rafmacalaba/datause-encoder-data huggingface dataset:
change in (A.1) as the trade policy shock, and describe the demand response changes that result.

The demand response for a given product is modeled in three consecutive steps: First, the model calculates how the allocation of expenditure on imports of a product changes across different country suppliers when tariffs are amended. These relative import changes are computed from the price change in (A.1), adjusted by the elasticity of substitution between exports. To isolate just the substitution effect, total exports are held constant.<sup>25</sup> More formally, the export substitution component of imports is calculated as where _σ_<sup>_ES_</sup> is the elasticity of substitution between exports from different supplier countries.

Second, the model calculates how the allocation of expenditure between domestic and foreign sources of a product is affected when the relative price of imports (to domestic goods) changes in response to trade reform.<sup>26</sup> This is implemented in two stages: First, relative demand changes are computed from changes in the aggregate (weighted average) price of imports, adjusted by the elasticity of substitution between domestic and foreign products. Individual suppliers then receive their share in the aggregate change in the demand for imported and domestically produced good respectively according to their share of the import market. Formally, we have where _σ_<sup>_DS_</sup> is the elasticity of substitution between imports and domestic production, _Xi_<sup>_∗_= �</sup> _m_ � _n_<sup>_x∗_</sup> _ij_<sup>is(initial)aggregateimports,andthepercentagechangeintheaggre-</sup> gate price of imports is 25This involves deflating post-substitution imports from each supplier, by multiplying the ratio of total imports of the product before tariff reform to the sum of imports of the product from all trading partners after the substitution

> 26As mentioned in the text, this effect cannot be taken into account in the case of this paper as sufficiently detailed sectoral production data is not available for Syria.

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Figure A4: School attendance, children aged 14-18, by poverty Figure A3: School attendance, children aged 6-13, by poverty <!-- Start of picture text --> 2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percent<br>Percent<br>Attending Not Attending Attending Not Attending<br>Total Total<br>Richest Richest<br>Q4 Q4<br>Q3 Q3<br>Q2 Q2<br>Poorest Poorest<br>Non Poor Non Poor<br>Poor Poor<br><!-- End of picture text -->

# Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017

Figure A5: School attendance, children aged 6-13, by gender Figure A6: School attendance, children aged 14-18, by gender <!-- Start of picture text --> 2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>2017 2017<br>2016 2016<br>2015 2015<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percent Percent<br>Attending Not Attending Attending Not Attending<br>Total Total<br>Girls Girls<br>Boys Boys<br><!-- End of picture text --> Source: Authors’ calculations based on HFSSS 2015, 2016 and 2017 19
51. The wage trajectory of lower skilled workers is challenging to piece together, due to more pronounced lack of comparability between the 2001 and 2004 surveys. In 2001, the lowest decile of urban workers earned a monthly average of USD 59, the lowest quintile a monthly average of USD 74, and the median urban worker earning USD 116. Unfortunately, it is possible reliably to compare only average wages in the urban formal sector in 2004 with these numbers. These can be seen in Table 11 below. Given the dominance of manual and service workers in employment, and the fact that Table 11 represents only the (presumably better-paid) formal sector, it seems safe to assume that the median wage in 2004 was below that of 2001.

**Figure 19: Wage-Setting as Reported by Informal Enterprises**

<!-- Start of picture text --> 50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Skills of worker Experience of Age of Worker Affordability Comparative Based on MW<br>Workers wages in private of USD<br>sector 85/month<br>Very important Important Somewhat importan t Not very important Not important at all DK<br><!-- End of picture text --> Source: UES, 2004 52. The survey also asked about wages for each urban area by category of worker and also overall by size of firm. The results need to be treated with caution, due to small cell sizes for some groups. However, some clear and apparently reliable patterns emerged. First, as one would expect, Dili had easily the highest wages across all categories of workers. Maliana and Baucau exhibit no consistent pattern of relative wages across worker categories, but this may be driven by small cell size.

53. As far as enterprise size and wages go, small and small/medium enterprises had the lowest and roughly similar average wage levels for manual workers, with a notable increase for medium sized enterprise manual employees and large enterprises paying the highest average wages for this group.

**Table 11: Average** **<u>Wages by Occupational Groups for Urban Formal Enterprises, 2000</u>**

||**Average Wages (USD/per month)**|
|---|---|
|Manual workers|90.72|
|Service workers|85.32|
|Administrative workers|148.09|
|Technical and Professional workers|187.70|
|Managerial workers|282.93|
|Source: TLSS, 2000.||

54. In 2004, the range of wages for younger workers was lower than that for older workers (see Table 12). This in fact, may be related to why enterprises have no such preference. If younger workers come with less experience, but also command lower wages, one would not 24
Figure 3: TFA effects: Time in export customs <!-- Start of picture text --> Effect of TFA<br>Cent Afr Rep<br>Guinea-Bissau Burundi<br>0 2 4 6 8 10 12 14 16<br>Days in export customs - 2012 (fitted)<br>Note: Circle size proportional to the country's value of merchandise exports.<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>Days in export customs - post-reform 2<br>0<br><!-- End of picture text --> Notes: The figure plots fitted time in export customs along the x-axis, and predicted time in export customs (assuming full TFA implementation) along the y-axis. Only WTO member countries are included.

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- 4 -

- (a) The theoretical modeling of export supply often raises controversial issues, particularly in connection with the transparence of its micro-foundations (for a review, see Faini (1986)3. Among the more prominent controversial issues, the use of either partial or general equilibrium models, the definition of the prevailing market structure, the assumed degree of substitution between domestically consumed and exported goods, the treatment given to factor costs and the role taken with regard to relative prices and productive capacity vis-a-vis more 'keynesian' variables like domestic absorption, appear prominently;

- (b) There are numerous reasons to object to the merit of empirical estimates of export supply functions, particularly those based on cross-country analyses. In fact, data on relative prices, trade deflators and productive capacity is usually obtained from very aggregate macro statistics, while data on elements like special export incentives, capital costs, risk and expectations is almost non-existent;

- (c) There are basic econometric problems in estimating an export supply model, namely simultaneity, aggregation biases and endogeneit, of explanatory variables, which usually make the empirical evidence highly debatable.

Most empirical studies (for instance, Balassa (1986,1987), Clavijo & Senhadji-Semlali (1987), Goldstein & Kahn (1985), Kahn (1974), Moran (1988)] have adopted a "neoclassical" approach, according to which the supply of manufactured exports is explained on the basis of relative product prices and productive capacity. As discussed by Faini (1985), this follows a certain tradition that attempts to explain both supply and demand purely in terms of
# Incomplete, Slow, and Asymmetric Price Transmission in Ten Product Markets of Bolivia.

Gonzalo J. Varela<sup>*</sup> Sector Board: POV Keywords: Spatial Integration; Commodity Prices; Asymmetric Transmission; Food Prices.

JEL Classification: Q11, Q18.

> * PRMTR, World Bank & University of Sussex & CARIS, e-mail: gvarela@worldbank.org.

I acknowledge with thanks help rendered by Julio Loayza and Barry Reilly, although they are not responsible for any flaws that may remain.

1
# **Other Outcomes**

We now turn to the impacts of the clubs on other tobacco and farm outcomes. These results are reported in panel C of Table 6. As expected, club membership has a positive impact on sales. In the model 4 (column 4), club members earn 89 percent more (per acre) than non-members from sales of tobacco at the auction floors. This impact is fairly consistent with the increase in yields reported before.

Notice, in addition, that club members fetch the same unit values for their tobacco as do non-members, implying that clubs cannot in fact secure higher tobacco prices at the auction floors. This is consistent with the responses from the TCS, where only a 33 percent of farmers claim to have enjoyed higher prices due to burley membership. As explained above, this is most likely because tobacco market at the auction floor is oligopsonistic so that a few buyers purchase tobacco from a competitive fringe of smallholders.

Furthermore, the land share allocated to tobacco is not affected by club membership. Behind this widespread inflexibility of land reallocation is the subsistence nature of smallholder farming in Malawi, which, besides all the complex mechanisms that affect faming strategies, basically induces farmers to grow food for own-consumption in a large part of their holdings.<sup>19</sup> Also, we showed that club membership significantly increases yields and thus household income. Thus, if the need for cash income is limited (because of the risk of food shortages associated again with subsistence farming in Africa), the expansion of tobacco land shares may not be observed in practice.

It follows that the bulk of the increase in household income due to tobacco club membership can be attributed to increases in yields rather than to higher unit prices or land shares. We can further explore the implications of club membership for household income with the following simple back of the envelope calculation of the income gain for the average tobacco club member.<sup>20</sup> In the Malawi Integrated Household Survey, the average share of income earned by farming tobacco is about 50 percent and the median plot size

> 19The analysis of the mechanisms behind farming strategies in Malawi is out of the scope of this paper mainly because of data constrains.

> 20Notice that our identification strategy cannot be utilized to estimate the impacts of clubs on income because the exclusion restriction is unlikely to be satisfied in this case.

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Box 1

# Colombia: The Regulation of Professional Services

In a recent study, the World Bank conducted a survey among Colombian professional service providers to assess the main barriers to trade they face. The study identified recognition of accreditations and licensing requirements as the main factors affecting their establishment abroad.

Colombian firms indicated in 30 percent of cases that the lack of title recognition or accreditation of professional service suppliers was a serious obstacle. Micro, small, and medium-sized enterprises considered these barriers to be moderate. Architecture service providers were found to face the most restrictions in terms of lack of recognition and residency requirements.

The regulation of licensing requirement and the speed of the process for granting licenses were considered to be serious barriers for 24 percent of the respondents. Regulations on technical standards were considered moderate or small obstacles in 56 percent of cases. For accounting and legal services providers these barriers were perceived to be important or serious, while for engineering and architecture services they were found to be mostly moderate or small barriers.

The survey also identified other performance and growth challenges that companies face, both those with a branch overseas and those considering setting up one. The greatest performance and growth obstacles faced in foreign markets for these companies were found to be the regulation for foreign suppliers, work permits, visas, residence and regulations related to license granting procedures.

Source: Trade in Professional Services: Demand and supply analysis of the Colombian market, World Bank, (2016).

Trade costs can also stem from the need for compliance with diverse regulatory requirements in multiple jurisdictions within the same political entity (for instance in countries with federal systems of governance where specific services are regulated at the sub-national level) or at the regional level. For example, in the EU context, Mustilli and Pelkmans (2013) find that despite significant services liberalization within the Single European Market, a high level of regulatory heterogeneity prevails among countries in several sectors, notably in professional services. Regulatory heterogeneity can thus alter the fixed costs of entry within segmented markets, and empirical evidence has been adduced showing its dampening effects on services trade flows (Kox and Lejour (2005); OECD (2017). The costs associated with regulatory heterogeneity may also affect decisions on preferred modes of market entry, tilting incentives away from cross-border supply towards establishment-related trade (Mode 3), and therefore changing the nature of the costs of doing business faced by firms.

4
an important and open question from the research and policy perspective: To what extent do educators understand the importance of early childhood development in Nepal, and what obstacles stand in the way of delivering education that promotes growth for children ages 3-5? This issue is particularly relevant for Nepal where ECD provision has expanded rapidly (see section 2) in the past few years, but the quality of provision remains uncertain.

While studies have shown short run positive impacts of providing information and incentives, the long run sustainability of impacts observed in these studies still remains an open question. Another related issue is that of the studies showing the impacts of ECD interventions on various outcomes often being very small in scale and sample sizes,<sup>3</sup> leaving the replicability of these interventions at a large scale as an open question for researchers. In order to implement interventions that are effective in achieving long run impacts and delivering these impacts at a large scale, policymakers need to consider three important questions: (i) in what areas of early childhood development is educator knowledge most lacking? (ii) what do these gaps look like for children with disabilities, who often need special attention to promote their development? (iii) what are the largest constraints to addressing these gaps?

In this paper, we try to address these questions in Eastern Nepal using a survey of 206 teachers and 225 head teachers in early childhood education and development (ECED) centers in one district. ECED centers are devoted to early childhood development and work with children in the critical age range: 0-5. This paper discusses results from two surveys conducted in April-May 2020. Results from these surveys highlight the gaps in teachers’ understanding of various domains of ECD and their understanding of disability in children. We also collect and report information on barriers the teachers face in providing effective education and early childhood development to their students.

The rest of the paper is organized as follows: Section 2 discusses background information on ECD in Nepal, section 3 discusses the data, section 4 discusses the results from the two

> 3See (Heckman and Karapakula (2019)) for impact on lifetime earnings; (Chinen and Bos (2016)) for impact on language, cognitive; (García and Heckman (2020)) for impacts on emotional skills, health behaviors and obesity; and (Masterov (2007)) for participation in crime and other risky behaviors.

2
# **4 Conclusion**

In this paper we built on the Senauer and Sur (2001) methodology to assess the consequences of higher food prices in 2008 and the 2009 growth contraction on the incidence of undernourishment. We show a range of estimates for the impact of the food crisis based on different assumptions about the extent prices rose and the pass-through of global to domestic prices. Our preferred, or most realistic, estimate suggests that the incidence of undernourishment could have increased by around 63 million people in 2008 due to the significant global food price spike. This is a 6.8 percent increase over the 923 million people estimated by FAO to be undernourished in 2007.

We also used our model to determine the impact of the 2009 slowdown in global growth on undernourishment. We estimate that the growth shortfall may have lead to a 4.4 percent, or 41 million, more undernourished people in 2009 than would have been the case had the economic crisis not taken place.

These results are indicative and are meant to highlight the fact that food security and nutrition ought to remain at the forefront of the development agenda especially following recent global shocks and in light of the fact that close to a billion people remain hungry. The specific policy implications are beyond the scope of this paper and most are country-specific. Broadly they relate to initiatives to promote growth, reduce food price volatility, insulate the poor from shocks and improve the effectiveness of nutritional programs.
7 industries underwent very different amounts of change in protection. Therefore, deviations of particular industries from the typical pattern of adjustment probably partly reflected the amount of new foreign competition that these irM'stries faced.

To see whether changes in trade exposure correlate with changes in structure and performance, we construct a set of industry-specific variables. First, we measure the change in protection between 1967 and 1979 with: where Rjt as the effective protection rate for the jth .ndustry in year t.6 Simildrly, we construct a set of industry-specific measures of change in size, factor use, and productivity. But for these variables, we provide extra detail by looking at the shape of the entire cross-plant distribution in each year -- just as in the middle panel of table 1. For example, we measure changes in the size distribution of plants wtih the following six variables: Here Qn. represents the output of the plant at the nth percentile during year t, given that plants in this industry and year have been sorted in order of increasing output. So if median plant size falls between the census years for industry j, RQ50j will be less than one. Also, if the shape of the size distribution changes, this will be reflected by different values for the different percentiles. We constuct analogous variables to 6/ Estimates of the effective rates of protection by industry and year are taken from Behrman (1977) and Aedo and Lagos (1986).
# _Selection of Census Tracts_<sup>_5_</sup>

The 2000 Brazilian Census was used to classify households as Nikkei or non-Nikkei. The Brazilian Census does not ask ethnicity, but instead asks questions on race, country of birth, and whether an individual has lived elsewhere in the last 10 years. Based on these questions, a household is classified as (potentially) Nikkei if it has any of the following:

- a) A member born in Japan

- b) A member who is of yellow race, and who has lived in Japan in the last 10 years.

- c) A member who is of yellow race, who was not born in a country other than Japan (predominantly Korea, Taiwan or China), and who did not live in a foreign country other than Japan in the last 10 years.

This procedure provides an approximate estimate of the number of Nikkei households, but will tend to be an overstatement due to misclassifying as Nikkei households comprising of individuals of Korean, Taiwanese or Chinese ethnicity who were all born in Brazil and hadn’t been in those countries in the last 10 years.<sup>6</sup> Table 1 tabulates the number of yellow race immigrants in Brazil in the 1980, 1991 and 2000 Censuses by country of birth. Individuals born in Japan are the second largest immigrant group in Brazil after the Portuguese, accounting for 11 percent of all immigrants in 2000. The number born in Japan has been falling over the last twenty-five years. In 2000, Japanese-born still accounted for 74 percent of all yellow race immigrants, Chinese 11 percent, Koreans 9 percent, and Taiwanese 5 percent. We classify as non-Nikkei yellow race individuals born in other countries, so our concern is with second or latter generation non-Nikkei Asians. If these generations occur in the same proportions as first generation, this would suggest we are overestimating the number of Nikkei by at most 35 percent. However, as Table 1 shows, Japanese were a greater share of yellow race immigrants in the 1980 and 1991 Censuses. This is illustrated further in Figure 1, which uses the 2000 Census to plot the mean year of

> 5 IBGE statisticians Kaizô Beltrão and Sonoe Pinheiro carried out the selection of the sample in consultation with the authors.

> 6 We will also misclassify as non-Nikkei Nikkei individuals who are married to Chinese or Koreans. Intermarriage is very low between Japanese and other Asian groups, so this will not induce much error.

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