rafmacalaba/datause-encoder-data huggingface dataset:
over marginal cost is fixed by the Dixit-Stiglitz elasticity of substitution (see equation 2.17). However, we have specific capital for each firm type in each imperfectly competitive sector. If there is entry into the sector by foreign or multinational firms, then rents to the sector specific factor for domestic firms will decline. This will lead to a decline in both costs and prices of the domestic firms. Further, the price charged by foreign firms will fall from the reduction of cost increasing barriers and the entry in the sector will lead to a decline in the quality adjusted price to users of the Dixit-Stiglitz aggregate due to the Dixit-Stiglitz variety externality. Consequently, even though price-cost margins are fixed, our models capture what is crucial to consumers and firms about increased competition: a fall in foreign prices due to reduced barriers to foreigners, a fall in domestic prices due to reduced rents to the specific capital in domestic firms and a quality improvement of their products. All of these can be measured in this model. Further, the model captures what is important to producers and what can explain lobbying for protection--their own sector specific rents.

## **Key Data**

# **Ad valorem equivalence of barriers to foreign direct investment in services sectors.**

Estimates of the ad valorem equivalents of barriers to FDI in services are key to the results. Consequently, we commissioned 20 page surveys from several Russian research institutes that specialize in these sectors and followed this with econometric estimates of these barriers based on these surveys.

These questionnaires provided us with data and descriptions and assessments of the regulatory environment in these sectors.<sup>26</sup> Using this information and interviews with specialist

> 26  This information was provided by the following Russian companies or research institutes: ZNIIS in the case of telecommunications, Expert RA for banking, insurance and securities; Central Marine Research and Design Institute

44
# **Figure 1b:  Net Disbursed Aid for Crisis vs. Non-Crisis Countries (excluding Korea)**

<!-- Start of picture text -->
-10 -5 0 5 10<br>t<br>mean net disbursed aid for crisis countries, excl. S. Korea<br>mean net disbursed aid for non-crisis countries<br>200<br>150<br>100<br>disbursed aid index with t0= 100<br>50<br><!-- End of picture text -->

Source: Authors’ calculations from OECD‐DAC aid data, using LV definitions of crisis‐onset dates Note: Crisis countries are defined as those that suffered any systemic banking crisis during 1977‐2007. For crisis countries, the figure shows the unweighted mean of the indices of real aid volumes (normalized to 100 in the last pre‐crisis year) before and after the onset of the crisis, with year 1 of all crises synchronized to t=1. The non‐crisis countries’ trends are calculated by taking the mean indices for all non‐crisis countries for each t=0, then averaging them.

32
# **Industry and Skill Wage Premiums in East Asia (*)**

**Emanuela di Gropello Human Development Unit East Asia Region The World Bank e-mail:** **<u>edigropello@worldbank.org</u>**

**Chris Sakellariou Economics, HSS Nanyang Technological University Singapore e-mail:** **<u>acsake@ntu.edu.sg</u>**

(*) This paper is part of the Study on Openness and Labor Demand in East Asia (DECRG Research Grant (RSB-P110368)), which contributes to the broader Regional Study on Skills in East Asia. The paper also counted with the contribution of Hong Tan for the processing and analysis of Philippines data and indicators.
than those that did not. Moreover, the timing of the increases indicates that foreign participation often increased in response to crises. However, post-crisis increases in foreign participation do not appear to be associated with improved provision of private credit. We hypothesize that this could be because recent foreign entrants tend to acquire distressed banks with loan portfolios in need of deep restructuring (i.e., write-offs). We cannot, however, rule out the possibility that the new entrants are simply poor intermediaries for the private sector. By contrast, countries with reasonably high and stable levels of foreign participation tend to have higher private credit levels than others before, during, and after crises.

One might also expect that the competitive pressure stemming from stable foreign participation would enhance banking sector efficiency. To test that proposition, we tried regressions with two dependent variables: net interest margin and the ratio of overhead costs to total assets. We could uncover no robust relationships between those measures, foreign participation, and the occurrence of crisis. This might be because those measures are imperfect proxies for efficiency, especially in a cross-country context. It might also be that efficiency improvements take longer to manifest themselves, and thus our time period is too short to detect them. It seems likely, however, that the post-crisis effects of foreign participation on banking sector efficiency are best detected in detailed micro-level studies of individual countries.

# **5. Conclusions**

This paper describes the recent trends in foreign bank ownership in developing countries, summarizes the existing evidence on the causes and implications of foreign bank presence and re-examines the link between banking crises and foreign bank participation. We find that foreign bank participation has risen strongly in two regions of the world: Eastern Europe and Central Asia and Latin America. Foreign bank presence has increased in Africa as well, but at a generally slower pace than in the other two regions. By contrast, foreign bank participation has remained stagnant or even declined in East and South Asia and in the Middle East and North Africa.

The literature on the drivers of foreign bank participation points to factors such as market opportunities, and cultural, regulatory and institutional similarities. In terms of the implications of foreign bank participation, the existing evidence suggests that foreign banks tend to foster efficiency and competition in the sector (except in cases of high banking concentration or when 26
column shows the percentage of enterprises that have attempted to secure government contracts through bribes. The last column of Table 1 shows Türkiye’s overall bribery rate in 2013. This rate shows the percentage of enterprises that encounter bribe requests during any of the six public transactions included in the bribery incidence index, plus the enterprises that used bribes to secure a government contract and the enterprises that reported positive average bribery spending.

Next, I present the amount of bribery spending in order to ‘get things done’. Table 2 summarizes bribery payments along both the intensive and extensive margins. Average bribery payments are reported as the percentages of value added (VA) to be consistent with the model I will present in the next section. The gross output to value added ratio in Türkiye is 2.1 according to input-output tables. Therefore, the bribery to value added ratio can be obtained by multiplying the bribery to sales ratio by 2.1. By doing so, I assume that total sales can be interpreted as gross output.

The second column of Table 2 reports the average percentage of VA spent on bribery in the extensive margin. This measure includes the enterprises that state that no such payments or favors are needed ‘to get things done’. The last column of Table 2 shows similar statistics in the intensive margin. In other words, the bribery amount to VA ratio in the intensive margin indicates the average bribe to VA ratio of the enterprises that admitted that they paid bribes.

The bribery data reveals that big enterprises pay larger bribes than small enterprises. However, bigger enterprises’ bribery payments, relative to their annual sales are smaller than the small enterprises’ bribery payments proportional to their annual sales.<sup>5</sup> On average, 1.4% percent of the annual VA of the enterprises was spent on bribery. However, if only the enterprises that were asked to pay bribes are considered, they spent 18.1% of their VA on bribery. The difference between small and big enterprises’ bribes to the sales ratio is remarkable. Although 6.8% of large enterprises and 8.7% of the small enterprises experience bribery requests, small enterprises spent 18.3% of their VA on bribery, while big enterprises only spent 5.2% of their VA. This observation concurs with the results in Bai _et al._ (2016) who show that as Vietnamese firms grow, they pay less in bribes relative to their size. Thus, we can conclude that while the amount of the big enterprises’ bribery payment is higher than that of the small enterprises, their bribe payment compared to their total annual sales is less than that of the small enterprises.

> 5The average VA of bigger plants is 6.23 times higher than that of the smaller plants in WBES.

7
overall contribution to regional inequality, while still marginal, to increase from 1.8 percent in 2003 to nearly 4.0 percent in 2012 (Figure 4a). Despite this increase in the between-country inequality, the increase in total Theil index between 2011 and 2012 (1.0 Theil point) was mainly due to the within component, which increased from 52 to 52.8 Theil points. Results are similar for the Mean Log deviation (Figure 4b).

Figure 4. Regional household income inequality decomposed into within and between countries (Theil index and Mean Log deviation), 2003-2012

- a. Theil index b. Mean Log deviation

Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: Since the Gini coefficient does not satisfy group decomposability, the regional Gini coefficient is computed based on pooled country-specific data for 17 countries. The methodology to pool these datasets is explained in footnote 1 and the years used for each country in the weighted average are detailed in Table A2 in the annex. Inequality measures include households with zero incomes, whose incomes are censored to 0.01 daily USD and who. Households with zero incomes represent 0.3 percent of the population. Results are robust to different censoring thresholds and to the exclusion of households with zero incomes.

While income inequality has unambiguously declined in all Latin American countries between 2003-12, the magnitude of change has varied a lot. Some general patterns emerge when grouping countries by sub-regions. Figure 5 presents the evolution of the Gini coefficient for total household income per capita grouped by three sub-regions: the Southern Cone (including Brazil) in panel (a), the Andean region in panel (b), and Central America and Mexico in panel (c).<sup>13</sup> The Gini coefficient strongly declined in each of the sub-regions between 2003 and 2010, but Gini trends have diverged since 2010. In the Southern Cone countries, the initial declining trend has continued throughout the period, with the Gini coefficient for the sub-region falling from 57.4 in 2003 to 52.5 in 2010 and 51.5 in 2012. Inequality declines continued through 2011 in the Andean region, but flattened between 2011 and 2012. This stagnation is explained in part by the slight increases in inequality in Bolivia and Ecuador (about 0.35 Gini points) and the slowdown in the reduction of inequality for Colombia. Even when Peru showed a small reduction in inequality between 2011 and

> 13 Southern cone countries include Argentina (urban only), Brazil, Chile, Paraguay and Uruguay (urban only). Andean countries include Bolivia, Colombia, Ecuador and Peru. Central America includes Costa Rica, Dominican Republic, El Salvador, Honduras, Nicaragua and Panama.

7
panel household survey, Bertoli and Murard (2020) find that Mexican migrant households have a 25 percent higher probability of receiving a new member within one year after the migration episode, and they also have a significantly higher probability of attrition, with the members of the household of origin of the migrant joining another household within Mexico. Bertoli et al. (2023) find that Mexican children left behind when their fathers migrate often experience a further change in their living arrangements by going to live with their grandparents, and a substantial share of them also experiences the dissolution of the marital union of their parents.

# **4 Evidence from the American Community Survey**

We use the 2017-2021 5-year sample of the American Community Survey (ACS) (Ruggles et al., 2023) to provide an illustration of a few stylized facts related to how frequently the marital status of immigrants changes after migration decisions. We focus on individuals aged 18 to 60 who migrated from a developing country to the U.S., and moved when they were adults between 18 and 40. The ACS asks respondents about both their year of immigration, and the year of their last marriage (independently of their current marital status). Comparing these two variables enables one to see what percentage of married immigrants were married prior to migrating, in the same year as migration, and after migrating, and (to some extent) to reconstruct the marital status of the migrant at the time of migration.<sup>14</sup>

## **4.1 New empirical evidence on marital changes with migration**

Table 1 reveals that 19.4 percent of the migrants aged 18 to 60 and who migrated to the U.S. when they were aged 18 to 40 were never married at the time of the survey, with this share being larger for males, and strongly declining with the age at migration. Panel A of Table 1 restricts the sample to ever-married migrants. Overall, only 38.5 percent of

> 14In particular, a migrant who never married, or who got married only once after the year of migration was certainly not married when moving to the U.S.; trivially, all currently married migrants whose last marriage predates migration were already married when they moved to the United States; the marital status at the time of migration cannot be inferred for the migrants who got married multiple times, and for the ever-married individuals who are not in a union at the time of the survey (separated, divorced, widowed) and whose last marriage predates migration, as the ACS does not report information on the year of separation or divorce from one’s own spouse.

16
We first imputed the household income for each census record for each round of the Monte-Carlo simulation without applying the adjustment coefficients. We then calculated poverty rates for each province using Eq(12) and plotted them on a map as shown in Figure 1, which we shall call the baseline map. The maximum, minimum and average standard error for the provincial-level estimate of poverty rate were 11.6 percent, 0.4 percent and 2.1 percent, respectively. Thus, while there are a few provinces with quite high levels of standard errors, provincial-level estimates are on average accurate enough to justify this presentation.

To see how our estimates correspond to others in the literature, we first calculated the poverty rate for Vietnam. The point estimate and its associated standard error were 34.6 percent and 0.7 percent, respectively. The difference between this estimate and the survey-only estimate is not significant. However, the gap is not as small as one would usually find in the standard small-area estimation. This is possibly because we need to estimate many more equations than the standard method. We also plotted the provincial-level estimates of our income poverty rates against the provincial-level consumption poverty rates calculated by Minot et al. (2003). There is a moderately strong correlation between the two measures with the correlation coefficient of 0.4. Overall, our baseline estimates of poverty seem reasonable.

In order to see how income poverty changes after Vietnam’s accession to the WTO under various scenarios, we applied the adjustment coefficients and re-calculated the household income for each census household and for each scenario. Then, we recalculated the poverty rates for each province. This yields ex post estimates of poverty. The ex post estimates of poverty in Vietnam has decreased by 0.8 percent and 6.8 percent under the UL and FL scenarios respectively. However, under the DSDT scenarios, the national poverty rate increased by 0.6 percent. Again, we see that the FL helps reduce poverty most.

We also looked at changes in poverty rates at the provincial-level. Figure 3 shows the spatial incidence of trade policy under the UL scenario. Figure 4 and Figure 5 are for the FL and DSDT scenarios respectively. In each map, lighter colours represent higher levels of improvement or lower levels of aggravation in terms of the provincial-level 21
Tabk 11I3

# New Product Development and Innovation Across Sectors

||Woven<br>Textiles<br>n=42|Auto<br> <br>parts<br>n=20|Elect.<br>pans<br>n-20|Factory<br>automption<br>n-40|
|---|---|---|---|---|
|(a) FREQUENCY OF NEW PRODUCT DEVELOPMENr<br>Average number of new products<br>introduced per year (1989-1991)|3.48|16.40|9.38|1.68|
|Proportion of firms that have not<br>introduced new products in 3 years|76.2|5.0|5.D|10.0|
|(b) NEWNESS' OF NEW PRODUlCTS<br>(1991)<br>1. No difference from existing<br>products|4|Number of Mentions<br>1|2|1|
|2. Moderate difference from<br>existing products|5|12|7|3|
|3. Iniermediate<br>difference from<br>existing products|0|2|5|3|
|4. Significant difference from<br>existing products|0|4|3|16|
|**_5._**Radical difference from<br>existing products|0|0|0|S|
|Mean score (between I and**_)'_**|1.5|2.5|**_2.5_**|3.8|
|(c) ORIGINALrrY- OF NEW PRODUCTS (1991)<br>1. Imitation of domestic models|0|Number of Mentions<br>1|0|0|
|2. Imitation of foreign models|1|9|11|0|
|3. Application<br>of existing<br>technology in domestie market|8|9|5|0|
|4. Adaptation of foreign technology<br>for domestic narket|0|0|1|5|
|5. Introduction of new products with<br>better perfornance than comparable<br>ones in foreign market|0|0|0|I8|
|6. Introduction<br>of new products never<br>previously deedoped in foreign<br>countries|0|0|0|5|
|Mean score (between 1 and 6)|2.9|2.4|2.4|5.0|

NOTE: ' Newness refers to the degree of difference of the new products compared to existing products produced by the same enarprise. The mean score indicates an ordinal position between I (no difference) and 5 (radical difference).

' Originality refers to the degree of difference of the new products from existing products or technologies available elsewhere. The mean score indicates an ordinal position between I (imitaon of domestic model) and 6 (compktly new producQ).

54
time tending the crop and less time on things that are good for children's health, and so child health outcomes can worsen even though incomes from coffee production increase.

Finally, Woldemichael _et al.,_ (2017) look at the impact of price inflation of teff, maize and wheat on a child’s health at different periods of development in the months both before and after birth. Using data from different rounds of the Demographic and Health Surveys and separate data on prices at the local level, they find a u-shaped relationship between food price inflation and a child’s height for age (stunting). The impact is more pronounced for teff than for wheat and maize. For wasting (weight for height), there is no significant impact of food price inflation on a child’s development. Overall, a common theme that emerges from this literature is that there are significant risks to children’s nutritional development from rising food prices.

# **3. Data on Prices, Households and Child Nutritional Outcomes in Ethiopia**

In this study we use information on individual (child) outcomes and individual, household and community characteristics from the two most recent rounds of the Living Standards Measurement Survey (LSMS) undertaken in Ethiopia.<sup>7</sup> The data set contains detailed information on health statistics for children under 5 years of age which allows the construction of measures of child undernutrition. The community data come with detailed geo-spatial household information including accessibility to markets. Table 1 summarizes the year, survey period and the number of children under 5 years.

**Table 1: Summary of Ethiopia LSMS data for children aged between 6 to 59 months**

|**Year**|**Survey period**|**No. of children (6-59 months)**|
|---|---|---|
|2013/14|September 2013-March 2014|2,880|
|2015/16|September 2015-April 2016|2,993|

We study individual nutritional outcomes of children and follow previous studies and compute indicators of undernutrition for children between 6 and 59 months.<sup>8</sup> To achieve this we compute z-scores for height for age (stunting).<sup>9</sup> A child is stunted if the height-for-age z-score is below -2 (that is two standard deviations below the mean of the reference group). According to WHO

> 7 There was also a first wave of the LSMS in Ethiopia in 2011/12.

> 8 Infants aged under 6 months are often excluded from nutrition surveys.

> 9 The z-scores are calculated using the 2006 World Health Organization (WHO) child growth guidelines. The analysis is done using zscore06 Stata module.

6
_Gender Equality and Economic Development: The Role for ICTs_ 20 With regard to the estimated coefficients, it can be seen that all of the ICT variables exhibit estimated coefficients that are of the theoretically postulated positive sign and are highly statistically significant when using the OLS estimation. When the IV technique is employed, all of the ICT estimated coefficients remain positive and statistically significant. As such, both OLS and IV results indicate that increases in the level of ICT infrastructure, when measured by any of the five ICT variables, tend to increase the ratio of female to male (primary and secondary) school students, which indicates an improvement in gender equality in education.

It was postulated that the trend value of the real GDP per capita, which is a proxy for the level of economic development would be positively associated with the student ratio and gender equality. The results show that while all of the estimated coefficients of the trend real GDP per capita variable are positive, none are statistically significant. Lastly, we see that the estimated coefficients of the youth sex ratio all of the five regressions exhibit the theoretically expected positive sign and are highly statistically significant with both OLS and IV estimations.

Also, we note that the estimated coefficient of the average years of school variable is generally positive and statistically significant when estimated using OLS<sup>14</sup> . However, some these coefficients become statistically not significant with the IV estimation, even though they continue to be positive. This fact, coupled with the observation that for this average years of schooling variable most of the OLS coefficients are larger than the IV coefficients, hints at a problem of reverse causality. On the other hand, it is noted that this variable, even with IV estimation, still exhibits a highly statistically significant positive coefficient in Reg 2.1a and a (mildly) statistically significant positive coefficient in Reg 2.5a. Thus, the results indicate to a certain extent that the level of general education among the population tends to have a positive effect on gender equality in education.

Lastly, the education expenditure variable produces estimated coefficients that are generally not statistically significant; when OLS is used, the coefficient is positive and statistically significant only when the ICT variable being used is the ICT expenditure (either as a share of GDP or in per capita terms). When IV estimation is employed, only when the ICT variable is ICT expenditure per capita, is the coefficient of the education expenditure mildly significant. Given that the regression samples are much smaller in both of the regressions where the ICT variable is ICT expenditures, we should be cautious in attaching too much weight to statistical significance of coefficients of the public education expenditure variable in those regressions.

> 14 The only exception is Reg 2.3.  Here the estimated coefficient of the average years of school variable is positive but not statistically significant at conventional levels.