rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendices:**

**Appendix 1: Descriptive Statistics of the Variables in Education Probit Regression**

|**Category**|**Description**|**Mean**|**Min**|**1988**<br>**Max**|**Std.**<br>**deviation**|**Mean**|**Min**|**2002**<br> <br>**Max**|**Std.**<br>**deviation**|
|---|---|---|---|---|---|---|---|---|---|
||Dummy, 1=if the adult child's education is more than primary school|0.52|0|1|0.49|||||
|Dependent variable|Dummy, 1=if the adult child's education is more than junior middle school|||||0.29|0|1|0.46|
||Daughters' education, years of schooling|5.8|0|14|2.2|9.0|0|16|2.4|
||Sons' education, years of schooling|6.8|0|14|2.7|9.0|0|16|2.4|
|Parents' education|Dummy, 1=either parents' education more than primary school|0.35|0|1|0.48|0.66|0|1|0.47|
||Average of mother's and father's years of schooling|3.7|0|14|2.6|6.0|0|15|2.3|
|Individual characteristics|Age|19.43|18|21|1.1|22.63|18|35|3.9|
||Age squared|379|324|441|43|527|324|<br>1225|193|
||Mother's age|46.53|35|81|5.88|48.61|35|76|5.84|
||Father's age|49.16|35|74|6.28|50.96|35|82|6.33|
|Parents' characteristics|Dummy, 1=if either parents in non‐farming|0.23|0|1|0.42|0.37|0|1|0.48|
||Dummy, 1=if either parents is Communist Party member or cadre|0.2|0|1|0.4|0.41|0|1|0.49|
||Dummy, 1=if either parents is ethnic minority|0.08|0|1|0.26|0.14|0|1|0.35|
||Number of adult male children|1.79|0|6|0.98|1.32|0|1|0.82|
||Number of adult female children|1.77|0|7|1.15|0.93|0|1|0.86|
||Dummy, 1=if female household head|0.03|0|1|0.16|0.03|0|1|0.18|
|Household characteristics|Dummy, 1=if irrigation available to the household|0.58|0|1|0.49|0.31|0|1|0.46|
||Land, unit is_mu_|14.27|0.3|956|39.9|5.91|0|180|7.09|
||House value, unit is current_yuan_|5307|3|80000|5730|26596|450|<br>360000|29684|
||Dummy, 1=if electricity available to household|0.87|0|1|0.34|0.99|0|1|0.06|
||Dummy,1= if topography is terrain (base is flat)|0.31|0|1|0.46|0.33|0|1|0.47|
||Dummy, 1=if topography is mountain (base is flat)|0.19|0|1|0.39|0.19|0|1|0.39|
||Dummy, 1=if old revolutionary base|0.15|0|1|0.35|0.22|0|1|0.42|
|Village characteristics|Dummy, 1= if ethnic minority region|0.08|0|1|0.28|0.13|0|1|0.34|
||Dummy, 1= if impoverished region|0.19|0|1|0.39|0.28|0|1|0.45|
||Dummy, 1= if suburb of big city|0.01|0|1|0.11|0.08|0|1|0.27|
||Dummy,1= if alongcoastal line|0.24|0|1|0.43|0.25|0|1|0.43|

45
|**Table 2. Household characteristics**|||||||
|---|---|---|---|---|---|---|
|**Isolation**|**Unit**|**Mean**|**Median**|**St.dev.**|**Min.**|**Max.**|
|Travel time to nearest local market|Hours|2.18|1.06|3.36|0.01|40.00|
|Travel time to nearest school|Hours|0.37|0.25|0.88|0.00|25.00|
|Travel time to nearest health facility|Hours|1.08|0.50|1.78|0.00|30.00|
|**Consumption**|||||||
|Total annual consumption expenditures|US$|862|563|1015|29|19940|
|Total value of assets|US$|9910|2445|29854|0|714789|
|**Household size and composition**|||||||
|Number of household members|Number|5.6|5.0|2.8|1.0|29.0|
|Share of adult females in the household|Share|0.26|0.25|0.16|0.00|1.00|
|Share of children aged 6 and under|Share|0.15|0.13|0.16|0.00|0.67|
|Share of youths aged 7 to 20|Ahare|0.32|0.33|0.22|0.00|1.00|
|Share of members aged 65 and above|Share|0.04|0.00|0.13|0.00|1.00|
|% households with female head||13.6%|||||
|**Household characteristics**|||||||
|Age of household head|Years|44.8|43.0|14.4|11.0|92.0|
|Years of schooling of household head|Years|3.6|0.0|5.0|0.0|17.0|
|Days lost to illness over preceding 12 mo|nth Days|2.3|0|6.57|0|70|
|% hholds with 1 or more members unemp|loyed|18.0%|||||
|% hholds that hire permanent or casual la|bor|80.1%|||||
|**Parental background**|||||||
|Inherited land|Hectares|0.81|0.36|1.69|0.00|32.05|
|Years of schooling of head's father|Years|0.9|0.0|2.5|0.0|16.0|
|% hholds in which head's father had non-f|arm job|17.0%|||||

Number of observations 3337
# **1. Introduction**

There has been a renewed interest in intergenerational economic mobility over the last few decades, with heightened concerns about widening inequality despite significant growth and poverty reduction in many developed and developing countries (World Development Report (2006), The Economist (2012)). Notwithstanding the recent interest, intergenerational economic persistence in developing countries remains an under-researched area, primarily due to data limitations.<sup>2</sup> A major issue that has stunted progress in this research agenda is that the standard household surveys suffer from truncation, because coresidency is used as a criterion to define household membership (Bardhan (2014), Behrman (1999), Deaton (1997)).<sup>3</sup> A standard household survey such as the Living Standards Measurement Survey (LSMS) done by the World Bank, or the Household Income and Expenditure Survey (HIES) done by national statistical agencies usually includes only the coresident parents and children.<sup>4</sup> Since the pattern of coresidence is not random, most of the studies suffer from potentially serious sample selection bias when estimating intergenerational persistence in economic status. This has discouraged research on intergenerational economic mobility in developing countries.<sup>5</sup> Although potential biases from the coresidency restriction have been a major stumbling block, to the best of our knowledge, there is no evidence on the direction and magnitude of the coresidency bias in the standard measures of intergenerational persistence in developing countries. Are the estimates from the coresident sample biased to such an extent that they

> 2The literature on intergenerational mobility in developed countries is rich with a distinguished pedigree. For excellent surveys of the literature, see Solon (1999), Black and Devereux (2011), Bjorklund and Salvanes (2011), and Corak (2013). A partial list of the contributions includes Bowles (1972), Becker and Tomes (1979), Atkinson et al. (1983), Solon (1992), Mulligan (1997), Arrow et al. (2000), Black et al. (2005), Bjorklund et al. (2006), Chetty et al. (2014), Lefgren et al. (2014), Black et al. (2015), Becker et al. (2015).

> 3Coresidency restriction results in a truncated sample, as the surveys do not gather any information on the family members who do not satisfy the coresidency criteria.

> 4Some of the children of the household head may not be part of the household at the time of the survey for a variety of reasons such as higher eduction, job, marriage and household partition.

> 5This is true even for a country such as India where there is a long tradition of high quality household survey data collection. Bardhan (2005) identifies intergenerational mobility as one of the under-researched areas of economic research in India.

1
Using three rounds of Demographic and Health Surveys (DHS) conducted in Nepal over the last decade linked to data on precipitation from an expansive network of rainfall stations, we find that a 10% increase in rainfall from historic norms during the most recently completed monsoon season is associated with a 0.15 standard deviation increase in weight-for-age across all children ages 0-36 months. However, a similar rainfall shock during the _current_ monsoon leads to a 0.02 standard deviation _reduction_ in weight-for-age. We can thus infer that the total impact of the past monsoon rainfall consists of a negative “disease environment effect” of no more than 0.02 standard deviations and a positive “income effect” of no less than 0.15 standard deviations, but as high as 0.17 standard deviations.

Our evidence also indicates that excess monsoon rainfall experienced in the _second_ year of life (though not in the first year, as found by Maccini and Yang, 2009) has a significantly positive impact on both male and female stature (not just on female stature as in Maccini and Yang). This timing pattern suggests heightened vulnerability immediately following the transition to the family diet. Importantly, however, these nutritional shocks appear to be largely transitory. By the time a child reaches age five, the negative height impact of drought experienced at age two completely dissipates.

The rest of the paper is organized as follows. Section 2 lays out the conceptual framework. Section 3 describes the anthropometric and rainfall data. Sections 4 and 5 present the empirical specification and estimation results, respectively. The final section recaps and discusses implications for policy.

# **2. Conceptual Framework**

A standard human capital production function specifies a child’s nutritional status at the end of period _t_ ( _Ht_ ) as a function _f_ of her nutritional status at the end of the previous period ( _Ht-1_ ), all of the relevant nutritional and health inputs she received during period _t_ ( _It_ ), and her period _t_ disease exposure ( _Et_ ). Thus, 4
19 pandemic) since the last national elections. Our exclusion restriction relies on the assumption that the share of social protection response spending allocated to job protection is correlated with a country’s electoral cycle, but the economic effects of the pandemic (our dependent variables) are not correlated with the electoral cycle.

There is substantial evidence that social spending follows an electoral cycle (Nordhaus, 1975): in particular, governments change the composition of spending during electoral years (Brender and <u>Drazen, 2013; Drazen and Eslava, 2010; Han, 2021). Governments in countries that had elections</u> just before the start of the pandemic may have implemented social protection policies differently from the governments that were up for reelection in the second part of 2020 or 2021.<sup>11</sup> Because the pandemic altered the election schedules in many countries, the variable we use to capture the electoral cycle is the number of months elapsed since the last election (before the pandemic).

<u>Table 6, column 1, presents the result of the first stage of instrumenting the share of the social</u> protection response budget allocated to job protection measures by the number of months since the last election. We exclude countries with suppressed political competition or with interim nondemocratic governments.<sup>12</sup> The number of months since the last election is highly significant and negatively correlated with the share of social protection spending allocated to job protection, indicating that the electoral cycle influences the composition of social protection response packages.

The results of the second stage of the IV estimation (presented in columns 3, 5, 7, and 9 of table <u>6) are qualitatively equivalent to our main results of estimation of equation (1) – a strong country’s</u> focus on job protection policies is associated with a higher GDP in 2021, a higher employment rate, a lower inactivity rate, and a lower poverty headcount rate. While the sign of the effects is consistent with our main results, the coefficients on the share allocated to job protection are not statistically significant. The associated F-statistic has a value corresponding to a size distortion of

> 11 While we are _a priori_ agnostic on the direction of the effect, evidence from Latin America suggests that governments expand cash transfers in the run-up to elections (Bueno, 2021), therefore implying in our case that income protection policies would be a priori more present in countries where elections were scheduled after March 2020 than in countries that had a recent election before that date.

> 12 We exclude countries where political competition is suppressed according to the Polity5 project (a value of 1 in the scale 1-10 of the _POLCOMP_ variable) and also countries with interim non-democratic governments in March 2020 (Bolivia, Sudan) or that have little effective control of their territories (Central African Republic). Singapore and Tajikistan are also excluded as political competition in these countries is virtually suppressed as well despite having a value higher than 1 in _POLCOMP_ .

19
# **Table 2**

**EU imports from the least-developed countries, 2001**

|total EU15<br>imports<br>‘000$US|duty free<br>imports<br>‘000$US|sector<br>share<br>of LDC<br>total|duty-<br>free<br>share<br>of<br>sector<br>total|share<br>subject<br>to<br>specific<br>duties|
|---|---|---|---|---|
|Agriculture<br>905,722|611,791|7.6|67.5|14.0|
|Forestry, Fisheries<br>258,714|174,782|2.2|67.6|0.0|
|Mining<br>3,982,709|3,973,127|33.5|99.8|0.0|
|Processed foods<br>1,035,968|32,188|8.7|3.1|4.2|
|Other manufactures<br>5,720,632|394,087|48.1|6.9|1.4|
|TOTAL<br>11,903,744|5,185,974|100.0|43.6|2.1|
|Source: WTO integrated database|||||

# **Table 3**

**Composition of EU15 processed food imports from LDCs**

||total EU15<br>imports<br>‘000$US|<br>free imports<br>(no duty<br>applied)<br>‘000$US|free<br>import<br>share of<br>category<br>total<br>|share of<br>total<br>processed<br>food<br>imports|EU<br>average<br>rate of<br>pro-<br>tection,<br>all extra-<br>EU trade|
|---|---|---|---|---|---|
|Animal products|92|0|0.0|0.0|16.0|
|Vegetable oils and fats|104,029|26,506|25.5|10.0|37.7|
|Dairy products|99|0|0.0|0.0|18.4|
|Processed rice|1,176|0|0.0|0.1|38.6|
|Sugar|37,818|0|0.0|3.7|36.7|
|Food producs nec|891,547|5,287|0.6|86.1|11.6|
|Beverages and tobacco|1,208|394|32.6|0.1|20.3|
|TOTAL|1,035,968|32,188|3.1|100.0|15.2|

source: WTO integrated database (trade) and GTAP database (protection) 28
Can Africa Replicate Asia’s Green Revolution in Rice?

Donald F. Larson<sup>1</sup> , Keijiro Otsuka<sup>2</sup> , Kei Kajisa<sup>2</sup> , Jonna Estudillo<sup>2</sup> , and Aliou Diagne<sup>3</sup>

**Keywords:** Africa, Asia, food security, Green Revolution, NERICA, rice

1 World Bank, Development Research Group.

> 2 National Graduate Institute for Policy Studies

3 Africa Rice Center
# Appendix 4 Estimation results

Table A.4.1 Sensitivity of long-term inflation expectations to inflation shocks

## A. 1990H2-2018H1

||Dependent va<br>|riable: Change in lo<br>|ng-term inflation e<br>Add|xpectations<br>|
|---|---|---|---|---|
||All countries|All countries|vance<br>economies|EMDEs|
|All countries|0.282***<br>(0.021)||||
|Advanced<br>economies||0.159***<br>(0.028)|0.154***<br>(0.032)||
|EMDEs||0.425***<br>(0.030)||0.425***<br>(0.030)|
|Observations|2,408|2,408|1,344|1,064|
|R-squared|0.069|0.086|0.019|0.169|

## B. 1995H1-2004H2

||Dependent va<br>|riable: Change in lo<br>|ng-term inflation e<br>Advanced|xpectations<br>|
|---|---|---|---|---|
||All countries|All countries|<br>economies|EMDEs|
|All countries|0.423***<br>(0.035)||||
|Advanced<br>economies||0.284***<br>(0.049)|0.278***<br>(0.052)||
|EMDEs||0.554***||0.558***|
|||(0.048)||(0.046)|
|Observations|1,139|1,139|696|443|
|R-squared|0.119|0.131|0.044|0.261|

## C. 2005H1-2018H1

||Dependent var<br>|iable: Change in lon<br>|g-term inflation e<br>Add|xpectations<br>|
|---|---|---|---|---|
||All countries|All countries|vance<br>economies|EMDEs|
|All countries|0.083***||||
|Advanced<br>economies||0.008<br>(0.028)|-0.001<br>(0.031)||
|EMDEs||0.201***<br>(0.034)||0.206***<br>(0.037)|
|Observations|1,269|1,269|648|621|
|R-squared|0.011|0.028|0.000|0.049|

Note: Standard errors are in parentheses. EMDEs = emerging market and developing economies. A.-C. Results for the full sample of 47 countries, 24 advanced economies, and 23 EMDEs, with country and time fixed effects. *** p < 0.01, ** p < 0.05, * p <0.1 significance level.

38
among public doctors reflected a different patient or sickness profile. Furthermore, the DH method, when combined with their inability to use medical personnel in the observation stage, did not provide any information on the accuracy of diagnoses; the researchers suspected that the accuracy of diagnosis was low, but could not validate their suspicions with data.

Therefore, in 2010, Das and others (2012) deployed _standardized patients_ —surveyors recruited from the local community and extensively trained to portray standardized presentations of limited cases—in a sample of health care providers in Delhi and the rural regions of an Indian state. Such standardized patients (SPs) are widely recognized as the “gold standard” in measurements of quality, but the use of SPs in large samples of randomly selected providers, many of them with low medical training, had not been previously attempted.

In Delhi, the study sought informed consent from all providers (where the providers knew that a standardized patient would be sent, but did not know the date) and follow-up surveys showed (A) a single case of adverse interaction between the provider and the patient (out of 230 interactions) and (B) less than a 1 percent detection rate for the SPs. On the basis of this pilot, the study was rolled out in the rural district _without_ informed consent from providers, who were all part of a larger study (with informed consent) similar to that in the DH Delhi studies. The reasons for the lack of informed consent stemmed primarily from the remoteness of the sampled populations. In rural villages, most patients were known to the doctor and although unknown patients often presented to the providers, the combination of announcing the SP study _and_ a remote rural setting would have raised suspicions, invalidating the study design. The study was cleared through Institutional Review Boards at Harvard University and Innovations for Poverty Action. For the purposes of the discussion, we note that the study demonstrated, for the first time, the very poor diagnoses rates in Indian health care (the correct diagnosis of unstable angina 20
significantly lower narrow and wide spreads.<sup>14</sup> This finding is quite interesting in light of the Nigerian consolidation exercise since it might indicate that a more concentrated banking sector as we find today after the implementation of the new capital requirements might indeed be potentially beneficial for increasing financial intermediation in Nigeria and lowering banking spreads. We are aware that the Herfindahl index has its caveats in capturing banking concentration such as its inability to account for market segmentation, efficiency considerations or threat of entry from new institutions (See Claessens and Laeven, 2004). But the consistency of the results across different spread definitions makes us somewhat confident that indeed there might be some benefit by having a more concentrated banking system after controlling for overhead costs. Of course, another caveat from this conjecture is that the pre-consolidated period faced a different market structure from the current market structure after 2006 so it might well be the case that findings from the pre-consolidated Herfindahl might not be projected to the postconsolidated period.

Finally, column 8 incorporates some macroeconomic control variables. We do find some strong evidence that both the inflation and real Treasury bill rate were key drivers of both spread measures during 2000-2005. The positive effect of the inflation rate on the spreads could be potentially explained by the fact that monetary shocks are not passed through the same extent to deposit and lending rates. Also, higher real Treasury bill rates lead to an increase of banks’ marginal costs of funds. Banks will incorporate these higher funding costs in their lending rates so this might cause the higher spreads. Also, higher t-bill rates might constitute an alternative investment opportunity for banks and might led to some substitution towards the T-bill market and away from pure lending. Finally, there is some evidence that a weakening exchange rate is associated with a higher wide spread but not narrow spreads.

Also, an inspection of the coefficients in both the narrow and wide spread regressions reveals that coefficients magnitude are in general lower for the wide spread measure. This might be explained by the fact that the explanatory factors are more suited to explain the narrow spread since it is based on only interest income from loans and

> 14 We note that the Herfindahl indices are scaled by 10,000 to allow for a more adequate measurement of the coefficient size.

18
The Return of “Patrimonial Capitalism”: Review of Thomas Piketty’s _Capital in the 21st Century_<sup>_1_</sup> Branko Milanovic<sup>2</sup> JEL classification: D31, D33, O4, N12, N14 Keywords: growth theory, income inequality, capital, labor, taxation

> 1 Thomas Piketty, “ _Le capital au XXe siècle_ ”, Paris: Editions du Seuil, 2013, 970 pages, list of graphs and tables, no index.

> 2 World Bank, Research Department. I am grateful to Christoph Lakner, Peter Lanjouw, Niels Pleven and Leandro Prados de la Escosura  for excellent comments. The paper was in part funded by the World Bank grant under the project “Changing inequalities: facts, perceptions and policies” TF012968.
||**N**<br>0<br>10000<br>20000<br>30000<br>40000<br>50000<br>60000<br>70000<br>80000<br>90000<br>US $ M nominal|**et PPI Infr**|**astructu**|**re Invest**|**ment 19**|**84-2003**||All<br>Urban|
|---|---|---|---|---|---|---|---|---|
||1980|1985|1990|1995|2000|200|5||
|$ M nomin|**Regional**<br>al|**Breakdow**<br>**EAP**|**Table**<br>**n of T**<br>**ECA**|**2:**<br>**otal PPI**<br>**LAC**|**and Ur**<br>**MENA**|**ban PPI**<br>**SAR**|<br>**SSA**|**Total**|
|All PPI||157,867|77,503|273,678|22,474|37,943|28,469|597,934|
|% of Total|by Region|26%|13%|46%|4%|6%|5%|100%|
|Urban||26,493|2,796|24,581|5,373|423|1,600|61,266|
|Urban as a|% of Region Total|17%|4%|9%|24%|1%|6%|10%|

The regional breakdown of total PPI illustrates what has already been noted elsewhere, that is, the concentration of PPI in two regions, Latin America and East Asia and the Pacific. These two regions also dominate the urban PPI, and to a more significant extent, accounting for 83% of total. The amounts of urban PPI in all other regions are virtually insignificant in relation to the size of their economies, with less than a billion dollars in South Asia, and barely more than that in Sub-Saharan Africa. Urban PPI is quite concentrated in a few countries, with 64% of the total investment flows accounted for by the top five countries. There is much less concentration in the transactions for total PPI, where the top ten countries account for a comparable percentage (67%) of the investment.

Because the database reports all PPI transactions including those that were cancelled, distressed or otherwise compromised, we netted out these problem transaction to get a better, albeit imperfect, measure of actual investment flows from urban PPI<sup>7</sup> . (See

> 7 Some transactions may have been cancelled after major investments were made, in which case, our adjusted figures may understate the investment. However, this is not likely to be a material error, given that most problems in water projects, for example, were encountered early in the project cycle (Guasch et alia (2003) and Guasch et alia (2005). On the other hand, the database does not measure the costs the government may have incurred as a result of contract termination. These numbers are not necessarily insignificant; it is estimated that the Mexican toll road bailout, probably one of the largest, cost over 1% of GDP. (World Bank 2005 in Serven and Irwin 2005)

5