rafmacalaba/datause-encoder-data huggingface dataset:
* t . v *

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**Women In Development** WPS 1045

This paper is a product of the Women in Development Divis )n, Population and Human Resources DepartmenL Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Abundo, room S2-270, extension 36820 (November 1992, 50 pages).

Subbarao and Raney explore the strength of female secondary education relative to, and in combination witi' family planning and health programs in reducing fertility and infant mortality. They find that family planning and health programs do influence fertility and mortality, but that the impact of expanding female secondary enrollments appears to be much greater, especially in countries with low female secondary enrollment. Fertility and infant mortality are more elastic with respect to female secondary education than to family planning and health programs. Their simulations suggest: Doubling female secondary school enrollment (from 19 to 38 percent) in 1975 would have reduced the total fertility rate in 1985 from _5.3_ to 3.9. Doubling the "family planning service score" (from 25 to 50 percent) in 1982 would have reduced the total fertility rate only from 5.5 to 5.0.

* Doubling female secondary school enrollment in 1975 from 19 to 38 percent would have reduced the infant mortality rate from 81 to 38.

Halving the ratio of population per physician would have reduced the infant mortality rate only from 85 to 81. Doubling per capita GDP from $650 to $1,300 would have reduced the infant mortality rate only from 98 to 92.

> * Doubling female secondary school enrollment (from 19 to 38 percent) in 1975 would have lowered the number of births by 29 percent of the 1985 number. Doubling family planning services would have reduced it by 3.5 percent.

* Doubling female secondary school enrollment would have reduced infant deaths by 64 percent. Halving the ratio of population per physician would have reduced it only by _2.5_ percent. Doubling per capita GDP has no effect on reducing infant deaths, all other factors being constant.

Female education affects desired family size by raising the opportunity cost of a woman's time in economic activities, increasing demand for family planning, and promoting more effective contraceptive use.

The Policy ResearchWorkimg PaperSeries disseminates the findings of work **under** way in theBanlc Anobjectiveof the series is to **get these** findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

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# **_Aid, Policies, and Growth: Revisiting the Evidence By Craig Burnside and David Dollar_**

_In Sub-Saharan Africa, 84% of opinion makers agreed with the statement that, “Because of corruption, foreign assistance to developing countries is mostly wasted.”_ – _Opinion survey commissioned by the World Bank and conducted by Princeton Survey Research Associates_ In “Aid, Policies, and Growth” (Burnside and Dollar 2000b) we provided evidence that aid accelerates growth in developing countries with sound institutions and policies, but has less or no effect in countries in which institutions and policies are poor. We found these results quite intuitive: a corrupt, incompetent government is not going to use aid wisely and outside donors are not going to be able to force it to change it habits. This evidence is supportive of the growing trend among aid agencies toward greater “selectivity” – that is, channeling relatively more aid resources to poor countries with reasonably good institutions and policies. The United States’s proposed Millenium Challenge Account is a recent example of this trend.

Since we initially presented our results in a working paper in 1996, critics have taken issue with them. However, our critics have not been able to agree among themselves about what the right model is: some find that aid never works anywhere (suggesting that it should be eliminated) while others find that it has a positive effect everywhere (suggesting that it should be given out indiscriminately). The main purpose of this paper is to take a fresh look at the issue based on a new data-set focusing on the 1990s.

It is useful to start with where there is agreement. Most development economists believe that underlying economic institutions and policies are the main determinants of long-term growth. Hall and Jones (1999), for example, argue that “differences in capital accumulation, productivity, and therefore output per worker are fundamentally related to differences in _social infrastructure_ across countries. By social infrastructure we mean the institutions and government policies that determine the economic environment within which individuals accumulate skills, and firms accumulate capital and produce output.” 3
(1998) finds that 6.5% of Hungarians have per capita incomes in the lowest quintile in all 5 years, indicating that persistent poverty in Hungary may be slightly higher than in Poland.

**Table 5. Poverty and Underclass**

||R|ussia|P|oland|
|---|---|---|---|---|
||Income|Expenditure|Income|Expenditure|
|_A._**_Fractions based on measured poverty_**|||||
|"Always" poor (4 out of 4)|2.2%|3.4%|5.3%|5.9%|
|"Sometimes" poor(1, 2or3 times out of 4)|45.4%|41.9%|33.5%|31.6%|
|"Never" poor (0 times out of 4)|52.4%|54.7%|61.2%|62.5%|
|**_B. Fraction based on simulation using_**<br>**_underlying poverty transition probabilities_**|||||
|"Always" poor (4 out of 4)|7.4%|10.0%|9.9%|10.3%|
|<br>"Sometimes" poor (1, 2 or 3 times out of 4)|28.2%|21.8%|22.0%|21.0%|
|"Never" poor (0 times out of 4)|64.3%|68.2%|68.1%|68.7%|
|**_C. Memo Items_**|||||
|Probability of remaining in underlying poverty|0.720|0.794|0.791|0.802|
|Probability of remaining out of underlying poverty|0.930|0.948|0.948|0.951|
|Mean absolute deviation of transitory shock (in %)|54.3%|45.3%|20.9%|19.2%|

Notes: Income and expenditure are adjusted for household size using an equivalence scale that equals household size raised to the power of 0.75. The poverty line is set such that in each year 20 percent of the population is poor.

Care needs to be taken, however, in interpreting such figures (see Jalan and Ravallion, 1998, and Baulch and Hoddinott, 2000). First, by construction, the underclass will shrink as the number of periods in the dataset increases. Second, this measure of underclass is very sensitive to transitory shocks and measurement error. A person whose underlying measure of well-being is consistently below the poverty line may occasionally appear as non-poor due to a transitory shock or measurement error. Hence, in countries with more transitory shocks and measurement error, the size of the underclass, as traditionally measured, will appear to be smaller than it really is. To illustrate this point, we simulate the fraction of the population whose underlying consumption or income measure is below the poverty line in all four periods using the transition probabilities of underlying poverty. These results are shown in panel B of Table 5. According to this simulation, the underclass in Russia is 7% to 10% of the population or about 3 times the size of the underclass based on measured poverty. The simulation shows that about 10% 28
just-style.com (2009c, November 24). 10 ways to develop a balanced sourcing strategy. _JustStyle._ Kapner, S. (2009, December 8). The unstoppable Fung brothers _. CNN Money._

- Kilduff, P., & Ting, C. (2006). Longitudinal patterns of comparative advantage in the textile complex part 2: sectoral perspectives _. Journal of Fashion Marketing & Management, 10_ (2), 150-168.

- Knappe, M. (2008). The textiles and clothing value chain: write-ups by ITC panelists. _UNCTAD XII: World Investment Forum Session II: Global value chains: opportunities and challenges for international and domestic firms,_ Accra, Ghana.

- MSN. (2009). _How will the global financial crisis affect the garment industry and garment workers?._ Ontario, Canada: Maquila Solidarity Network (MSN).

- McCormick, D., & Schmitz, H. (2001). _Manual for value chain research on homeworkers in the garment industr_ y. Institute of Development Studies.

- O'Connell, V. (2009, November 14). Wal-Mart looks to bolster suppliers _. Wall Street Journal._ Rasmussen, J. (2008). Part I: State of the industry 2008 _. Review, Ma_ y, 28-35.

- Sauls, J. (2008). _Excellence in sourcing: an Apparel research study and analysi_ s (A Supplement to Apparel Magazine No. August)Apparel Magazine & Kurt Salmon Associates.

- Talking strategy: expert views on US apparel sourcing.(2008). _Global Apparel Markets,_ (1), 5-9.

- Technopak. (2007). Strategies for textile and apparel manufacturers in the post-quota era: prospects to 2015 _. Textile Outlook International, July-Augus_ t, 152-167.

- Tewari, M. (2006). Is Price and Cost Competitiveness Enough for Apparel Firms to Gain Market Share in the World after Quotas? A Review _. Global Economy Journal,_ 6(4), 1-46.

- Thoburn, J. (2009). _The impact of the world garment recession on the textile and garment industries of Asia._ Seoul (Korea) Workshop: November 13-15: United Nations Industrial Development Organization (UNIDO).

- Tucker, R. (2009). Not only the recession: industry hit by resizing, trade issues and more _. Women's Wear Daily, 19_ 7(89)

- U.S. Census Bureau. (2009a). _Apparel - Summary 2008 Report MQ315A(08)-5_ . Washington, DC: U.S. Department of Commerce.

- U.S. Census Bureau. (2009b). _Socks production - summary 2008: Report MQ315B(08)-5._ Washington, DC: U.S. Department of Commerce.

- Vernon, R. (1966). International investment and international trade in the product cycle _. Quarterly Journal of Economics, 80_ (2), 190-207.

- World Bank. (2009). _Swimming against the tide: how developing countries are coping with the global crisi_ s. Background Paper Prepared for the World Bank. New York, NY: World Bank.

- WTO. (2009). _International Trade Statistics 2009._ Geneva, Switzerland: World Trade Organization (WTO).

- WTO. (2010). _Time series on international trad_ e. Geneva, Switzerland: World Trade Organization. Retrieved from <u>http://stat.wto.org/Home/WSDBHome.aspx?Language=E</u>

27
15 has substantially lower voter turnout—over a long period of regular elections from 1960 to 2005, the mean and median turnout in this group is around 52 percent, contrasted against 66 percent for the higher governance group.

Alternate explanations focusing on underlying determinants of poverty, inequality, and social or political institutions would therefore need a theory of why these factors would be correlated with higher share of capital spending, without growth (as discussed in the previous paragraph), if not through the channel of rent-seeking as argued in this paper. That is, even if the correlation between voter participation and budget composition is viewed as the outcome of underlying socio-economic conditions that contribute simultaneously to reduced turnout and greater share of capital spending, the most plausible channel for such correlation is the use of infrastructure spending for rent-seeking by political agents rather than the actual provision of broad public goods for citizens at large.

Perhaps the most puzzling issue presented by the observed negative correlation between voter turnout and share of capital spending is its inconsistency with household survey evidence on reported prioritization of capital infrastructure—roads, water, electricity—by an overwhelming majority of respondents.<sup>16</sup> New experimental evidence on the nuances of voting behavior provides some answers. Finan and Schechter (2010) explore the role of social preferences such as reciprocity, the psychological pressure to return a favor received from someone, in shaping electoral strategies of vote buying. Combining experimental games that measure the strength of reciprocal feelings among individuals with survey data on vote-buying

> 16 Reverse causation, that greater relative allocation to capital spending would contribute to lower turnout, also does not seem plausible given this survey evidence—if voters care more about capital infrastructure relative to social sectors, greater government activity in their preferred sector may be expected to increase their participation. Unless, of course, they are more likely to be satisfied with government and greater satisfaction keeps them away from the polls. However, there is nothing in the literature or data to suggest that actual access to infrastructure in the low governance states is of the quality that would support such an argument.
# **1. Introduction**

Household choices shape cities. Households select housing units based on affordability and the amenities they provide. A growing literature in urban economics seeks to better understand these choices using information revealed by market prices to estimate the value households assign to various housing characteristics. Arguably the most important characteristic is location, which greatly influences access to amenities and employment opportunities.

Infrastructure investments and public services interact with these choices. By investing in roads and transit services, public policies can make different parts of a city more or less attractive, which will be reflected in property values. A better understanding on how households value transport options can help in designing transport policies that increase private, but also social welfare—for instance by making sustainable transport more attractive. Such improved understanding is especially needed in fast growing cities in emerging and developing economies. However, most of the literature in these countries has focused on dwelling unit characteristics rather than on access to opportunities, specifically income earning opportunities.

Studies in emerging and developing economies offer interesting insights because of the spatial and socioeconomic segregation that these cities experience. Recent literature (Suárez et al. 2016; Negrete and Paquette Vassalli, 2011) suggests high-income households have a higher likelihood to own and use private cars, and spend less time commuting to their workplace. On the contrary, low-income households are located farther away from employment centers, and have access to more limited opportunities, whether it is because they spend more time accessing formal employment, or because they segregate economically finding informal or lower-paid jobs closer to their places of residence (Negrete and Paquette Vassalli, 2011).

This paper uses a unique, geographically referenced data set on new housing developments to estimate how households value accessibility in Mexico City. Accessibility is measured by access to employment subcenters, access to the historic center (or Central Business District) and access to the two main transportation infrastructures in the city: The Bus Rapid Transport 2
WPS3696

# **Growing together or growing apart?**

# **A village level study of the impact of the Doha round on rural**

# **China**

by Marijke Kuiper & Frank van Tongeren* Chapter 8 in _Poverty and the WTO: Impacts of the Doha Development Agenda_ , Thomas W. Hertel and L. Alan Winters (eds.) forthcoming from the World Bank, Washington, D.C.

## World Bank Policy Research Working Paper 3696, September 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._

> *Agricultural Economics Research Institute (LEI) – Wageningen UR
industries which require more external financing grow faster in more financially developed countries. Financial inclusion encourages investment by reducing liquidity constraints and therefore affects firm size, competition, and industrial structure (Beck et al. 2005). Moreover, Beck et al (2005) find that the effect of financial underdevelopment on a firm's growth depends on the firm's size. The latter highlight that the smallest firms are consistently the most adversely affected by negative investment climate in general but especially by financial underdevelopment. Finally, heavy regulatory environment negatively affects firms’ performance. Djankov et al (2002) show that heavy regulation related to the starting of businesses increases the size of the unofficial economy. Moreover, Botero (2004) finds that heavy regulation of labor is associated with a larger unofficial economy, lower labor force participation, and higher unemployment. Klapper et al (2006) show that entry regulations hamper establishment entry and that the value added per employee grows more slowly in countries with high entry barriers (Klapper et al., 2006) and small firms may be more dissuaded from entering than large firms.

Related to the investment climate and productivity, the impact of corruption and firm productivity is examined in detail by De Rosa et.al. (2013) in their analysis of Eastern and Central European economies. They examine the impacts of the “bribe tax”, which is the small payments to reduce transaction costs of the day-to-day operation and the “time-tax”, which is the time devoted by managers to deal with red tape. The authors note that the impact of the bribe tax can be either positive or negative, though they find a negative association between both variables. They find insignificant the impact of the time tax.

B. Descriptive statistics and Stylized facts of Nigerian Firms’ Output, Factors, and Business Climate This paper uses the World Bank’s Enterprise Surveys in Nigeria including more than 7,000 firms for the three years 2007, 2009 and 2014. The 2007 survey included 2,387 firms in 26 states, the 2009 survey 3,157 firms in 11 states and the 2014 survey included 2,676 firms in 19 states. We combined the first two surveys to ensure statistical representativeness at the national level. The surveys did not cover the agricultural or mining sectors. Tables 1 and 2 summarize the sample composition by size and sector, for the total sample.

4
type COMPOUND D, which is cheaper. Further, less than 4 percent of the club members do not bale or grade their own tobacco. Instead, around 20 percent of the non-members do not grade or bale their tobacco. Finally, tobacco club members benefit from more visits from agriculture field assistants, show more experience growing tobacco and hire less temporary work.

Both members and non-members allocate almost 30 percent of their plot to grow tobacco. In fact, very few producers specialize intensively in tobacco and almost all tobacco growers also produce food crops, such as maize, potatoes or beans, for their own consumption as well as other cash crops such as hybrid maize, groundnut or sugar cane. Although club members report higher values of output (per acre) of both cash and food crops, these differences are in fact not significant.

# **3 Empirical Strategy and Results**

We turn next to the estimation of the impacts of burley tobacco club membership on tobacco farming outcomes in rural Malawi. The analysis is based on the data from the Integrated Household Survey. The estimating equation is: where _yi_ denotes an outcome of household _i_ , a tobacco grower; _C_ is a binary variable that indicates whether the person in charge of cropping activities in the household (usually, but not necessarily, the household head) has _ever_ been member of a tobacco club in the last 5 years; **x** is a vector of additional controls including characteristics of the person who makes cropping decisions, like gender, education, age, marital status and health, as well as characteristics of the household such as size, demographic composition, and land holdings. In some specifications, the vector **x** also includes tobacco related variables such as tobacco experience, fertilizer use, baling procedures, and temporary work use (ganyu). The equation also includes district dummies _d_ to control for differences in district infrastructure, local institutions, and demographic-cultural factors (such as the matrilineal or patrilineal nature 17
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gender and having a cousin in New Zealand. This interaction is negative and insignificant. Therefore females are just as affected by having cousins in New Zealand as males, if not more so.

# **6.6 No male wage premium in Tonga?**

Table 6 shows that male and female immigrants have very similar expectations of the wages that they would earn if they were working in Tonga. Mean expected wages are 158 pa’anga for males and 157 pa’anga for females. These are close to the mean prior incomes of 146 pa’anga for males and 172 pa’anga for females. Among our sample of unsuccessful ballots, we also find that females have higher mean incomes than males, although the difference is not significant.

In Table 11, we explore the differences in work income between male and female workers in Tonga after controlling for years of education, age, and marital status. Restricting ourselves only to principal applicants for the Pacific Access Category, we see males having a positive and insignificant wage premium for the prior income of immigrants in New Zealand, and a negative and insignificant wage premium for the current income of unsuccessful ballots. We then look to broader samples of workers in Tonga. Column 3 shows that for all Tongan wage workers in our survey, the male dummy is negative, small, and statistically insignificant, while Column 4 shows a small positive and insignificant coefficient using the Tongan Labor Force survey. It therefore appears that there is no male wage premium in Tonga. We can not reject that male and female workers of the same age and education earn the same amount.

Tongan workers moving to New Zealand therefore face quite a different wage distribution than they do in Tonga. In particular, columns 5 and 6 of Table 11 show that male immigrants under the PAC earn significantly more than female immigrants. Male principal applicants earn $163 more per week on average than female principal applicants. Recall that the degree of male underestimation of income in New Zealand relative to females was $165-187 in Table 10. Therefore almost all of the male difference in underestimating earnings in New Zealand can be accounted for by the male wage 22