rafmacalaba/datause-encoder-data huggingface dataset:
countries that were not former colonies, former British colonies and former French colonies). All estimations control for year fixed effects and regional or country fixed effects.

**Table 5 Growth of the working-age share of the population can increase real GDP per capita**

||Fist-Diffe|rence||Panel Fix|ed-effects||Generalized|Method of|Moments|
|---|---|---|---|---|---|---|---|---|---|
|Variables|S1|S2|S3|S1|S2|S3|S1|S2|S3|
|Δ share of WAP|1.61***|1.53***|1.71***|1.36***|1.096**|1.66***|1.87**|1.96**|1.55**|
||-0.453|-0.425|-0.396|-0.511|-0.456|-0.394|-0.845|-0.799|-0.641|
|Initial GDP-pc||-0.489**|-0.533**||-2.321***|-2.213***||-0.0257|-0.353|
|||-0.207|-0.227||-0.406|-0.514||-0.545|-0.539|
|Schooling (years)|||0.618**|||-0.162|||0.965|
||||-0.242|||-0.538|||-0.987|
|British colony|||0.295|||-|||1.011|
||||-0.277|||-|||-1.43|
|French colony|||-0.0886|||-|||-0.266|
||||-0.329|||-|||-1.989|
|Non-colony|||0.344|||-|||-0.997|
||||-0.39|||-|||-1.542|
|Landlocked|||-0.273|||-|||-0.0828|
||||-0.288|||-|||-0.521|
|Latitude|||-0.00014|||-|||0.00296|
||||-0.0102|||-|||-0.0203|
|Observations|1,796|1,776|1,307|1,796|1,776|1,427|1,796|1,776|1,307|
|Fixed Effects||||||||||
|Year (time)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region|Yes|Yes|Yes||||Yes|Yes|Yes|
|Country||||Yes|Yes|Yes||||

Source: Authors’ estimations.

Note: First-difference Ordinary Least Square (FD); Panel Fixed Effects (FE); Generalized Method of Moments (GMM). Data are from Penn World Tables, UN (2015), World Development Indicators, Treisman (2007), and Barro and Lee (2014). Standard errors clustered at the country level are reported in parentheses. *** p<0.01, **p<0.05, *p<0.1.

Additional covariates were tested (e.g. openness for trade) and results are robust. In the GMM specification (S1), lags 2 to 8 of changes in the share of working age population were used. In the GMM specifications (S2) and (S3), lags 2 to 8 of changes in the share of working age population and the initial per capita GDP were used. Geographic and time variables were used as instruments. Results are also significant when reducing the number of instruments. Using the GMM estimation as a baseline (S3), the results suggest that an increase of 1 percentage point in the share of working age population would lead to an increase in GDP per capita of approximately 1.5 percentage 15
non-farm sector and hence increase income. The households that are short of land but rich in labor are more likely to participate in non-farm activity. The participation in non-farm activity may raise the income of poor households to a larger extent than that of rich households, and reduce the income inequality in rural areas. The LSMS data show that the households that participate in non-farm activity have higher (total) income: the average income of these households is 7% higher than that of the households that participate only in farm activity. But these households with higher total income may not be the households that have high farm income. In fact, the participation in non-farm activity is a long-term rational choice of the rural households. The households that choose to participate only in farm activity are usually those with comparative advantages in farming and with a higher expected farm income. If the access to the non-farm sources of income is absent, the poor households, usually those with low level of land resource per capita, are more likely to suffer from the binding constraint and face the cornered solution, because their opportunity costs of labor are lower and their ability to weather negative shocks are weaker, other things being equal. In addition, if those currently employed in the nonfarm sector were engaged in some alternative employment, such as being agricultural labor, agricultural wage rates might be lower and overall income inequality might rise. So rather than raising inequality, in this case, the non-farm sector actually prevents inequality from rising even further.<sup>33</sup> In conclusion, the results show that, the non-farm activity participation reduces rural income inequality in China by widening the occupation choice that disproportionately favors the poor households.

# **6 Conclusions**

In rural China, non-farm activity has grown rapidly since the economic reforms in the late 1970s. Although farm income is still a principal income source of rural households, non-farm income also plays a significant role. In the provinces of Hebei and Liaoning in 1995/1997, twothirds of the rural households had non-farm income, and non-farm income represented almost a third of the total household income. The average income of the households that have participated in non-farm activity is higher than that of the households that participate only in farm activity.

> 33 See also Barrett et al. (2001); Chapman and Tripp (2004).

20
# **4. Growth and Other Instruments**

I now turn to the policies that emanate from having these goals. The first question that one is forced to grapple with is an age-old one.<sup>6</sup> Can growth alone do it or do we need specially targeted policy interventions by government?

To begin, some relatively mechanical exercises can help us form an opinion on this. One such exercise is to assume that each nation will grow at the rates that it did on average during the past two decades and the distribution of income will remain unchanged. If this happens over the next 17 years, it is easy to calculate that global poverty will decline to 7.7%. In other words, we will not make the target of less than 3% poor.

Table 3 describes the global breakdown of where we will be in terms of poverty in 2030 if there is growth of the above kind.

# **Table 3: Poverty in 2030 at $1.25 per day PPP**

**(Assuming country-specific National Accounts-based growth rates over the past 20 years)**

|**Region**|**Headcount Poverty**<br>**(%)**|**Number of Poor**<br>**(millions)**|
|---|---|---|
|East Asia and Pacific|0.6|12.0|
|Europe and Central Asia|0.7|3.5|
|Latin America and the Caribbean|4.4|30.3|
|Middle East and North Africa|1.7|7.2|
|South Asia|4.4|91.3|
|Sub-Saharan Africa|36.5|492.0|
|**Total Developing World**|**8.8**|**636.3**|
|**World**|**7.7**|**636.3**|

Source: World Bank Poverty and Inequality Research Group staff estimates.

During the first decade of this century, growth in most nations was, however, faster than in the decade preceding that. What happens if we assume

> 6 The literature debating this is large; see, for instance, Rodrik (2005); Himanshu (2008); Lopez-Calva and Lustig (2010); Dercon (2011); Bhagwati and Panagariya (2013); Dreze and Sen (2013); Rieff (2013).

13
properly controlling for the fact that on-the-frontier innovation data is limited to patents within the United States.

One surprising result shown in Table 1 is that export growth net of non-export GDP growth, which is meant to capture the relative profitability in searching for new exports, is insignificant. In Klinger and Lederman (2006), this variable was found to be positively related to discovery, particularly as barriers to entry increase, suggesting that fears of imitation dull the incentives to experiment and retard discovery activity. In order to verify the robustness of this finding, we split the sample into low barrier and high barrier countries and find that returns do in fact have a positive relationship with discovery in high-barriers countries. Furthermore, the relationship between net export growth and discovery was the only relationship to be affected by differences in barriers to entry. The other variables shown in Table 1 did not significantly change when the sample was split according to the Klinger and Lederman (2006) barriers index, confirming the conclusions therein.

In addition, we extended the analysis of barriers to on-the-frontier innovation by examining the effects of the intellectual property rights (IPR) regime, taken from Park (2001), on the relationships of interest. As expected, the quality of the IPR regime enters the full estimation of patent performance as positive and significant. Furthermore, when the sample is split into the best and worst halves with respect to quality of the IPR regime, the elasiticty of R&D spending on patenting changes significantly, becoming much higher in countries with better IPR regimes. This conforms with intuition: just as discovery is higher in countries where innovators are able to appropriate the returns of their discovery, the value of the search for on-the-frontier innovations (measured by 13
groupings. We then analyze how the program affects labor and then analyze how each of the program interventions affects total child labor and child labor by the two types of activities presented in the model. We conclude this part of the analysis by sub-grouping children into types (by age and gender).

In order to ensure that no outliers bias our results we cut 1% in the upper tail of the distribution for all (2005 and 2006) continuous variables in the analysis. Some data is missing or made missing in the survey due to sample attrition (2 percent) or digitizing errors, this is a small percent of the total sample and does not affect our findings. All estimations are clustered at the community level, following the data collection design. Lastly we conduct several robustness exercises (bootstrapping and widening of the sample to include younger children) and find the results are unchanged.

# **VI.1      Basic specification**

We begin this section by investigating the relationship between income and child labor. We then proceed to investigate how a program that increases household income as well as contribute to human capital opportunities through various investments affects child labor. In the data section we established the success of the randomization which allows us to use the cross section of households in the second period to test the hypothesis that the relationship between income and child labor is linear up to a point and non-linear thereafter. We test the relationship using all three treatment groups and control group in our sample (<sup>_t_</sup> _n_ = ( _t_ 0, _t_ 1, _t_ 2, _t_ 3) ).

We investigate the effect of income on child labor. The intensity of child labor is measured by the total average hours worked _LHihc_ (LH is child labor, L for physical child labor and H for non-physical child labor) in all activities, including domestic work, as a dependent variable. Domestic work accounts for a non-trivial number of children who reported zero hours worked in other non-domestic activities but had more than zero hours in domestic duties.

The child labor data has a left hand side censoring problem, particularly for non-physical labor, resulting from our inability to observe negative work hours for children who would normally have less than zero if it were possible. We use a special case of censored regressions (tobit) to accurately calculate the effect of our variables of interest on child labor. We use tobit for all estimations to keep all results consistent and easily comparable with each other. We first estimate: 14
but living on less than US$14 per day PPP, follows a similar path, increasing on impact 0.5 percentage points and 0.3 percentage points higher in the projected horizon. The increase in poverty and vulnerability is to the expense of the middle class, which falls 0.8 percentage points and is still 0.5 percentage points below the baseline in the projected horizon. That is, without additional mitigation policies, a temporary shock can have protracted effects on the evolution of poverty and inequality.

_An economy subject to stochastic drought shocks_

**The second exercise shocks the economy with stochastic droughts to understand their role on aggregate macro volatility and simulate their dynamic effects on selected macro variables** . The baseline simulation uses the historical risk curves presented in Figure 7. That is, it responds to the following question: how would an otherwise stable Uruguayan economy behave if it was only subject to drought shocks of the same frequency and magnitude as in the past?

**The volatility of a simulated Uruguayan economy only subject to drought shocks is 22 percent of observed GDP volatility in Uruguay** (Figure 15).<sup>10</sup> Economic growth in Uruguay over the past 40 years has generally ranged from -10 to +9 per growth per year, with a mean real growth rate of 2.3 percent for the sample 1980 – 2019. The historical standard deviation of economic growth is quite large, at 4.7 percent. On the other hand, the growth rate of the simulated Uruguayan economy only subject to drought shocks fluctuates 95 percent of the time between -0.7 percent and 3.4 percent, with a standard deviation of 1 percent.

**Figure 15: Uruguay real GDP growth and historical variance**

<!-- Start of picture text --> 10 10<br>5 5<br>0 0<br>-5 -5<br>Historical variation<br>Climate variation<br>-10 -10<br>Real GDP growth<br>Mean growth<br>-15 -15<br>1982 1992 2002 2012 2022<br>Source: WBG databases and analysis<br><!-- End of picture text --> 10 The volatility of a simulated economy with climate shocks (droughts and floods) is also 22 percent of historical volatility.

22
contraceptive methods at the time of the follow-up survey. Because of the difference in attrition rates between the treatment arms, we present results also of regressions including controls for characteristics of the women, their households and the CHWs in their villages.

Overall, 56 percent of the women reported having an additional pregnancy after 2010. As can be seen in Table 6, there is no statistically significant impact of the performance payments on the likelihood of having an additional pregnancy since baseline. There is also no significant effect detected on the likelihood of reporting the use of modern methods of contraception. Fifty percent of the women report using some modern contraceptive method while 58 percent reported using any method. As in the Rwanda DHS 2014-15, the most commonly used method is injectables, followed by contraceptive pills. Women in the sectors implementing the cooperative performance payments are less likely to be using modern contraceptive methods by 3 percentage points but the difference is not statistically significant.

Because the attrition rates were unbalanced between the treatment arms, rather than stating that there was no impact of the CHW cooperative performance payments on the use of modern family planning, we say we cannot reject that there is no impact. We can also reject big impacts of the performance payments on use of modern family planning. For example, under the extreme assumption that all missing women from the treatment sectors used modern family planning methods and those missing from the other sectors have not, the utilization rate would be higher among the treatment group by less than seven percentage points.

Another check we conducted to assess difference in fertility among the treatment arms is to compare the characteristics of women who were recently pregnant at the follow-up round (the ‘recently pregnant’ sample). If women respond to the intervention by increasing use of modern family planning methods, we would expect to see differences in characteristics such as age and number of previous births among pregnant women in the different treatment arms. As reported in Table A2 in the appendix, the women in the sectors exposed to the performance payments and in the other sectors are similar. Not only is the average age similar, but the 25, 50 and 75 age percentiles are identical. There is no statistically significant difference in the number of household members, whether the women are covered by health insurance, the number of lifetime births and the number of living children. The only statistically significant difference, at the 10 percent level, is in the share of women with at least some secondary education. These similarities also suggest there has been no impact of the CHW cooperative performance payments on use of modern family planning.

12
(e.g. Pagano and Jappelli 1993)<sup>5</sup> , credit registries are typically public institutions created for bank supervision (e.g. Powell et al. 2004).

There is recent evidence that policy efforts directed at introducing credit registries can reduce information asymmetries between borrowers and lenders and can improve access to finance at the firm level (Galindo and Miller 2001; Love and Mylenko 2003; Brown, Jappelli, and Pagano 2009; Love, Martinez Peria, and Singh 2014). <mark>Martinez-Peria and Singh (2014) use comprehensive data across a larger number of countries and a difference-in-difference approach to show that credit bureau reforms have a significant and robust effect on firm financing and this is more pronounced on smaller, less experienced, and more opaque firms. Ayyagari, Juarros, Martinez-Peria, and Singh (2016) use the introduction of credit bureaus as an exogenous shock to the supply of credit in over 4 million firms in 29 developing countries and find that the resulting access to finance results in higher employment growth, especially among micro, small, and medium enterprises.</mark> Finally, Beck, Lin, and Ma (2014) show that better credit information sharing systems are associated with lower tax evasion especially among smaller firms and firms requiring more external finance.

Another strand of the literature has used natural or randomized experiments to examine the implementation of credit information systems that showed variation in either the firms covered under the information system or in the use of information by lenders. Studies such as - Liberti, Seru, and Vig (2016) in Argentina, Luoto,

> 5 Bruhn, Farazi, and Kanz (2012) show that bank concentration is negatively associated with the probability that a credit bureau emerges. In credit markets dominated by a few large banks, the banks try to maintain market share by holding onto information.

13
Table 4 lists the reported type of conflict. Just over half of conflicts were categorized as civil strife. The ratio was almost three-quarters on Java. Ethnic strife accounted for only 3.3 percent of reported conflict incidents. Ethnic conflict was more important off-Java. Omitting Aceh, ethnic conflict accounted for 6.5 percent of conflict incidents off-Java (but only 1 percent on Java). Within conflict types, the majority of instances were reported as violent, except in the case of school strife (student clashes).

# _The resolution of conflict_

Almost three-quarters (72 percent) of conflicts were reported to have been resolved. The security apparatus was most frequently mentioned as the agency that ended the conflict (45.7 percent). This ratio tended to be higher on Java.

**Table 5: Agency in Conflict Resolution**

||National<br>(%)|Rural<br>(%)|Java<br>(%)|Off-<br>Java<br>(%)|Off-Java,<br>non-Aceh<br>(%)|Aceh|
|---|---|---|---|---|---|---|
|Population|21.0|23.0|13.1|25.5|15.0|74.9|
|VillageApparatus|33.2|34.6|34.7|32.4|37.4|9.0|
|SecurityApparatus|45.7|42.4|52.2|42.1|47.7|16.0|
|(N)|3,544|2,605|1,269|2,275|1,876|399|
|Source:<br> PODES2003 (Q|uestions17.04e)||||||

Village institutions were attributed with ending conflict in a third of the cases, and were relatively more important off-Java. Aceh stands in marked contrast to all other provinces. Not only were the vast majority of conflicts classified as “other”, but the comparative rates at which village or security apparatuses were credited with resolving conflicts was only about one-third (compared with more than three-quarters in the rest of the archipelago).

15
44

**Table 7 Second Stage Fixed Effects Regression (8)**

|**Variable**|**Coefficient**<br>**[std. error]**|
|---|---|
|Coop|-13.109***<br>[2.639]|
|Zcar|15.244***<br>[4.139]|
|Ztur|10.880***<br>[3.936]|
|Zaten|-3.567<br>[5.151]|
|Zcot|17.480***<br>[4.140]|
|Zsan|3.832<br>[3.344]|
|Zgen|20.472***<br>[4.900]|
|Zgua|1.61<br>[4.427]|
|Zsar|32.456***<br>[4.968]|
|Constant|-10.361***<br>[1.609]|
|Observations|155|
|R-squared|0.46|
|White’sTestfor Heteroskedasticity|χ<sup>2</sup>(9) = 3.10<br>Prob>χ<sup>2</sup>=0.93|
|F-test|F(9,145)= 10.76|
||F(3,142 ) =0.02|
|RamseyResetTest|Prob > F = 0.99|

Robust standard errors in brackets *significant at 10% ** significant at 5%; *** significant at 1% The included regressors explain almost half of the variation in mill-specific fixed effects and almost every explanatory variable in the model of (8) is well-determined.<sup>47</sup> The estimated coefficient for the cooperative status dummy is significantly negative: On average and _ceteris paribus_ , cooperative mills record fixed effect mark-down measures over 13 US cents/kg lower than non-cooperative mills. Note that the omitted category is all non-cooperative mills, including foreign-owned mills that were seen to have _smaller_ mark-down measures than the omitted category of domestic owned, noncooperative mills. Even with the presence of these mills in the omitted category of (8), the coefficient on cooperative mills is negatively significant. Mill-specific fixed effects represent the omitted characteristics of the mills that are discernable thanks to the panel dimension of the model. The negative coefficient on the cooperative dummy variable confirms that there is something structural about cooperative mills that lead them to have very low mark-down measures relative to the fixed effects of all other mills.

> 47 Note that the standard diagnostic tests (White’s Test, F-test, Ramsey Reset Test) reported in earlier tables are also included in Table 7, but the distribution of the diagnostic tests in a WLS framework is not known, so the interpretation of such should be treated with caution. Note also that the reported R<sup>2</sup> is here calculated as the squared correlation coefficient between the actual and predicted values, using the original un-weighted data and WLS coefficient estimates.
8 about 3 percentage points between 2007 and 2012 to 28.1 percent and 23.8 percent, respectively. Increased youth unemployment has longer term implications for the young, as many experience long-term unemployment from the start of labor market entry, according to the International Labor Organization—a situation that has not been observed during earlier cyclical downturns.

**Figure 2. Rising unemployment worldwide, with differences across regions and income levels**

<!-- Start of picture text --> 6.6 205 12<br>6.4 200 % 2007 2011<br>195 10<br>6.2<br>190<br>6.0 8<br>185<br>5.8 180<br>6<br>5.6 175<br>170<br>5.4 4<br>165<br>5.2<br>160 2<br>5.0<br>155<br>4.8 150 0<br>Global unemployment rate (%)<br>Global unemployment (Mn, RHS)<br>2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 World Advanced CEE East Asia SAP South Asia LAC Middle East North Africa SSA<br><!-- End of picture text --> _Source_ : International Labor Organization 2013.

**Figure 3. The unemployment rate before and after the crisis**

<!-- Start of picture text --> 0 10 20 30 40<br>unemployment average 2008-2011 (%)<br>East Asia and Pacific Central and Eastern Europe Western Europe Latin America/Caribbean<br>Middle East and North Africa South Asia Africa North America<br>40<br>30<br>20<br>10<br>unemployment average 2005-2007 (%)<br>0<br><!-- End of picture text --> _Source_ : Authors’ computations, based on World Bank World Development Indicators Database _Note_ : Countries below the 45 degree line had a higher average unemployment rate in the post crisis period.