rafmacalaba/datause-encoder-data huggingface dataset:
Table D.1 (continued): Program impacts on antisocial behaviors, 12–13 month survey only

|||||ITT regre|ssion (N=94|7)||
|---|---|---|---|---|---|---|---|
||Control|Ther|apy only|Cas|h only||Both|
|Outcome|mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|In the last 4 weeks, did you let others see<br>your frustration when you were<br>frustrated?|0.555|-0.027|[.080]|0.015|[.080]|-0.085|[.082]|
|In the last 4 weeks, have you threatened<br>other people?|0.360|-0.042|[.071]|-0.027|[.069]|-0.137|[.072]*|
|In the last 4 weeks, have you taken things<br>from behind other people without asking<br>them?|0.421|-0.047|[.075]|0.074|[.079]|-0.159|[.076]**|
|In the last 4 weeks, have you easily<br>controlled your vexation when vexed? (-)|1.010|-0.051|[.090]|0.099|[.095]|-0.031|[.098]|
|Do you get vexed when you lose a game?|1.704|-0.163|[.094]*|-0.242|[.096]**|-0.251|[.095]***|
|Can you feel fne when you hit or yell at<br>somebody?|0.828|-0.158|[.091]*|-0.051|[.092]|-0.269|[.092]***|
|If you are under attack can you hit that<br>person to defend yourself?|1.867|-0.010|[.093]|-0.020|[.091]|-0.051|[.094]|
|When someone teases you, does that<br>make you vexed?|1.493|-0.128|[.097]|-0.013|[.098]|-0.144|[.100]|
|Do you ever fght to show that you are<br>the stronger person?|0.833|-0.104|[.094]|-0.011|[.094]|-0.153|[.098]|
|Do you ever damage things as a joke or<br>for fun?|0.680|-0.162|[.084]*|0.010|[.083]|-0.250|[.084]***|
|Do you ever hurt the person you are<br>playing football with for you to win?|0.882|-0.174|[.091]*|-0.177|[.090]*|-0.141|[.100]|
|Do you ever use force on somebody to do<br>something for you?|0.567|0.041|[.086]|0.016|[.081]|-0.105|[.086]|
|Do you ever cuss somebody to do<br>something for you?|0.596|0.054|[.084]|0.066|[.086]|-0.110|[.082]|
|Verbal/physical abuse of partner, z-score<sup>_†_</sup>|-0.071|0.142|[.100]|0.233|[.113]**|0.059|[.104]|
|Last month, did you accuse your woman<br>for getting boyfriend?|0.388|0.115|[.083]|0.120|[.087]|-0.030|[.084]|
|Last month, did you ever tell your woman<br>you will beat her?|0.381|0.084|[.078]|0.095|[.082]|0.027|[.079]|
|Last month, did you ever cuss your<br>woman?|0.218|0.023|[.064]|0.123|[.075]|0.036|[.071]|
|Last month did you push, hit, slap or<br>throw something at your wife or<br>girlfriend?|0.152|0.076|[.056]|0.151|[.065]**|0.087|[.057]|

_Notes:_ The table reports long-run (12–13 month) intent to treat estimates of antisocial behavior outcomes. We calculate the impact of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1<sup>_†_</sup> These variables were not collected during every phase/round, so their regressions have a smaller sample size.

xxiii
**Figure 13. PPG External Debt Service** _(% Exports)_

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br>PPG Multilateral PPG Bilateral PPG Bonds PPG Commercial Banks PPG Other Private Creditors<br><!-- End of picture text --> _Note: PPG = Public and publicly-guaranteed. Source: The World Bank – World Development Indicators_

# **3.2 Sovereign Bond Issuances in Sub-Saharan Africa**

In the aftermath of the global financial crisis, countries in the region started looking for alternative sources of development finance —notably, infrastructure financing— as bilateral loans and grants from Euro Area countries and the United States diminished. In addition to resorting to financing from non-Paris Club governments, Sub-Saharan Africa’s frontier economies had measured success in tapping international bond markets. Early issuers of Eurobonds included the Democratic Republic of Congo ($454 million), Gabon ($1 billion) and Ghana ($750 million) in 2007.

_Sub-Saharan African countries have been issuing Eurobonds at an increasing pace._ During the post-global financial crisis period, several low- and lower-middle-income countries in the region have been issuing Eurobonds at an accelerating pace ( **Figure 14** ). The conditions for international issuances have been benign, with strong and continuous demand from investors and suppressed risk spreads, reflecting investors’ “ _search-for-yield_ ”. By 2018, 16 countries in the region have taken advantage of high and steady demand and issued in international markets.<sup>7</sup>

> 7 The group of countries under analysis in the figures excludes South Africa given its continuous presence in international markets. In addition, the Republic of Congo and Côte d’Ivoire issued bonds in the context of debt exchange/restructuring, in 2007 and 2010 respectively.

14
Over the past two years, the government has changed its public stance to argue for modernization of the economy with an emphasis on foreign direct investment as the vehicle to achieve this goal. However the increase in state control of productive assets, limitations on foreign direct investment in several questionable areas, and increased use of import-substitution industrialization all work against this objective. These tendencies emphasize the internal conflicts of the current government regarding its economic policy in general and with regard to foreign direct investment in particular.

To increase the attractiveness of Russia as a destination for FDI, the Russian Government should work in several important directions. First, it needs to improve domestic institutions to make Russia a better place for doing business. Russian rankings in the Doing Business Survey and Enterprise Surveys are below the means in almost all respects and they worsened in recent years<sup>25</sup> . In 2009, 50% of the surveyed firms in Russia mentioned corruption as a major constraint for development. Enormous efforts should be put just to change this tendency and they involve legislative and court reform among others.

Second, important steps should be done toward making Russia a better place for locating some part of production process. It reflects the current situation with major FDI flows being vertical ones and it possesses very specific requirements not just for the business climate in the country but to a high transparency of national borders as well. In this respect Russia also falls behind its major competitors for FDI flows being ranked only 99<sup>th</sup> out of 150 countries evaluated by International Logistic Performance Index.<sup>26</sup> The situation with customs is especially dreadful with Russian ranking only 137<sup>th</sup> out of 150. Unfortunately customs reform was not on the list of priorities aimed at modernization of Russian economy highlighted by President Medvedev in his annual address to the Federal Assembly on November 12, 2009.

# **IX. Improving Customs—Any Role for Uniform Tariffs or Pre-Shipment Inspection**

Given the problems in customs performance in Russia, some have recommended uniform tariffs and pre-shipment inspection services. In our view, there is enormous merit in uniform tariffs, but pre-shipment inspection services are likely to produce only marginal benefits at best.

In response to a request from the Russian Ministry of Trade, Tarr (1999) has analyzed the advantages and disadvantages of a uniform tariff for Russia.<sup>27</sup> Tarr (1999; 2002) argues that there is little merit for Russia in the various arguments against a uniform tariff. That is, there is

> 25 The data from the Doing Business Surveys are  available at: <u>http://www.doingbusiness.org; and  the data</u> from the Enterprise Surveys are  available at:  http://www.enterprisesurveys.org/CountryProfiles/

> 26 The data from the Logistics Performance Index is available at: http://info.worldbank.org/etools/tradesurvey/mode1b.asp

> 27 These ideas were developed further in Tarr (2002).

18
_Eshnales and Projetions of Mortality by Cause_ _74_ Table C2. Expert assessments of mortality rates and deaths (contlnued)

|**_Rcion_**_and_|_Raks_||_Dea_|_ths_|_Rate_|_Deaths_|
|---|---|---|---|---|---|---|
|_age grmup_|_Malt_|_Femal_|_Male_|_Femak_|_Male_<br>_Femake_|_MA_<br>_Female_|
|Sub-Sahara|||||||
|Total|2.4|1.4|5.4|3.3|54.7|126.0|
|0|0.0|0.0|0.0|0.0|0.0|0.0|
|1-4|0.1|0.0|0.0|0.0|0.0|0.0|
|5-14|0.3|0.2|0.2<br>|0.1<br>|0.6<br>|0.4<br>|
|15-44|0.6|1.3|0.6|1.2|128.6|121.5|
|45-64|15.2|5.9|3.4|1.4|17.3|4.2|
|65+|19.5|7.9|1.2|0.6|0.0|0.0|
|MENA|||||||
|Total|2.3|0.5|4.4|0.9|20.8|38.0|
|0|0.0|0.0|0.0|0.0|0.0|0.0|
|<br>1-4|<br>0.0|<br>0.0|<br>0.0|0.0|0.0|0.0|
|5-14|0.1|0.0|0.1|0.0|0.2|0.1|
|15-4|3.7|0.4|3.1|0.3|46.3|36.2|
|45-64|4.6|1.8|0.9|0.4|8.4|1.7|
|65+|5.9|2.5|0.4|0.2|0.0|0.0|
|Asia|||||||
|Total|0.5|0.2|6.1|2.9|24.8|294.0|
|0|0.0|0.0|0.0|0.0|0.0|0.0|
|1-4|0.0|0.0|0.0|0.0|0.0|0.0|
|5-14|0.0|0.0|0.0|0.0|0.1|0.2|
|15-44|0.3|0.1|1.8|0.3|52.2|288.2|
|45-64|1.6|0.8|2.7|1.2|3.4|5.6|
|65+|3.0|2.3|1.5|1.4|0.0|0.0|
|||Diabe|bs||||
|World|||||||
|Total|8|12|188|290|||
|0|3|3|2|2|||
|1-4|1|1|3|2|||
|5-14|0|0|1|1|||
|15-4|1|1|15|16|||
|45-64|16|21|56|78|||
|65+|90|117|110|192|||
|Developed|||||||
|Total|10|17|58|100|||
|o|0|0|0|0|||
|1-4|0|1|0|0|||
|5-14|0|0|0|0|||
|15-44|1|1|2|1|||
|45-64|11|9|13|12|||
|65+|87|107|42|86|||
|Developing|||||||
|Total|7|11|130|189|||
|0|3|4|2|2|||
|1-4|2|1|3|1|||
|<br>5-14|<br>0|<br>0|<br>1|1|||
|15-44|2|2|13|14|||
|45-64|18|28|43|65|||
|65+|95|130|68|106|||
|hi (Total Non-Africans)|||2.93-5<br>(1.11-4)|||_438-5_<br>(1.1-4)|
|---|---|---|---|---|---|---|
|In (Wage Bill<br>Temporary Workers)|12E4'<br>(7.01-5)|124'<br>(6.7B5)|12E4<br>(6.9BS)|12-4'<br>(758-5)|92-S<br>(7.11-5)|1.11.4<br>(72<br>)|
|In K|0.494'<br>(0.043)|0511*<br>(0.042)|0.467*<br>(0.046)|0574*<br>(0.046)|0571*<br>(0.04S)|0SOS<br>(0.019)|
|TIme<br>Industry|.054<br>(0.016)|0.062!<br>(0.016)<br>|.057<br>(0.016)<br>|.0.0<br>7.<br>(0.017)<br>|4075<br>(0.016)<br>|-0.007<br>(0.016)<br>|
|Intercepus|Yes|Yes|Yes|Yes|Yes|Yes|
|R<sup>2</sup>|088S|0.883|0.880|0.884|0.8|0.8B1|
|N|386|386|386|375|375|375|

- - Sinificant at the S percent statistical test<sup>leveL</sup> + - Significant at the 10 percet statistical test<sup>leveL</sup> Source: 80 firm sample
domestic securities companies were allowed to do business with each other. This made possible limited foreign ownership of Korean equities through the country funds. The government permitted individual foreign securities companies to own up to 10 percent of the paid-in capital of large domestic securities firms, provided that the total stake of foreign securities companies in a domestic securities firm did not exceed 40 percent. Under a guideline announced in November 1990 foreign securities companies were allowed to establish branch offices or joint ventures in Korea. In 1989 and 1990, Government prepared for the imminent opening and expand foreigners' indirect investment by increasing the foreign investment funds. A matching fund, which will invest in both domestic and foreign securities, was created in 1990. From January 1989, foreign securities companies were entitled to increase their share in a domestic joint venture from 10 percent to a maximum of 40 percent on a global basis.

In 1992, Government announced plans to liberalize the capital account in several steps. Starting January 1992, foreigners were allowed to invest directly in Korean stocks with certain limits, and substantial overseas capital flowed into the Korean stock market. In 1994, the Korean bond market was opened to foreign investors on a limited basis: foreigners were allowed to invest in nonguaranteed bonds of small-scale industries. Since February 1995 Koreans have been allowed to hold foreign currencies without any restrictions, invest up to $300,000 in overseas real estate, and deposit up to $30,000 in overseas banks. In June 1993, the government announced a three stage program for complete liberalization of financial, capital and foreign exchange market by 1998 (Table 3.1 in World Bank 1996).

Although Korea took several steps to liberalized its financial sector and capital account, many internal and external factors triggered a severe financial crisis at the end of 1997. The causes of the crisis have been subject to heated debate, including, inter alia, a weak legal and regulatory framework, regulatory forbearance and weak enforcement capacity, and excessive government intervention to promote certain industries. Nonetheless, the tremendous progress made by Korea in economic reform and human development over the past 40 years cannot be ignored.

# References

World Bank, 1998. East Asia: The Road to Recovery. Washington DC.

World Bank. 1996. Managing Capital Flows in East Asia, by John D. Shilling and Yan Wang. Development in Practice Series. Washington DC 1996.

World Bank, '"The East Asian Miracle," A World Bank Policy Research Report, 1993 World Bank, "Korean Managing the Industrial Transition," World Bank Country Report, Num. 6138-KO, 1986 Work Bank, "Korean Country Economic Memorandum," 1986, 1989 and 1992.

58
One of the primary innovations in the literature on firm learning and exports has yet to be investigated in the context of the firm’s decision to import: establishing that the basis for information spillovers is that the firm’s knowledge of foreign markets is not known with certainty. It follows that firms learn about the characteristics of foreign markets, such as the quality of inputs, reliability of suppliers, and ease of importing, through the imperfect signals they receive from firms that currently engage internationally. Fernandes and Tang (2014) explicitly incorporate this into a model of the firm’s decision to export and show that it is not only important how many of a firm’s neighbors export to a certain location, but also the success those neighboring exporters are finding in the foreign market. In this paper, we bridge this gap in the literature by combining the statistical learning framework for specifying the signal firms receive about foreign markets from their neighbors in Fernandes and Tang (2014) within the framework of the optimal sourcing decision as developed in Antras, Fort, and Tintelnot (2017).

Specifically, we study the effects of firm learning on firm sourcing strategies using firmlevel panel data from Serbia. Our data combines information about firm characteristics with customs activities for all Serbian firms between 2006 and 2015. We find that market-specific import experience of firms in the same local area and industry increases the likelihood of a firm to start sourcing from that market. Given the detail of our data, we are able to estimate multiple specifications with broad sets of fixed effects. Specifically, we find that a one standard deviation increase in the log density of neighboring firms with experience importing from a specific market results in a 1.06 percentage-point increase in the probability of firm import entry into that market in a given year. Additionally, we find that a one standard deviation increase in the neighboring firms’ signal about the profitability of sourcing from a market (as defined in Fernandes and Tang (2014)) results in a 1.15 percentage-point increase in the probability of import entry in a given year. The effect of the neighboring firms’ signal is further magnified by the number of firms with import experience. Given the import premia we observe in Serbia, these results are economically significant and explain 3
factors -- in other words, of endogeneity bias. Certain environmental variables should have no effect on the probability of dying from certain causes. If it turns out that they do, then these environmental variables may be picking up unobserved attributes of households or communities that are correlated with mortality outcomes, the presence of which would invalidate any causal inferences.<sup>3</sup> Information on cause of death is collected in only a few DHS, usually by selfreports combined with verbal autopsy methods. Aside from the well-known reliability issue, one problem is that, given the typical DHS sample size, the number of deaths from any particular cause is likely to be small. For this reason, we turn to a national health survey conducted in China in 1992, and modeled after the DHS, which does provide information on cause of child death. An advantage of the China survey is its vast sample size – more than 4,000 child deaths are recorded in rural areas – and its broad geographical coverage, which ensures substantial variation in environmental risk factors. Another advantage of looking at China, especially rural areas, is that local provision of public goods, such as piped water, is likely to be less responsive to local tastes than in a more pluralistic and market-oriented economy. As a result, the issue of purposive program placement (Rosenzweig and Wolpin, 1986) is likely to be less important in our context.

The paper is structured as follows. Section 1 provides a summary of the China health survey. In section 2, we lay out the different hazard models used in our empirical analysis and discuss the other econometric procedures. Section 3 reports results. Section 4 concludes.

# **1. Data**

The 1992 China Health Survey (CHS) was designed and implemented jointly by the China Statistical Bureau (CSB) and UNICEF. It covers all 29 provinces (except Tibet), using a sampling frame based on the 1990 census. The survey is representative both at the national and at the provincial level. Within rural areas, which are the focus of our analysis, stratification was done by geographic characteristics: hills, mountainous

> 3 Galiani, et al. (2002) make a similar argument in the context of their evaluation of a water service privatization program in Argentina using aggregate data.  They show that the program reduces mortality from water-borne disease, but does not reduce mortality from other causes.

3
# **How Do Special Economic Zones and Industrial Clusters Drive China’s Rapid Development?**

## **Douglas Zhihua Zeng**

China’s meteoric economic rise over the past three decades is an unprecedented “growth miracle” in human history. Since the Open Door policy and reforms that began in 1978, China’s gross domestic product (GDP) has been growing at an average annual rate of more than 9 percent, with its global share increasing from 1 percent in 1980 to almost 6.5 percent in 2008 (see figure 1.1) and its per capita GDP increasing from US$193 to US$ 3,263 (see figure 1.2). Total exports have been growing at an average annual rate of 13 percent (21.5 percent from 1998 to 2007), with China’s share of total exports increasing from 1.7 percent in 1980 to 9.5 percent in 2008 (see figure 1.3).

**Figure 1.1 China’s GDP Growth, 1980–2008**

<!-- Start of picture text --> GDP (constant 2000<br>US$)<br>GDP (% of Global total) 16%<br>2,500<br>14%<br>GDP growth (annual %)<br>2,000 12%<br>10%<br>1,500<br>8%<br>1,000 6%<br>4%<br>500<br>2%<br>0 0%<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Source: World Bank GDF and WDI central database Sep 2009<br>Billions (constant 2000 USD)<br><!-- End of picture text --> In 2007, China’s incremental growth in real GDP actually exceeded its entire real GDP in 1979. In 2010, China outpaced Japan and become the world’s second-largest economy. China has indisputably become an important growth engine of the global economy and a leader in international trade and investment. Rapid growth in the past decades has helped lift more than 400 million people out of poverty. These results are truly impressive.

While China’s rapid rise has become a hot topic for development debate among policy makers, business people, and scholars all over the world, the 2
schools were closed. In fact, we find some evidence that more vulnerable children (poor and foodinsecure) are more likely to rely on this type of learning. Although we cannot observe the quality of education for any form of learning in this paper, it is important to remark that alternative education may be the one with the highest heterogeneity in this regard, since in this case children depend to a higher degree on the skills of their relatives and/or community members.

Finally, remote and alternative learning do not seem to be substitutes when schools close. Rather, the data suggest that these worked as complementary strategies during times of need, and children that engaged in either of the two were more likely to also take part in the other.

# _5.2. Return to School_

## _5.2.1. Attendance_

Between January 25 and March 25, 2021 (wave four data), approximately 84 percent of Kenyan children attended schools again, while the share of those who relied on either remote or alternative education fell below 1 percent.<sup>32</sup> However, a substantial number of children, especially of pre-primary age did not return to school immediately after the lockdown policies were lifted (Figure 2).<sup>33</sup> To allow for a more nuanced analysis, in this section we split the sample by age group. The explanatory variables remain largely the same.<sup>34</sup> Starting with children in pre-primary education, we again find significant benefits of living in households with college-educated heads, for which attendance rates are on average 3.3 percentage points higher (Table 2).<sup>35</sup> Also consistent with previous results, households headed by women are less likely to send their young children back to school, which can be interpreted as a more risk-averse behavior in this context (Kowalik and Lewandowski 2021). Although those in urban areas are more likely to return to school (by almost 3 percentage points), children with internet access are 7 percentage points less likely to attend classes, suggesting that, for children in pre-primary age, parents may have been more likely to delay their return to school if remote learning options were available.<sup>36</sup>

> 32 For comparison, according to World Bank data, in 2016 (the last year for which information is available) gross school enrollment rates in Kenya were 103 percent for primary- and 76 percent for pre-primary-aged children. 33 More than 25 percent of children aged between 3 and 6 did not return to school in the first term of 2021 (wave four).

> 34 Differently to the previous section, however, from January 2021 (wave four) on we also have information on whether households have internet access, which we include as an additional control variable.

> 35 Table A4, in the appendix, reports comparable results when we estimate these models using probit instead. 36 Approximately 3 percent of children between the ages of 3 and 6 had remote access to education between September and November 2020.

12