rafmacalaba/datause-encoder-data huggingface dataset:
Table A.2: Deprivation Status among children living in top consumption level households <u>(unweighted)</u>

||**Monetary pover**|**ty 1.9USD**||**Multi-Dimens**<br>**in**<br>|**ional Poverty**<br>**%**<br>|||**Multi-**<br>**in absolute**<br>|**Dimensional Pov**<br>**number of obse**<br>|**erty**<br>**rvations**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||||**Not**<br>**Deprived**|**At least one**<br>**Deprivation**|**At least two**<br>**Deprivations**|**Missing**<br>**Information**|**Not**<br>**Deprived**|**At least one**<br>**Deprivation**|**At least two**<br>**Deprivations**|**Missing**<br>**Information**|**Total**|
||_Top 1%_|_Not Monetary_||||||||||
|_3_|<br>_consumption_|<br>_Poor_|_49%_|_46%_|_5%_|_6%_|61|57|6|7|125|
|_Wave_|_Top 5%_<br>_consumption_|_Not Monetary_<br>_Poor_|_63%_|_34%_|_6%_|_2%_|406|220|36|16|642|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_63%_|_34%_|_7%_|_3%_|808|437|89|39|1,284|
|_2_|_Top 1%_<br>_consumption_|_Not Monetary_<br>_Poor_|_32%_|_63%_|_35%_|_5%_|43|84|46|6|133|
|_Wave_|_Top 5%_<br>_consumption_|_Not Monetary_<br>_Poor_|_52%_|_38%_|_19%_|_10%_|351|251|124|67|669|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_53%_|_36%_|_16%_|_11%_|709|487|213|142|1,338|
|_1_|_Top 1%_<br>_consumption_|_Not Monetary_<br>_Poor_|_52%_|_27%_|_9%_|_22%_|72|37|13|30|139|
|_Wave_|_Top 5%_<br>_consumption_|_Not Monetary_<br>_Poor_|_42%_|_35%_|_11%_|_22%_|294|245|75|156|695|
||_Top 10%_<br>_consumption_|_Not Monetary_<br>_Poor_|_36%_|_44%_|_16%_|_19%_|505|618|221|266|1,389|

Source: Authors’ estimates based on GHS Wave 1-3 Note: For each age group the respective maximum number of deprivations is considered. If a child is not deprived along any dimension, it is considered not deprived, if it is deprived along one or more dimensions it is considered deprived, irrespective of missing information on some other dimensions. If a child is for instance not deprived along four out of possible five dimensions applicable in its age category with missing information on one deprivation, it is excluded, as it is unknown whether the child is indeed not deprived of any dimension or is in fact deprived in the dimension where the information is missing.

68
provided as an alternative measure. Based on this measure, exports to the United States account for more than 60 percent in the case of Costa Rica, El Salvador and Guatemala.

**Table 5. Central America’s Trade Structure: Bilateral Exports/Total Exports**

|**Bilateral Exports / Total Exports**|**(average: 1995-**<br>|**2001)**<br>||||||||
|---|---|---|---|---|---|---|---|---|---|
||**Costa Rica El**|**Salvador G**|**uatemala**<br>**H**|**onduras **|**Nicaragua**|**Argentina**|**Mexico**|**Canada**|**France**|
|**Costa Rica**||4.4%|3.5%|1.1%|4.8%|0.1%|0.2%|0.0%|0.0%|
|**El Salvador**|2.3%||9.9%|3.1%|11.1%|0.1%|0.2%|0.0%|0.0%|
|**Guatemala**|3.2%|12.4%||2.5%|2.8%|0.1%|0.4%|0.0%|0.0%|
|**Honduras**|1.7%|6.8%|2.0%||5.3%|0.0%|0.1%|0.0%|0.0%|
|**Nicaragua**|2.9%|3.8%|3.1%|2.2%||0.0%|0.1%|0.0%|0.0%|
|**Mexico**|1.1%|0.7%|2.3%|0.3%|2.8%|1.2%||0.5%|0.4%|
|**Brazil**|0.1%|0.0%|0.0%|0.0%|0.0%|26.9%|0.5%|0.4%|0.7%|
|**USA**|21.3%|11.1%|50.7%|61.1%|38.0%|9.4%|87.1%|85.3%|7.3%|
|**Germany**|3.6%|6.1%|3.3%|3.8%|9.9%|2.3%|0.9%|0.9%|15.7%|
|**European Union**|16.0%|10.7%|10.4%|12.2%|23.1%|18.5%|3.6%|4.9%|61.6%|
|**Memo:**||||||||||
|**Free trade zone**|39.1%|54.5%||||||||
|**USA reported imports CIF**|62.4%|68.1%|66.3%|||||||

_Note_ : Interpretation of this table is as follows. The table should be read column -wise, where each row represents the share in total column -countries exports. As an example, the top-left figure indicates that exports from Costa Rica to El Salvador represent 2.3% of Costa Rica’s total exports.

Source: Direction of Trade Statistics

**Table 6. Central America’s Trade Structure: Bilateral Exports/GDP**

|**Bilateral Exports / GDP (avera**|**ge: 1995 - 2001)**<br>|||||||||
|---|---|---|---|---|---|---|---|---|---|
||**Costa Rica El**|**Salvador Gu**|**atemala**<br>**H**|**onduras **|**Nicaragua**|**Argentina**|**Mexico**|**Canada**|**France**|
|**Costa Rica**||0.8%|0.7%|0.6%|1.2%|0.01%|0.05%|0.01%|0.01%|
|**El Salvador**|0.8%||1.8%|1.5%|2.9%|0.01%|0.05%|0.00%|0.01%|
|**Guatemala**|1.1%|2.3%||1.2%|0.7%|0.01%|0.11%|0.01%|0.00%|
|**Honduras**|0.6%|1.3%|0.4%||1.4%|0.00%|0.03%|0.00%|0.00%|
|**Nicaragua**|1.0%|0.7%|0.6%|1.1%||0.00%|0.01%|0.00%|0.00%|
|**Mexico**|0.4%|0.1%|0.4%|0.2%|0.7%|0.1%||0.2%|0.1%|
|**Brazil**|0.0%|0.0%|0.0%|0.0%|0.0%|2.4%|0.1%|0.1%|0.1%|
|**USA**|**7.1%**|**2.1%**|**9.5%**|**30.1%**|**9.8%**|0.8%|24.1%|30.3%|1.6%|
|**Germany**|1.2%|1.1%|0.6%|1.9%|2.6%|0.2%|0.3%|0.3%|3.3%|
|**European Union**|5.3%|2.0%|1.9%|6.0%|5.9%|1.6%|1.0%|1.7%|13.2%|
|**Memo:**||||||||||
|**Free Trade Zone (USA - Intel)**|13.0%|10.1%||||||||
|**USA reported imports CIF**|19.4%|11.8%|11.7%|||||||

_Note_ : Interpretation of this table is as follows. The table should be read column -wise, where each row represents the share of bilateral exports in the column -countries GDP. As an example, the top-left figure indicates that exports from Costa Rica to El Salvador represent 0.8% of Costa Rica’s GDP.

10
- 27 -

55. The government debt market remains the centerpiece of Hungary’s capital market and developed further during this period, as indicated by the increase in the share of traded securities from 53 to 74 percent of the total debt stock, and the corresponding increase in the stock of traded government securities from 30 to 43 percent of GDP between 2000 and 2004. Moreover, 15-year bonds have been introduced and all auctions for these bonds have been oversubscribed, revealing a strong demand for these instruments, while the share of short-term T-bills has declined from 35 to 24 percent between 2000 and 2004. Institutional investors contributed to these positive outcomes by holding an increasing share of the stock (Figure 12) and participating actively in the auctions of long-term securities.

56. At the same time, the issues of long-term securities are still a small share of total issues, as shown in Figure 13. As a result, the increase in the average maturity and duration of the stock has been modest, as shown in Figure 14. Moreover, the annual turnover of all government securities has dropped from 6-8 times the debt stock in 2000 to 3- 4 times the stock in 2004, and bid-ask spreads have increased in the same period. Therefore, whereas there has been progress in developing the market for government securities, this progress should not be exaggerated either.

# **Figure 12: Holders of government debt (% of total), 1997 – 2004**

<!-- Start of picture text --> 100%<br>Other<br>80%<br>Rest of the World<br>60%<br>Pension and Insurance Sectors<br>40%<br>Mutual Funds<br>20%<br>Banks<br>NBH<br>0%<br>1997Q4 1998Q4 1999Q4 2000Q4 2001Q4 2002Q4 2003Q4<br><!-- End of picture text --> Source: AKK.
|||Table of Contents||
|---|---|---|---|
|I.|Intro|ducton|1|
|II.|Debt|, Equity, and Taxes under Symmetric Information|4|
||A.|Modigliani-Miller<br>Theorem|_5_|
||B.|Miller Equilibrium|8|
||C.|The Financing Choice of Existing Firms|9|
|**Im.**|Fina|ncing Choices under Asymmetric Information|15|
||A.|Bankruptcy as an Inadequate Explanation|17|
||B.|Asymmetric Information and Bankruptcy Risk|18|
||C.|Suboptimal Investment in the Presence of<br>Asymmetric Information|20|
|IV.|Rural|Credit Market Solutions to Asymmetric Information|33|
|V.|Polic|y Implications|39|
||A.|Equity Finance as an Alternative to Debt Finance|39|
||B.|Regulation of the Bankinig Sector|43|
||C.|Tax Instruments|44|
||D.|Other Measures|47|
|App|endices||_51_|
|Refe|rences||54|

This is one of a series of papers commissioned by the Tax Incentives Evaluation Research Project of the Public Economics Division of The World Bank. The project is directed by Anwar Shah.

I wish to thank Karla Hoff for many useful suggestions and especially Anwar Shah for his guidance of this project.
49 consumption cities (Bogot ́a is a “medium” consumption city but Barranquilla is an “extreme” consumption city).

# **B Robustness for the Mapping Analysis**

**Non-Linearities.** We obtain similar residuals to those in the main text if we presented also include the square, cube, and perfect fourth of the urban share in 2000, and their interactions with the population dummies (coefficient of correlation = 0.99), in case there are non-linearities in the relationship between MFG+FIRE employment, urban economic development, and city size.

**Per Capita GDP.** We obtain a coefficient of correlation of 0.97 if we use log per capita GDP in 2000 (PPP and constant international dollars) instead of urbanization (including the square, cube, and perfect fourth of log per capita GDP, and their interactions with the population dummies). This is not surprising since urbanization rates and log per capita GDP are highly correlated cross-sectionally (correlation of 0.91 in 2000; N = 178).

**Omitting Urbanization.** The correlation is 0.99 if we do not control for the urban share and do not interact the population dummies with it. We then do not allow the relationship between city employment and size to change with urban economic development.

**Using the Raw Employment Shares.** We obtain a coefficient of correlation of 0.89 if we simply consider the raw, i.e. non-residualized, city-specific employment shares. While the residualization was a priori important to ensure we compare apples with apples, the very high correlation indicates the residualization is not entirely necessary.

**Weights.** We obtain a coefficient of correlation of 1.00 if we do not modify the weights so as to over-weigh developed countries (which are under-represented in IPUMS). In that case, the weights are only based on the FUAs’ population levels c. 2000. We obtain a coefficient of correlation of 1.00 if we do not use weights at all.

**Alternative City Categorizations.** If we do not combine the top two population categories into one category, we obtain a coefficient of correlation of 1.00. If we use 5 population categories instead of 10 categories, we still get a correlation of 0.99.

**Urban Definition.** We can control for the urban definition used by the country c. 2010. We include dummies identifying whether the definition is based on a population threshold, another condition, an administrative function, or a combination of these, and the log of the threshold (U.N., 2011). We then interact these variables with the population dummies. The correlation of the residuals remains very strong (about 0.9). Another related question
analyze to what extent skilled migrants from various countries are employed in unskilled jobs in the US labor market. Their study finds evidence of significant variation in immigrants’ occupational placement which persists even after accounting for education and experience accumulated in the US. However, a large part of the variation is explained by country of origin attributes that affect the quality of human capital on one hand, and by US immigration policy affecting selection of migrants on the other. Therefore, the authors conclude that the “underplaced” immigrants suffer primarily from low (or poorly transferable) skill levels rather than skill underutilization. Chiswick et al. (2005) design and test on Australian longitudinal data a model of immigrants’ occupational mobility. The model predicts that occupational mobility from the last job in the country of origin to the first and then the subsequent ones in the destination countries follows a U shape, the depth of which depends on the similarity between origin and destination countries, but also on migrants’ skill levels. Evidence of downward occupational mobility is also found, for the United States, by Akresh (2006b), a study relying on data from the US New Immigrant Survey-Pilot. Exploring unique dimensions of the same database, such as information on migrants’ occupational placement before, immediately after, and several years post-migration, Akresh (2008) assesses how performance paths differ across various immigrant admission categories.

# **3. DYNAMIC PATTERNS IN OCCUPATIONAL PLACEMENT OF SKILLED MIGRANTS BY COUNTRY OF ORIGIN**

Our estimation focuses on occupational placement as an indicator of performance and aims to identify how migrants’ performance patterns differ by country of origin. We control for some important human capital characteristics, and especially account for the part of the assimilation effect that is common to migrants irrespective of origin. Finally, we attempt to disentangle the inferred country of origin effects by identifying various country attributes explaining it.

The evaluation of assimilation patterns uses the “synthetic cohort” approach introduced by Borjas (1985) for earnings and implemented by Green (1999) for occupational choice. By using country of origin attributes to explain performance, the results parallel the conclusions of the static occupational placement analysis by Mattoo et al. (2008), but also are related to Borjas (1987, 2000) and Jasso and Rosenzweig (1986) who focus on earnings. We evaluate the convergence in immigrants’ occupational placement indicators, employing a model which has also been used by Borjas (2000). Finally, our results of negative correlation between entry levels of occupational placement and improvements over time and by country (while controlling for 6
# **Table 8: Effect of Commodity Price Shocks on Tax Revenues**

|**Model**|**Country**|**and Year Fixed Effec**|**ts**|
|---|---|---|---|
|**Outcome (over GDP)**|**Tax revenues including**|**social contributions e**<br>**revenues**|**xcluding resource**|
||**(1)**|**(2)**|**(3)**|
|Log difference of commodity Net Export Price Index (historic rolling<br>weights)|-5.220***|||
||(1.450)|||
|Log Difference of Hydrocarbons Commodity Index||-0.345*||
|||(0.194)||
|Log Difference of Metals and Minerals Commodity Index||-0.500||
|||(0.321)||
|Log Difference of Agriculture, Food, and Beverages Commodity Index||-0.748*||
|||(0.442)||
|Log Difference of Crude oil Commodity Price Index|||-0.255|
||||(0.177)|
|Log Difference of Coal and Natural Gas Commodity Index|||-6.160**|
||||(2.411)|
|Log Difference of Base Metals Commodity Index|||-0.851***|
||||(0.307)|
|Log Difference of Agricultural Raw Materials Commodity Index|||-0.440|
||||(2.742)|
|Log Difference of Food Commodity Index|||-0.805*|
||||(0.474)|
|Log Difference of Beverages Commodity Index|||-0.406|
||||(1.387)|
|Log Difference of Fertilizer Commodity Index|||0.839|
||||(0.612)|
|Log Difference of Precious Metals Commodity Index|||9.863*|
||||(5.210)|
|Constant|17.214***|18.805***|18.810***|
||(0.496)|(0.541)|(0.543)|
|Country Fixed Effects|YES|YES|YES|
|Year Fixed Effects|YES|YES|YES|
|Number of observations|4,766|3,439|3,439|
|R2|0.106|0.130|0.132|
|Adjusted R2|0.099|0.120|0.121|

note: *** p<0.01, ** p<0.05, * p<0.1, robust standard errors clustered at the country level 20
# WPS3761

## **IS SKILL–BIASED TECHNOLOGICAL CHANGE HERE YET? Evidence from Indian Manufacturing in the 1990s**<sup>**1**</sup>

Eli Berman Rohini Somanathan Hong W. Tan<sup>2</sup> World Bank Policy Research Working Paper 3761, November 2005 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The World Bank World Bank Institute Finance and Private Sector Division 1
Table A.7 – Treatment effects, accounting for attrition (women’s empowerment and wellbeing)

||||KL bounds|+/- .25 SD|Behag|hel et al. bo|unds|
|---|---|---|---|---|---|---|---|
||(1)<br>Main<br>specifcation|(2)<br>IPW|(3)<br>Lower<br>bound|(4)<br>Upper<br>bound|(5)<br>Using<br>relative date|(6)<br>Using inten|(7)<br>sive tracking|
||||||Exact<br>trimming|Lower<br>bound|Upper<br>bound|
|**Women agency index**|-0.045|-0.036|-0.089**|-0.011|-0.047|-0.094**|-0.026|
|**Women fnancial index**|(0.038)<br>0.408***|(0.039)<br>0.414***|(0.035)<br>0.338***|(0.035)<br>0.478***|(0.038)<br>0.438***|(0.037)<br>0.141***|(0.038)<br>0.448***|
||(0.088)|(0.091)|(0.080)|(0.080)|(0.093)|(0.044)|(0.093)|
|**Women well-being**<br>Cantrill’s ladder:||||||||
|Current life satisfaction|0.268***|0.242***|0.196***|0.321***|0.253***|0.139**|0.285***|
||(0.062)|(0.062)|(0.057)|(0.057)|(0.062)|(0.056)|(0.062)|
|Life satisfaction one year ago|0.238***|0.231***|0.166***|0.294***|0.223***|0.139**|0.256***|
||(0.060)|(0.060)|(0.055)|(0.055)|(0.060)|(0.057)|(0.060)|
|Predicted life satisfaction in three years|0.219***|0.195**|0.120|0.303***|0.191**|0.089|0.231***|
||(0.081)|(0.081)|(0.075)|(0.075)|(0.082)|(0.079)|(0.082)|
|Relative wealth|0.243***|0.231***|0.166***|0.311***|0.216***|0.107*|0.251***|
||(0.067)|(0.068)|(0.062)|(0.062)|(0.068)|(0.063)|(0.068)|
|MHI-5 score|1.426*|1.480*|0.582|2.204***|1.000|0.035|1.800**|
||(0.783)|(0.790)|(0.721)|(0.721)|(0.791)|(0.756)|(0.772)|
|**Observations**|1824|1,824|2,000|2,000|1,776|1,776|1,776|

Notes: This table reports the results from three methods to test the sensitivity of our results to attrition. Column 1 reports the estimates from Table 2 for reference. Column 2 reports the results weighting the households by their inverse probability of responding to the survey (Inverse Probability Weighting or IPW). Columns 3 and 4 replace outcome values for the attrited households with semi-extreme values as in Kling and Liebman (2004). Missing treatment and control observations are set to have a 0.5 SD difference in their outcomes: that is we impute mean + 0.25 SD for missing treatment and mean – 0.25 SD for missing controls, and vice versa (KL bounds). Columns 5-7 use the approach proposed by Behaghel et al. (2015) to identify the observations that should be trimmed in the treatment group to fully close the attrition differential. In Column 5, we exploit the fact that respondents that are hard to track are typically interviewed much later than other respondents in the same village, and we trim the 48 observations in the treatment group with the latest interview dates relative to the median interview date in their village. Columns 6 and 7 report the results excluding the 30 interviews in the treatment group that were done during the intensive tracking. For the remaining 18 additional observations in the treatment group, we use standard Lee bounds. Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

49
two the one including a constant term only and the second one including all controls.

We start the description of our results by considering the January 2011 survey (table 4). The three regressors of interest enter significantly at 0.1 percent level in all models for both, probit and logit regressions. The estimates of _β_ 1, _β_ 2 and _β_ 3 are comparable across models even if in the probit the point estimates are lower than in the logit. While the sign and significance of the coefficients in the regressions is informative, the magnitude has no specific meaning. For this reason we report the marginal effects of each variable keeping all other variables at their mean values. The marginals for ’Information’, ’Delivery’, and ’Partisanship’ are shown in figure 4, 5, and 6. Being informed about the reform increases the probability of being satisfied by around 20 percentage points. Similar effects are found for the variable ’Delivery’ and the variable ’Partisanship’. It is also possible to estimate the joint effect of the three variables. While the unconditional satisfaction rate is 30.0 percent (on a total of 1,032 observations), it increases to 50.0 percent for informed people (204 observations). If we further condition on being confident about receiving the transfer (99 observations in total), meaning that the variable ’Delivery’ = 1, the satisfaction rate increases to 59.6 percent. Finally, if we also condition on being an FMLN voter, the satisfaction rate jumps to 75.0 percent. As a measure of goodness of fit we rely on the PCP and on the area under the ROC curve. These two synthetic measures are reported at the bottom of table 4 together with the Akaike (AIC) and Bayesian (BIC) information criterion. Although the estimates of the coefficients of interest are slightly different, the goodness of fit of all models is high (ranging between 0.7 and 0.8) indicating a high power of the model to explain the variance of the dependent variable.

The results of the regressions run on the four intermediate surveys are reported in table 5. The focus of these regressions is on the significance of the variables ’Delivery’ and ’Partisanship’. For each survey we run model (1) twice, the first time including a constant term only while the second time adding all controls. The point estimate of the variable ’Partisanship’ is positive and progressively more significative: the coefficient is not significant in the August 2011 survey but it is in the May 2012 survey and it becomes significative at 0.1 percent level in the August 2012 one. On the other hand the variable ’Delivery’ remains significant in all surveys. Overall, our results show that over time the personal characteristics lose power in explaining the satisfaction about the reform in favor of our main regressors.

Finally, the results for the September 2013 survey are reported in table 6. As for the January 2011 survey 20
**Figure 10: Turkey Foreign Currency Bond Issuance by year**

<!-- Start of picture text --> 7000<br>14<br>6000<br>12<br>5000<br>10<br>4000<br>8<br>BB+<br>3000<br>BB BB 6<br>2000<br>4<br>1000 2<br>0 0<br>2010 2011 2012 2013 2014 2015 2016<br>EUR USD LT FX Credit Rating<br>Millions in USD Eq.<br><!-- End of picture text --> Source: Bloomberg, Standard & Poor’s The Turkish Treasury does not engage in cross-currency swaps (CCSs), but uses them to calculate what would be the USD cost-equivalent. Given the current level of the basis-swap and the cost of credit risk, Turkey pays more for an obligation denominated in EUR compared to a USD bond.<sup>27</sup> In addition, Turkey finds value in choosing the ‘non-standard’ tenors for its placements in euros. Instead of being forced into the typical 10 or 30-year maturities of the USD market, Turkey taps other tenors (for example, 6, 8 or 11-year sectors) which at times offer more flexibility to manage its redemption profile. On the other hand, the EUR market makes it more difficult to access the long-end of the curve.

Based on political and economic fundamentals, Turkey sees its financing in EUR strategic in the long term. This rationale is complemented by the need to diversify its base of foreign investors. Since the Turkish Treasury prefers to keep the currency benchmark internal, it is not possible to state whether the current share of the EUR in the government debt portfolio is bound to change in the future.

> 27 The same applies to the Japanese market in JPY, where Turkey has also been issuing at a slightly higher cost but seeking the benefit from having a broader investor base. In this case, Turkey has recently issued three bonds with a guarantee from the Japan Bank for International Cooperation (JBIC) and plans to access the market by itself in the near future.

18