rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Descriptive Statistics: Main Variables

|Household Type:|Two|-Parent||Single|
|---|---|---|---|---|
|Variables|Mean|Std. Dev.|Mean|Std. Dev.|
|Child Care Choices:|||||
|Child Care Center Providers|||||
|Use: =1 if main form of care is a day care center<br>|0.27||0.41||
|Units: 0–1|||||
|Price: Hourly fee for a 2 year old attending full-time|4.54|1.74|3.88|2.41|
|Units: 2003 US dollars per hour|||||
|Quality: Average overall ITERS score|4.26|0.94|4.35|1.16|
|Units: ITERS scale 1–7|||||
|Non-relative Caregivers|||||
|Use: =1 if main form of care is|||||
|non-relative to the child|0.06||0.04||
|Units: 0–1|||||
|Relative Caregivers|||||
|Use: =1 if main form of care is|||||
|a relative other than a parent|0.18||0.23||
|Units: 0–1|||||
|Child’s Cognitive Score:|||||
|Bailey Short Form Mental Ability Score|128.9|11.52|123.8|9.97|
|Units: Original Score Unit<br>|||||
|Parents’ Characteristics:|||||
|Mothers|||||
|Labor Supply|||||
|=1 if working full-time for pay<br>|0.32||0.42||
|=1 if working part-time for pay<br>|0.16||0.13||
|=1 if not working for pay|0.52||0.45||
|Units: 0–1|||||
|Wage: Gross hourly wage|20.14|13.99|10.13|4.90|
|Units: 2003 US dollars per hour|||||
|Education|||||
|=1 if college completed or more|0.49||0.12||
|=1 if high school completed or some college|0.45||0.58||
|=1 if incomplete high school|0.06||0.30||
|Units: 0–1|||||
|Fathers|||||
|Labor Supply|||||
|=1 if working full-time for pay|1.00||||
|Units: 0–1|||||
|Wage: Gross hourly wage|23.22|16.41|||
|Units: 2003 US dollars per hour|||||
|Education|||||
|=1 if college completed or more|0.51||||
|=1 if high school completed or some college|0.41||||
|=1 if incomplete high school|0.08||||
|Units: 0–1|||||
|Both|||||
|Correlation between parents wages|0.54||||
|Units: 2003 US dollars per hour|||||
|Sample Size|||||
|Number of Households||600||150|

Note: Table reports definitions and descriptive statistics of the estimation sample’s main variables. Sample 45 extracted from the second wave of ECLS-B for children born in 2001. Sample size is rounded to the nearest 50 to comply with ECLS-B confidentiality rules.
when combined with the necessity to use a proxy respondent (groups 2 and 3) or not (group 1).

The criterion of household membership, generally defined as all individuals who normally live and eat their meals together, affects whether migrants are captured in a survey. In the case of migration questions, additional restrictions are generally imposed to refine the concept, for example by asking about the number of months individuals have been absent over the previous 12 months. In most standard surveys, if the absence is more than 3 (or 6) months, the person will no longer be considered a household member and thus excluded from data collection past the basic household roster. However, many of these people maintain linkages – such as through remittances – and they should be considered in the survey, especially if the goal is to assess the impact of migration on those left behind. Another question is whether these groups should include all former household members (i.e. any individual who used to live in the household at any point in time) or only members of the nuclear family. Although the latter approach may result in an underestimation of the total number of international migrants, it may be preferable. This method has been applied to internal migration in a nearly nationally representative survey collected in China (de Brauw et al., 2002), and again in the Mexican National Rural Household Survey (Richter and Taylor, 2005).

An alternative method takes advantage of the fact that many household surveys already contain a fertility module, in which information on all children ever born from all female members of reproductive age is collected. A drawback is that it will miss all children of women no longer in the household or who have passed away. A similar approach is to list in a separate module all adult children of the head of the household 16
# **Impacts of Policy Instruments to Reduce Congestion and Emissions from Urban Transportation: The Case of Sao Paulo, Brazil**

Alex Anas, Govinda R. Timilsina

# **1. Introduction**

São Paulo is the largest city in Brazil and in South America, and the São Paulo metropolitan area, with an estimated population of 21,616,060 in 2008 spread over an area of 7,944 square kilometers, ranks among the most populous metropolitan areas in the world (Instituto Brasileiro <u>de Geografia e Estatística, 2002; 2008). Sao Paulo is a highly congested and polluted megacity</u> due to the rapid increase in car ownership resulting from income growth without adequate expansion of road capacity. The city emitted more than 15 million tons of CO2 in 2003, with 76% of that total from energy use, of which gasoline and diesel combustion accounted for 68%. The city government has identified climate change mitigation as a high priority and has presented an ambitious proposal to reduce CO2 emissions by 30% by 2012, using both economic and fiscal incentives (Goldman, 2008). Moreover, it has been reported that particulate matter (PM) and the ozone precursors (nitrogen oxides and volatile organic compounds) from vehicular emissions pose a grave threat to local air quality in the São Paulo metropolitan area (SánchezCcoyllo et al., 2009).

Vehicular transportation is the primary source of congestion and environmental externalities in Sao Paulo as one of the most desirable normal goods is the ownership of a private vehicle (Ingram, 1998). Income growth stimulates the demand for car ownership, and with more car ownership, congestion and pollution, including CO2 emissions, increase. The increases in emissions that could come from some of the developing countries in the future can be staggering, due to the enormous urbanization and real income growth that potentially lies ahead. In particular, traffic congestion and pollution tends to be worst in the megacities of the rapidly developing nations such as China and Brazil (see, for example, Gurjar et. al. 2008).

With increased urbanization and higher incomes, suburban and exurban sprawl accelerates with urban areas spreading out in low density patterns that are believed to favor mobility over longer distances by private motorized vehicles rather than by public transit or by bicycle. Meanwhile, high densities that can be achieved in urbanized areas support potential investments in rail transit systems that could greatly reduce reliance on the automobile, than if the same 2
**Table 6:  Taxes,** **<u>party ideology and party ag</u>**

|Dependent variable|(1)<br>Tax reve<br>Base specification|(2)<br>nues/GDP<br>Add'l political vars|
|---|---|---|
|**Largest govt. party is left wing**|**0.92**<br>**(0.016)**|**0.89**<br>**(0.020)**|
|**Age of largest govt. party if left-wing**|**-0.011**<br>**(0.004)**|**-0.011**<br>**(0.004)**|
|Political system (1 = presidential; 2 =<br>semi-presidential; 3 = parliamentary)||-0.65<br>(0.36)|
|Proportional (0) or plurality (1) lower<br>house elections||1.25<br>(0.20)|
|Mean District Magnitude House||0.0079<br>(0.012)|
|Continuous years of competitive elections|-0.035<br>(0.52)|-0.031<br>(0.57)|
|Log of ppp-adjusted per capita income,<br>2005 US dollars|1.80<br>(0.21)|1.19<br>(0.41)|
|Log total population|-3.34<br>(0.29)|-3.83<br>(0.23)|
|Percent population rural|-0.23<br>(0.074)|-0.25<br>(0.059)|
|Percent population 65 and older|-0.27<br>(0.28)|-0.15<br>(0.54)|
|Observations|1,245|1,212|
|R-squared|0.036|0.047|
|Number of countries|110|106|

Note: All specifications are ordinary least squares, controlling for country fixed effects. Clustered, robust p-values in parentheses. Only country-years with competitively-elected leaders are included.

# **8. Conclusions**

Altogether, then, evidence from a variety of sources strongly supports the conclusion, already present in the literature, that left-wing parties redistribute more. The analysis also identifies an important new influence on redistribution, the age of left-wing parties. A number of arguments point to organizational features that older parties are likely to have and younger parties to lack, including mechanisms to replace leaders and to control leader and member shirking. All of these lengthen the horizons of leaders and improve the reputations of parties, which in turn substantially alter the redistributive policies that such parties have an incentive to pursue.

22
2. Calibrate the production function (1) using parameters estimated in Duranton _et al._ (2015).

3. Calibrate the distributions of _φ_ , _L_ , _K_ , and _T_ . This can be done in four different ways:

- (a) Full-sample-correlation calculated from data (correlation 1).

- (b) Average-within-district-correlation calculated from data (correlation 2).

- (c) Full-sample-correlation between _φ_ and ( _L_ , _K_ , _T_ ) and zero between _L_ , _K_ , and _T_ (correlation 3).

- (d) Average-within-district-correlation between _φ_ and ( _L_ , _K_ , _T_ ) and zero between _L_ , _K_ , and _T_ (correlation 4).

4. For each of the four cases, simulate random numbers for these four variables.

5. Calculate the following quantities _ΣY_ / _ΣL_ , _M_<sup>_Y_</sup> , _M_<sup>_L_</sup> , _M_<sup>_T_</sup> , and _M_<sup>_K_</sup> associated to this baseline 6. Slightly change the correlations of ( _φ_ , _L_ ), ( _φ_ , _K_ ), ( _φ_ , _T_ ), respectively.

7. Simulate random numbers for these four variables again.

8.

9. Calculate _∆_ ( _ΣY_ / _ΣL_ )/ _∆M_<sup>_X_</sup> and _∆M_<sup>_Y_</sup> / _∆M_<sup>_X_</sup> for _X_ = _L_ , _K_ , _T_ .

10. The number of simulation is 800 In the interest of space, the full results (4 correlations and 5 years of data with different sampling options) are not reported here. Table 20 reports only results for 2010. Recall that we can consider four different correlations among factors and productivity: (1) those that we observe in the data in the aggregate, (2) those than we observe on average within district, (3) the aggregate misallocation measures that we observe in the data but setting the covariances across factors to zero, and (4) the average misallocation measures that we observe within districts but setting covariances across factors to zero.

41
Chaudhuri, K. N. "World Silver Flows and Monetary Factors as a Force of International Economic Integration 1658-1758 (America, Europe and Asia)." The Emergence of a World Economy 1500- 1914. Papers of the Ninth International Congress of Economic History Part I: 1500-1850. Eds. Wolfram Fischer, R. Marvin McInnis and Jiirgen Schneider. Wiesbaden: Steiner Verlag, 1986. 61-82.

Chaudhury, Sushil and Michel Morineau. "Introduction." Merchants. Companies and Trade: Europe and Asia in the Early Modem Era. Eds. S. Chaudhury and M. Morineau. Cambridge: Cambridge lJniversity Press, 1999. 1-18.

Chia Siow Yue. ASEAN: 30 Years of Existence and Challenges Ahead. Seoul: Korea Institute for Intemationa] Economic Policy, 29 Oct. 1997.

Clark, Colin. Population Growth and Land Use. London: Macmillan, 1977.

DeRosa, Dean A. "Regional Trading Arrangements Among Developing Countries: The ASEAN Example." Research Report 103 Abstract. International Food Policy Research Institute, September 1995. <http://www.ifpri.org/pubs/abstract/abstr103.htm>.

Desai, M. "Demand for cotton textiles in nineteenth-century India." Indian Economic and Social History Review 8 (1971): 337-61.

Elek, Andrew and Hadi Soesastro. "Ecotech at the heart of APEC: Capacity-building in the Asia Pacific." Asia Pacific Economic Cooperation rAPECI: Challenges and tasks for the twenty-first century. E]d. Ippei Yamazawa. London: Routledge, 2000. 218-254.

Fan, Gang. "Compatible Opening in China - Lessons from the Asian Financial Crisis." Developing Economies in the Twenty-First Centurv." Ed. Ippei Yamazawa. Chiba, Japan: Institute of Developing Economies, JETRO, 2000. 27-37.

Feinberg, Richard E. and Ye Zhao. Eds. Assessing APEC's Progress: Trade. Ecotech & Institutions. Singapore: Institute of Southeast Asian Studies, 2001.

Feldbak, Ole. "Country lTrade Under Danish Colours; A Study of Economics and Politics Around 1800." Asian Trade Routes: Continental and maritime. Ed. Karl Reinhold Haellquist. London: Curzon Press, 1991. 96-103.

Frank, Andre Gunder. ReORIENT: Global Economy in the Asian Age. Berkeley: University of California Press, 1998.

75
4

# _II.2 Measures ofpoverty, consumption, and inequality_

Three poverty measures of the FGT (Foster, Greer, and Thorbecke, 1984) class are used, and each of them is computed for both the lower and upper regional poverty lines. The incidence of poverty, which is simply the percentage of the population living in households with a per capita consumption below the poverty line, is measured by the headcount index (denoted by HL for the lower poverty line and HU for the upper poverty lines). The depth of poverty is measured by the poverty gap index (denoted by PGL or PGU), which estimates the average distance separating the poor from the poverty line as a proportion of that line (the mean is taken over the whole sample with a zero distance allocated to the households who are not poor.) The severity of poverty is measured by the squared poverty gap index (denoted by SPGL or SPGU), which takes into account not only the distance separating the poor from the poverty line, but also the inequality among the poor. Denoting by Ci the nominal per capita consumption for household i, by N the population size, by wi the weight for household i (equal to the household size times the regional expansion factor, the sum of the weights being N), and by Z the set of regional poverty lines, the three poverty measures are obtained for values of 0 equal to 0, 1, and 2 in: Two additional measures of well-being are used: the welfare ratios (denoted by WL or WU) and the Gini indices (denoted by GL or GU). Welfare ratios are simply mean consumption levels normalized by the poverty lines so that differences in costs of living between areas are taken into account: W = Xi (wi IN) (Ci /Z) (2) the transformation table giving caloric intake from food consumption. The differences in poverty lines result in fairly small differences in poverty measures which are not statistically significant.
<!-- Start of picture text --> 5.b Share (%) of direct transfers received by market income deciles<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Mali Niger<br><!-- End of picture text --> _Source:_ Authors The amount of total direct transfers is small, especially for the poorest people **.** Although direct transfers are pro-poor in Mali, their small size may limit their effect in improving the well-being of the population (figure 5.a). Direct transfers account for less than 1 percent of market income for each of the four bottom deciles. This is also true for Niger, where in addition to not being propoor, direct transfers are marginal.

# _Health and Education Expenditures_

Public expenditures on education and health are considered through their unit costs, which are imputed to every individual who uses public health and education services. Public spending on education includes salaries, operating expenses, and investments in physical infrastructure. The total number of individuals enrolled in primary, secondary, and higher education is estimated directly from the survey, and the unit costs of each level of education are obtained by dividing public spending on education by total national enrollment. Then, the amount of education spending to be transferred to each household is determined by multiplying the number of individuals in the household enrolled in primary, secondary, and tertiary education in 2013/14 by the respective unit costs. For health, the number of individuals who visited a public health care provider or were hospitalized during 2013/14 is estimated based on the survey. The unit cost is determined by dividing total health spending by the total number of individuals. This assumes more-equitable distribution among health service users than happens in reality; health service users do not receive the same levels of benefits depending on the nature of the service received, but it is not possible to distinguish health spending for different types of services in the budget data. The calculation of the unit costs of education and health is refined here by estimating specific unit costs according to region.

Health and education spending are globally progressive in Mali and Niger. In-kind transfers in health and education are overall progressive in the two countries (figure 6). Their progressivity is 10
# IV. ECONOMIC **AND INSTITUTIONAL FACTORS FOR SUCCESSFUL CREDIT POLICIES 16**

The list of theoretical arguments and practical considerations set out in the introductory sections of this paper suggest that credit policy could be an effective policy instrument for economic development. The question then arises why did credit policies fail in so many countries around the world and what factors explain their relative success in East Asian countries.

The experience of East Asia shows that both economic and institutional factors are very important. Economic factors include the maintenance of macroeconomic stability, export orientation, domestic competition, reliance on private sector, and bias toward industrialization. Institutional factors cover the creation of effective monitoring systems, the use of extensive consultation arrangements and the development and propagation of credible visions.

# A. ECONOMIC **FACTORS**

**Macroeconomic Stability.** Most East Asian countries were able to maintain macroeconomic stability, with small exceptions now and then. Macroeconomic stability in itself does not, however, seem to be a sufficient condition. Several other countries in other parts of the world, such as the Middle East and North Africa, Southern Europe, and South Asia, also avoided the high inflation rates of some Latin American and African countries. For instance, India maintained better price stability than Korea. Yet neither their economic performance nor their credit policies were as successful as those of Japan and other East Asian countries. Nevertheless, macroeconomic stability seems to be very important for encouraging the growth of financial savings. India and other countries with moderate inflation, compared to other developing countries with similar level of per capita income, experienced substantial deepening of their financial sector. Japan, whose macroeconomic environment was very stable, also experienced steady and rapid growth of its financial system. Korea, on the other hand, had relatively high inflation and poor growth of financial sector in the 1970s, although its experience was reversed in the 1980s. The recent rapid growth of holdings of findncial assets in China, Where the macroeconomic environment was relatively stable compared to other developing countries, also supports the importance of price stability.

**Competitive Product Market Environment and Export Orientation.** The main difference between the high performing East Asian economies and other countries lied in the fact that macroeconomic stability in East Asia was combined with intense domestic competition, strong export orientation, and reliance on the private sector. In Japan and Korea, although there were import protection, strong competition prevailed in the domestic niarket among large industrial firms. Even in the economically stable countries of Middle East and North Africa, Southern Europe, and South Asia

Ih 6 This section draws on JDB/JERI (1994), Cho and Hellmann (1993), and Vittas and Kawaura (1994).
for studying labor market dynamics. The list of countries and years that are covered is shown in Table 1.<sup>10</sup> All of the information is aggregated by sector of activity using the standardized NACE classification Rev. 1.1 at 1 digit.<sup>11</sup> Table 2 shows the labor market characteristics of low and high FD sectors in 2007, one year before the financial crisis. High FD sectors are larger in terms of total employment, and have a higher share of middle and high-skill occupations (82% versus 77%). 46% of employment in high FD sectors is rural, versus 42% in low FD sectors. Their differences on other dimensions are not striking. The share of workers on temporary contracts is 11% in high FD sectors and 12% in low FD sectors. The share of employment accounted for by small firms (10 employees or less) is about 23-24% in both types of sectors. They have roughly similar distributions of workforce by age, education and gender.

Our proxy variable for the availability of credit is private sector credit flow, consolidated as a share of GDP (from Eurostat). The private sector credit flow represents the net amount of liabilities that firms and households have incurred throughout the year. As seen in Figure 3, which shows the average of this variable for the two country groups, this variable displays significant variation throughout the period. During the financial crisis, the net amount of liabilities as a share of GDP declined from around 20 percent or more to around zero. In order to facilitate the interpretation of the results, we create the variable ������ ������� , which is equal to private sector credit flow (as a share of GDP) multiplied by -1. Alternatively, we also use a dummy variable equal to one if private sector credit flow (as a share of GDP) is negative or falls below half of the previous year’s level.

> 10 Data for Germany and the two candidate countries (Macedonia and Turkey) were not included in the 2014 release of the EU-LFS microdata. Malta was excluded because data for this country are only available from 2009 on.

> 11 Unfortunately, the public version of this survey does not include a more disaggregated version by sector of economic activity.

10