise imports and rebates on all merchandise exports based on carbon content in domestic production;_ **_BTAFR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in imports;_ **_BTADR:_** _Industrial countries alone reduce emissions by 17% and impose tariffs on energy-intensive merchandise imports based on carbon content in domestic production_ 41
points respectively, with similar effects for both heavy and light overdrafters. SSP also significantly increased the share of individuals who strongly agreed that the product helps – manage transfer requests from people within the household (column 1) this is broadly consistent with results in Appendix Table A9.

However, we cannot reject the null that the effects in Appendix Table A10 are the same for heavy and light overdrafters. Why are effects on downstream outcomes so different by overdrafter status when perceived benefits of the product are so similar? One possibility is that differential treatment effects are not driven by underlying behavioral mechanisms so much as they are driven by differences in financial fundamentals. Consider, for example, the “lumpy savings goal” hypothesis: in order for SSP to have an impact on the likelihood of meeting a savings goal, a saver’s baseline probability of meeting a goal would have to be neither too low nor too high. It may be that light overdrafters, by virtue of being in a better savings position to begin with, were more likely to be marginal savers.

Ultimately, data constraints prevent us from testing the hypotheses discussed in this – section more directly we therefore leave further investigation of these questions to future research.

# **4 Conclusion**

We use six rounds of detailed survey data as well as administrative bank data to study the impact of a commitment savings product for salaried workers in Ghana. Overall, the product increased savings at the partner bank, both during and after the commitment period, with limited crowd out of cash savings held in other places. These savings gains are offset by an – increase in debt as a result, the point estimate on net savings is small and insignificant. This suggests that people with access to liquidity may fail to benefit from commitment savings products because they can undo their commitment with other financial instruments.

However, we find divergent results when we examine heterogeneity in treatment effects – by baseline propensity to take overdrafts with our partner bank. Heavy overdrafters who – had less liquid savings and more debt at baseline see (marginally significant) declines in net savings when offered the product, while light overdrafters – who had considerably better – access to liquidity save more, both with the partner bank and in other savings devices, and do not take on more debt. Strikingly, these treatment effects manifest both during and after the commitment period.

Our results for heavy overdrafters are consistent with the hypothesis that time inconsistent agents make inefficient savings commitments, especially when they have access to liquidity that allows them to unwind their commitments. The results for light overdrafters 13
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# **_1. Introduction_**

This paper is motivated by the ongoing debate among economists and policy makers about the efficiency and other economic effects of privatization of state-owned enterprises (SOEs). Our goal is to evaluate what we have learned to date about the effects of privatization from the experiences during the last 15-20 years in the post-communist (transition) economies and, where relevant, China.

The transition economies – economies in Central and Eastern Europe (CEE) and in the Commonwealth of Independent States (CIS) that replaced most of the former Soviet Union -- provide a useful laboratory, having experienced major changes in the values of many relevant variables as they changed their economic system. Unlike most other developing countries and until recently also China, the transition economies for instance did not merely privatize a number of key state-owned firms or strive to improve the functioning of their legal and institutional framework. As may be seen from Table 1, they carried out a major transformation that made the share of private sector in GDP increase from extremely low levels to between 60% and 90% (see EBRD, 2007) and they instituted from scratch a market-oriented legal and institutional system. The transition economies therefore share with many other developing countries numerous characteristics associated with “weak” institutions, such as poorly conceived and/or ineffectively enforced property rights and insufficiently developed capital markets (see Daron Acemoglu, Simon Johnson and James Robinson, 2001), but they have carried much larger privatization programs than have been observed in other developing countries and until very recently also in China. One can hence obtain valuable insights about the impact of privatization by focusing on the large literature dealing with the transition. It is appropriate to undertake a study of this type now because it has been nearly twenty years since the start of transition so work has emerged based on datasets of sufficient size, length and quality to allow the use of more sophisticated methods and to address more robustly issues of causality.

There has already been one major attempt to survey this literature, by Simeon Djankov and Peter Murrell (henceforth D-M) in 2002. D-M applied a meta-analysis to the findings from a large number of diverse early studies of the transition economies (but not China), combining – controversially - various indicators of performance into one 1