rafmacalaba/datause-encoder-data huggingface dataset:
the other hand, emphasizes these intergenerational correlations. However, the empirical research in this literature has focused mostly on intergenerational income correlations in the context of developed countries.<sup>36</sup> In this paper, we present some first empirical evidence on intergenerational occupational correlations in non-farm participation in a developing country, Nepal.

The empirical results show that there are strong intergenerational correlations, especially for daughters, in non-farm participation arising from 'intangible' factors like role model effects, learning externalities, and transfer of reputation capital. The results of Two Stage Conditional Maximum Likelihood analysis that correct for potential enodgeneity of education and assets in the regressions, suggest dramatic impact of a mother's employment status on daughters' nonfarm participation rate. The probability of a daughter's non-farm participation increases by 200 percent if mother is employed in the non-farm sector, even after controlling for a large number of relevant variables including education, assets, and non-farm opportunities. The linkage between mother and daughter is especially strong in case of skilled non-farm jobs, thus implying very restricted occupational mobility for women out of agriculture and low skilled non-farm activities. In contrast, the intergenerational link between father and son is found to be nonexistent, except for the case of skilled non-farm jobs. The difference in the strength of intergenerational correlations between son-father and daughter-mother implies that the observed correlations in case of mothers and daughters are not primarily due to intergenerational correlations in genetic ability or taste. Other intangible factors such as role model effects and learning externalities are perhaps more important in determining these correlations. The much stronger and positive intergenerational correlations between mothers and daughters also suggest the existence of a strong gender bias against women in occupational mobility. Such robust occupational correlations, on the other hand, provide a strong argument in favor of policy interventions for promoting non-farm participation of women due to the dynamic multiplier effect. It will be interesting to see if the pattern of intergenerational linkages found in case of Nepal is valid for other developing countries, especially in South Asia and Africa where the initial non-farm participation rates are strongly skewed against women.

> 36 Solon (1999) in his survey of studies on intergenerational mobility cited only one paper on income mobility in the context of developing countries; the paper cited being Lillard and Kilburn (1995) that uses data from Malaysia.

23
# **IV. Impact of the Taiping Rebellion on Population Levels**

We now examine the relationship between the Taiping Rebellion and the prefecture population levels. We use the panel data for the 266 prefectures and the seven specific snapshots across two centuries (i.e., 1820, 1851, 1880, 1910, 1953, 1982, and 2000). The pre-treatment years are 1820 (the default year) and 1851; the post-treatment years are the rest. The baseline regression is as follows: ln⁡popit = dt + ηi + Xi,t′ β+ ∑βt tDtZi + + ∑αt 1tDtTPi + ∑αt 2tDtTP_Oci + ei,t (1) Here dt and ηi are the year and the prefecture fixed effects. The prefecture fixed effects capture all time-invariant factors such as geography and culture. Xi,t is a vector of time-varying controls including the duration of treaty ports, concessions, and leased territories, and war frequency, which has been shown to matter for China’s development (Bai and Kung, 2015; Jia 2014). Zi is a vector of time-invariant controls including geographical and historical variables (i.e., the distance to Yangzi River, to the coastline, and to the Grand Canal; the number of neighboring provinces; the pre-war level of land taxes per mu; the total number of palace graduates per million people from 1793 to 1820; the silk and tea prefectures dummies; the four post designations of the Qing government in 1820), which are allowed to have period-specific effects so that the local endowment could have a different effect over time. e is the error term. i,t _The OLS Results_ . Column (1) in Table 2 presents the regression results using the two Taiping treatments. To allow for potential selection bias, we allow the period immediately before the Taiping Rebellion to have distinct effect for the Taiping areas — as a test of selection bias. The estimate of the Taiping coefficient before the rebellion (1851) is small (at about one log point lower than for the control group) and statistically insignificant, suggesting a lack of selection bias by the Taiping areas after proper controls are employed. After the war, the Taiping coefficients increase dramatically in magnitude (in absolute value). There is no sign of convergence in population growth relative to the control group after the war from 1880 to 2000. In 1880, the Taiping coefficient is -0.47, which implies that the population growth of Taiping areas from the pre-rebellion years to 1880 is 37 percent lower than that of the control group.<sup>38</sup> In 2000, the Taiping coefficient is -0.48, indicating that the pre-rebeellion-to-year-2000 population growth of the Taiping areas remains 38 percent lower than that in the control group even after one-and-a-half centuries, and that the Taiping Rebellion has had a long-lasting impact on population growth. The initial drop in population for the Taiping areas of 37 percent

> land. In addition, there was family planning in effect. Population growth  thus differed little for the two regions from 1982 to 2000.

> 38 That is, e−0.469 −1= -37%.

26
- 34 -

cardiovascular conditions in adults. These two effects provide the basis for our estimates of the impact of lead. A thorough review of health outcomes associated with lead exposure is provided by U.S. EPA<sup>5</sup> 4 D and ATSD.6Y Effects of Lead on Blood Pressure in Adults. The association between lead and increased blood pressure was first observed in animals. This effect has been shown across a range of doses and in several species," and has been examined in occupational and population-based epidemiologic studies. The population-based studies will be briefly reviewed here.

Several investigators66 have used NHANES II. data, published by the National Center for Health Statistics, to investigate the relationship between blood lead level and blood pressure. NHANES IL is a large, individual-level database that includes information on a variety of potentially confounding factors. Therefore, these studies avoided common study design problems (e.g., healthy worker effect, workplace exposures to other toxic agents, selection bias, and problems of control group selection).

Using these data, Harlan er al.< demonstrated statistically significant linear associations (p<O.OOl) between blood lead concentrations and both systolic and diastolic blood pressure among males aged 12 to 74 years. Further analyses reported by Pirkle et alt<sup>3</sup> focused on white males, aged 40 to 59 years. This age group was used to reduce any potential confounding effects of age on blood pressure. In the subgroup studied, significant associations were found between blood lead and blood pressure even after controlling for most risk factors known to be correlated with blood pressure. Furthermore, no threshold For the effect was observed across a blood lead range of 7 to 34 micrograms per deciliter of blood (ug/dl). Schwartz reanalyzed the data of Pirkle eE al. and showed that the association decreased but remained significant for both systolic and diastolic blood pressure when adjusted for site.'"' In addition, an association between blood lead and the likelihood of hypertension (diastolic blood pressure greater than 90 mm Hg) was reported.
# **Appendix 5.  Comparison of Mobile Phone Ownership in November 2017 WFP Survey to 2014 Household Budget Survey**

||2014 Hou|sehold Budget|Survey|Novemb|er 2017 WFP Sur|vey|CI<br>Overlap|
|---|---|---|---|---|---|---|---|
|Region|_________<br>Lower<br>Bound-<br>95% CI|___________<br>Upper<br>Bound-<br>95% CI|_______<br>Mean|__________<br>Lower<br>Bound- 95%<br>CI|_____________<br>Upper<br>Bound- 95%<br>CI|_____<br>Mean|_______|
|National|2.17|2.72|2.45|2.08|2.44|2.26|Y|
|Abyan|1.43|2.02|1.72|2.03|2.73|2.38|N|
|Aden|2.58|3.03|2.81|2.14|2.91|2.52|Y|
|Al_Bayda|2.40|2.95|2.67|1.70|2.22|1.96|N|
|Al_Hudayda|1.73|2.15|1.94|1.60|2.10|1.85|Y|
|Al_Jawf|2.00|3.13|2.56|2.21|3.00|2.60|Y|
|Al_Mahrah|2.44|3.72|3.08|2.38|3.98|3.18|Y|
|Al_Mahweet|1.66|2.38|2.02|1.67|2.38|2.02|Y|
|Amran|1.86|2.43|2.15|1.77|2.45|2.11|Y|
|Dhale|1.83|2.67|2.25|1.99|2.48|2.23|Y|
|Dhamar|1.76|2.51|2.14|1.68|2.14|1.91|Y|
|Hadramout|2.98|3.85|3.41|2.38|3.16|2.77|Y|
|Hajja|1.65|2.18|1.91|1.58|2.05|1.81|Y|
|Ibb|1.91|2.48|2.20|1.99|2.53|2.26|Y|
|Lahij|1.56|2.03|1.80|1.72|2.15|1.94|Y|
|Mareb|2.78|3.60|3.19|2.27|3.30|2.79|Y|
|Raymah|1.74|3.04|2.39|1.53|1.95|1.74|Y|
|Saada|0.96|2.47|1.72|2.23|2.90|2.57|Y|
|Sanaa|1.86|3.23|2.54|1.70|2.20|1.95|Y|
|Sanaa_City|3.33|3.89|3.61|2.45|3.26|2.86|N|
|Shabwah|2.82|3.69|3.25|2.34|3.10|2.72|Y|
|Taiz|2.32|2.78|2.55|1.85|2.24|2.04|N|

Notes: This table compares mobile phone ownership in the November 2017 WFP mobile phone survey and the 2014 Household Budget Survey (HBS), where the 2014 HBS summary statistics are restricted to the share of the population that resides in a household that owns at least one mobile phone. The 2014 HBS is the last known population estimates of these variables for Yemen.

36
1% level. Likewise, these same businesses report up to 43% higher capability of remaining open with current cash flow, have 71% greater number of employees, and report 67% higher business investment than the control group. We find qualitatively similar findings for new businesses in Panel B of Table 4. Hence, these results suggest business grants are vital for maintaining day-to-day business operations, which then translates to higher sales and profits.

Finally, table 5 focuses on individual level welfare outcomes and presents analysis on confidence and life satisfaction. These outcomes are based on self-reported survey questions with responses recorded on a sliding scale, which are then normalized. The results show that compared to the control group, youth assigned to grants or both grants and BDS report 29% higher life satisfaction (0.26 SD effect size), 5% more confidence their households will maintain living standards (0.12 SD), and 10% more preparedness to navigate the business and job environment (0.21 SD). There are no statistically significant differences in the number of days youth believe the effects of COVID-19 will last, though the coefficients for “Grants” and “Grants and BDS” are negative.

Overall, these findings show that grants are instrumental in keeping businesses afloat during times of aggregate crises. The mechanism appears to operate through stronger business operations and also higher levels of entrepreneurial confidence and life satisfaction. While we acknowledge that we cannot identify the causal chain, as better individual satisfaction could lead to better performance just as much as better performance could result in higher satisfaction, nevertheless our results do highlight the importance of these individual factors in crisis recovery. Moreover, our evidence suggests that COVID-19 is not only an economic shock but also a psychological shock that is felt at the personal level, and recovery from both is key to eventual improvements in well-being.

# **<mark>6. Conclusion and Discussion</mark>**

The goal of this study is to learn how a large shock – to both demand and supply – affects micro and small businesses in an emerging economy, and whether policy measures such as business grants and/or business development services can mitigate adverse outcomes. We do so in the context of the COVID-19 pandemic and a field study among youth entrepreneurs across Kenya. Our results show that beneficiaries who were assigned to receive business grants of $360 (with or without BDS in addition) are significantly less likely to report a decline in income; are up to twice as likely to maintain their business in operation; report significantly higher sales and profits; and maintain significantly higher life satisfaction and confidence levels.

Our results also show that business development services in the form of business training and counseling are ineffective in curtailing the negative effects of the crisis. Given that the original study envisioned a different purpose for business development services rather than crisis mitigation, it is unsurprising that we find no impact on any of the outcomes that we 14
Table 3. Sub-Saharan Export Products Facing MFN or Preferential Duties in the<sup>European Community (No. of tariff line items).</sup>

|Exporting Country|Tariff Lines<br>Facing a<br>Zero MFN Duty|Zero<br>Preference<br>.ate Lines|Total Lines<br>with Zero<br>Duties'|Non-Zero<br>Preference<br>Rate Lines|Zero Duty<br>or Preference<br>Rate Lines'|Non-Zero<br>MFN Duty<br>Lines|Total<br>Tariff Lines<br>Exported|
|---|---|---|---|---|---|---|---|
|Angola|25|106|131 (98)|1|132 (98)|2|134|
|Botswana|33|94|127 (99)|0|127 (99)|1|128|
|Cameroon|95|411|506 (98)|4|510 (99)|4|514|
|Central African Republic|41|83|124 (98)|0|124 (98)|3|127|
|<br>Chad|21|51|72 (97)|1|73 (99)|1|74|
|Congo|57|173|<br>230 (100)|0|230 (100)|0|230|
|<br>Cote d'lvoire|121|595|716 (97)|6|722 (98)|14|736|
|Ethiopia|67|111|178 (98)|1|179 (99)|2|181|
|<br>Gabon|55|261|<br>316 (100)|0|316 (99)|1|317|
|Ghana|63|250|313 (99)|2|315 (99)|2|317|
|Guinea|50|109|159 (100)|0|15) (100)|0|159|
|Kenya|108|438|546 (98)|4|550 (98)|10|560|
|<br>Liberia|57|84|141 (96)|1|142 (97)|5|147|
|Madagascar|63|278|<br>341 (97)|2|343 (98)|8|351|
|Malawi|28|140|168 (98)|2|170 (99)|1|171|
|Mali|34|146|180 (99)|1|181 (99)|1|182|
|Mauritania|18|125|143 (99)|0|143 (99)|4|144|
|Mauritius|46|588|634 (99)|2|636 (99)|6|642|
|Niger|29|151|180 (99)|1|181 (99)|1|182|
|Nigeria|127|392|519 (99)|3|52i (99)|4|525|
|<br>Senegal|90|375|465 (98)|4|469 (99)|6|475|
|<br>Sierra Leone|30|78|<br>108 (98)|0|108 (98)|2|110|
|Sudan|55|84|139 (99)|1|140 (99)|1|141|
|Swaziland|20|118|138 (98)|5|143 (96)|6|149|
|Togo|48|170|218 (96)|2|220 (97)|6|226|
|Uganda|25|60|85 (97)|0|85 (97)|3|88|
|<br>United Republic Tanzania|79|219|298 (99)|1|299 (99)|2|301|
|Zaire|97|217|314 (98)|0|314 (98)|7|321|
|Zambia|48|125|173 (93)|8|181 (97)|5|186|
|Zimbabwe|91|451|<br>542 (97)|8|550 (99)|6|556|
|MEMO ITEM||||||||
|Taiwan, China|192|0|192 (4)|0|192 (4)|4.080|4,272|
|Republic of Korea|153|0|153 (4)|0|153 (4)|3,350|3,503|

'Figures in parentheses show the percentage of all tariff lines covered. The EEC customs<sup>schedule differentiates between some 9,506 line item products.</sup> Source: World Bank-UNCTAD SMART Data Base.
_Political Economy of Policy Reform in Turkey 11_

# Table 2: Results of the Major National and Municipal Elections 1983-1991

|General<br>of Nov|Elections<br>ember 1983<br>|Gene<br>of No|ral Elections<br>vember 1987|Municipal Elections<br>of March 1989|Gene<br>of O|ral Elections<br>ctober 1991|
|---|---|---|---|---|---|---|
|Share of<br>vote (70)|Share of<br>Seats in<br>parliament(%)|Share of<br>vote(%)|Share of<br>Seats in<br>parliament (%)|Share of<br>vote (%)|Share of<br>vote(%)|Share of<br>seats in<br>parliament(%)|
|ANAP<br>|45|53|36|66|22|:2426|
|HP<br>|31|29|--|--|--|--|
|SHP<br>|--|--|25|22|29|2120|
|DYP<br>|--|--|19|12|25|2740|
|DSP<br>|--|--|9|--|9|112|
|MqP<br>|--|--|3|--|4|----|
|MDP<br>|23|18|--|--|----||
|RP<br>|--|--|7|--|10|1714|
|DP<br>|--|--|0.8|--|0.9|----|
|SP<br>|||--||--|0.4--|
|Independents|I|--|0.5|--|0.6|0.1--|

_lNotes: Results of the 1984 municipal elections are not inclhded because they are broadly parallel to the outcome of the preceding national elections. ANAP (Motherland Party); HP (Populist Party); SHP (Social Democratic Populist Party); DYP (True Path Party); DSP (Demnocratic Left Party); M('P (Nationalist Work Party); MDP (National Democracy Party); RP (Prosperity Party); DP (Reformist Democracy Party); SP (Socialist Parry)._
# A. Trade Policies that Affect Imports

# 1. Tariffs

The simple average applied MFN tariff level and the standard deviation in the applied tariff level for the latest year available, as well as the average level of binding, the average difference Ietween applied and end of UR bound rates and the proportion of tariff lines unbound for the 42 developing countries in the sample are presented in Table 4. The table shows the great variability in developing countrv trade regimes. Average applied rates range from zero in Ilong Kong and Singapore, to a iange of 10-20 per cent in many countries in Latin America, to over 30 per cent in Egypt, India, Kenya, Pakistan, Tunisia and Thailand and several African countries.<sup>4</sup> The simple average applied tariff rate for the countries in the sample was 19 per cent.<sup>5</sup> i ariff rates also vary substantially within each country with overall standard deviations ill excess of 10 for several countries; and similarly high coefficients of variation. Interestingly enough however, the variability in the applied tariff rate structure of the developing countries in the sample is not substantially different from that of many developed countries (See OECD, 1997, Tables 1.1-1.4). The main reason for this increasing similarity is the increased variability of the agricultural tariffs in developed couniries as a consequence of tariffication in agriculture.

The Table also shows the significant variability in the proportion of total tariff lines developing countries have bound in the UR. On the whole , of course, the proportion of tariff lines bound by developing countries increased during the UR. But, while WTO Members have bound all their agricultural tariff lines, many developing country members have bound only a small proportion of the

> 4The main source of the data is the WTO Integrated Data Base (IDB) which is based on country notifications. In a few cases where the TPRs contain more up to date information on country applied rates than those notified, these later estiniates have been used and are noted with an asterisk in Tables 4 and 5. TPR applied tariff information is sometimes availabl: at the 2- digit HS classification.

> 5 This average needs to be used with caution as it refers to applied rates in different countries in different years, and some countries have subsequently reduced their tariff schedules. Unfortunately the data do not permit a calculation of an applied tariff average for the grodp of countries as of a given recent year post URA.

10
Econometric analysis allows us to identify rigorously the effect of backward GVC participation on productivity growth. Labor productivity has a multitude of drivers. To investigate whether GVC participation is among the significant ones in the context of the developing East Asian countries, we draw on the econometric framework in Constantinescu, Mattoo and Ruta (2017). We apply that framework to data on value‐added, output and trade from the OECD ICIO and TiVA and on employment and capital from OECD STAN, WIOD (Release 2013) and UNIDO INDSTAT2.<sup>20</sup> We estimate the impact of GVC‐related trade and non‐GVC‐related trade on labor productivity for the average country in the world, and then use interaction terms to investigate whether a differential effect exists for the developing East Asian group. Annex B provides the details of the econometric analysis.

The analysis shows that GVC participation has been a significant driver of productivity growth for developing East Asian economies. Results are presented in table 5. For the average country/sector in the world, they confirm the findings in Constantinescu, Mattoo and Ruta (2017) that total imports and total exports influence productivity (specification 1), that the imported input channel is relatively more important (specification 2), and that imported inputs matter for productivity whether or not they are related to GVCs (specification 3).<sup>21</sup> The coefficients of the interacted terms which aim to capture differential impact for developing East Asian countries relative to the average country/sector in the world are generally not significant. Gross final imports have a negative impact on productivity that is significant at the 5 percent level, suggesting that the positive effect of international competition may be outweighed by the resulting contraction in the scale of production. Our GVC results suggest that, on average, a 10 percent increase in the backward GVC participation of manufacturing sectors in developing East Asian countries corresponds to a 1.4 percent increase in labor productivity, other things being equal. In other words, developing East Asia also sees a productivity boost from GVC‐related imported inputs or backward linkages, and the magnitude is not significantly different from the global average.

> 20 The resulting data set covers 55 countries, 12 sectors and period 1995 through 2011. It includes the following developing East Asian countries: China, Indonesia, Malaysia, Philippines and Vietnam. Estimations are restricted to manufacturing sectors (excluding manufacturing of coke, refined petroleum and nuclear fuel).

> 21 While all trade variables included in the regressions have endogeneity concerns associated with them, our focus is on identifying the causal effect due to backward GVC participation, for which we were able to find a plausible instrument. Hence, we examine this effect using IV estimation.  Backward GVC participation of sector s in country c is instrumented by the average value added from the United States, Japan and Germany embodied in the exports of industry s of three countries in the sample that are closest in income to the country c.

42
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