rafmacalaba/datause-encoder-data huggingface dataset:
ENADID data. Consequently, if women who would have chosen to abort are included in the sample of women who miscarried, this sample is more favorably selected than the sample of women who gave birth. The results from estimation carried out on these samples would be biased towards finding adverse effects of teenage childbearing.

To address the issue of the potential bias, we can take advantage of the fact that ENADID reports the amount of time elapsed between the start of the pregnancy and the date when miscarriage occurred. It is reasonable to assume that those women, who would choose to abort, would prefer to do so during the early months of pregnancy. Medical literature confirms that the likelihood of complications due to abortion increases with every month of pregnancy (Bartlett et al., 2004). Many pregnancies are associated with physical discomfort. Additionally, it is easier to conceal a pregnancy during earlier months. Overall, it appears that a pregnant woman who does not want to give birth does not have any incentive to extend the length of her pregnancy. Therefore excluding women who miscarried during the first two months of pregnancy from the control group seems to be a reasonable safeguard against contaminating the sample with women who are not comparable to the “treated” in their latent status. Intuitively, we include in the control group only women whose pregnancies lasted long enough to conclude that they were not planning to have an abortion. However, it is important to keep in mind that such modification reduces the sample size, which may increase the standard errors and result in lower significance of the estimated coefficients.

Table 11 shows that our results are highly robust to such modification in the sample. Raising a child has a significant and positive effect on the level of social assistance income, the likelihood of employment and on participation in _Oportunidades_ . Indeed, nearly all of the outcomes that appear to be significantly impacted by bearing a child in the full sample of potential teenage mothers remain significant in the reduced sample. Their magnitude is nearly identical. The only exception is education – the coefficient is positive, but no longer significant. Overall, the results in Table 12 suggest that our findings are not driven by erroneous inclusion of not truly comparable women into the control group.

Another consideration that may cast doubts on the interpretation of our results is that miscarriage may not be without costs. Therefore, an alternative interpretation of our findings is that rather than reflecting the consequences of bearing a child, these results reflect the costs of having suffered a miscarriage. To rule out this explanation, we test whether having had a miscarriage has an effect on 18
# **III. BENEFITS AND COSTS OF AN EMU BETWEEN**

# **THE DOMINICAN REPUBLIC AND HAITI**

During the past few decades, the economic performance of the Dominican Republican and Haiti has diverged markedly. As shown in Figure 1, using 1963 as a base year, by 2007 real output in the Dominican Republic had increased almost ninefold (more than twice the increase in the United States during the same period), compared to quasi stagnation in Haiti. In per capita terms, real GDP tripled to about US$2,500 between 1960 and 2005 in the Dominican Republic, whereas it halved to US$430 in Haiti (Jaramillo and Sancak (2007)). This divergence in economic performance has put the two countries at the opposite ends of the spectrum in Latin America and the Caribbean.

Figure 1 Dominican Republic and Haiti: Real GDP Index, 1963–2007 **___** Dominican Republic ___ Haiti --- United States <!-- Start of picture text --> 1,000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003 2007<br>Source: World Bank.<br><!-- End of picture text --> Editora Alfa & Omega, 1995.

7
improved understanding of where coca is now growing in Colombia.” Summarizing, according to the two sources, total cultivation in the three Andean countries shows a large decrease between 2001 and 2003, in large part due to the large decreased observed in Colombia after the implementation of Plan Colombia. And, if anything, coca cultivation has remained relatively stable during the last three years reported. Although the observed figures for the last few years are not enough evidence to conclusively support a ballooning effect, where a decrease in cultivation in one area due to “effective” anti-drug policies would lead to the reallocation of crops to new areas and, as a result, total cultivation remains unchanged (or increases), it does send a warning signal of the potential for large increases in Bolivia and Peru if anti-drug policies and monitoring are not maintained in all areas where coca can and has been grown in the past (see Department of State, 2005).

Since UNODC only started the Illicit Crop Monitoring System in Colombia in 1999, in Peru in 2000, and in Bolivia in 2003, data between the two main sources of information can be compared only for those years. Figure 3 shows the evolution of coca bush cultivation in Colombia according to the two main sources. Although the levels are different, with an almost constant average difference between the two sources of 30,000 hectares, the tendency is the same: first increasing between 1999 and 2000, and then decreasing until 2004. The same pattern is observed for Bolivia and Peru, that is, an almost constant level of coca cultivation with a small increasing tendency in the last year. However, while ONDCP’s estimate of coca cultivation in Colombia is larger than UNODC’s in the last four years, the opposite is true for Bolivia and Peru for those years when the two sources gathered data separately.

ii. Intermediate Prices 10
- 32 -

Only with large changes in policy variables would one expect observable shifts in the rate (or even the direction) or rural-urban migration. Some observers contend that in several countries such as Ghana, Nigeria, and Tanzania, where profound policy changes altered the domestic terms of trade in favor of the rural sector, urban dwellers are returning to the farm. Because of poor demograplhic data, however, these patterns have been difficult to substantiate.

In the case of Ghana, however, recent survey data from the World Bank's Ghana Living Standards Measurement Survey offers an opportunity to authenticate the pattern more concretely. In a survey of over 8,000 individuals in Ghana, information on current and prior employment was collected. From this data, respondents were broken down by principal occupation into agricultural and non-agricultural, and similarly for previous occupation. For those who changed occupations between mid-1984 and 1988 - the time of the survey -- prior and current occupation was tabulated as shown in table 7.

Table 7. Rural-urban migration in Ghana since structural adiustment (principal occupation 1984-88, in percent)

|||Current Occupation|
|---|---|---|
||Agriculture|Non-agriculture<br>Total|
|Peius<br>ocMuation|||
|Agriculture|63|2<br>65|
|Non-agriculture|4|31<br>35|
|Total|67|33<br>100|

Source: Ghana Uving Standards Survey (World Bank). Note: sample size = 5570 These data reveal a significant evre migration from urban (or nonagricultural) to rural (agricultural) occupations since the reform program was initiated in the early 1980s. Among individuals that have changed occupations during the period, those moving from nonagricultural into agriculture outnumber those moving in the opposite direction two-to-one. Given the proportions of the rural and urban populations as a whole (and assuming it is roughly the same for the agriculturalto-nonagricultural breakdown) these data indicate a two-percent net increase in the share of the population earning a living in agriculture since 1984. This contrasts with aggregate data prior to 1984 indicating migration in the other direction at about 1 percent per year. The impact of this reversal on productivity of both food and export agriculture appears **to** have been considerable: In the late 1980s Ghana substantially improved food production and dramatically increased cecoa exports (figure 17 below).

These data confirm what many observers have suspected or observed anecdotally; that government policy has a profound effect on migration from rural to urban areas. As a corollary, these policies have also had an important effect, both directly and indirectly, on the growth in Africa's food imports.
under five years of age were included in the sample only if they had access to other arrangements for child care. We refer to this as our _potential business owner_ sample.

Based on this screening exercise, we selected a sample of 628 current business owners and 628 potential business owners. This sample was stratified to take approximately 63 of each type in each D.S., in order to have equal sized groups at each training location. A baseline survey of these 1256 individuals was conducted in January 2009. (See timeline in Figure 1.) Table 1 provides summary statistics on the two subsamples. The current business owner sample consists of women in a wide range of industries typical of female self-employment in Sri Lanka, such as tea (snack) shops, beauty shops, bag and mat manufacturing, tailoring and sewing, selling fruit and vegetables or groceries, making and selling lunch packets and string hoppers, baking cakes, and flower shops. The typical owner in our sample is 36, married, with ten years of schooling, and has been running the business for just over six years. Mean monthly business income is around 4000 Rs (US$34, or around a dollar a day at market exchange rates), and constitutes about one-quarter of total household income for the median and mean business owners. At baseline few enterprises were implementing the types of business practices taught in the SIYB training program. Our business practices index measures the marketing, costing and record-keeping, stock control, and financial planning practices used in the firm, with higher scores indicating better management practices (see appendix 1). The mean firm has a baseline score of only 4.6 out of a possible 29. Only 17 percent of firms keep written records, only 4 percent had done any advertising in the last six months, only 9 percent have a sales target for the next year and only 3 percent had made a budget of what costs are likely to be over the next year.

Only 18 percent of these current business owners reported ever having had any form of business training, and the majority of this was technical training. Fewer than 4 percent of owners had ever taken a training course which covered record-keeping, marketing, customer and supplier relations, or management of employees.

Among the potential business owners, only 18 percent have never worked before, but only 8 percent have previously engaged in self-employment. Half of them claim to have taken concrete steps towards opening a business in the past year. These women are about two years younger on average than the current business owners, but are otherwise similar in many respects. In particular, the potential and current business owners have similar levels of education, similar cognitive ability (as measured by digitspan recall and raven tests), similar attitudes towards risk, 6
international experts. A consortium of Gallup Europe<sup>51</sup> and International Lesbian Gay Bisexual Trans and Intersex Association (ILGA) Europe<sup>52</sup> implemented the survey. Over a period of four months any person over 18 years<sup>53</sup> that identified as LGBT could participate in the anonymous online survey. The online questionnaire and sampling through selfidentification provided a number of advantages such as easy distribution across 28 countries, a collection of a large data set and the development of a tool which could be readily applied in other contexts.<sup>54</sup> The questionnaire covered the following areas:

- Public perceptions and responses to homophobia and/or transphobia;

- Discrimination;

- Rights awareness;

- Safe environment;

- Violence and harassment;

- Social context of being an LGBT person; and

- Personal characteristics, including age and income group.

The survey discovered that despite anti-discrimination laws across the EU, one-third of the respondents felt discriminated against in at least one of the following areas: housing, health care, education, social services, and access to goods and services. Transgender respondents were even more vulnerable, with 35% reporting being attacked or threatened with violence in the previous 5 years. Together with bisexual women, they are also most likely to report income levels in the lowest quartile.<sup>55</sup> As with the other surveys, extrapolating from the results to the total LGBT population or non-LGBT people is difficult given the sampling method. Respondents were a segment of the population that had access to the Internet, and frequent LGBT friendly websites, events or other services where the survey was advertised. Overall, the vast majority of respondents were younger than 40, mostly gay and well educated. An unusually high number of students answered the survey.<sup>56</sup> FRA opted to use an online survey for two reasons: First, the necessary sample size for a representative sample in each country would have needed to be extremely large in order to achieve a robust analysis. They estimated that around 800,000 screening interviews would

> 51 Gallup is an international survey and consultancy company.

> 52 ILGA-Europe is the European umbrella organization of civil society organizations working on LGBTIQ issues.

> 53 FRAs focus on respondents over 18 years is very common among these types of surveys. Unfortunately, this makes invisible the experiences of younger LGBTI+ people.

> 54 FRA. 2012. EU LGBT Survey Technical Report.

> 55 Ibid.

> 56 Ibid.

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**<u>Figure 4: Domestic Debt by Maturity.</u>**

<!-- Start of picture text --> Maturity composition Maturity composition excluding Central Bank advances<br>Year Year<br>Long term Short term Long term Short term<br>Non classified Non classified<br>Maturity composition in HIPCs Maturity composition in Non-HIPCs<br>Year Year<br>Long term Short term Long term Short term<br>Non classified Non classified<br>1996199719981999200020012002200320042005200620072008200920102011 1996199719981999200020012002200320042005200620072008200920102011<br>1996199719981999200020012002200320042005200620072008200920102011 1996199719981999200020012002200320042005200620072008200920102011<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Maturity composition (as % of Domestic Debt) Maturity composition (as % of Domestic Debt)<br>100 100<br>80 80<br>60 60<br>40 40<br>20 20<br>0 0<br>Maturity composition (as % of Domestic Debt) Maturity composition (as % of Domestic Debt)<br><!-- End of picture text --> _Source: our elaboration on the LIC domestic public debt dataset._

# **4.5. Investor base**

Investors in LIC government debt are few is nature and often also in number. Domestic public debt instruments are held primarily by commercial banks, the Central Bank, financial institutions in the non-banking system (e.g., mutual funds, pension funds, and insurance companies), and non-financial institutions (e.g., non-financial corporations and individual investors). The investor base in local financial markets is typically narrow and highly concentrated (Arnone and Presbitero, 2010).

Previous studies underlie the benefits of a diverse investor base in terms of lowering borrowing costs as well as reducing market yield volatility. Broadening the investor base attenuates the monopoly power of a particular group of financial institutions, bringing down interest rates and rollover risks (Christensen, 2005). Larger crowding out effects are to be expected when the investor base is strongly biased towards commercial banks. As indicated above, the banking system generally has a strong incentive for buying government debt and seeking profitability in lending to the public sector. This may lead to relatively weaker incentives to extend credit to riskier private borrowers and even lower efficiency in banking operations and financial intermediation (Hauner, 2006). Crowding out effects are especially harmful in LICs because small- and 16
What role do securities markets play in economic development? How do they emerge in the development process? And how can regulation make them more effective? Like many a business school professor, I will use a case study to explore these issues. The case is the history of U.S. securities markets, a history that I believe is very relevant to policymakers and financial reformers concemed with developing countries today.

Some might doubt that lessons of financial history from the United States could be relevant to developing and transition economies. I would remind these doubters that twvo centuries ago the United States was a small and undeveloped country with serious financial problems. But it confronted those problems and creatively reformed its financial system, which then became a foundation of the U.S. economic infrastructure and a bulwark of the country's long-term growth. Several background papers-Sylla and Smith (1993, 1993a) and Sylla, Wilson, and Jones (1994)-examine this history in great detail. Here I will mention only the most interesting points and add some additional evidence regarding the importance of foreign investment in U.S. history and the role of securities markets in promoting foreign investment.

**Securities Markets in** U.S. **History**

Securities markets in the United States have a long history. The commercial paper market, as far as financial historians can discern, did not begin until the 1 830s, but the bond and stock markets date back to the first term of George Washington.

As an economist and financial historian I have been interested in the question of what we can learn about early U.S. economic growth from financial data. Aggregate data on production are to sketchy
# **I.  Introduction**

The determinants of firm size and, more broadly, firm life cycle are still largely a blackbox. We do know that in developed countries such as the U.S. new businesses start small and, if they survive, grow fast as they age (e.g. Evans, 1987; Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh, 1996; Caves, 1998; Hsieh and Klenow, 2013). It is an open question as to whether firms in developing countries face a similar size-age profile as in the US given that they face entirely different business environments and operating conditions. An understanding of the firm life cycle in developing countries and its determinants is critical to the policy debate on productivity improvements and achieving convergence between rich and poor countries.

In this paper, we examine the relationship between plant size, age, and growth in the formal sector across 120 developing economies. Specifically we examine if older plants are much larger than young plants and if there are any differences in the size-age relationship across country income groups and industries. To overcome concerns with cross-country analysis, we investigate the firm size-age relation using detailed manufacturing census data from India. We then try to understand how financial institutions are associated with the size-age gap.

Our cross-country sample consists of 69,224 formal firms in 120 countries, surveyed over the period 2006-2012. The strength of our data is that we cover most of the developing countries in the world. The country with the highest GDP/capita in our sample of countries is Slovenia. Our data are comparable across countries since it they sourced from the World Bank Enterprise Surveys (ES) database which samples formally registered firms from over 100 countries to study the business climate constraints to private sector growth and performance. The surveys use 2
stock of public capital. However, neither one of these contributions examines the composition of aid and its links with public investment, or Dutch disease effects, which may alter the long-run impact of aid and public investment on growth. Moreover, the models developed in these papers are parsimonious analytical tools, which are not designed to guide practical policy decisions.

This paper fills an important gap in the literature by developing a quantitative macroeconomic framework that captures the links between foreign aid, the level and composition of public investment, growth, and poverty, in the context of a “typical” low-income country. The model focuses on the fiscal and supply-side effects of aid, as well as the stock and flow effects of public investment, while accounting at the same time for potential congestion effects associated with the use of public services. It is designed to examine how increased aid and aid-funded levels of public investment, possibly coupled with changes in the allocation of public investment, can stimulate growth and lead to sustained poverty reduction.<sup>3</sup> At the heart of the model is a production function that accounts explicitly for the effect of public capital (in health and infrastructure) on output and the marginal productivity of private production inputs. Public capital in education also plays a role in the production process, because “raw” labor must be turned into educated labor to become productive. The domestic (composite) good is imperfectly substitutable with the foreign good. By accounting for changes in relative prices, the model allows us therefore to analyze potential Dutch disease effects associated with aid flows (as discussed earlier) in both the short and the long run. In addition, the model captures explicitly the link between nonfood aid and public investment, and the possible adverse effects of large inflows of foreign aid on fiscal accounts (as emphasized in fiscal response models). Finally, although by its very nature the model is silent on distributional issues (only one aggregate household is accounted for), the impact of policy shocks on poverty is assessed either by linking the model to a household survey, or by using partial elasticities relating consumption growth to poverty, using a range of available estimates for low-income countries.

8
Figure 4 confirms that the increased concentration of OECD imports on China occurs at the intensive margin: In recent years, no new product line opened between China and its OECD trade partners.

Table 5 Regression results, OECD import concentration on time trend, excluding China

|Regressors:|Herfindahl|Theil|
|---|---|---|
|time|-0.004***|-0.013***|
||(-17.08)|(-24.61)|
|timesq|0.000*|0.000|
||(1.925)|(0.0720)|
|Constant|0.419***|3.302***|
||(218.4)|(677.4)|
|Observations|53763|53763|
|R-squared|0.531|0.581|
|Product FE|yes|yes|

Notes: t statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1 Figure 4 Contribution of the intensive and extensive margin to China-OECD imports growth, 1963-2006 <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1963-1975 1975-1993 1993-2006<br>extensive margin intensive margin<br><!-- End of picture text --> Thus, although their imports are increasingly concentrating on Chinese products, OECD countries continue to open new imports lines with extra-OECD countries. That is, China‘s expanding exports to the OECD do not seem (yet) to crowd out the entry of new exporter/product pairs on OECD markets.

13