rafmacalaba/datause-encoder-data huggingface dataset:
These findings are further buttressed by Figure 2. At these rates of missingness, the mean values of the multiply-imputed outcome variables of interest are within the 95 percent confidence intervals of the corresponding true mean values.

_Figure 2: Mean Values of Plot Area & Plot-Level Yield by Rate of Missingness Above a Given Distance Threshold_ <!-- Start of picture text --> Mean values of outcome variables by missing rate<br>Malawi Malawi<br>Area (Ha) Maize Yield (Kg/Ha)<br>0 20 40 60 80 100 0 20 40 60 80 100<br>% of plots missing beyond threshold % of plots missing beyond threshold<br>Ethiopia Ethiopia<br>Area (Ha) Value of output per Hectare (1000s Br/Ha)<br>0 20 40 60 80 100 0 20 40 60 80 100<br>% of plots missing beyond threshold % of plots missing beyond threshold<br>Mean Outcome variable and 95% confidence interval<br>Mean imputed outcome variable 500m Threshold<br>Mean imputed outcome variable 1km Threshold<br>1 800<br>.99<br>750<br>.98<br>(Ha) 700<br>.97 (Kg/Ha)<br>Mean of Area .96 650<br>.95 Mean of Maize Yield 600<br>.26 150<br>.24<br>.22 100<br>(Ha) .2 50<br>Mean of Area .18 (1000s Br/Ha)<br>.16 0<br>Mean of Value of output per Hectare<br><!-- End of picture text --> Overall, further investment into the operationalization of this evidence could result in significant savings in terms of time and resources. Our findings are particularly relevant for household and farm surveys that visit sampled households at least twice and in sync with a given agricultural season, such as the LSMS-ISA supported surveys in Ethiopia, Malawi, Niger, Nigeria, and Mali. In these surveys, the sampled households are visited for the first time during the post-planting period for parcel and plot demarcation and area 28
**Table 14:** Job Flows by Firm Size - the Role of Labor and Product Market Regulations (Difference-in-Difference Analysis)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant<br>USA SUM<br>|0.1225<sup>_∗∗∗_</sup><br>[0.0126]<br>0.8379<sup>_∗∗∗_</sup><br>[0.0700]|0.0753<sup>_∗∗∗_</sup><br>[0.0131]<br>0.8579<sup>_∗∗∗_</sup><br>[0.0409]|0.1150<sup>_∗∗∗_</sup><br>[0.0109]<br>0.8401<sup>_∗∗∗_</sup><br>[0.0988]|0.0660<sup>_∗∗∗_</sup><br>[0.0147]<br>0.8371<sup>_∗∗∗_</sup><br>[0.0435]|
|USA SUM <sup>_∗_</sup>EPL (Adj)|||-0.0546<sup>_∗∗_</sup><br>[0.0203]||
|USA SUM <sup>_∗_</sup>EPL <sup>_∗_</sup>_<_20 workers|-0.0499<sup>_∗∗∗_</sup><br>[0.0124]||||
|USA SUM <sup>_∗_</sup>EPL (Adj) <sup>_∗_</sup>_<_20 workers||-0.0632<sup>_∗∗∗_</sup><br>[0.0090]||-0.0540<sup>_∗∗∗_</sup><br>[0.0139]|
|USA SUM <sup>_∗_</sup>EPL <sup>_∗_</sup>20-49 Workers|-0.0739<sup>_∗∗∗_</sup><br>[0.0129]||||
|USA SUM <sup>_∗_</sup>EPL (Adj) <sup>_∗_</sup>20-49 Workers||-0.0895<sup>_∗∗∗_</sup><br>[0.0100]||-0.0649<sup>_∗∗∗_</sup><br>[0.0188]|
|USA SUM <sup>_∗_</sup>EPL <sup>_∗_</sup>50-99 Workers|-0.0853<sup>_∗∗∗_</sup><br>[0.0131]||||
|USA SUM <sup>_∗_</sup>EPL (Adj) <sup>_∗_</sup>50-99 Workers||-0.1012<sup>_∗∗∗_</sup><br>[0.0104]||-0.0793<sup>_∗∗∗_</sup><br>[0.0206]|
|USA SUM <sup>_∗_</sup>EPL <sup>_∗_</sup>100+ Workers|-0.0997<sup>_∗∗∗_</sup><br>[0.0148]||||
|USA SUM <sup>_∗_</sup>EPL (Adj) <sup>_∗_</sup>100+ Workers||-0.1140<sup>_∗∗∗_</sup><br>[0.0133]||-0.0537<sup>_∗_</sup><br>[0.0319]|
|USA SUM <sup>_∗_</sup>Bus. Reg. (Adj)|||0.0235<br>[0.0255]||
|USA SUM <sup>_∗_</sup>Bus. Reg. (Adj) <sup>_∗_</sup>_<_20 Workers||||-0.0096<br>[0.0225]|
|USA SUM <sup>_∗_</sup>Bus. Reg. (Adj) <sup>_∗_</sup>20-49 Workers||||-0.037<br>[0.0309]|
|USA SUM <sup>_∗_</sup>Bus. Reg. (Adj) <sup>_∗_</sup>50-99 Workers||||-0.0321<br>[0.0338]|
|USA SUM <sup>_∗_</sup>Bus. Reg. (Adj) <sup>_∗_</sup>100+Workers||||-0.1003<sup>_∗_</sup><br>[0.0530]|
|Observations|940|940|940|940|
|Adjusted R-squared|0.73|0.73|0.69|0.73|

Standard errors in brackets.<sup>_<u>∗</u>_</sup> significant at 10%,<sup>_<u>∗∗</u>_</sup> significant at 5%,<sup>_<u>∗∗∗</u>_</sup> significant at 1%. All regressions include country dummies. USA SUM: industry/size job reallocation in the United States. EU denotes the OECD European countries. Transition denotes the countries in Central and Eastern Europe. LAC denotes the countries in Latin America. EPL is the index of stringency of hiring and firing regulations. EPL (Adj) is the indicator of hiring and firing adjusted to take into account different degrees of enforcement of regulations (see main text). Bus. Reg. is the indicator of the stringency of business regulations. Bus. Reg. <u>(Adj)</u> is the same indicator adjusted to take into account different degrees of enforcement of regulations.

Source: Own calculations based on harmonized firm-level database.

39
measure of human capital flows at the state level. Using available evidence from the fiveyearly rounds of the National Sample Survey, we estimated the likely magnitude of interstate migration and these are reported in Table 3.<sup>4</sup> As a percentage of students completing their higher education and entering the labor market, inter-state migration of the highly skilled varies between 0.48% (Andhra Pradesh) and 3.01% (Haryana). Since annual data on interstate migration are not available, we cannot control for migration related effects in our regressions. However, given its relatively small magnitude, the assumption that there is negligible interstate migration is unlikely to affect our results significantly.<sup>5</sup> Using the two assumptions above, we can rewrite (1) as a “4<sup>th</sup> difference per capita” equation as follows: where _Pst_ is the total population of state _s_ in year _t_ , _H st_ − _H st_ − 4 is the addition to the stock of the highly skilled in the labor market over _t_ , _t-4_ period in state _s_ which is equal to the total number of students enrolled in Bachelor of Arts, Science and Commerce courses in year _t-4_ and state _s_ . In the remainder of the paper, we will use the terms human capital, enrollments and skilled workers interchangeably for _H st_ − _H st_ − 4 / _Pst_ .

> 4 These estimates are based on ongoing work on interstate migration in India by Utsav Kumar, Aaditya Mattoo and Arvind Subramanian.

> 5 Other studies also suggest that inter-state migration in India is quite small. See, for example, Cashin and Sahay (1995) and Topalova (2004).

4
Policy Research Working Paper 10068

# **Abstract**

How does trade policy affect exporters’ ability to respond to foreign demand shocks? Faced with a sudden change in the demand for their goods, exporting firms must optimally change their inputs and/or input sources. This paper tests whether a country’s own trade policy makes such adjustments harder for firms that rely on imported inputs. The analysis exploits new time-varying data on tariffs and non-tariff measures faced by Indonesian firms and focuses on the impact of exchange rate shocks on exports to Japan. In response to a depreciation of the yuan, which makes Chinese exports more competitive, the findings show that firms that face non-tariff measures on their inputs see a much larger drop in their export values compared to firms that do not face any non-tariff measures. That is not the case for import tariffs on inputs, which do not affect the export response to the shock. This difference is consistent with the (partial) fixed costs imposed by non-tariff measures on imports in contrast to the pure variable costs of tariffs. The magnitude of this effect depends on the type of non-tariff measure and on firms’ characteristics, such as their participation in global value chains, size, and product quality.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at amontfaucon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
To assign the PROBEMS scholarships, SEP issues one and sometimes two calls for applications per year depending on budget availability. The call for applications is usually issued in March of each year, targeting students starting or continuing EMS in the following academic year (August). Interested candidates start their application process by filling in a form capturing household income, the availability of household assets, parents’ education and basic academic information such as the school and grade of enrollment. All applicants must have a personal identifier known as the CURP, issued by the Government of Mexico.<sup>8</sup> SEP uses the CURP to identify applicants who are beneficiaries of the conditional cash transfer program (PROGRESA / Oportunidades) at the time of applying, which automatically excludes them from PROBEMS. The self-declared household income and assets information included in the on-line application format is used to rank students in terms of their likelihood of being below the “assets” poverty line. Finally, as stated by the call for applications, SEP gives priority to first-year EMS students. The likelihood of being poor, budget availability and EMS grade of enrollment determined a list of pre-selected candidates.

When a candidate is pre-selected–usually one month after the call for applications is closed, he or she is notified by email with instructions on how to complete the application process. The next step is to enroll in EMS or continue to the second or third grade, and present, at the school, all the relevant documentation to validate the information uploaded in the program’s portal. School directors receive the list of pre-selected candidates via email and are responsible for validating the information provided by the candidates through the program’s portal. In particular, the school director has to validate that the student is indeed enrolled in his or her school, that the student has no subject areas of previous semesters below the passing mark, and validate the GPA as declared by the student. Once the school director validates the information, the candidates receive a second email notifying them that they have been awarded a scholarship. SEP issues debit cards under the name of the beneficiary. As a final step the student, together with their father, mother or legal guardian, collects the debit card at the closest branch of the commercial bank

> 8Mexican citizens have a unique personal identifier, known as _Clave  ́Unica de Registro Poblacional, CURP_ , formed by an algorithm combining name, surname, date of birth, sex, state of birth, plus 2 randomly generated digits.

9
Figure 2. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by size <!-- Start of picture text --> 100%<br>38.0% 52.5% 51.2% 63.3% 33.0% 49.2% 54.5% 63.7%<br>90%<br>80%<br>70%<br>60% 36.0%<br>36.8%<br>50%<br>40% 34.4% 32.1% 33.0% 29.8%<br>30% 27.3% 25.2%<br>31.0%<br>20%<br>25.2%<br>10% 16.7% 17.8% 15.7%<br>0% 13.1% 9.4% 11.1%<br>GVC1 GVC2 GVC3 GVC4 GVC1 GVC2 GVC3 GVC4<br>(a) Owned (b) Managed<br>Small Medium Large<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

Figure 3. The Percentage of Firms Owned/ Managed by Female integrating into GVCs, by region <!-- Start of picture text --> 100.0%<br>90.0%<br>80.0%<br>51.2%<br>70.0%<br>60.0% 69.6% 64.0%64.3%72.3%65.7%71.1% 62.5% 74.7%81.8%79.4% 70.7% 74.1% 78.4%<br>88.5% 86.8%89.0%95.1%84.9%92.7% 88.1%93.6%95.0%87.0%<br>50.0%<br>40.0%<br>30.0%<br>48.8%<br>20.0%<br>10.0%0.0% 30.4% 36.0%35.7%27.7%34.3%28.9% 37.5% 25.3%18.2%20.6% 29.3% 11.5%25.9%13.2%11.0% 4.9% 15.1% 7.3% 21.6%11.9% 6.4% 5.0% 13.0%<br>GVC1 GVC4 GVC1 GVC4<br>(a) Female Ownership (b) Female Top Manager<br>Yes No<br>SSA EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR SSA EAP ECA LAC MENA SAR<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Note: SAR stands for South Asia, MENA Middle East and North Africa, EAP East Asia and Pacific, SSA Sub-Saharan Africa, LAC Latin America, and the Caribbean and ECA Europe and Central Asia.

We argue that the effect of GVC on women’s participation is moderated through internal and external factors. Generally, both factors might be gender blind as rules and regulations (to start a business or in trade agreements) do not have a gender lens (see Figure 4). However, their impact differs across individuals, and must be either gender mainstreamed or gender focused to improve women economic empowerment in a more explicit way. This is the case of gender provisions in trade agreements or rules that might be more gender-friendly.

7
There could be two potential mechanisms leading to a positive and significant effect of HSAA on educational attainment of girls. First, to the extent that girls’ asset ownership and educational attainment are complements rather than substitutes, parents may have an incentive to provide both in equal measure. Second, improved educational outcomes of generation III girls could be the result of intrahousehold bargaining. With differential preferences for men and women, stronger inheritance rights could increase women’s bargaining power leading to an increase in their daughters’ educational attainment. Unfortunately, neither the generation III girls’ mother’s level of asset ownership nor whether the mother’s father is still alive (if not when he passed away) are observed, preventing us from exploring the underlying mechanisms more directly.<sup>33</sup> However, using a proxy of bargaining power, measured by age at marriage of the mother, shows no differential effects on generation III girls’ education (in column 4 of Table 4) providing suggestive evidence in favor of complementarities. A caveat is in order when interpreting these results, as an ideal test would involve estimating the impact on children’s schooling based on the inheritance patterns of both parents. However, the use of generation III girls’ mother’s age at marriage, given the lack of survey information on mother’s father, provides evidence in support of complementarities between land and schooling within this context.

# **6 Conclusion**

While developing countries continue to make considerable progress to equalize economic opportunities for women, inheritance legislation remains, in many cases, strongly biased against women in a way that may seriously reduce equality, especially in rural areas. Reform of inheritance laws in India, in the form of state-level amendments to the Hindu Succession Act, provide an interesting natural experiment to explore whether and to what extent such efforts have been effective, thus providing potentially important lessons for India (where similar changes have been made, in 2005, on a national scale), and for other

> 33The REDS survey does not contain information on the father of the wife of the household head (when the head was male) nor information on the father of the head of the household when the head was female, thus restricting our ability to shed light on the underlying mechanism that mediates the increase in girls education after the amendment.

27
**Table 8: Effect of Program Availability on Mother’s Time Spent on Sleep**

|||**Minutes per t**|**ypical school day s**|**pent on sleep**||
|---|---|---|---|---|---|
||**Pooled OLS**||**Fixed**|**Effects**||
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|Treatment District|-15.331|||||
||(17.812)|||||
|Round 3|71.102***|70.223***|70.896***|23.815|20.666|
||(9.029)|(9.146)|(14.098)|(23.508)|(24.424)|
|Round 4|57.401***|58.115***|60.004***|59.234***|52.211***|
||(9.176)|(9.249)|(17.098)|(17.805)|(19.656)|
|Treatment District * Round 3|0.917|-0.992|2.476|-4.202|-4.280|
||(18.940)|(19.137)|(19.567)|(20.316)|(20.533)|
|Treatment District * Round 4|37.231*|35.758*|40.379**|28.752|26.205|
||(19.580)|(19.658)|(20.241)|(20.947)|(21.002)|
|Constant|581.581***|578.394***|576.294***|496.830***|498.930***|
||(6.964)|(4.781)|(78.223)|(84.996)|(84.815)|
|Number of observations|1,354|1,354|1,354|1,353|1,353|
|Number of mothers||473|473|473|473|
|R2 within||0.107|0.121|0.148|0.150|
|Controls|None|None|Basic|Extended|Full|

note: *** p<0.01, ** p<0.05, * p<0.1. For columns 1 and 2, the mean of the dependent variable at baseline is 0.9883. For columns 3 – 7, the mean of the dependent variable is 599 minutes per typical school day. Baseline controls: a series of indicator variables that fully describe the composition of the household by age and gender. For example: an indicator for each number of adult men, women, teenage boys, teenage girls, pre-teen girls, pre-teen boys as well as infant boys and girls. Extended: In addition to basic specification includes age of mother and father as well as indicators for whether this information is missing. Includes an asset index measured as the first principal component of a list of 16 assets over all rounds of the data. Also includes a series of indicators for wealth status of household as measured by wealth relative to five years ago and harvest relative to last year. Full: In addition to extended controls a series of indicator variables for quarter in which household was interviewed. Round 3 is the first post-program year for which LEAPS data on this outcome is available. Heteroskedasticityrobust standard errors in parentheses. Standard errors adjust for clustering at the mother level.

38
and wages of four-digit-level occupations in Brazil, Chile, Mexico, and Peru—–four countries that have household survey data with a homogeneous occupation classification during the 2000s (288 occupations in Brazil, 210 in Chile, 184 in Mexico, and 113 in Peru). Each panel shows the changes (in percentage) in employment over time for detailed occupations that are ranked by skill percentile in the base year.<sup>11</sup> Since the sum of the shares must equal one in the baseline and end years, the total change in the shares depicted is zero. The figure also shows the log changes in the wages of each skill percentile.

With the exception of Chile, figure 6 shows no sign of job polarization. In Chile, employment growth was concentrated among high-skill and, to a lesser extent, low-skill occupations, while there was a contraction in employment in the middle of the distribution. The other three countries had modest growth of similar magnitude among occupations in the middle- to high-wage range. Only occupations below the 30th percentile in the skill distribution display employment losses. If anything, there are mild signs of skill-biased technical change in the changes in occupational structure. However, compared with the United States during the 1980s and 1990s, growth among high-skill occupations in the four countries was modest. Using census data, Maloney and Molina (2016) study changes in occupations in 21 developing countries, including Brazil, the Dominican Republic, Ecuador, El Salvador, Mexico, Nicaragua, Panama, and Peru. Their findings are consistent with those observed in the household surveys. They report no significant decline in occupations that are easily codifiable, such as operators and assemblers. Moreover, elementary occupations appeared to be in decline, and high-skilled occupations such as professionals and technicians exhibited small positive employment growth.

> 11Skill content here is approximated by average wages in the base year. The figure looks very similar if instead occupations are ranked by the average years of education in the base year; see Messina and Silva (2018).

13
Policy Research Working Paper 5759

# **Abstract**

Stakeholder dialogue, as an alternative institutional strategy for environmentally and socially sustainable development, has received little attention from researchers and practitioners in developing countries such as China, even though the dialogue strategy can potentially lead public governance to a more efficient level. This paper first discusses the potential of stakeholder dialogue as an institutional tool for promoting sustainable development in China, and then presents a pilot program of stakeholder dialogue recently developed in China—the community environmental roundtables. Community leaders organize roundtable dialogues where representatives from government agencies, companies and the local residents exchange their views toward certain environmental issues they are facing and discuss possible ways to resolve the issues. Informal agreements are reached during the dialogues and implemented after them. This community roundtable dialogue strategy has been piloted in dozens of Chinese municipalities, addressing various environmental issues. A survey of dialogue participants shows that significant impacts have been generated on environmental protection, community management, as well as social and institutional development at the community level. Mutual understanding and trust among the government, companies, and local citizens are enhanced, environmental and social conflicts are reduced, and the public performance of various parties has been improved. This approach is expected to help solve other conflicts and public governance issues in China as well. The potential challenges of institutionalizing such a program in China are also discussed in the paper.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at hwang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team