rafmacalaba/datause-encoder-data huggingface dataset:
Almost all studies of _inter-industry_ effects rely on industry-specific measures of foreign presences in downstream sectors. There are, however, two studies that explicitly identify suppliers of multinationals operating in their country and thus test _directly_ whether suppliers are more productive than non-suppliers. Chung, Mitchell, and Yeung (2003) examine this question in the context of the American automotive component industry in the 1980s. They find that Japanese FDI into automotive assembly was associated with overall productivity improvements in the US auto component industry. Somewhat surprisingly, their results indicate that Japanese assemblers tended to purchase components from _less productive_ US suppliers and that the productivity growth of US suppliers affiliated with Japanese assemblers was not greater than that of other non-affiliated US suppliers. Javorcik and Spatareanu (2009a) use data from the Czech Republic to make an explicit distinction between _self-selection_ (i.e., the possibility that more productive firms become suppliers to foreign affiliates) and the _learning_ effect (i.e., the productivity benefits that accrue to suppliers from their interactions with affiliates). They find evidence consistent with both high productivity firms having a higher probability of supplying affiliates and suppliers learning from their relationships with affiliates.

The studies mentioned so far focus primarily on manufacturing sectors, but FDI inflows into the retail sector can also generate knowledge externalities and pecuniary spillovers. A case study by Javorcik, Keller, and Tybout (2008) finds that 24
for regulations, we obtain more objective indicators that are less influenced by stages of economic development or recent events.<sup>3</sup> These indicators, in turn, can then be used to analyze specific regulations that enhance or constrain investment, productivity and growth.

Out of seven indicators in the database, we have chosen those that are clearly applicable to test the linkages between the inflow of foreign investment and economic growth in the host economy. That leaves us with five sub-components, namely, starting a business, labor market regulations, contract regulations, creditor rights and insolvency regulations. The starting-abusiness indicator is the (arithmetic) mean of the average number of procedures to start a business, the number of days and the costs required to complete that process,<sup>4</sup> while the labor market regulation index is an average of three indexes: flexibility of hiring, conditions of employment and flexibility of firing. The contract regulation index is an average of three indexes covering the number of judicial procedures to enforce a contract, the duration and the cost, whereas the creditor rights index measures four powers of secured creditors in bankruptcy. Finally, for the insolvency index we use the goals of insolvency index from the Doing Business dataset that reflect the difficulties in closing down a business.

To get an overall index of regulations we compute a weighted average of the five individual components of regulations, taking factor loadings in principal components analysis as weights.<sup>5</sup> Higher values for all five sub-components (and, thus, for the aggregated regulation index) reflect more regulations, that is, regulations with a lower quality for business operations. While the Doing Business indicators are available for a total of 145 countries, we had to restrict our sample to 89 countries. Included in the following analysis are all countries for which data on regulations, the dependent and all independent variables are available.<sup>6</sup> Base year for the regulation index is January 2003. For our country sample, the combined indicator ranges from -4.24 to -0.42, with a mean of -2.1 and a standard deviation of 0.85.

> 3 For an extended discussion of the advantages of the Doing Business indicators, see World Bank (2004). In general, the Doing Business database is widely recognised (and used) as a high-quality measure of regulations across countries. For instance, Bolaky and Freund (2004) and Pica and Mora (2005) have used the Doing Business indicators in their empirical work.

> 4 Before taking the average for this indicator, all three sub-components are rescaled to 0-1 by dividing all observations by the highest figure in each of the three sub-components.

> 5 Before combining the five regulation indicators, we have taken the logarithm of each of them. This procedure yields negative values for the regulation index for all countries.

> 6 See Appendix C for the country sample.

7
# _Students_

While less dramatic than in healthcare, trends in the costs and affordability of higher education have become adverse in many industrial countries. At a time when globalization and technological developments are increasing the premium on skill, students face a scissors effect: on the one hand, they need more education, on the other the cost of education is increasing, in part due to fiscal constraints.

Increases in the costs of education may well reflect, as Martin Wolf has argued, the workings of democracy in which demographic changes are shifting the balance of power from the young to the populous elderly. One way of effecting inter-generational transfers is to tax the young—in this case by increasing the cost of education. For example, in the UK, the government recently lifted the ceiling on annual tuition at English universities from about $5,200 to $14,200. As a result, some 200,000 young people will graduate each year with average debts of nearly $70,000.<sup>11</sup> Similarly, average tuition in the US in secondary institutions has risen faster than inflation. It is estimated that tuition fees have risen 29 percent in private schools and 72 percent in public institutions over the past decade. More ominously, student indebtedness has risen sharply. Twothirds of students who graduated in 2011 took on debt to finance their education, compared with 59 percent in 1996;<sup>12</sup> meanwhile average debt has risen from $17,100 to nearly $26,600.<sup>13</sup> As with healthcare, one way of keeping costs down is to exploit the gains from trade. This would involve addressing the many barriers that prevent fragmentation of the education value chain across industrial and developing countries, just as is happening in many goods and services. Higher education too can be broken down into a standardized component and a more specialized, sophisticated and even customized component. For example, the early stages of a medical education involve the acquisition of fairly routine knowledge and skills (anatomy, physiology) which could in principle be imparted at relatively low cost in developing countries. Consider the cost advantages from the following example. A medical degree in the United States, including living expenses, today costs about $60,000 per year. The equivalent amount in a reputable private sector medical college in India costs about $15,000 per year. If say the first 2 years of an American medical education (where the learning is mostly standardized) can be traded for an

> 11 http://www.guardian.co.uk/commentisfree/2012/jan/27/how-will-tuition-fees-affect-students

> 12 Online education will lower costs but there might be limits to this because it cannot substitute for learning that takes place in the class-room and in the university.

> 13 http://projectonstudentdebt.org/state_by_state-data.php

9
# **6 Concluding remarks**

Agnostic about the pros and cons of urbanization per se, this paper starts from the observation that the next wave of urban expansion is predicted to be concentrated in large cities (1 million plus) (UN, 2011) and explores whether the nature of the occupational and spatial transformation matters for poverty reduction (as opposed to growth alone). In so doing, the study differentiates itself from most of the literature which usually only applies a sectoral (agriculture versus non-agriculture) or spatial (rural versus urban) lens and draws attention to that the fact that the urbanization pattern may be more important for poverty reduction than urbanization itself.

The study starts from a rather unique panel of individuals from Kagera, Tanzania who have been tracked over more than 15 years (1991/4 – 2010). Poverty among these individuals almost halved (from 58 percent to 30 percent) and almost half of the poverty decline could be attributed to rural diversification and migration to small towns (i.e. the transition out of agriculture into the middle). Only 1 in 7 of the people who exited poverty, did so through migration to the larger cities. The remainder did so while remaining in farming, underscoring the continuing importance of increasing agricultural productivity in poor settings such as Kagera. But, the case study findings point especially to the importance of rural diversification and the better ability of poor rural households to connect with the rural economy and smaller towns in exiting poverty (the size effect), even though average consumption growth was substantially higher among those moving to the big cities, albeit at the expense of higher joblessness.

Cross-country panel data analysis for developing countries (including Sub-Saharan Africa) further suggested a broader empirical regularity to these phenomena observed in the micro-data. Transitioning out of agriculture into the middle was associated with faster poverty reduction, while agglomeration in large cities was not. The results were robust to controls for political regimes as well as a series of other measurement issues and other competing hypotheses. They also suggested that these superior poverty reducing effects of the 23
Some reasonably robust conclusions have emerged from studies of public spending incidence, including some in PSP. Spending on _basic_ services-notably primary and secondary education and basic health care-is found to reach the poor almost universally. The case for "broad targeting" by expanding the share of public spending on these services is well substantiated. Yet even here, care is required in monitoring that marginal investments are not lavished on increased quality for the betteroff. Certain food subsidy and distribution schemes, social cash transfers (such as are common in the former Soviet Union countries and Eastern Europe), public employment schemes and other targeted transfer schemes have at times been quite propoor. However, many programs whose stated rationale is to reduce poverty, have instead been dismal and expensive failures. A popular reaction has been to clamor for reforms of public spending towards finer targeting of benefits to the poor.

Most public spending programs are to some degree "targeted." The key question is: what _degree_ of targeting is optimal? Other things being equal, the more ways one discriminates between beneficiaries, the greater the impact of targeting on poverty. However, other things are not equal. Fine targeting sometimes comes at a cost to the poor. Administrative costs may escalate, political support may vanish, and behavioral responses may create extra costs to targeted interventions. There is no simple answer to how much targeting is desirable, but there are some clear principles to guide choice, and some suggestive empirical evidence from past experience, including many of the studies reviewed here.

The optimal mix of targeted and universal programs in fighting poverty will depend on a number of factors, including the characteristics of the poor (who the poor are, how many there are, and why they are poor) and country specific circumstances (initial conditions, infrastructure development and administrative capabilities). When poverty is widespread and administrative capacities are low, broad targeting will be particularly desirable and results from incidence of public spending studies should help guide sectoral and intrasectoral allocations. In general, what is needed is 43
|Durable goods: index (_durables_)<br>**Market conditions**|-0.026<br>(-1.08)|
|---|---|
|Average prices, 2000 quetzals/lb (_Price_ave_)|-0.104<br>|
||(-9.62)|
|**Role of time in adoption**||
|Historical time: origin of adoption spell (_start_a_)|-0.121|
||(-4.16)|
|Historical time: adoption epoch dummy: 1990-2001 (_epoch_90-01_)|0.633|
||(7.03)|
|Historical time: adoption epoch dummy: 2002-2005 (_epoch_02-05_)|1.202|
||(4.68)|
|Village time: % adopters in village (_Village__t)|0.021|
||(16.24)|
|**Community fixed effects**||
|San Mateo|0.303|
||(4.39)|
|El Rejón|0.460|
||(6.11)|
|Sta. Maria de Cauque|-0.146|
||(-2.55)|
|Pacul|-0.229|
||(-2.70)|
|Pachali|0.107<br>(1.39)|
|**Constant**|2.294|
||(8.85)|
|Ρ|3.28|
|σ=1/ρ|0.305|

Note: Shaded coefficients are significant at 95% percent, or higher.

Consistent with the hypotheses, the age of the head of household slows adoption while, contrary to expectation but in line with the cited study in the same communities (Carletto et al., 1999), family labor plays no significant role in the decision to adopt. This may reflect that fact that households would enter NTX production regardless of the amount of family labor available. However, labor supply is likely to have affected the extent of adoption, not captured by our model. The coefficients of the education of the household head, in quadratic form, are both significant, suggesting that higher education is associated with earlier adoption, but only above a threshold of about 4 years of schooling. Agricultural assets go hand in hand with more rapid adoption, 25
ROMANIA: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990._ SPAIN: The IMR is tl'* average of official vital registration estimates for 1985 to 1988 as published by Eurestat (1990). Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

SWEDEN: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth estimates are from _World Population Prospects 1990._ SWITZERIAND: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

UNITED KINGDOM: The IMR is the average of official vital registration estimates for 1985 to 1989 as published by Eurostat (1990). Life expectancy at birth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

YUGOSIAVIA: The IMR is the average of official vital registration estimates for 1985 to 1989 as published in _PVSR._ Life expectancy at tIirth is from Coale-Demeny model life tables, selected on the basis of the official CDR and age-sex distribution.

U.S.S.R.: The IMR is the average of official estimates from 1985 to 1988 as published by Blum and Pressat, 1987, and in _PVSR_ 10/90. Life expectancy at birth estimates are from _World Population Prospects 1990._ 23
- 93 -

# REi -NCES

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III. Cross-Country Correlations between Period Averages of the Indicators of Financial Depth and Stock Market Development This section preserts correlations and graphs to highlight the relationship between indicators of financial depth and stock market development. There are two sample periods under consideration. One is from 1978 to 1990, and the sample for this period includes 20 economies which are constituents of the IFC's Composite Index. The other is from 1984 to 1990, and the sample for this period includes all 32 economies in the EMBD. Arithmetic averages are taken for all the five variables over these two periods respectively.

# A. Correlations in the period 1984-1990

The levels of financial depth, GNP per capita and stock market development vary widely among the sample countries. But the level of stock market development has a much larger variation among the countries than the variations in financial depth. Table 1 presents the relevant uni-variate statistics<sup>3</sup> . This suggests that stock market development is affected to a greater degree by country specific factors than are measures of financial depth.

In the richer developing economies (as defined by GNP per capita), financial depth and the degree of stock market development are typically higher. Figures 1 and 2 show the distribution of MCY and QMY across the countries. In the figures, the countries are arranged in an ascending order from left to right according to the levels of GNP per capita. It is clear that there is a rising trend in the two variables. In this period, on average Malaysia, Kuwait, Taiwan, Jordan, Korea and Chile are the economies which have larger stock markets.

Countries with more developed stock markets (in terms of MCY) also have more financial depth as defined by the ratio of QMY to GDP or the ratio of MI to GDP. But the 3All tables and graphs are placed at the end of the paper.

4
By contrast, using proxy instead of self-reports involves substantial savings. The use of self-reports increases the length of field work because more days are spent in each sample village to locate and interview respondents. This survey experiment was carried out in conjunction with a larger consumption expenditure experiment, which required survey teams to spend a full two weeks in a village anyway. We cannot determine the additional field days that would be needed to complete self-report compared to proxy labor modules. However, based on field experience, we can roughly calculate that for two days spent in a village using proxy respondents, the survey team would need at least one more day to track down self-reports. This corresponds to a 33 percent increase in the length of time spent on actual field work. We can assume that all variable costs of field staff (per diems, lodging costs), often the largest category of survey costs, would increase by 33 percent. Transport costs may also raise if field teams used a team vehicle to track down respondents for selfreports. Given that the results of our experiment indicate that using self-reports instead of proxy respondents does not alter significantly the employment statistics collected, we can conclude—even without a rigorous cost-benefit calculation—that using self-reports in this case (for this sample and this type of statistics) would not be worth the extra cost required.

# **5. Conclusions**

Child labor has received increasing attention over the past decade and empirical measurement has now become common practice. How child labor is measured does differ across countries and within countries over time, potentially creating problems of comparability. Little is known about whether different survey methods generate different results for child labor statistics or whether the fluctuations we observe in child labor data are explained by other factors. This paper presents a randomized experiment whereby we use two commonly varied survey designs, the level of detail in the questionnaire and the choice of respondent, to estimate the effects of these survey features on the labor statistics they generate.

Our findings suggest that using a short employment module generates a much lower incidence of child labor, once the percentage of boys and girls who declare their main occupation was “domestic duties” are correctly classified—as per the ILO definition 20