rafmacalaba/datause-encoder-data huggingface dataset:
tariff cut for import customs and a 1.2 percentage point export tax equivalent reduction for exports. These average figures obscure substantial variation across countries; the countries with the largest implied cuts in implied ad valorem tariff equivalents tend to be countries that trade relatively little.

We conduct a further thought experiment, calculating the global welfare gains associated with full implementation of the TFA by WTO member countries. We adopt a simple partial equilibrium framework so that our calculations are transparent. Our tentative estimate of the global welfare gains attributable to full implementation through reductions in time spent at customs is US $210 billion per year, with a more certain lower bound of $101 billion. These estimates range from $16 to $33 annually per resident of WTO member countries. As for national gains they are concentrated among a set of countries that are large traders, but for which the OECD policy data suggest that significant but not large policy changes would be necessary if the countries are to reach global best practice. China, the largest trader with room for significant policy improvement, accounts for roughly one-fourth of global gains from TFA implementation in our model.

# **References**

- Cameron, A Colin, and Pravin K Trivedi (2005) _Microeconometrics: methods and applications_ (Cambridge university press)

- Decreux, Yvan, and Lionel Fontagn ́e (2011) ‘Economic impact of potential outcome of the DDA.’ CEPII Working Paper 2011-23, Centre d’ ́etudes prospectives et d’informations internationales, October

- Djankov, Simeon, Caroline Freund, and Cong Pham (2010) ‘Trading on time.’ _Review of Economics and Statistics_ 92(1), 166–73

- Freund, Caroline, and Nadia Rocha (2011) ‘What constrains Africa’s exports?’ _World Bank Economic Review_ 25(3), 361–86

- Hornok, Cec ́ılia (2012) ‘Need for speed: Is faster trade in the EU trade creating?’ Working Paper, Central European University

- Hornok, Cec ́ılia, and Mikl ́os Koren (2011) ‘Lumpy trade and the welfare effects of administrative barriers.’ CeFiG Working Papers 14, Center for Firms in the Global Economy

- Horton, Nicholas J, and Stuart R Lipsitz (2001) ‘Multiple imputation in practice.’ _The American Statistician_ 55(3), 244–254

- Huffbauer, Gary, Martin Vieiro, and John S. Wilson (2012) ‘Trade facilitation matters!’ `http: //www.voxeu.org/article/trade-facilitation-matters`

- Hummels, David L., and Georg Schaur (2013) ‘Time as a trade barrier.’ _American Economic Review_ 103(7), 2935–59

- Kauffman, Daniel, Aart Kraay, and Massimo Mastruzzi (2010) ‘The worldwide governance indicators: Methodology and analytical issues.’ Policy Research Working Papers 5430, World Bank

37
Poor health in urban areas is directly correlated with poverty and to the best of our knowledge, rapid economic growth remains the most efficacious way of climbing out of poverty.

Among the leading causes of ill health in urban areas are the unavailability of clean water, the inadequacy of sanitation, and extraordinarily crowded, squalid living conditions.<sup>79</sup> These not only are responsible for high infant and child mortality but by compromising the growth of young children, their nutritional status, their psychomotor and cognitive functions, and their ability to attend school, they affect their future earnings while raising their susceptibility to chronic diseases at later ages (Case, Fertig, and Paxson 2003; Keusch and others 2006). There are well known solutions to these problems and they take the form of investment in water, sanitation, and housing infrastructure which is capital intensive and absorbs a lot of resources. Achieving the MDG target of halving the proportion of people without access to water and sanitation would cost about $10 billion per year even using low-cost technology (UNDP 2006). Such resources are more abundant (and less costly) when an economy is growing rapidly, including the resources to maintain and expand this infrastructure in line with needs.<sup>80</sup> In many low and middle income countries, chronic diseases now loom as large as infectious diseases in urban areas. These diseases are the result of sharply decreased physical activity, air pollution, a higher intake of calories mainly from refined carbohydrates and fats, cigarette smoking, substance abuse, and unsafe sex. They are also traceable to urban poverty which contributes to the low birth weight of babies born to poorly nourished mothers, to inadequate intake of micronutrients during childhood, and to multiple episodes of diarrhea in early years (Keusch and others 2006). These lifestyle trends can possibly be ameliorated through the better design of transport and land use policies, and by means of health campaigns backed up by a primary health care

> 79 The periodic cholera epidemics which hit European cities in the nineteenth century - in particular the severe cholera epidemic that swept through Hamburg in 1892 - were the result of inadequate public and private sanitation and waste disposal as well as the adulteration and contamination of food (Evans 1987). 80 The cost of providing safe water is high, estimated to be around $10 per month per household (Foster and Yepes 2006).  Using 5% of income as cut-off for the affordability, 30-50% of urban household in Honduras, Nicaragua, and Bolivia will find it difficult to afford access to water at such price.  35% of the households in East Asia and 55% of households in India and in sub-Saharan Africa countries would also face difficulties ("Clean Water" 2006; Foster and Yepes 2006).  Since the price is the most effective way of encouraging economical use of resources, water usage fees which reflect the degree of scarcity would need to be in place ("Clean Water" 2006).

39
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# **4 Effect of Changes in Minimum Wages on Labor Supply**

## **4.1 Identification strategy**

The methodology we employ here is to use individual-level pooled cross-sectional data (for the years 2004 to 2014), to estimate how changes in minimum wages have affected the employment of workers and number of hours worked holding constant other factors that might affect wages. The approach is similar to a number of approaches in the literature including most notably that of Gindling and Terrell (2007), who estimated employment effects for a similarly complex system of minimum wages in Costa Rica.

We estimate an equation of the following form: EEiiii � αα � ββββββMMMMiiii � XXiiii γγ � δδVVVVzzii � DDss � DDii � εεiiii ... (1) Where EEiiii = 1 if individual i is employed in the covered sector at time t EEiiii = 0 if individual i is an own-account worker, an employer or unemployed worker who has worked before at time t.

XXiiii = individual characteristics (gender, experience, education, and interactions).

- ββββMMMMiiii = logarithm of real hourly minimum wage that applies to individual i at time

- t.<sup>9</sup>

DDDD , DDDD = sets of dummies for industry/occupation and year VVVVzzii = value added of sector z at time t.

The coefficient ββ is an estimate of the impact on employment in the covered sector of changes in the minimum wage.<sup>10</sup> The minimum wage is assigned to each worker based on their RO category. We were unable to assign specific wages based on job title and years of experience. We have taken the minimum wage for each RO category to be the lowest stipulated wage in each RO for that category of RO workers.

> 9 Minimum wages are set in either hourly, daily or monthly terms. Where they are not in hourly terms, they have been standardized to a real hourly minimum wage using 8 hours as the reference number of hours for a workday and 168 hours as the reference number of hours for a work month. All real wages are in 2012 prices.

> 10 Note that this specification including self-employed and unemployed workers who have worked before in the covered sectors assumes that workers who lose their jobs in the covered sectors either become unemployed or self-employed in those covered sectors they left.

12
On the other hand, if there is only one donor, i.e., _N_ = 1, equations (5) and (6) hold simultaneously. However, as _N_ increases, the gap between the Pareto optimal effort level and the individually optimal effort level widens. This result illustrates the inefficiency of aid proliferation.

In contrast, when donors are fully altruistic and the aid effectiveness translates into pure public goods, the individual solution becomes socially optimal. In other words, the free-rider problem arises when there are multiple donors who are motivated by self-interest.

# **3. Empirical Results**

Empirically, we examine the free-rider problem by estimating the following equation: where _σit_ is a proxy for the variable, _si_ , which is quantified by the average aid per donor as a share of the recipient’s GDP in country _i_ in year _t,_ and _N_ , as before, is the number of donor agencies in the recipient country. _X_ is a vector of country specific controls, such as GDP per capita and population size. The last two terms in the right-hand side of equation (7) are the country fixed effects and a well-behaved error term, respectively.

With regard to the data sources, we employ the Creditor Reporting System (CRS) database of the Organization for Economic Cooperation and Development (OECD) to compute the average aid per donor. The CRS provides detailed information on every activity funded by the foreign aid provided by member countries of the OECD or the OECD’s Development Assistance Committee (DAC). To calculate the aid variable for each recipient, we use the committed amount of bilateral and multilateral foreign aid. Other macroeconomic data, such as GDP per capita and population size, were extracted from the Penn World Table Mark 6.2 (Heston, Summers, and Aten, 2006). We use a cross-country data set of 163 countries for the 5
**<u>Table 10: Regulatory Environment, Investment Climate and the Growth Pattern</u>**

||Manufacturi|ng Employment-|Employm|ent-intensive|Producti|vity-intensive|
|---|---|---|---|---|---|---|
||intens|ive Growth|Growth inS|econdary Sector|Growth i|n Agriculture|
|Overall Score|-0.000333<br>(-1.27)||-0.00117*<br>(-2.00)||0.000113<br>(0.54)||
|Business Regulation||-0.000124||0.000207||0.000652**|
|||(-0.85)||(0.38)||(2.13)|
|Trade||-0.000447**<br>(-2.09)||-0.000731*<br>(-1.72)||0.000329<br>(1.59)|
|Fiscal||0.0000855||-0.000591||0.000112|
|||(0.24)||(-0.89)||(0.38)|
|Government||0.0000945||0.000357||0.0000921|
|||(0.63)||(1.40)||(0.61)|
|Monetary||0.000177***||0.000191||-0.000129*|
|||(3.34)||(1.45)||(-1.89)|
|Investment||0.0000443<br>(0.22)||-0.000105<br>(-0.28)||0.000218<br>(0.93)|
|Financial||-0.000247*||-0.000559**||-0.000213|
|||(-1.78)||(-2.23)||(-1.36)|
|Property Rights||0.000000891||-0.000671||-0.000587**|
|||(0.00)||(-1.57)||(-2.63)|
|Corruption||-0.0000427||0.000182||0.0000411|
|||(-0.42)||(1.22)||(0.52)|
|Constant|0.0155|0.0175|0.0608*|0.104*|-0.00914|-0.0347|
||(0.99)|(0.51)|(1.73)|(1.99)|(-0.71)|(-1.36)|
|Observations|67|67|67|67|67|67|
|AdjustedR-squared|0.017|0.093|0.092|0.251|-0.011|0.102|
|*p<.10,**p<.05,**|*p<.01.T-stat|isticsinparenthese|s.||||

Table 11 shows the results for the employment intensity of growth in the manufacturing sector and in the secondary sector, as well as the productivity-intensive growth in agriculture. Given that the variables are likely to be correlated (e.g., being landlocked limits trade, as does an appreciated exchange rate), and therefore that it might not be possible to identify the effects in a joint regression, each variable was tested on its own and jointly. The evidence suggests that openness to trade is positively correlated with employment-intensive growth in manufacturing and the secondary sector, as is favorable terms of trade (the price of exports relative to the price of imports). As would be expected, overvalued exchange rate decreases employment-intensive growth, by making 29
|_Regulatory and institutional va_<br>|_riables_<br>|||||
|---|---|---|---|---|---|
|Restrict|7225|7.604|1.338|3|11|
|Rights|6353|4.120|1.208|0|5|
|Self-dealing|6818|0.561|0.173|0.080|1|
|Capital|7048|6.112|1.189|3|10|
|Official|7177|12.002|1.844|4|14|
|Diversification|7228|1.748|0.455|0|2|
|Financial freedom|7364|67.045|21.648|10|90|

36
26. Sources of the price data for goods and services in China include _Price Statistical Yearbook of China_ (State Statistical Bureau, 1987), local _Price Inforimation_ in some provinces, _The Price Handbook in Heavy, Machinery and Transportation Industies_ (Processed), newspapers, magzines, and other sources. Where national average prices are not available, this project's research team conducted a market survey through field observation and correspondence inquiry in ten Chinese cities: Beijing Shangbhai, Shenyang, Taiyuan, Xan, Chengdu, Nanjing, Wuhan, Kunming, and Guangzhou. The average of prices in these cities was taken as the national average pice. Some price data that are not for 1986 were converted to 1986 pIices by relevant price index nunibers. If prices for some goods were obtained in only one city, they were converted to national averages using the regional diffbrences issed by the State Price Management Authorities.

27. Since there are multiple prices existed for many items in China, thus it was very difficult to adjudge whether the prices used in this study are matched to the prices used by the compiler of GDP except m some cases, for exmle, the mixed average prices provided in the _Prices Sistical Yearbook of China_ and the prices in the _Prices Handbook in Heavy, Machinery and Trarportton Industries._ This will lead to errors in the PPP for fte basic headings and the final results.

28. Sources of price data on the U.S. side include _Stoaistcal Abtact of the United States_ (U.S. Bureau of the Census, 1987, 1988,1989), _CPI Detailed Report_ (J.S. Bureau of Labor Statistics, 1986), _Producer Prices and Price Indexes Data_ (U.S. Bwueau of Labor Statistics, 1984), _Producr Price Indexes Data_ (U.S. Bureau of Labor Statistics, 1985, 1986), adverements in such newspapers as _7he New York Times_ etc., the price lis of several supeimarkets, the Chinese version of the market survey report by consulting firms in the United States, and interviews with ten Cminese scholars who lived in diffrent cities in the United States in 1986 and had more than one year of stay in the U.S.

14
16 _Table 6. Coefficients of Variation for Selected Variables_

||**1992**|**1993**|**1994**|**1995**|
|---|---|---|---|---|
|**I. Total budget expenditures**|.80|.68|.99|.94|
|- **National economy**|.86|.65|1.10|1.09|
|**expenditures**|||||
|**Housing**|--|.99|1.39|1.14|
|**Agriculture**|--|.67|.61|.71|
|**Transportation**|--|.69|.92|.90|
|**Other (93/94)/Industry (95)**|--|.71|1.33|2.84|
|**Food**|.91|1.37|2.47|n.a.|
|- **Total investment**|.92|.75|1.21|1.51|
|- **Budget loans**|1.64|1.34|2.68|4.16|
|**H. Total budget revenue**|.97|.67|1.02|.98|
|- **Pre-transfer revenue**|.94|.76|.97|1.08|
|- **Transfers**|2.20|1.60|1.90|1.91|
|**III. Household incomes**|.54|.51|.57|.73|

"--m means data are not available for these categories in 1992. "n.a." means not applicable.

Table 6 shows a growing disparity across regions in per capita national economy expenditures. This means that the overall decline in these expenditures seen in Table 5 is not tending to equalize differences between regions. On the contrary, the decline has led to greater inequality. This tendency is also evident in total expenditures but to a lesser degree, suggesting the greater sensitivity to changes in a region's overall fiscal situation of national economy expenditures within overall expenditures.

Table 6 presents results complementary to those observed in earlier studies (Stewart, 1995; Rutkowski, 1996): as a rule, budget social expenditures by Russian regions are much less elastic than expenditures on national economy.<sup>2</sup> ' Budget social spending (including those on health and education) are treated as priority items and are more protected against various fiscal strains, while spending on national economy are treated as 'less important" goods, with the real level of spending being more flexible in response to changes in the local budget situation.

Of note in Table 6 is the high degree of variability over the period in the coefficients of variation of national economy expenditures themselves. As noted above, the decline in housing subsidies (by far the largest individual component part of national economy expenditures) on 21 The simnilar coefficient of variation for budget health expenditures in 1994 amounted to 0.80, and for education expenditures, 0.92 (Stewart, 1995). It is worth noting that Russian annual budget laws, as a rule, determine some types of budget expenditures as protected, which requires regional governments to give them some priority in budgetary allocations. A larger part of overall budget social spending falls under the definition of such protected expenditures. While _de facto_ the federal government does not have real power to force regional governments to follow such established priorities, it appears that regional administrations are quite keen on prioritizing their social liabilities versus those in the area of national expenditures.
# TABLE OF CONTENTS

|Introduction<br>.... . . . . . . . . . . . . . .<br>.<br>.<br>1|
|---|
|Demographic<br>Projections<br>.... . . . . . . . .<br>. . . . . . . . .<br>...<br>2|
|Life-Cycle<br>Saving<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>4|
|A Simulation Model forHousehold<br>Saving<br>. . . . . . . . . . . . . . . . .<br>8|
|Simulation Results .... . . . . . . . . . . . . . . . . . . . . . .<br>.<br>10|
|Sensitivity to Assumptions ............<br>....<br>....<br>.<br>.<br>12|
|Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>14|

# FIGURES

# BIBLIOGRAPHY

We have benefited from Jacques van der Gaag's suggestion of the simulation methodology and from comments by Bela Balassa, Vittorio Corbo and other colleagues in CECMG. We thank My T. Vu for helping us use the demographic projections.
the educational opportunities of children born to college educated fathers, a failure to account for the eects of family background on conditional variance vastly overstates the educational opportunities of the most disadvantaged children with father having no schooling.

Ignoring the conditional variance can also lead to wrong conclusions in inter-group comparisons. For example, In India, the urban and rural daughters appear to enjoy similar relative mobility according to the standard IGRC estimates (0.60 (urban) and 0.59 (rural)), but the RIGRC estimates reveal a substantial disadvantage faced by the rural daughters (0.92 (rural) and 0.79 (urban)). The estimates of both RIGRC and IGRC for decade wise birth cohorts show that the evolution of intergenerational educational mobility has been very dierent in China compared to India and Indonesia. China has become less mobile from the 1950s to the 1980s while mobility has improved monotonically from the 1950s to the 1980s in India and Indonesia, and the magnitude is substantial. While both measures pick the trend correctly, the standard IGRC substantially underestimates the improvements over time in India.

The rest of the paper is organized as follows. The next section discusses the relevant conceptual issues with a focus on the economic mechanisms that can give rise to a negative or positive eect of father's education on the conditional variance of children's schooling, and lays out the estimating equations. Section (3) is devoted to a discussion of the surveys and data sets used for our analysis: CFPS 2014 (China), IHDS 2012 (India), and IFLS 2014 for Indonesia. These three surveys are dierent from many other household surveys available in developing countries as the samples do not suer from signicant truncation. This is important as truncation of a sample is expected to reduce the estimate variance. Section (4) reports the evidence on the conditional variance. In section (5), we develop a methodology for estimating relative mobility that takes into account both the conditional mean and conditional variance, and provide estimates of the risk adjusted relative mobility measure. The paper concludes with summary of the ndings and points out the central contributions of the paper to the literature.

# (2) Conceptual Issues and Estimating Equations

The standard estimating equation for intergenerational educational mobility is: 5
railways (which is standard) is the only command-and-control regulation picked up by the sectoral questions.

# **CONCLUSION**

Over the last years, Bulgaria has implemented substantial structural reforms on many fronts, including in improving the regulatory environment for doing business. As a result, it is now performing as well or better than the average for the OECD on a number of indicators. As shown in Figure 20, Bulgaria has been very successful in reducing barriers to trade and investment, and in eliminating regulatory barriers and discriminatory procedures against foreign investors. Tariffs were above the OECD average in 2006 but are now in line with EU rules. With respect to barriers to entrepreneurship, Bulgaria has managed to eliminate a number of them (such as antitrust exemptions) or is on par with the OECD average for several others (licenses and permits, and legal barriers to competition).

**Figure 20 Indicators in which Bulgaria performs as well as the OECD average (or is about to): outward-oriented policies, barriers to competition and licenses and permits.**

<!-- Start of picture text --> Licenses and permits<br>3.0<br>2.5<br>Administrative burden for corporations Tariffs<br>2.0<br>1.5<br>1.0<br>Communication and simplification of<br>0.5 Foreign ownership barriers<br>rules and procedures<br>0.0<br>Regulatory barriers to trade and<br>Legal barriers to competition<br>investments<br>Discriminatory procedures against<br>Antitrust exemptions<br>foreign investors<br>Bulgaria OECD<br><!-- End of picture text --> However, an important agenda lies ahead if Bulgaria is to continue eliminating barriers to entrepreneurship and further reduce state control (Figure 21).

27