rafmacalaba/datause-encoder-data huggingface dataset:
port facilities and inefficient customs procedures, the main constraints on international trade flows appear to be due to transport costs and administrative costs. Our survey evidence suggests that a 50percent reduction in either of these costs would boost the volume of trade by about a quarter, thus implying an elasticity of about one-half.

_Table 8: Predicted Trade Expansion – Cross Tabulation_

|||**Percentage in**<br>**f**<br>|**crease in trade vol**<br>**or 50-percent fall i**<br>|**ume predicted**<br>**n:**|
|---|---|---|---|---|
||||||
|||**ranspor**<br>**costs**|**mnsrave**<br>**costs**|**Border delays**|
||Mean|102|79|12.5|
|**All exports**|Median|37.5|25|5|
||_N_|_12_|_12_|_10_|
||Mean|31|39|17|
|**All imports**|Median|20|25|10|
||_N_|_36_|_39_|_29_|
|**Td**|Mean||51||
|**rae**<br>**Kinshasa → Brazzaville**|Median||40||
||_N_||19||
|**T**|Mean||41||
|**rade**<br>**Bill Kih**|Median||12.5||
|**razzave → nsasa**|_N_||_8_||

**A general fall in trade costs would stimulate DRC and RC exports more than it would stimulate their imports.** Interestingly, the results shown in Table 8 suggest that the sensitivity of trade volumes to trade costs is somewhat larger for exports than for imports. Hence, trade facilitation would be beneficial for both countries’ balance of trade.

**The strongest predicted response to lower trade costs is for exports from Kinshasa to Brazzaville, with an implied elasticity of 0.8.** A halving of trade costs is predicted by our sample firms to trigger a 40-percent increase in the volume of trade. Hence, a reduction in trade barriers would pay particularly rich dividends if applied between the two capital cities. Table 8, however, also suggests that there is greater latent potential for exports from Kinshasa to Brazzaville than in the other direction.

**Our findings imply that cuts in tariffs and transport prices could be revenue neutral.** The true potential for trade expansion comes from three sources: expansion of existing trading relationships, opening of new trade routes by existing operators, and entry of new operators. By considering almost exclusively the first source of trade changes, our approach implies a certain status-quo bias and must therefore be considered as yielding lower-bound estimates. In view of this, our computed trade elasticities of up to 0.8 (for Kinshasa-Brazzaville trade) can be taken as pointers to large latent potential for trade expansion. Importantly, given the in-built downward bias, our estimated elasticities may well be compatible with true elasticities of one, meaning that any cut in trade costs would be offset by an equiproportional increase in the volume of trade. If the true elasticity were even larger than unity, something that appears entirely possible given our estimates, tariff cuts could even yield increases in tariff revenue.

**Regression analysis offers a more rigorous evaluation of survey responses and confirms our findings based on simple cross tabulations.** The summary statistics reported in Table 8 do not allow us to test whether observed differences in projected trade responses are statistically

21
**Figure 4.1c: Regional disparity trends in federal countries**

<!-- Start of picture text --> 0.18 e-...;<br>0.16<br>0.14<br>0.12<br>X 0.1-.. ,-,. . ..,. '''<br>.e 0.08-_ i'-;; --<br>0.06- .. . .~ _-<br>0.02 -- : , .-.<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999<br>-- Brazil Canada Indiab~ Mexico -@ Pakistan +t Russia -United States<br><!-- End of picture text --> _Mexico's_ regional inequality is around five times that of the US and has remained more or less stable in the l990s. There is a slight dip in _1995,_ which was the year when the Mexican economy contracted 6.2%, the worst recession since the Great Depression. Mexico is the most centralized of the federal countries in our sample. The state governments' are largely dependent on central transfers for their revenues. Transfers to states from revenue sharing were almost six times as large as states' own revenues in 1996 (Giugale et. al., 2000,19). This highly centralized nature of the Mexican federation may be one of the reasons for its high level of regional inequality (Shah and Shankar, forthcoming).

_21_
However, the FDI boom has also raised two major concerns. The first one involves the uncertain future prospects of FDI to developing countries, following the near completion of the privatization drive in major economies (most notably in Latin America). As just noted, a considerable portion of the FDI inflows received by these economies over the last decade reflected M&A transactions related to the acquisition of public enterprise assets, and hence the end of privatization might be followed by a sharp decline in FDI inflows, which, given the predominant role acquired by investment flows in overall external financing during the late 1990s, could generate major external difficulties in these countries.

Whether this concern is warranted, however, depends to a large extent on the relationship between M&A and greenfield FDI. Specifically, if the former tends to set the stage for the latter, then stagnating M&A need not cause undue worries, because the surge in mergers in the 1990s is likely to be followed by rising greenfield investment, thus ensuring the continuation of external financing in the coming years.

The second concern relates to the growth impact of FDI flows, which has attracted renewed interest in the wake of the FDI boom. While the theoretical literature has pointed out that FDI may boost growth, both by raising aggregate investment and through technological spillovers – i.e., technology transfers that go beyond those firms directly receiving foreign capital -- the empirical literature shows considerable disagreement about the relevance of these impacts. On the one hand, firm-level studies often find no significant productivity effects of FDI.<sup>2</sup> On the other hand, macroeconomic studies tend to conclude that FDI boosts growth via higher productivity

> 1 See for example Calvo and Reinhart (1996), Fernández-Arias and Montiel (1996), Fernández-Arias (2000), and Albuquerque, Loayza, and Servén (2003).

> 2 Aitken and Harrison (1999), Kokko, Tansini and Zejan (1996), and Haddad and Harrison (1993) find no evidence of productivity spillovers; Blömstrom and Sjoholm (1999) find no evidence of technology spillovers but do find some evidence of productivity improvements stemming from greater competitive pressure in local markets.

2
after locating numbers embedded in printed materials; examples of such calculations include determining the interest on a loan, calculating a tip and balancing a checkbook.

For Chile, the survey is representative of 98 percent of the population between the ages of 16 and 65 (it excludes residents of institutions and remote areas) and the total number of respondents was 3,583. A four-stage stratified sample was used and stratification was performed according to region and type (urban/rural). As is the case for the rest of the IALS country data, in the Chilean data the three skills are very highly correlated. Consequently, the average of the three scores will be used in the analysis as an aggregate IALS score measure (see also Blau and Khan 2001; Devroye and Freeman 2001; Leuven _et al_ . 2004).

As is the case with the other countries in the IALS dataset, for Chile as well, the relation between skill level and years of schooling is positive. However, the slope of the skill-schooling profile is less steep compared to all but three other countries – Germany, the Netherlands and Sweden – while the slope is steepest in the Czech Republic, the United States, Slovenia and Canada (see Leuven _et al_ . 2004).

# **Methodology**

In the basic Mincerian human-capital model (Mincer 1974), schooling is assumed to be independent of ability/skills, and that the return from schooling investments is equal for all individuals. However, in the contemporary literature it is acknowledged that the return to schooling must be different for different skill levels. Intuitively, an estimate of the average return to schooling probably over-estimates the return for low skill individuals and under-estimates the 7
Expenditure Survey,” _Food Policy_ , SI: Food Counts, 72, 72–80, 2017. doi:10.1016/j.foodpol.2017.08.014

- Farfán, G., M.E. Genoni and R. Vakis, “You Are What (and Where) You Eat: Capturing Food Away from Home in Welfare Measures,” _Food Policy_ , SI: Food Counts, 72, 146–56, 2017. doi:10.1016/j.foodpol.2017.08.020

- Ferreira, F.H.G., S. Chen, A. Dabalen, Y. Dikhanov, N. Hamadeh, D. Jolliffe, A. Narayan, E.B. Prydz, A. Revenga, P. Sangraula, U. Serajuddin and N. Yoshida, “A Global Count of the Extreme Poor in 2012: Data Issues, Methodology and Initial Results,” _Journal of Economic Inequality_ , 14, 141–72, 2016. doi:10.1007/s10888-016-9326-6

- Gibson, J., K. Beegle, J.D. Weerdt and J. Friedman, “What Does Variation in Survey Design Reveal About the Nature of Measurement Errors in Household Consumption?” _Oxford Bulletin of Economics and Statistics_ , 77, 466–74, 2015. doi: 10.1111/obes.12066

- Holden, K., and D.A. Peel, “On Testing for Unbiasedness and Efficiency of Forecasts,” _The Manchester School_ , 58, 120–27, 1990. doi: 10.1111/j.1467-9957.1990.tb00413.x

- Jäntti, M., V.M. Törmälehto and E. Marlier, “The Use of Registers in the Context of EU-SILC: Challenges and Opportunities,” Eurostat Statistical Working Papers, Publications Office of the European Commission, 2013.

- Jolliffe, D., “Measuring Absolute and Relative Poverty: The Sensitivity of Estimated Household Consumption to Survey Design,” _Journal of Economic and Social Measurement_ , 27, 1–23, 2001. doi:10.3233/JEM-2003-0192

- Jolliffe, D., P. Lanjouw, S. Chen, A. Kraay, C. Meyer, M. Negre, E.B. Prydz, R. Vakis and R. Wethli, _A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals_ , World Bank, Washington, DC, 2014.

- Jolliffe, D. and E.B. Prydz, “Estimating International Poverty Lines from Comparable National Thresholds,” _Journal of Economic Inequality_ , 14, 185–98, 2016. doi:10.1007/s10888-016- <u>9327-5</u>

- Karshenas, M., 2001, ‘Measurement and Nature of Absolute Poverty in Least Developed Countries’. _Economic Working Paper Series_ no. 129, School of Oriental and African Studies, University of London, London.

- Karshenas, M., “Global Poverty: National Accounts Based versus Survey Based Estimates,” _Development and Change_ , 34, 683–712, 2003. doi: 10.4236/me.2014.52013

Korinek, A., J.A. Mistiaen and M. Ravallion. “Survey Nonresponse and the Distribution of Income,” _The Journal of Economic Inequality_ 4, 33–55, 2006. doi:10.1007/s10888-005- <u>1089-4</u> 32
scheme and a third adopted rate of return (RoR) regulation while the last third adopted hybrid regimes (HR) in which some costs changes are automatically passed through to tariffs. This is clearly a very rough classification of regulatory regimes since the incentive for improvements in efficiency are strongly correlated with the specific design of the regime. In the case of HR, for instance, the larger the share of costs enjoying an automatic pass-through rule, the closer the regime is to RoR regulation and hence the lower the expected incentive to minimize costs. The classification is, however, useful enough to allow an explicit modelling of the influence of broad categories of regulation on the efficiency of the operators.

# _Data and model specification_

The baseline econometric model used to analyze the impact of alternative regulatory schemes on labor productivity is the same as the one used above. The information about type of regulatory regime was obtained from Espinasa (2001) and checked by asking regulators and governmental agencies. A classification of the type of regulatory regime in each of the countries in the sample is listed in Table A.2 (see Appendix).

The translog average labor requirement function is specified as where _l_ , _y_ 1, _y_ 2, _y_ 3, and _y_ 4 are the natural logarithms of labor, sales, customers, area, and distribution lines, _Dj_ is an indicator variable equal to one for private firms operating under regulatory regime j (see Table 5 for their descriptions), _t_ is the time trend, and _v_ is the random error term.

17
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non-financial sector, again normalized by the GDP level in a given country. _Total credit_ represents the ratio of all credit to the private sector over GDP. The variable thus includes credit provided both by deposit-taking banks and by other financial institutions.

Our main measure of the depth of financial markets in the exporting country is called _Stock markets_ . It represents the ratio between stock market capitalization (the market value of all listed stocks) and GDP of a given country, and it is a standard measure of well-developed stock markets. _Stock market turnover_ (the ratio between the value of stock market transactions and the stock market capitalization) serves as a proxy for an active and liquid (rather than large) stock market. In additional tests, we also use _Stock market value traded_ (the value of stock market transactions divided by GDP).

The data are from the Global Financial Development Database that builds on the earlier work by Beck et al. (2000) and has been substantially extended by Cih ́ak<sup>ˇ</sup> et al. (2012). The database contains various indicators of financial development across countries and over time and is regularly updated.

All measures representing financial institutions and markets vary over time. We measure them at the beginning of a given export spell in order to capture the impact of the predetermined financial environment on the subsequent export performance. The direct effects of these time-varying country-level financial variables are thus captured in our regressions by the included country-time fixed effects ( _δc∗t_ 0), with time referring to the beginning of a given export spell ( _t_ 0). Consequently, the variables described in this subsection enter the regressions explicitly only as part of interaction terms with the industry-level measures of financial vulnerability.

# **3.3 Product-Related Trade Variables**

The export survival rate in the US market and all trade-related control variables at the product level (initial export, total export, number of suppliers, a multiple spell dummy) are computed using the BACI dataset developed by the CEPII and described in Gaulier and Zignago (2010). The dataset provides harmonized bilateral trade flows for more than 5,000 HS 6-digit products and 143 countries. Export flows are reported annually 15
# **_3.2 Legal Discrimination and Gender Norms_**

We use indicators on legal discrimination against women and manifestations of gender norms from two key sources. First, we use data from the World Bank’s Women, Business and the Law (WBL) database that constructs a number of variables with regard to laws governing a women’s ability to earn an income, either as an employee or entrepreneur.<sup>12</sup> WBL variables are based on codified law and regulations, not the implementation or practice of that law. Therefore, these indicators do not take into account customary law, unless that customary law has been codified. Exceptions to this rule are common law or religious codes where decisions of such common or religious courts or schools of jurisprudence have legal standing equivalent to that of codified law.

We consider those legal indicators that might affect women’s demand for financial services, as compared to men’s, such as women’s access to institutions and ability to own, manage, control, and inherit property. In particular, we consider the ability of women: (i) to work, (ii) to head a household, (iii) to choose where to live, and (iv) to inherit property, as well as (v) requirement by law of wives to obey their husbands.<sup>13</sup> While it is not a gender norm per se, we also include a dummy variable that indicates whether separation is the default marital property regime (as opposed to a variant of community) as it affects the assets that a women has control over if they are married. Variables are coded such that the dummy variables take the value of one if the answer is yes; a dummy variable in general equals one if there exists (legal) equality between the genders, except for the variable capturing whether women are required by

> 12 For details on methodology of the World Bank’s WBL database see http://wbl.worldbank.org/methodology 13 Many variables in the World Bank’s WBL database are available for both unmarried women (compared to unmarried men) and married women (compared to married men). If both are available, we consider the situation of married women.

15
districts) of Bangladesh.<sup>5</sup> A second round, conducted in 1998/99 with the help of BIDS, could not retrace 131 of the 1,769 households from the 1991/92 survey, and so surveyed 1,638 available households, implying an attrition rate of 7.4 percent. The 1998/99 survey also included new households from old villages and newly selected villages; in total, 2,599 households were surveyed, of which 2,226 were from old villages and 373 were new ones. Among the 2,226 households in old villages, 279 were newly sampled ones and 1,947 were from the 1,638 households of 1991/92 which split to form new households in the years between the two surveys.

These households were surveyed a third time in 2010/11 with the help of the Institute of Microfinance (InM). The third survey round tried to revisit all of the households (2,599) surveyed in 1998/99. But 2,342 households were traced (attrition of about 10 percent). In all, 3,082 households were interviewed in 2010/11, with 740 households splitting-off during this period to form new households. The survey began in March 2010/11 and was completed in September 2010/11.

The analysis of this study is based on 1,509 households from 1991/92 that are common in all three surveys. Of course, because of household split-off, we have higher number of households in 1998/99 (1,758) and 2010/11 (2,322) (Table 1). As Table 1 indicates, over the three survey years, household membership in microcredit programs grew steadily - from 26.3 percent in 1991/92 to 48.6 percent in 1998/99 and to 68.5 percent in 2010/11. The only exception is the Bangladesh Rural Development Board (BRDB), a government program, which lost a good share of its members due to reorganization between the second and third survey rounds. Membership in Grameen Bank, the largest of all the programs, increased from 8.7 percent in 1991/92 to 15.1 percent in 1998/99 and to 27.4 percent in 2010/11. In addition to the four major programs (i.e., Grameen Bank, BRAC, BRDB, and Association for Social Advancement (ASA)), many other programs developed over the past 20 years and are now serving rural communities in a large capacity. In 2010/11, the coverage of

> 5 An _upazila_ is an administrative unit smaller than a district, consisting of a number of villages.

5
-3-

specific goods. This model was based on the assumption that developing countries should have a comparative advantage in goods that employ relatively large labor inputs. 4/ Given the obvious importance of issues concerning changing comparative advantage, this paper attempts to extend the related empirical analyses in several directions. First, an attempt is made to evaluate the predictive power of comparative advantage indices for future changes in trade by drawing on the NBER study. This will be done by analyzing the 1965-1986 trade changes in products identified as labor intensive in the earlier year. Aside from the ability to identify industries where major changes are likely to occur in trade or production, an attempt will also be made to determine the time frame for which predictions based on comparative advantage indices are valid. 5/ Other issues that will be addressed include: the importance oi textiles and clothing in the initial stages of a labor intensive trade expansion; how the product composition of "maturing" developing country

- 4/ See Lary (1968) for details. According to the United Nations product classification scheme, manufactured products are those items which fall in: SITC 5 (Chemicals); SITC 6 (Manufactured Goods Classified by Material), less SITC 68 (Non ferrous Metals); SITC 7 (Machinery and Transport equipment); and SITC 8 (Miscellaneous Manufactured Articles). The National Bureau of Economic Research study adopted a somewhat broader definition of manufactures in that it includes some processed foods falling in SITC 0, cigars (in SITC 1), shaped wood (in SITC 2), and fish oil (in SITC 4). See Appendix 1 for a listing of the labor intensive manufactured products identified in the NBER study.

- 5/ There have been several attempts to develop "early warning" systems for disruptive imports with studies by Watkins and Karlick (1981) and Tuong and Yeats (1975) being representative of the earlier work. Indices of comparative advantage could play a major role in the development of such systems if it were established that these measures are accurate predictors of future trade patterns. This study shows that the NBER labor intensive indices are generally accurate predictors of changes in South-North trade flows and that they may provide a lead time of as much as one or two decades.
**Table 2. Unemployment Regressions and Married vs Single Regressions**

Panel A. Response Variable: Change in Unemployed Females

|Change in Exports Exposure (billion USD)|(1)<br>-1,387.8<br>(1,617.9)|(2)<br>147.3<br>(2,611.8)|(3)<br>272.0<br>(2,749.1)|
|---|---|---|---|
|Panel B. Response Variable: Change in Unemployed|Males|||
|Change in Exports Exposure (billion USD)|(1)<br>-1,750.4<br>(1,680.8)|(2)<br>-602.6<br>(2,243.9)|(3)<br>-1,279.6<br>(2,156.9)|
|Panel C. Response Variable: Change in Female Emp|loyment, Mar|ried Women||
|Change in Exports Exposure (billion USD)|(1)<br>-343.177<br>(1,348.004)|(2)<br>-4,540.806**<br>(1,993.872)|(3)<br>-4,605.166**<br>(1,897.571)|
|Panel D. Response Variable: Change in Female Emp|loyment, Sing|le Women||
||(1)|(2)|(3)|
|Change in Exports Exposure (billion USD)|-1,446.643<br>(1,127.362)|-2,750.395*<br>(1,549.210)|-2,501.416<br>(1,429.758)|
|Panel E. First-Stage. Response Variable: Change in|Exports Expo|sure (billion U|SD)|
||(1)|(2)|(3)|
|ChiFiDdEbilliUSD|.0058***|.0046***|.0045***|
|ange n oregn eman xposure (on )|(.0006)|(.0008)|(.0004)|
|F-statistic|77.82|30.25|139.95|
|Time Fixed-Efects||✓|✓|
|District Fixed-Efects||✓|✓|
|Socio-Demographic Controls|||✓|
|N|120|120|120|

District cluster robust standard errors in parenthesis. * ** *** _p <_ 0 _._ 1; _p <_ 0 _._ 05; _p <_ 0 _._ 01 14