rafmacalaba/datause-encoder-data huggingface dataset:
could for example be larger, while the space and population affected by the metropoles may also be smaller than this affected by all the secondary towns taken together.

Thus, while urban concentration may be more conducive to aggregate economic growth—and important caveats<sup>11</sup> remain—the pro-poor marginal incidence of nonfarm employment expansion may be higher for secondary towns. Overall, the relationship between urbanization and poverty reduction, beyond its effect on growth, remains little studied, with theoretical expositions by Anand and Kanbur (1985), Ravallion (2002) and Fields (2005) and an initial empirical exploration by Ravallion, Chen and Sangraula (2007) being notable exceptions. Nonetheless, the question of urban concentration is pressing, as policymakers prepare to accommodate the next wave of rural migrants as the structural transformation proceeds, with China and India for example both contemplating the development of supercities (Henderson, 2010) and Africa also thought to be urbanizing rapidly<sup>12</sup> , while finding itself already at high levels of urban primacy (Behrens and Bala, 2013). The lock-in of urbanization patterns, including through infrastructural lock-in, adds further urgency.

Building on Ravallion, Chen and Sangraula (2007) and also drawing on the global cross-country experience, this study takes a comprehensive and comparative perspective and empirically examines whether the nature of the rural-urban transformation process (i.e. urban concentration versus rural diversification and secondary town development, as opposed to urbanization per se) matters for the speed of poverty reduction. In doing so, it does not seek to establish causality as such, but rather explores whether worldwide, empirical regularities along these lines can be uncovered.

To do so, the population in each country is classified into three groups according to their occupation and location: 1) those living in rural areas and employed in agriculture, 2) those living in mega cities and employed in industry and services, and 3) those living in rural areas and secondary cities and employed outside agriculture. The latter group will be referred to as

> 11 As political factors, diseconomies of scale in living and transport and the nature of industrial activities all have to be taken into account in analyzing the relationship between urban concentration and economic growth, important caveats remain regarding its empirical robustness, shape and universal applicability across types of industries and countries (Henderson, 2003; Henderson, 2010; Behrens and Bala, 2013). 12 See Potts (2012) for a critical discussion.

5
|_Log of Population_|-0.147***|-0.130***|-0.137***|-0.138***|-0.143***|-0.138***|
|---|---|---|---|---|---|---|
||(0.029)|(0.029)|(0.032)|(0.035)|(0.035)|(0.036)|
|_Log of GDP per capita_|0.239***|0.223**|0.222|0.275**|0.226|0.250|
||(0.076)|(0.093)|(0.139)|(0.108)|(0.189)|(0.195)|
|_Openness_|-0.190***|0.217|0.195|0.179|-0.077|0.155|
||(0.072)|(0.224)|(0.220)|(0.275)|(0.293)|(0.251)|
|_Ethnic fractionalization_|-0.739***|-0.607**|-0.617**|-0.465||-0.462|
||(0.269)|(0.267)|(0.280)|(0.330)||(0.321)|
|_High income_||0.490*|0.472*|0.433|0.333|0.430|
|||(0.285)|(0.280)|(0.367)|(0.379)|(0.349)|
|_High income * Openness_||-0.448**|-0.413*|-0.463*|-0.233|-0.437*|
|||(0.211)|(0.213)|(0.262)|(0.280)|(0.249)|
|_Age dependency ratio, young_|||-0.001||-0.007|-0.002|
||||(0.005)||(0.007)|(0.008)|
|_Age dependency ratio, old_|||-0.003||-0.003|-0.001|
||||(0.005)||(0.007)|(0.007)|
|_Gini_||||0.002|0.003|0.003|
|||||(0.008)|(0.010)|(0.011)|
|_Constant_|0.904|0.587|0.718|0.058|0.819|0.376|
||(0.743)|(0.795)|(1.439)|(0.801)|(1.616)|(1.751)|
|_Observations_|286|286|284|223|224|223|
|_Number of countries_|80|80|79|72|73|72|
|_Country FE_|No|No|No|No|No|No|
|_R-2_|0.347|0.355|0.358|0.352|0.372|0.351|
|_Robust standard errors cluster_|_ed by countr_|_y in parenthe_|_ses_||||
|_*** p<0.01, ** p<0.05, * p<0.1_|||||||

This result is driven by the assumption of homogeneity of the coefficients across all countries. When that assumption is relaxed and heterogeneity across groups of countries (by income group) is allowed, results change. When a dummy variable for high-income countries is included and interacted with openness (Table 9, Column 2), a robust negative coefficient associated with the interaction term is present in most specifications. Hence, openness is negatively associated with public employment in high-income countries, but positively associated in the rest.<sup>10</sup> In the case of ethnic fractionalization, we find a negative association, implying that more fragmented countries have lower public sector employment, while the age dependency ratio and income inequality are not statistically significant determinants of public

> 10 Openness appears imprecisely estimated, but when the dependent variable is changed to public employment over population instead of labor force (to increase the sample size by 15%), results are similar, with the openness coefficient being positive and statistically significant (Table 13 in the Appendix).

18
- iv. Countering disorderly FX markets: Liquidity shortages in FX markets can threaten currency convertibility and trigger financial stress. Among the central banks surveyed by BIS, 41 percent had intervened to supply liquidity to thin FX markets.

- v. Managing FX reserves: Reserve accumulation has often been a central bank priority, especially in the aftermath of currency crises—as, for example, in Turkey in 2001 and South Africa in 2002. A key objective has been to ensure an adequate level of reserves against debt repayment obligations and bolster market confidence. Another objective is to build sufficient buffers against volatility of capital flows and market contagion. However, correcting excessive reserve accumulation can also be an aim of FX intervention, as holding excessive reserves entails quasi-fiscal and opportunity costs. When pursuing goals of FX reserve management, central banks seek to minimize the impact on the exchange rate, contrary to the other objectives of FX intervention where a maximum impact on the exchange rate is generally sought.

To ensure consistency with the broader objectives of monetary policy, central banks usually need to sterilize FX interventions. This is done through changes in their net domestic asset positions (by issuing interest-bearing liabilities; selling government bonds; issuing reverse repos) that offset changes in their net FX position resulting from FX interventions. However, sterilization entails a quasi-fiscal cost, equivalent to the interest paid on the liabilities used by central banks to insulate the monetary base from FX interventions. Sterilization costs can be non-negligible: An IMF study estimated the average cost of FX interventions in six Latin American countries over 2004-10 at 0.5 percent of GDP (Adler and Tovar, 2011).

Intervention typically takes place in the spot FX market because this market is more liquid than forward FX markets, which helps the objective of influencing the spot exchange rate or managing the outstanding stock of FX reserves. Some central banks also practice interventions in forward markets—the markets for currency delivery in more than two business days. As the forward FX price is linked to the spot FX price through the “covered interest parity” condition, 6
<!-- Start of picture text --> Difference between MNE and National Average (%) Share of firms that export (%)<br>-10 0 10 20 30 40 60 10 20 30 40 50<br>|—| [Vietnam||)-—___—————-e<br>| Malaysia || |~=9~ oe<br>Madagascar -——____—_9<br>Romania -———_———_<br>_| KenyaChina “oe-——e—_0<br>Poland -—____—_<br>Nigeria -——_e—_—_—_———o<br>Ethiopia +-—o——_e<br>Philippines t——__o—_<br>Indonesia eo<br>Peru -——_o—<br>Egypt Ho<br>Morocco Ho<br>Ghana Ho<br>Uzbekistan ||-——o—@<br>Guatemala te—————_©<br>Argentina || -——e—o<br>Mexico ||-/——®<br>India Heo<br>Chile || -—e<br>Colombia || -—-®@<br>Mali || Hoe<br>Cote d'Ivoire || He<br>Malawi || +®<br>Uganda ||F-«0<br>| ] | | | RussiaDRC |||| Heo©<br>Angola ¢<br>Venezuela<br>Qo . e ° '<br><!-- End of picture text -->
positive association uncovered with various gender outcomes and other composite measures may indicate there is some degree of enforcement otherwise none of these results is likely to hold. Second, certain laws typically apply to the formal economy but not the informal economy, and therefore the implications of legal gender disparities in the informal economy is beyond the scope of this measure. Third, while the underlying data are rich and collected through the rigorous efforts of the _Women, Business and the Law_ project of the World Bank, the laws explored are not the universe of all possible laws. Some laws may be omitted due to difficulty in obtaining a consistent indicator across economies, or simply due to the fact that they may be covered in future reports. Finally the data are cross-sectional in nature, however a panel dimension can eventually be developed. Thus in time, it can be expected that the proposed measure will be more comprehensive. This will be a boon for future research, as well as policy makers who wish to measure progress of gender equality in relation to the Sustainable Development Goals (SDG).

22
industrialized economies that never had colonial relationships with each other. From a sampling perspective, it is probably also true that an indicator variable for colonial ties does not belong in the set of gravity covariates for a sample of OECD countries.

In sum, the estimates from this section reveal that the structural gravity model performs quite well with services data. Many of the standard gravity covariates are significant and their estimates make good economic sense. At the same time, we document important differences in the effects of standard gravity covariates between goods and services trade. Our benchmark results point to avenues for further research in modeling trade costs in services, which we leave for future work. Instead, we now focus attention on estimated border

# **4.3 Results on International Border in Services Trade**

Equation (8) delivers a multi-dimensional data set of sector-country-time estimates of the border effects in services trade. Due to the large number of _SMCTRY_ estimates, we first display the overall distribution of border effects across countries, sectors and years. Then we characterize border effects separately along each of the three dimensions of our new database (and their interactions whenever possible).

Figure 1 shows the full distribution of _SMCTRY_ estimates across all 12 sectors, 28 countries and 4 years. Our data enabled us to estimate 1,231 (out of 1,344 possible) border effects. The median coefficient estimate is 5.35, which points to substantial border effects in services trade. To get a sense of the magnitude of the border barriers, note that the median border effect (exp _{_ 5 _._ 35 _} −_ 1) _×_ 100 = 20 _,_ 961, which suggests that the border enormously deflects international trade in services. We attribute the large estimates of border effects in services trade to the fact that consumption of services is highly localized.

Figure 1 also reveals that there exist some negative _SMCTRY_ estimates, suggesting that internal trade is smaller than international trade. The negative estimates are concentrated 15
# **Help or Hindrance?**

# **The Impact of Harmonized Standards on African Exports**

Witold Czubala, Ben Shepherd and John S. Wilson<sup>*, †</sup> The World Bank 1818 H St. NW Washington D.C. 20433

**Keywords:** International trade; Technical barriers to trade; Product standards; Economic development; Textiles and clothing.

**JEL Codes:** F13; F15.

* The first author is with the Bank of America, and second a Consultant and third author a Lead Economist in the Development Research Group—Trade, The World Bank. This work is part of a broader project on trade facilitation and development supported through a Trust Fund of the U.K. Department for International Development. Compilation of the World Bank EU Standards Database (EUSDB) was supported by a Trust Fund of the U.S. Agency for International Development. Sincere thanks to the following for their superb assistance in assembling the World Bank EUSDB: Jérôme Beaufils, Teresa Bianchi, Françoise Blarez, Anne-Claire Hoyaux, Mark-André Saucier-Nadeau, and Mark Troger. We are also grateful to a number of World Bank colleagues for their comments and suggestions: Enrique Aldaz-Carroll, Silja Baller, Paul Brenton, and Bernard Hoekman, as well as participants at a World Bank workshop on Trade Costs on April 30, 2007.   Comments to: <u>bshepherd@worldbank.org</u> (corresponding author), <u>jswilson@worldbank.org, or witold.czubala@gmail.com.</u>

† 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.
are consistent with the ndings in the previous section with two exceptions: the investment wedge contributes to the recovery of output and labor in Canada, and the worsening in the risk premium wedge prevented an even larger drop in output and labor in Canada.

# The 1995 Mexican Crisis

The upper panel of Figure 1 shows output and the estimated wedges in the 1995 Mexican crisis. The series have been normalized to equal 100 at the beginning of the episode and the units of the risk premium wedge are shown on the right vertical axis. Output falls 12 percent in two quarters and remains below trend until 2000. The eciency, labor, and risk premium wedges deteriorate throughout the recession, although by 1998, the labor wedge fully recovers. The investment wedge, on the other hand, improves substantially throughout the episode, suggesting that investment decisions were, in fact, relatively less distorted during the Mexican crisis. The solid blue lines in Figure 2 display Mexican data. Labor (hours) is normalized to equal 100 at the beginning of the recession; investment and net exports are normalized by the initial level of output, and are shown in percentage points. Labor initially drops 3 percent but soon recovers, and by 1998 it is over 4 percent above its initial value. Investment drops abruptly and co-moves closely with output. Finally, net exports move sharply from a decit of 4 percent to a surplus of 4 percent and remain in surplus until 1998.

The contribution of the wedges to the Mexican crisis is shown in Figures 2 and 3. These gures report counterfactual experiments in which the active wedges are set to their estimated values and the inactive wedges are set at their values at the beginning of the crisis. Consider, rst, the direct contribution of the eciency wedge. The model with just the eciency wedge, shown in the upper panel of Figure 2, matches the evolution of output and labor remarkably well until 1997. Thereafter, the model predicts a somewhat slower recovery compared with that in the data. The model also misses the evolution of investment and net exports throughout the episode: predicted investment declines by a small amount and predicted net exports actually drops instead of increasing.

24
**Table 26: Progress in Adult literacy (figures in percent).**

|**.Items**|**1987**|**1988**|**1991**|
|---|---|---|---|
|read literacy|38.70|39.40||
|Read&Write literacy|36.61|37.03|48.8|
|Read, Write & Maths literacy|36.20|36.84||
|Read and Write Literacy||||
|Male|48.18|48.51|60.8|
|Female|25.44|25.97|38.5|
|Age-group (by cohort)||||
|15-19 yrs|36.64|34.57||
|20-24 yrs|50.46|47.26||
|25-44 yrs|44.33|47.86||
|> 44 yrs|19.06|19.71||
|Urban|48.54|54.29|_65.0_|
|Rural|29.73|27.94|40.0|

**Source: Authors' own calculations using GLSS 1,2,3.**

**Note: A person is defined as "Literate" if he is able to read and write.**

39
# **Figure 2**

<!-- Start of picture text --> Distribution of regimes over the years LYS classification (1974-2004) – All countries<br>100%<br>90% % Interm<br>80%<br>70% % Float<br>60%<br>50%<br>40%<br>30% % Fix<br>20%<br>10%<br>0%<br>1974 1980 1990 2000 2004<br><!-- End of picture text --> 22
is correlated with ability (Griliches and Mason, 1972; Spence, 1973), however, and there is no more plausible proxy available in the WVS.

Measuring morality, defined as an aversion to earning a living through appropriative as opposed to productive work, is even more difficult. There are several survey questions in the WVS that are potentially relevant. In selecting our primary indicator of morality from among them, our first criterion was that the question pertain directly to work or employment choices. Our second criterion was that it must bear on the willingness to sacrifice income (or job-related leisure) to do work that may be regarded as more moral. No WVS question fully meets these two criteria, but the question that comes closest inquires about particular job characteristics that would be “most important” to the respondent if he/she “were looking for a job.” Specifically, we construct a “morality” dummy coded 1 for respondents who place the highest value on “doing an important job which gives you a sense of accomplishment,” and coded 0 for those who place greater value on high income, job security or likable co-workers.<sup>10</sup> The results we report below rely primarily on this morality proxy. However, in a subsequent section we present results for several alternative morality proxies from the WVS.

Following the theory, the empirical investigation is mainly concerned with identifying whether or not it matters for economic performance if workers with more ability are also more “moral.” Therefore, the key independent variable in our analysis is the _correlation_ of morality and ability among workers _within_ each country.<sup>11</sup> In computing the correlation and in generating country-level aggregates for morality and ability, we drop all respondents not in the labor force

> 10 See the Data Appendix for the complete survey question. If we measure morality more expansively as the share of respondents selecting “doing an important job which gives you a sense of accomplishment” as either their first or second priority among the four choices, the results reported in tables below change very little. The mean of this more expansively-defined variable is 40%, compared to 21% for the more restrictive definition based only on respondents’ first choices. However, the two versions of the indicator are correlated at .95, and the ability-morality correlations computed using both versions are correlated at .86.

> 11 Note we are _not_ testing an _interaction_ effect at the country level between morality and ability, as the theory does not imply that morality will matter more (or less) for economic performance in countries with more ability.

16
INTRODUCTION XXiii Figure 6 Projected Population (billions) in Less Developed-Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections <!-- Start of picture text --> 7.5 _<br>7 in _<br>6.5 -.<br>Preyvious<br>6.0 ,<br>5 5 - X 2 > / ~~~~~~~~~Current<br>5.5-<br>5. 0<br>4. 5<br>4.0<br>3.w- 5 I I I I I I<br>1985 1990 1995 2000 2005 2010 2015 2020 2025<br>Figure 7 Projected Population (billions) in More Developed Countries,<br>1985-2025, Compared with U.N. and Previous World Bank Projections<br>1.36<br>1.35-<br>1 .34 -<br>1.33 N.<br>1 ;32 - Previous<br>1.31<br>1.30 Current<br>1.29<br>1 .28<br>1.27<br>1 .26<br>1 .25<br>1 .24<br>1.23<br>1.22<br>1.21<br>1 .20<br>1.19<br>1.18<br>1.17<br>1985 1990 1995 2000 200S 2010 2015 2020 2025<br><!-- End of picture text -->
not have predicted this e¤ect since all agents in the model are employed. In the data, however, about 32 percent of the individuals do not participate in the labor force or are unemployed. The model in this paper is kept simple to emphasize what the reform in Mexico can say about the di¤ering views of informal business owners. In order to capture more of the e¤ects of the reform, the model would have to be more complex. Agents who are not employed could, for example, be introduced into the model if agents had di¤erent costs of participating in the labor market.

Another extension of the model for future work is the inclusion of non-eligible industries. In the model, the entire formal business sector is subject to the reform. However, as Tables 10 and 11 show, the overall fraction of wage earners and overall employment did not increase signi...cantly. Instead the increase in wage earners and employment in low-risk (eligible) industries went along with a decrease in wage earners and employment in high-risk (non-eligible) industries. Column 3 in Panel B of Table 7 illustrates that the decrease in wage earners in high-risk industries is due the to fact that former high-risk wage earners are less likely to continue working in these industries after the reform. Instead, they move into low-risk industries, as shown in Column 2. The coe¢ cients in Columns 2 and 3 in Panel A further suggest that all past occupation groups are less likely to be high-risk wage earners after the reform and are more likely to be low-risk wage earners instead. In order to interpret these e¤ects, it is important to point out that the Mexican labor market is quite mobile, with large and symmetric ‡ows among sectors and occupations (Maloney 1997). The reforms seems to have redirected some of these ‡ows from high-risk sectors to low-risk sectors.

# 6.3 Prices

The model above predicts that entry of new registered businesses after the reform decreases prices. My measure of the price level is the Mexican consumer price index (CPI), which is constructed by the Bank of Mexico. Price data are only available at the city level, not at the municipality level. In Mexico, a city can consist of one or several municipalities. I thus convert the price data to the municipality level by assigning each municipality the price index of the city where it is located<sup>18</sup> . CPI data exists only for 20 out of 34 municipalities in the sample since the other 14 municipalities do not fall into cities for which the CPI is calculated. As opposed to the ENE data, which have a quarterly frequency, the price data come at a monthly frequency. The speci...cation used to analyze the e¤ect on the price level is thus log(CPIcm) = � + �c + �m + �SAREcm + �EC1999 � t + "cm,

> 18 In my sample, only three counties lie in the same city (Guadalajara, Zapopan and Tlaquepaque in Guadalajara). These three counties are thus assigned the same price data. All other counties have unique observations.

23