rafmacalaba/datause-encoder-data huggingface dataset:
Figure 2: Dynamics of Adjustment under Different Shocks with Alternative Pension Systems and Regulations

**Fertility Shock**

<!-- Start of picture text --> Longevity Shock<br><!-- End of picture text --> <!-- Start of picture text --> Capital Accumulation, k(z*)<br>0.32<br>0.31<br>0.30<br>0.29<br>0.28<br>0.27<br>0.26<br>0.25<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Utility, U(z*)<br>1.63<br>1.62<br>1.61<br>1.60<br>1.59<br>1.58<br>1.57<br>1.56<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Capital Accumulation, k(z ̅)<br>0.33<br>0.32<br>0.31<br>0.30<br>0.29<br>0.28<br>0.27<br>0.26<br>0.25<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Utility, U(z ̅)<br>1.63<br>1.62<br>1.61<br>1.60<br>1.59<br>1.58<br>1.57<br>1.56<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>DC DB DA<br>Capital Per Worker<br>Lifetime Utility<br>Capital Per Worker<br>Lifetime Utility<br><!-- End of picture text --> <!-- Start of picture text --> Capital Accumulation, k(z*)<br>0.32<br>0.31<br>0.30<br>0.29<br>0.28<br>0.27<br>0.26<br>0.25<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Utility, U(z*)<br>1.64<br>1.63<br>1.62<br>1.61<br>1.60<br>1.59<br>1.58<br>1.57<br>1.56<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Capital Accumulation, k(z ̅)<br>0.35<br>0.35<br>0.34<br>0.34<br>0.33<br>0.33<br>0.32<br>0.32<br>0.31<br>0.31<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>Utility, U(z ̅)<br>1.70<br>1.68<br>1.66<br>1.64<br>1.62<br>1.60<br>1.58<br>1.56<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Periods<br>DC DB DA<br>Capital Per Worker<br>Lifetime Utility<br>Capital Per Worker<br>Lifetime Utility<br><!-- End of picture text --> Notes: The parameter values used in the simulations above as follows: In all cases _A_ = 10, _α_ = 0.33, _β_ = 0.25, and _γ_ = 0.15. These reflect commonly used calibrations such as the share of capital in the economy and time preferences.were made when needed: _a_ = 1.6, _z_ ̄ = 0.394 in defined annuity case with mandatory retirement, andIn addition, the following assumptions _z_ ̄ = 0.579 in defined benefit and defined contribution cases. These values are chosen to make the initial conditions and changes comparable with alternative social security systems. Longevity and fertility rates are kept constant in fertility and longevity shock scenarios, with values _l_ = 0.9 and _n_ = 0.2, respectively. The longevity shock is an increase in _l_ from 0.9 to 0.95 in period 5, and the fertility shock denotes a decrease in _n_ from 0.2 to 0.137 in the same period.

17
_Africa’s Macroeconomic Story_ future (IMF 2012a, p. ix and p. 97). One might expect that the dramatic rise in commodity prices over the past few years would boost growth in resource rich countries. However, the historical record is not so clear cut. In a series of seminal papers, Sachs and Warner (1995 and 2001) observed that resource-rich countries have tended to growth more slowly than other countries. Collier and Goderis (2007) argue that increases in commodity prices boost output in the short run (through the improved terms of trade channel) but tend to have adverse consequences over the longer term by impeding the development of governance and sound institutions needed to manage the resource wealth effectively and advance a development agenda more generally. The empirical evidence on the “resource curse” hypothesis is mixed. A recent IMF (2012a) study finds that countries highly dependent on resource revenues have experienced significantly higher volatility in exports, revenue, and non-resource GDP growth. Mehlum, Moene, and Torvik (2006) and Arezki and van der Ploeg (2007) find that the quality of institutions and openness to trade determine whether resources impede or enhance growth. In contrast, Cavalcanti, Mohaddes, and Raissi (2011) find that oil abundance has had a net positive effect on income levels and growth and hence has been more of a blessing than a curse.<sup>2</sup> Similarly, Smith (2013) finds that resource wealth tends to raise GDP per capita but erodes democratic institutions. Moreover, McMahan and Moreira (2013) show that low- and lower-middle countries highly dependent on mining revenues have posted higher growth rates that other countries over the past two decades, even in cases where governance indicators are weak.

Dutch disease plays a central role in the debate. Resource development can lead to a large real exchange rate appreciation and thereby undermine competitiveness of the non-resource tradable sector. An important element of the argument is that the development of a vibrant, diversified nonresource export sector is critical for sustaining growth over the longer term (especially where resources are being depleted over a relatively short horizon). The proposition that export-led growth is critical for sustained growth spurts is controversial. Yang (2008) finds that higher growth often precedes an expansion in the export sector, which brings into question the direction of causation. Collier and Goderis (2008) find that, although exchange rate appreciations have a statistically significant effect on growth, it is only a minor part of the overall explanation. They argue that macroeconomic volatility plays a more important role. Recent experience does not indicate that

> 2 See Van der Ploeg, F. (2011) for a survey of the empirical literature on whether resources are a curse or a blessing.

7
<!-- Start of picture text --> Accra Antananarivo<br>|<br>y<br>/ \ | \<br>Nn i<br>S<br>—<br>a i a ie ai se<br>0 500 1000 1500 0 5000 10000 15000<br>2017 PPP US$ 2017 PPP US$<br>Dar es Salaam Addis Ababa<br>“\<br>ly<br>1 \ oN<br>In| /<br>/<br>/ ~ .<br>IY tees<br>] YL _<br>J +S ~-~N a,<br>a cs a<br>0 2000 4000 6000 8000 0 5000 10000 15000<br>2017 PPP US$ 2017 PPP US$<br>—-—-—-—- Non directly affected by flood ———_ Directly affected by flood<br><!-- End of picture text -->
Nicita, Alessandro and Marcelo Olarreaga. 2004, Trade, Trade reforms and Poverty in Ethiopia, mimeo World Bank.

Ravallion Martin and Shaohua Chen 2004, “Household Welfare Impacts of WTO Accession in China”, World Bank Economic Review, Vol. 18(1), pp. 29-58.

Taubman, Paul, and Michael L.Wachter. 1986. “Segmented Labor Market,” in Ashenfelter, O. and Layard P.R.G. (eds.) _Handbook of Labor Economics_ , Vol.2. Amsterdam: North Holland.

Winters, L. Alan, Andrew McKay and Neil McCulloch, 2004, “Trade Liberalisation and Poverty: The Evidence so far” Journal of Economic Literature vol. XLII, pp. 72-115 World Bank (2005) DR-CAFTA: Challenges and Opportunities for Central America, Washington, DC: World Bank.

World Bank. 2000. _Trade Blocs_ . New York: Oxford University Press.

World Bank. 2001. _Nicaragua Poverty Assessment: Challenges and Opportunities for Poverty Reduction_ . World Bank Report No. 20488 NI, Washington, DC: World Bank.

World Bank. 2004. _Global Economic Prospects 2005: Trade, Regionalism, and Development_ . Washington, DC: World Bank.

48
- Vyas, Seema. 2020. “Women’s economic status and sexual negotiation: re-evaluation of the ‘normative precedent’ in Tanzania.” _Culture, Health & Sexuality_ 22(10):1097–1111. PMID: 31429378. **URL:** _https://doi.org/10.1080/13691058.2019.1652933_

- Wilson, Robin Fretwell. 2007. “The Overlooked Costs of Religious Deference Symposium: Gender Relevant Legislative Change in Muslim and Non-Muslim Countries.” _Washington and Lee Law Review_ 64(0):1363.

- World Health Organization. 2021. _Violence against women prevalence estimates, 2018: global, regional and national prevalence estimates for intimate partner violence against women and global and regional prevalence estimates for non-partner sexual violence against women_ . World Health Organization: Geneva.

42
# **1. Introduction**

In recent decades, China’s economy has been characterized by rapid economic development accompanied by equally rapid urbanization. The resulting benefits have not, however, been shared equally across the country. Many rural areas saw considerable improvements in living standards and large reductions in poverty rates, but gains in urban areas and those in China’s coastal regions in particular have been considerably larger. The Chinese government has always had a keen interest in the country’s spatial economic development. Its main policies have been aimed at restricting the flow of migrants to the big cities, while at the same time trying to develop interior regions by better connecting them to the booming coastal regions.

Two policies stand out in this regard.<sup>1</sup> The first is the Hukou, or household registration, system. Every Chinese citizen’s Hukou status is determined by that of their parents at the time they were born. A Hukou is tied to a particular urban or rural location and represents an entitlement to welfare benefits and public services (such as education, health care, etc.) in that place. When migrating to a different city, a person’s Hukou status does not change, so that migrants are unable to claim many welfare benefits or public services in their destination city. Despite the Hukou system, many rural migrants have still migrated to the cities. A 2013 survey by China’s National Bureau of Statistics estimated a “population that is separated from their household registration” of 289 million (NBS 2014, also Chan, 2013). There is growing consensus that if China’s continuing urbanization process is to be economically productive and socially inclusive, the Hukou system—put in place to restrict migrant flows into the cities—will have to be loosened or even abolished (World Bank and DRC 2014). Chinese policy makers seem to agree, and have recently issued significant reform proposals (State Councel, 2014) The second policy is the huge investment in large-scale infrastructure. In recent decades China has constructed an extensive 96,000 km network of highways connecting the largest cities in the country, vast intra-city transport infrastructure (ring roads, metros), and is currently building the longest high speed rail network in the world connecting its main population centers (see Baum-Snow and Turner, 2012; Roberts _et al_ ., 2012, Baum-Snow _et al._ 2012; Zheng and Kahn, 2013; Faber, 2014; among others). The explicit aim of these projects, and that of the construction of the highway network in particular, is to spread development from the more developed coast to the cities in the interior. They make it cheaper for these cities to import materials and intermediate goods, and to ship their own products to the rest of China and the world. Indirectly, this policy is also aimed at alleviating migration pressures. By

> 1 Another is land reform. The risk of losing one’s plot of land keeps many rural residents from moving to the city, even if they were able to obtain an urban Hukou. Land reform, whereby people would be able to sell their land would be a way to alleviate this problem.

2
**Table 6: Sectoral Commitments Adjusted for Mode of Supply Weights, Proportion of Service Sectors Excluded from Liberalization, and Sector Weight in GDP**

||'HIC'Group|Developing countries|
|---|---|---|
|Market access commitments|48.5|11.4|
|National treatment commitments|53.0|12.6|

Table 7 and 8 contain country-specific data for the 'HIC' and 'large developing country' groups, respectively. These Tables will not be described in detail, but have been included so as to give a flavor of the variance of commitments across individual countries. It is worth repeating that sectoral coverage indices are of only limited usefulness from an economic perspective, as no account can be taken of the actual restrictiveness of the policy stance maintained by different countries by sector/mode of supply. The least ambiguous measure in this respect is the 'no restrictions' coverage ratio.' This is 28 percent on average for the 'HIC' group, and 6.4 percent on average for the other GATS Members. Much remains to be done therefore. As it seems reasonable to presume that the 'easiest' sectors were scheduled by countries, these relatively low coverage indicators suggest that for the foreseeable future most trade in services will remain subject to market access and/or national treatment restrictions.

The average coverage of sectoral commitments is of course greater than the 'no restriction' measure. Weighted by sectoral contributions to GDP, it is about 50 percent for 'HIC' and 11 percent for the developing country group. The average sectoral coverage ratio of the 'bottom' 50 developing countries (which account for over 60 percent of the group in number) is substantially below the group average: only 3 percent. As shown in Annex 3, four countries--Algeria, Bangladesh, Fiji, and Tanzania-made commitments for only one sector; five others scheduled two sectors--Belize, Burkina Faso, Madagascar, Sri Lanka, and Uganda. While many of these countries are small and have very low per capita incomes--reducing pressure from trading partners to schedule more sectors--this by no means justifies such a passive stance.

**26** The reciprocal of the 'no restrictions' ratio--with services weighted by shares in GDP--is an indicator of the extent to which access to services markets is 'affected' by policy. It can be seen as a direct analogue of the standard NTB frequency indicator if unweighted by GDP shares; and the NTB coverage ratio if weighted.

20
to come through the grants enabling firms to purchase more capital and hire more labor, with no changes in business networks, mentors, self-efficacy, or uses of other sources of finance. The results show there is a group of constrained entrepreneurs with the ability to grow their businesses beyond a small scale, and that the business plan competition was successful in both attracting such individuals, and in helping them overcome their constraints. Nevertheless, conditional on getting to the stage of submitting a full business plan, we find that personal, business, and business plan characteristics have low predictive power for identifying which entrepreneurs will grow faster, and which will respond best to treatment.

This paper contributes to three main literatures. The first addresses the sources and constraints to job growth, which is one of the most fundamental concerns of policy makers globally. An increasing body of work examines which types of firms create more jobs, with recent evidence highlighting the importance of business start-ups and young firms in job creation.<sup>2</sup> Related work has found that a small number of firms, variously termed “gazelles”, “high-growth entrepreneurs” or “high-impact firms” represent a tiny fraction of the overall firm population, but make a disproportionally large contribution to job growth.<sup>3</sup> New job creation by firms is of particular importance in Sub-Saharan Africa given that it currently has the lowest share of the labor force in wage work of any region in the world, and that demographic forces result in the working-age population growing 2.8 percent per year (ILO, 2012). Schoar (2010) posits that “transformational” entrepreneurs who aim to create large, vibrant businesses that grow and hire workers are fundamentally different from these subsistence businesses, a point echoed by a recent survey of African entrepreneurs which concluded that “The culture of entrepreneurship in Africa is largely defined by necessity driven entrepreneurship; that is, entrepreneurship as a means of survival. Entrepreneurship is viewed as a last resort, as opposed to the pursuit of an opportunity or aspiration” (Omidyar Network, 2013, p. 18). While the study felt these attitudes were changing, it concurred with a long literature that suggests a perceived lack of high-growth entrepreneurship in many African countries (e.g. Elkan, 1988) may be in part due to multiple market failures that

> 2 See, for example, Haltiwanger et al. (2013) for the U.S.A,, Lawless (2014) on Ireland, and Ayyagari et al. (2014) for evidence from a range of developing countries.

> 3 For example, Morris (2011) finds that 4 percent of entrepreneurs in the Global Entrepreneurship Monitor surveys account for close to 40 percent of all jobs created; see also reviews by Henrekson and Johannsson (2010) and the OECD (2010).

3
US$2,900 for high-income), the difference in income level implies that tertiary education becomes much more affordable in high-income countries than in LAC countries.

The share of non-governmental education is an important factor behind the cost differences. Countries with a higher share of students in non-governmental universities evidently tend to have a higher education costs than countries with a higher share of students in public universities. The extent of cost-recovery in public institutions also plays a role for the cost difference.

<u>Living costs</u> for accommodation and food for the academic year vary significantly between the countries in this study ( Figure 2 ).

**Figure 2. Living Costs**

<!-- Start of picture text --> Mexico<br>Ireland<br>Belgium (Flemish)<br>Germany<br>Canada<br>Belgium (French)<br>Italy<br>Netherlands<br>United States<br>Finland<br>Austria<br>High Income<br>France<br>Sweden<br>Japan<br>Average<br>Australia<br>LAC Average<br>Brazil<br>United Kingdom<br>New Zealand<br>Colombia<br>Peru<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>Living Cost/GDP per capita (%)<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries Students in LAC countries have to pay a comparatively large share of income on living costs, 29 percent as a share of GDP per capita, compared to peers in high-income countries, 19 percent.<sup>18</sup> Brazil, Colombia and Peru are found to have considerably higher living costs relative to their GDP per capita. Mexico is the only LAC country that shows a lower percentage, 11 percent.

> 18 The difference in living in Peru could possibly originate from the difference in available information. As explained, the Peru household survey collects information regarding actual accommodation costs of students, while this is estimated in a conservative manner in the case of the other LAC countries. Also, the living costs in a number of continental European countries are lower because of subsidized accommodation. This subsidization policy is absent on a significant scale in the other countries considered in this study, include the LAC countries.

14
turnover ratios to obtain an overall index of stock market development, INDEX-1. Thus, INDEX-I gives equal weights to the market capitalization, value traded, and turnover ratios.<sup>3</sup> INDEX-2 is the second conglomerate index and incorporates the IAPM estimate of market integration. We adjust the IAPM measure of integration so that great values imply greater integration. To compute adjusted-IAPM measure, we simply multiply the original IAPM measure by negative one. Thus, INDEX-2 equals the average of the means-removed values of the adjusted-IAPM integration measure, the market capitalization, valued traded, and turnover ratios.

# _B. Summary information_

Given our focus on the association between major policy changes and stock market development, we highlight developing countries and use industrial countries mainly for comparison purposes. Our sample includes Argentina, Austria, Australia, Brazil, Chile, Colombia, Spain, Finland, France, India, Italy, Jordan, Korea, Mexico, Malaysia, Nigeria, Netherlands, Norway, Pakistan, Philippines, Portugal, Sweden, Thailand, Turkey, Taiwan, Venezuela, and Zimbabwe. Furthermore, as a benchmark, we compute stock market development indicators for the three most developed stock markets: Japan, the United Kingdom, and the United States. Data are obtained from the International Finance Corporation's Emerging Market Data Base (EMDB), the International Finance Corporation's annual Factbook and the International Monetary Fund's Intenational Financial Statistics.

Table 1 presents the means for each of the six stock market indicators for the 16 countries that we study plus the United States (USA), the United Kingdom (UK), and Japan (JPN) for comparison. The six indicators exhibit considerable variability both across countries and across indicators within the same country. While Malaysia ranks among the top five countries 3 Furthermore, we used principal component analysis to construct another conglomerate measure. Specifically, given the market capitalization, total value traded, and turnover ratios, we compute the one principal component that is the best linear predictor of the three original indicators. The principal components measure gives very similar results to the INDEX-I results.

9
Overall, the results from child development tests applied to children and caregivers in the household (ASQ and TVIP) and to teachers in primary school (EDI) consistently demonstrated robust positive effects of preschool on child development as measured in the domains of cognition, communication, precise motor and socio-emotional development of young children. To obtain an aggregate measure of preschool’s effects on child development, we combined the ASQ and TVIP into a single summary variable via factor analysis, summarizing the 5 ASQ domains and the internally standardized TVIP score into a “child development factor”. The first factor (“principal”, or “g”) explains 92% of the variance. As shown in model 1 of Table 9, the presence of a preschool in the community increases the mean index by 0.17σ, while attending a preschool increases the index by 0.337σ.

# 6.2. Schooling

By stimulating child development, the preschool program aimed to improve school readiness and facilitate the transition of children into primary school. Table 6 presents the ITT and IV impacts of preschool on the probability of being enrolled in primary school at the time of the follow-up survey, of ever enrolling in primary school, enrolling at the appropriate age, and of dropping out of primary school. Children who enrolled in preschool had an increased likelihood of being enrolled in primary school at the time of the survey of 21.2 percentage points and an increased probability of ever enrolling of 18.2 percentage points. Particularly important in the Mozambican context is that preschool increased the probability of enrolling at the appropriate grade for age (defined as 6 years old in 1<sup>st</sup> grade). Children who attended preschool were 14.9 percentage points more likely to enroll in school at the appropriate age, a relative increase of 44.3% compared to the control. The effect of preschool on primary school dropout was negative but not significant. This is not surprising given that at endline children had only a short exposure to primary school, and dropout rates are below 6%. We also calculated an aggregated schooling index, summarizing those outcomes in a single variable. Preschool attendance is associated to an increase of 0.36σ on the aggregated schooling outcome (Table 9, model 2).

In addition to primary school enrollment, we measured the amount of time spent by children on school related activities as reported by the child’s primary caregiver. Table 7 analyzes the impact of preschool on time use. We observe that time reported on schooling and homework activities increased by 5.89 hours per week for children who enrolled into preschool, above the average of 19