rafmacalaba/datause-encoder-data huggingface dataset:
margins. Moreover, these firms are often highly mobile, and more likely to compare taxes across alternative locations (Wells 1986). Hence, taxes can be an important part of their cost structure, and the firms can easily move to take advantage of more favorable tax regimes.

The impact and the nature of incentive schemes may also differ if they apply to new or existing companies. For example, Rolfe et al. (1993) shows, using a survey of managers of US firms, that start-up companies will prefer incentives that reduce their initial expenses (equipment and material exemption), while expanding firms will prefer tax incentives that target profit. He also reports that manufacturing industries will prefer incentives related to depreciable assets because they utilize more fixed assets than service industries.

In an interesting study, Coyne (1994) suggests that small investors are generally more responsive to tax incentives than large ones. Taxes may play a more important role in the cost structure of small companies because they do not have the financial and human capacity to developed sophisticated tax avoidance strategies. Large multinational companies are also more likely to receive special tax treatments, whatever the tax laws applied by the host country. Oman (2000) reports some evidence that large firms, especially in the automobile sector, are more likely to negotiate secret advance agreements on how much they will pay in both industrial and emerging countries.

There exist a few studies that estimate separate equations for FDI financed by retained earnings and external funds (equity plus debt) (Hartman (1984) and Boskin and Gale (1987)). They typically found that FDI financed by retained earnings is more strongly influenced by host country tax rates. However, they do not offer any clear explanation for this result, but it is possible that equity and debt financing are also influenced by the tax policy in the home country, thereby reducing the impact of the host country's tax regimes (see next section for more explanations).

Finally, there is growing evidence that low taxes might be a key factor for firms that are not operating in one specific but multiple markets such as Internet related 10
12

-

some parts of Latin America,<sup>8</sup> a high level of project appraisal capability has become established in public sector utilities and other implementing agencies to which the Bank has lent; in other countries of the region, the picture is less encouraging. In SubSaharan Africa, there have been many attempts to develop project appraisal techniques in core ministries. Despite all this effort, relatively few countries have systematically established procedures or institutional capacity for economic project appraisal at either s.ectoral or core ministry level. Even in Turkey, a recipient of five structural adjustment loans, a 1985 public expenditure review found "important eeficiencies in project preparation and selection even in the case of agencies with high technical competence." Political considerations seems to dominate in project choice. There is still a widespread ignorance and/or mistrust of cost benefit techniques, well justified in many sectors. Such techniques are, perhaps, most widely accepted in transport where they are more refined, and where there is reasonably good knowledge of recurrent cost implications and of trade-offs between capital and recurrent expenditures. In other areas (especially agriculture and the social sectors), such knowledge is less developed and hard data is often difficult to come by. This is not to argue against the use of cost benefit techniques with the appropriate institutional backing; at best, they can lead to vast improvements in public investment programming, and at worst they can be conducive towards a more rational political debate on project choice. Perhaps that partially explains the slow progress in promoting their use. In poorer countries, with a high proportion of donor-financed projects, there may perhaps be more projects subject to economic analysis, though donor pressures to proceed are also strong. In any event, few projects financed by other donors seem to meet Bank standards of analysis.

(c) The lack of priorities and ranking of projects. Governments, together with the Bank, have tried to address this through the formulation of "core" investment programs. An agreement is reached that the government abandon a policy of partially funding all or most projects, and distinguishes between "core" projects which should receive funds under all circumstances and standby projects which are funded only when additional resources are available. It is important to emphasize here that "funding' includes not merely capital, but all current resources required to complete and operate a priority project. "Core" programs should also be concentrated on strategic projects and should exclude white elephants even where funding is assured. However, such programs need to be formulated against the background of consistent overall macroeconomic objectives and should take account of the constraints faced by the public sector and by the economy at large through the balance of payments, etc. Their 8/ For example, in Chile and, at least until recently, Panama.
<!-- Start of picture text --> Figure 3b: Job separation Rates towards Unemployment (Trends and Cycle): Mexico an Brazil<br>Mexico Brazil<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>S-U I-U S-U I-U<br>F-U F-U<br>1983q1 1988q1 1993q1 1998q1 2003q1<br>1987q1 1991q3 1996q1 2000q3 2005q1<br>S-U (HP) I-U (HP) S-U (HP) I-U (HP)<br>F-U (HP) F-U (HP)<br>.15 .1<br>.08<br>.1<br>.06<br>.04<br>.05<br>.02<br>0<br>0<br>.4<br>.4<br>.2<br>.2<br>0<br>0<br>-.2<br>-.2<br>-.4<br>-.4<br><!-- End of picture text --> Notes: The graph shows the transition rates from the three employment sectors (S=Informal Selfemployed, I=Informal Salaried, , F=Formal Sector) into unemployment (U=Unemployment). Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. The bottom panels shows the series logged and de-trended using an HP filter with lambda 1600. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2. Shaded areas indicate recessions.

34
# **1 Introduction**

Public policies aimed at increasing access to formal preschool education are high on the political agenda in a number of countries. There are probably two main reasons for this. First, a higher supply of formal preschool education is by many seen as a crucial tool for achieving higher (female) participation rates in the labor market.<sup>1</sup> Second, there is now a sizable body of evidence for both developed and developing countries indicating that there are important long-term individual and public benefits to enrollment in preschool education (Cunha et al., 2006; Engle et al. 2007, 2011; Almond and Currie, 2011; Behrman et al. 2013).

Access to formal child care is a particularly important policy issue in developing countries, where enrollment rates are generally much lower, and where private institutions constitute a much larger share of the formal preschool sector, than in developed countries.<sup>2</sup> The generally low and uneven access to preschool education is arguably reflected in the observation of large disparities in cognitive development at the start of primary school in many developing countries. For example, Paxson and Schady (2007) and Schady et al. (2015) document a widening gap in cognitive development between poor and non-poor children in several Latin American nations.<sup>3</sup> After entering primary school, the gap tends to remain constant. Why do these gaps emerge and why do they stop growing? In light of the previously cited studies, a possible explanation lies in the differences in opportunities available _−_ in particular, access to formal child care _−_ to poor and non-poor children in their early years.

In order to remedy such problems, an available policy option is to increase the public supply of (free or widely affordable) preschool education. The intended effects of such a policy are two-fold: (i) to provide more equitable access and (ii) to increase the total supply 1See, e.g., Havnes and Mogstad (2011) and references therein. See also Besamusca et al. (2015) for further discussions and results on the relationship between pre-school enrollment and female labor force participation.

> 2See UNESCO (2008) for statistics and further information about formal preschool in developed and developing countries. Bastos and Cristia (2012) examine supply and quality choices of private suppliers in the city of S ̃ao Paulo, Brazil.

> 3Paxson and Schady (2010) show that conditional cash transfers are unlikely to contribute towards narrowing this gap.

2
# **Figure 2a – Virtual-C intensity in production, domestic final demand, and imports (tons** **<u>per 1,000 dollars of Gross National Income)</u>**

<!-- Start of picture text --> 2.5<br>Virtual carbon in domestic final demand<br>(foreign sources)<br>Virtual carbon in domestic final demand<br>2 (domestic sources)<br>Virtual carbon from domestic production<br>1.5<br>1<br>0.5<br>0<br>BRA CAN CHN E15 eit IND JPN liy MEX RUS USA xhy xmy xx1 ZAF<br>Note: virtual carbon flows have been estimated using the MRIO approach.<br>Source: authors<br>Figure 2b – Virtual-C intensity in production, domestic final demand, and imports (tons<br>per 1,000 dollars of PPP adjusted Gross National Income)<br>1<br>Virtual carbon in domestic final demand<br>0.9 (foreign sources)<br>Virtual carbon in domestic final demand<br>0.8 (domestic sources)<br>Virtual carbon from domestic production<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>BRA CAN CHN E15 eit IND JPN liy MEX RUS USA xhy xmy xx1 ZAF<br>Tons of CO2 per 1,000 USD ofGNI<br>Tons of CO2 per 1,000 USD of GNI (PPP adj.)<br><!-- End of picture text --> Note: virtual carbon flows have been estimated using the MRIO approach. Source: authors 22
are rendered unusable. Consequently, the domestic water supply in rural parts of Bangladesh is disrupted and the water supply becomes unsustainable.

Mean depths to dry-season groundwater levels were estimated from weekly monitoring records from 236 boreholes from 2004 to 2013 (Figure 2c). These monitoring boreholes belong to a network of some 1,250 monitoring wells across the entire country that have been managed by the Bangladesh Water Development Board (BWDB) since the early 1960s. Depth from the well head to groundwater level at each station is referenced to a common horizontal datum known as the Public Works Datum (PWD), originally set approximately at the mean sea level (msl) with a vertical uncertainty of around ±0.45 m.<sup>14</sup> We estimated depth to mean dry-season groundwater levels (i.e., maximum depth below ground level) using the ground surface as a reference level. The map of dry-season groundwater levels shows that the central, north-central, eastern and northwestern parts of Bangladesh have deepest water levels with mean depth acceding 15 mbgl that is twice more than the suction limit of hand pumps. Depths to water levels in much of the southern Bangladesh and along major river floodplains are generally shallower (<8 mbgl) than the maximum suction limit of the No. 6 pump.

# **Demography, access to water supply, and social vulnerabilities**

Demographic datasets on population, poverty, tubewells, and access to pipe water supplies in Bangladesh at the upazila level are collated from a GIS database ( _The Bangladesh Interactive Poverty Maps_ ) published by the World Bank.<sup>15</sup> The country-level demographic database allows one to explore and visualize socioeconomic data at both Zila (district) and Upazila (sub-district) level. The online GIS-based mapping tool enables an easy access to different types of indicators including poverty, demographics of the population, children’s health and nutrition, education, employment, and household access to energy, water, and sanitation services.<sup>15</sup> These maps (see maps in supplementary Figure S1) were constructed by combining three different data sources all of which are publicly available: (i) 2010 Bangladesh Poverty Maps, (ii) 2011 Bangladesh Census of Population and Housing, and (iii) 2012 Undernutrition Maps of Bangladesh. Upazila-level GIS data with a geographic projection (WGS 1984 Web Mercator coordinate system) have been used in this study.

Upazila-level total population and percentage of poor population (i.e., percentage of the population that lives below the official national upper poverty line, which is based on household's poverty status assessed using per capita consumption) are shown in Figure S1.

27
Sachs, Jeffrey, and Andrew Warner.<sup>1997a. "Natural Resource Abundance and Economic</sup> Growth - Revised." Working Paper<sup>of the Center for International Development, Harvard</sup> University, Cambridge, Massachusetts.

Sachs, Jeffrey, and Andrew<sup>Warner. 1997b. "Fundamental Sources of Long-Run</sup> Growth." American Economic Review.<sup>Papers and Proceedings 87(2): 184-188.</sup> Sachs, Jeffrey, and Andrew Warner.<sup>1999. "The Big Push, Natural Resource Booms and</sup> Growth." Journal of Development<sup>Economics 59: 43-76.</sup> Sachs, Jeffrey, and Andrew Warner.<sup>2001a. "Fundamental Sources of Long-Run</sup> Growth." American Economic Review.<sup>Papers and Proceedings 87(2): 184-188.</sup> Sachs, Jeffrey, and Andrew Warner.<sup>2001b. "Natural Resources and Economic</sup> Development. The Curse of Natural<sup>Resources." European Economic Review 45(2001):</sup> 827-838.

Sala-I-Martin, Xavier. 1997. "I<sup>Just Ran Two Million Regressions." American Economic</sup> Review. Papers and Proceedings<sup>87: 178-183.</sup> Sevestre, Patrick and Alain Trognon<sup>(1996), "Dynamic Linear Models" in Matyas, L. and</sup> P Sevestre, The Econometrics of<sup>Panel Data, (Boston: Kluwer Academic</sup> Publishers),100-1 18.

Serven, Luis. 1998. "Macroeconomic<sup>Uncertainty and Private Investment in Developing</sup> Countrires: An Empirical Investigation."<sup>Policy Research Working Paper 2035, The</sup> World Bank, Washington, DC.

Smith, Adam. 1776 [1976]. An<sup>Inquiry into the Nature and Causes of the Wealth of</sup> Nations. Oxford: Clarendon<sup>Press.</sup> Solow, Robert. 2001. "Applying<sup>Growth Theory across Countries." The World Bank</sup> Economic Review 15(2):<sup>283-289.</sup> Stijns, Jean-Philippe (2001), "Natural<sup>Resource Abundance and Human Capital</sup> Accumulation". Mimeo University<sup>of California at Berkeley.</sup> Trefler, Daniel. 1995. "The Case<sup>of the Missing Trade and Other Mysteries." American</sup> Economic Review 85(5):<sup>1029-46</sup> Wacziarg, Romain. 2001. "Measuring<sup>the Dynamic Gains from Growth". The World</sup> Bank Economic Review. Vol<sup>15: 393-430</sup> Wacziarg, Romain, and Karen<sup>Horn Welch. 2002. "Integration and Growth: An Update."</sup> Mimeographed. Stanford University.

19
bias while obtaining a large number of observations at the same time, our approximate completed fertility measures of 2011 are calculated based on women aged 37 to 42 (cohorts 1969-1974)<sup>3</sup> .

Figure 1 illustrates the evolution of completed fertility rates in Turkey. The line is divided in three parts: The first one (bold line) presents completed fertility for women aged 50+, who have already completed their childbearing period (census 2000, cohorts until 1950)<sup>4</sup> while the second one (dashed line) is for women aged 40 to 50 (census 2000, cohorts 1950 to1960). The endpoint of the third line (dot line) presents the average number of children observed for cohorts 1969 to 1974 reported by SILC for the year 2011 - a number we call ‘approximate” completed fertility rate as the measure is downward biased.

**figure 1. Completed fertility rates in Turkey**

<!-- Start of picture text --> CFR<br>6<br>5,5<br>5<br>4,5<br>4<br>3,5<br>3<br>2,5<br>2<br>1,5<br>1<br>1910 1915 1920 1925 1930 1935 1940 1945 1950 1955 1960 1965 1970 1975<br>Genera ons<br><!-- End of picture text --> Data Source: Census (i-pums) 2000 and SILC CS 2011 (women aged 37 to 42) With 2.3 children per women, approximate completed fertility rates are in Turkey still above the weighted EU(26) average (1.72 SILC; 1.79 for the unbiased measure of the Human Fertilty Data Base).

Census data illustrated in figure 2 report an important decline for the proportion of women having four or more children between the cohorts 1930 and 1960, while women having two and three children are on the rise. Childless women and those having one child only represent a constant minority in Turkey until the 1960 cohort.

> 3 Note that in comparison to unbiased measures of completed fertility provided by the Human Fertility Database (year 2012, cohort 1970: 2.9 children per women), the SILC reports lower completed fertility rates (year 2011, cohort 1969: 2.4 children per women). A comparison with census data suggests that the downward bias in SILC measures of completed fertility concerns mainly women with 4 and more children. These women risk having their first children at relatively early ages, and these children probably already moved out when their mothers are around their forties. The empirical analysis of this paper focusses on the arrival of a first, second and third child, for which the downward bias in SILC is much lower.

> 4 The bold shows specific peaks for every  “round” generation ( born in a ’00 or ‘05 year) due to the fact that especially for older cohorts, their exact age is unknown and hence women report “round” birth years as proxies.

7
29 Figure 3: Decomposition of indices by policy instrument, 1980-84 and 2000-04 (percent) <!-- Start of picture text --> (a) Decomposition of WRI, border measure only components<br>1980–84 2000–04<br>250<br>250<br>200<br>200<br>150<br>150<br>100<br>100<br>50<br>50<br>0<br>0<br>ANZ NA ECA EU EFTA Ja/Ko ANZ NA ECA EU EFTA Ja/Ko<br>Import tax Export tax Import tax Export tax<br>Export subsidy Import Subsidy Export subsidy Import Subsidy<br><!-- End of picture text --> <!-- Start of picture text --> Export subsidy Import Subsidy Export subsidy<br>(b) Decomposition of PDI, border and domestic components<br>1980–84<br><!-- End of picture text --> <!-- Start of picture text --> 150<br>100<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>ANZ NA ECA EU EFTA Ja/Ko<br>Border measures<br><!-- End of picture text --> <!-- Start of picture text --> ANZ NA ECA EU EFTA Ja/Ko<br>Border measures<br><!-- End of picture text --> <!-- Start of picture text --> Production tax<br>Production subsidy<br>Input subsidy<br><!-- End of picture text --> <!-- Start of picture text --> Production tax<br>Production subsidy<br>Input subsidy<br><!-- End of picture text --> Source: Authors’ calculations based on data in Anderson and Valenzuela (2008).
(2004). We calculate as distance weighted average GDP of all other countries in our sample. The measure of distance originates from the CEPII data base and represents the geographical distance between the capital cities. We refer to these control variables as _Tropics, Pop100km,_ and _Remoteness,_ respectively _._ Finally, given the possible noise introduced by initial conditions in terms of natural resource endowments, we include dummy variables for countries with subsoil assets (World Bank 2006). We refer to this dummy variable as _Resources_ . It should be noted however that the effects of natural endowments on long term economic growth is unclear. Sachs and Warner (1997) find support to the hypothesis that countries rich in natural resources tend to grow slower. The authors include the ratio of natural resource (fuels and non-fuel primary products) exports to GDP in the base year and find it to be negatively correlated with economic growth. Similarly, Barro (1997) who includes a dummy variable for oil-rich countries in the growth regression finds it to be negative and statistically significant. However, several more recent studies found that the presence of natural resources does not necessarily present an impediment to higher growth; it depends on other policies pursued by the countries (e.g., Lederman and Maloney, 2007).

The data on GDP per capita and the amount of net oversees development assistance per capita (ODI) originate from the World Bank World Development Indicators data base.

# **3.2 Stylized facts**

As noted, Freedom House’s Civil Rights and Political Liberties indices are closely related (Table 1). When looking at the most recent data available i.e. 2007 we note that all observations on civil rights and political liberties are located along the 45 degree line with the highest single cluster of countries representing developed countries with high levels of civil liberties and political rights (Figure 1). The indices has been reversed as compared to the original Freedom House indices, so that the higher the level of index the higher the level of freedom.

**Table 1 Spearman rank correlation coefficients between the indices of civil liberties, political rights and economic freedom (1970-2007).**

||**CL**|**PR**|**EFW**|**IEF**|
|---|---|---|---|---|
|**CL**|1||||
|**PR**|0.927|1|||
|**EFW**|-0.621|-0.602|1||
|**IEF**|-0.608|-0.603|0.786|1|

Source: Freedom House and Fraser Institute, 2007.

13