rafmacalaba/datause-encoder-data huggingface dataset:
and their determinants in India. The first is the presentation of internal migration patterns in India in greater detail by using district-to-district census-based migration data, disaggregated by age, education, duration of stay and reason for migration. Most existing studies in India use household survey data that suffer from sampling and aggregation biases and are rarely bilateral. Our data allow us to control for origin and destination specific factors (such as natural endowments, economic and social conditions, and climate) through fixed effects in a gravity model. Thus, we are able to focus on the bilateral variables emphasized in the literature. Among these are the – – critical contiguity variables being in the same state and/or being neighbors in addition to the standard physical distance and linguistic overlap measures. Furthermore, by using bilateral migration data between 585 districts, instead of the standard state-to-state analysis in other papers, we are able to solve many of the aggregation problems that arise in large countries like India. For example, Uttar Pradesh would rank as the fifth most populous country in the world if it were independent, and treating it as a single observation creates many biases.

The second and more substantive contribution is to demonstrate the role played by administrative barriers, particularly state borders, in limiting internal migration in India. Our empirical analysis shows that, even when we control for numerous barriers to internal mobility, such as physical distance, linguistic differences and economic and social features of origin and destination districts (through district fixed effects), state borders continue to be important impediments. Migration between neighboring districts in the same state is at least 50 percent larger than migration between districts which are on different sides of a state border. This gap varies by education level, age and reason for migration, yet it is always large and significant.

– – The low level of internal mobility in India including the role of state borders cannot be attributed to restrictions imposed by the state or federal governments. In China, for example, federal government policies have constrained migration through measures such as the _hukou_ system. No such administrative measures exist in India, and anyone is legally free to move from one district or one state to another. Moreover, 3
# <u>Trade and Connectivity</u>

Caribbean countries have historically been net importers, with deficits in their current account balances. Food and fuel absorb important shares of imports representing on average 20 percent and 22 percent of total merchandise imports, respectively (Figure 2).

<!-- Start of picture text --> Figure 2: Key imports (Most recent year available for the period 2008-2012 as a percent of merchandise imports)<br>a. Food b. Fuel<br>40 40<br>35 35<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>% of Imports<br>% of Imports<br><!-- End of picture text --> _Source: World Bank, (2013b). For graph (a) and (b) data for St. Lucia is from 2008, Grenada from 2009, for Trinidad and Tobago from 2010, St. Kitts and Nevis and Suriname from 2011. Data for Haiti not available._ From the economic viewpoint, this implies that anything that can reduce the cost of key input factors for the economy is a top priority. The compound effect that high food prices can have on the competitiveness of the tourism industry or that high fuel prices can have on the competitiveness of the service industry, small and medium enterprises and agribusiness development, is massive. Because the economies of these countries are not self-sufficient, and their domestic GDPs depend mainly on the tourism industry, an improved trade sector is fundamental to ensure that imports are sufficient to satisfy domestic consumption and the countries’ major industry. Furthermore, these countries need to be able to compete in international markets with their own produce, in order to start diversifying their fragile economies. Currently, with the economy concentrated in one activity, the countries in the region could benefit from improved performance in processes associated with international trade, as well as road connections to and from major production and consumption zones.

From the transport sector viewpoint, trade relies on good logistics. Therefore, connectivity factors are a centerpiece of this discussion. Improvements in both air and maritime connectivity can set the stage for a better business environment, and are key to improving the outlook for international trade and to increasing competition. Small islands face an important challenge due to their huge diseconomies of scales in terms of volume of trade, small populations, and isolation from big consumer centers.

This report focuses on characterizing the air and maritime connectivity in the Caribbean countries. It structures the analysis in four sections. Section II presents a quick benchmarking using existing indicators. Section III introduces regional aspects and key international agreements. Sections IV, V and VI discuss key connectivity aspects for air and maritime –both passenger and freight.

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8 crucial as the median voter, who is approximately 48 today, is anticipated to have an age of 52 by the year 2025.

Revisions of the first pillar in the year 2000 have led to a number of important structural changes. Firstly, family/household benefits have been replaced by _individual benefits_ . Secondly, individuals with responsibilities for children up to 16 years or other dependants are now entitled to _child care credits_ . Thirdly, contributions during marriage, including child care credits, are split between the spouses. This change led to a substantial improvement for divorced women, but reduced the entitlements of couples with a non– working spouse and no children. Fourthly, the legal retirement age for women has been raised stepwise from 62 to 64 years, and most probably, it will be further raised to 65.

**Table 1  AHV: Revenues, Expenditures** **<u>and GDP (in millions) explored until 2015</u>**

|Year|Total<br>Revenues|Total<br>Expenditures|Gap|AHV<br>Fund|Nominal|Expenditures<br>as % of GDP|Gap as<br>% of GDP|
|---|---|---|---|---|---|---|---|
|2000|28,792|27,722|1,070|22,720|415,529|6.67%|0.26%|
|2001|29,620|29,081|539|23,259|422,485|6.88%|0.13%|
|2002|28,903|29,095|-192|23,067|434,744|6.69%|0.04%|
|2003|31,958|29,981|1,977|25,044|446,203|6.72%|0.44%|
|2004|31,525|30,588|937|25,981|455,027|6.72%|0.21%|
|2005|32,062|31,532|530|26,511|461,917|6.83%|0.11%|
|2006|32,349|31,485|864|27,375|468,532|6.72%|0.18%|
|2007|32,852|32,817|35|27,410|475,854|6.90%|0.01%|
|2008|33,144|33,138|6|27,416|483,875|6.85%|0.00%|
|2009|33,767|34,634|-867|26,549|492,086|7.04%|0.18%|
|2010|34,001|34,662|-661|25,888|500,043|6.93%|0.13%|
|2011|34,553|36,541|-1,988|23,900|507,618|7.20%|0.39%|
|2012|34,744|36,664|-1,920|21,980|514,941|7.12%|0.37%|
|2013|35,232|38,503|-3,271|18,709|522,208|7.37%|0.63%|
|2014|35,340|38,485|-3,145|15,564|529,529|7.27%|0.59%|
|2015|35,806|40,508|-4,702|10,862|536,905|7.54%|0.88%|

Source: Bundesamt für Sozialversicherungen (2004a), T16 on p.70

# **_2.1.2  Contribution Rates in the First Pillar_**

The contribution rate is a percentage of all labor income without any lower or upper limit. The current rate is 8.4 percent for employed workers, of which the employer has to pay half. Self-employed workers pay 7.8 percent on what they declare as income. The contributions accumulated during a marriage are split between the partners in case of divorce, or when the first partner reaches retirement.

For parents of children up to 16 years, child care credits are automatically granted on top of the contributions of regular labor income. The credits are equivalent to a labor income of three times the minimum benefits. The same is true upon request for individuals caring for sick or frail relatives.

Non-working individuals, including students, are required to contribute at least 425 Swiss francs a year to insure a full contribution period, unless their spouse contributes at least 850 Swiss francs a year. The contributions of individuals without labor income increase as a fraction of total wealth, which includes savings, assets and real estate, as well as regular benefits (such as unemployment, second pillar income, annuities) multiplied by
# **Introduction**

The collapse in oil prices that started in 2014 has put diversification at the forefront of the policy debate in many nations that have been dependent on fossil fuel production. Many oil rich countries have indeed either announced or already put in place policies to help transform their economies and move away from the dependence on oil. Diversification strategies have been pursued in the past and historically those managed by the state have not worked. That is because top-down management almost inevitably obstructs the economic diversification process because it does not empower managers of the companies (and their teams) who are best able to guide the process and adapt to new circumstances. In other words, states should not concentrate on the end goal—namely diversification—and focus instead on what is required to get there, no matter how disruptive that transformation process might be to traditional production.

Energy markets are subject to changes in technologies that affect producers and consumers alike. These changes—such as certain innovations in oil drilling techniques or in battery technology for automobiles—can be risky for oil companies and national economies that depend on fossil fuel production.<sup>3</sup> But technological change can also offer new opportunities for growth.

<!-- Start of picture text --> Figure 3.1 Crude Oil Price, Actual and Forecast<br>(US$ per barrel)<br>120<br>100<br>80<br>60<br>Actual (WB)<br>40 WB (f)<br>IMF (f)<br>20 EIU (f)<br>0<br>2008 2010 2012 2014 2016 2018 2020 2022<br>Sources: World Bank (WB), Commodity Markets Outlook October 2017; International Monetary Fund (IMF), World<br>Economic Outlook January 2018 Update; The Economist Intelligence Unit (EIU), World Commodity Forecasts ,<br>March 2018 Edition; and WB staff calculations.<br><!-- End of picture text --> The biggest risk for oil producers and oil-dependent economies comes from changes that cause oil price collapses followed by a protracted period of low prices, as is occurring now (see Figure 3.1). What is more is that low prices could strand oil reserves—which will be left in the ground because they are no longer economical to extract—a sharp blow to economies whose national wealth is heavily bound up with fossil fuel reserves.

The opportunities associated with technological change include potential improvements in extraction efficiency that permit profitable production of oil at lower prices. Other changes not directly associated with oil, such as the development of technologies around renewable energies, can allow economies that have say a high potential for solar irradiation to limit the risks of trying to develop non-fossil fuel industries and better align with the goals set by the 2015 Paris Climate Accord. The accord, if adhered

> 3 Oil and fossil fuels are used interchangeably thereafter.

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Vergunst, F., Jenkinson, C., Burns, T., Anand, P., Gray, A., Rugkåsa, J., and Simon, J. (2017) Psychometric validation of a multi-dimensional capability instrument for outcome measurement in mental health research (OxCAP-MH), Health and Quality of Life Outcomes, 16: 250 Vinayagamoorthy, A., 2017. Women empowerment through self-help groups: A case study in the north Tamil Nadu. Small Enterprises Development, _Management & Extension (Sedme) Journal_ , 34(1).

Weber, O. and Ahmad, A., 2014. Empowerment through microfinance: The relation between loan cycle and level of empowerment. _World Development_ , 62, pp.75-87.

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These district-level results do not account for changes in school attendance patterns that are the product of changes in the characteristics of children and their families. We now turn to multivariate analysis based on the 1994 and 1997 LSMS to see whether the positive effects of FONCODES expenditures are still apparent when we control for other factors.

We consider four measures of educational outcomes in the multivariate regressions: the school attendance rate for children aged 6-13, the school attendance rate for children aged 14-16, the probability that children aged 8-10 will be at least at the appropriate grade for their age, and the amount of time (in minutes) it takes children aged 6-13 to get to school1<sup>9</sup> In each case (except for the measure of school proximity) we run regressions at both the "household" and "individual" levels. The "individual" regressions take a given child as the unit of observation, while the "household" regressions take a given household which has at least one child in the relevant age group as the unit of observation: for example, the first set of individual regressions estimates the probability that a child aged 6-13 is attending school, while comparable household regressions estimate the probability that all of the children aged 6-13 in a household are attending school. All of the results we present for regressions with dichotomous dependent variables (that is, those for the probability of attending school or being on track for a given age) are based on the linear probability model. The results from probit regressions (not reported) are very similar throughout.

We start by showing results of "naYve" regressions in Table 6.2. These specifications correspond to equation (2) above, when the sample is limited to observations in the 1997 LSMS (similar regressions were estimated using the 1996 INEI, with similar results). The "naYve" estimates illustrate the problems with drawing inferences from bivariate relationships between treatments and outcomes based on a single cross-section of data. The coefficients on FONCODES expenditure when no controls are included (rows 1 and 3) tend to be small and imprecisely estimated. Adding a set of controls for household and district-level characteristics (rows 2 and 4) substantially alters the results. FONCODES funding becomes positively and significantly related to school attendance for both younger and older children, although there is no relationship between spending and the likelihood of being at the right grade level, or spending and the time it takes to travel to school.

The naive estimates may be badly biased because the targeted and demand-driven nature of FONCODES projects induces a correlation between the parameter for FONCODES expenditures and the error tern in the regression. We now turn to estimates of the effect of FONCODES

**'9** When testing the fraction of children at appropriate grades for their age we limit the sample to the youngest children because of the cumulative effect of repetition.

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productivity in three ways. (i) Internet can lower prices by lowering search costs; (ii) Internet use can cut the cost of holding inventories by allowing large suppliers to bypass retailers and contact customers directly; and (iii) Internet usage can improve the transparency of the host countries and make it comfortable to do business. It is expected that the improvement of SI will have a positive impact on the trade.

Because trade flows are expected to be positively related to national incomes, and negatively related to distance, b10 and b11 are expected to be positive and b14 is expected to be negative. Trade flows are also expected to be positively associated with regional integration, language, and adjacency.

In contrast to cross-section data, panel data permit more general types of heterogeneity. For a single cross-section, these controls can only depend on observed country-pair attributes such as common language, and estimates can thus be biased if there is additionally an observed component to the country-pair propensity to trade. With panel data, such heterogeneity can be controlled through a country-pair fixed effect. In this paper, we use country-specific effect when a country is an exporter. With this specification, distance, adjacency, and language are eliminated because they are fixed over time<sup>10</sup> .

We use three sub-samples. The first concerns bilateral trade flows between 100 countries. The second is, a sample comprising 25 African countries (see table 8 in appendix). The last one concerns intra-African trade and trade with the rest of the world<sup>11</sup> . The coefficients of the three sub-samples are assumed stable over the period 2003-2004.

> 10 See Cheng and Wall (2005) for the various specifications to controlling for heterogeneity in the gravity models of trade.

> 11 Due to endogeneity problem and space, we don't present the results of the estimations with GNI per capita as regressor. However, the results with all the variables, including GNI per capita, are available on request to the authors
exception of certified midwives, all clinicians in this setting, are equally qualified to provide high-quality care.

Although we already know that capacity has a one-to-one relationship with knowledge in this setting, we can still examine the role of equipment in the know-can gap in Table 3. This regression corresponds to equation 5, _C_ = _α_ 2 + _β_ 2 _I_ + _ν_ 2 _K_ ( _T_ ) + _γ_ 2 _K_ ( _T_ ) _∗ I_ + _E_ 2. In the analysis of the relationships, it is important to include both the inputs of knowledge _K_ and equipment _I_ and the interaction between the two inputs. The level of equipment might help to explain the intercept on Graph 4 of the Three-Gap model: do increased levels of equipment improve the overall capacity of health workers? The interaction of know and the measure of infrastructure can help to explain the slope: as knowledge increases, does it make a difference if the equipment score is higher? In this case, unsurprisingly, the level of equipment available does not significantly alter either the intercept or the slope. This does not mean every facility has adequate equipment, only that their performance is not explained by the level of equipment.

To examine the determinants of the can-do relationship, we summarize the results of 54 questions on the attitudes and motivation survey, coming up with five summary measures of motivation: **self-satisfaction** , **feeling valued by the facility** , **job satisfaction** , **positive** 4 **facility characteristics** and **positive worker behavior** . The individual questions that make up the summary statistics are listed in Appendix B and each score is described briefly here:

> 4When the survey was conducted, the answers to the motivation questions were scored on a scale of 1 to 5, where 1 corresponds to “strongly disagree” and 5 corresponds to “strongly agree.” Afterwards, we flipped the scores of all negatively worded motivation statements for which answering “strongly disagree” indicated high motivation and “strongly agree” indicated low motivation. For example, the score for the negatively worded statement, “I do not get feedback from my superior so it is hard to improve my performance,” has been flipped so that a 1 means that the worker strongly feels that she does not get feedback from her superior and a 5 means that she feels she does.  Therefore, all motivation questions used in the analysis in this paper are measured on a scale of 1 to 5 where 1 indicates low motivation and 5 indicates high motivation.

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of financial development (Rajan and Zingales, 2008). For instance, the financial crisis might have a greater impact on entrepreneurship in countries where new firms are more dependent on bank financing.

It is commonly observed that financial development is highly correlated with the level of overall development in a country. For example, in our data the correlation is 0.72 (Table 2). Next we investigate which result dominates – the financial development or the income level - by including two interactions simultaneously in column 3. We find that financial development remains significantly negative, while GDP per capita is no longer significant. This suggests that earlier results showing that the crisis more significantly affected more developed countries was in fact capturing the impact of the crisis on countries with deeper financial markets.

Finally, we explore whether the variation in the severity of the crisis has affected new firm creation in different countries. For example, Calderon and Didier (2009) estimate an index of “turbulence” which measures the degree to which a country has been affected by the crisis. We use their index of “financial turbulence” which is a principal component of three measures: (a) variation in the real effective exchange rate in March 2009 (% year‐on‐year), (b) rate of change in the aggregate stock price index in March 2009 (% year‐on‐year), and (c) change in the country credit rating from Institutional Investor (variation in March 2009 vis‐à‐vis March 2008). Here we test whether the degree of “turbulence” has a significant impact on new business creation in a country. To do that, we add an interaction of our crisis dummy and the degree of turbulence (which is time invariant since it only measures the overall response of a country to crisis). In other words we use the degree of crisis severity as a country-level variable (denoted as Xi) in model 4. Higher values of the turbulence index indicate the country was _less_ affected by the crisis. We find that this turbulence measure has a significant impact on new business 21
also be that households that are induced to send their girls to school are unwilling to do so without also sending their sons. While the school census data used in this analysis can say nothing about spillovers within the household, a companion piece (Hasan, 2009) explores the effects of this program on time allocation within households using household survey data.

I take a holistic approach to the policy process and consider if the success of the program is able to shed any light on pitfalls policymakers must prepare for down the road. The school census data allow an examination of the student teacher ratio which suggests that while the student teacher ratio is increasing it is not increasing at the same rate as enrollment. This is precious little solace however if it indicates that things are not as bad as they could be. Policymakers must pay particular attention to the bottlenecks in the learning process that might appear if a business-as-usual approach is taken to staffing decisions. The CCT program is showing consistent evidence of being successful in terms of its stated goal of increasing girls’ enrollment. However, this means that adjustments must be made to ensure that the students who are enrolling in school are also learning. That enrollment and learning are often divorced from each other is a point that has been repeatedly underscored in the literature on education.<sup>13</sup> Likewise I consider whether schools from urban areas are better at producing enrollment gains. The evidence I find suggests that urban schools are driving the gains to the program. Rural schools are contributing to the success of the program but not by nearly as much as their urban counterparts. This goes to the very heart of the rationale for this program. The program documents state that the size of the stipend is intended to cover transport costs for girls. Transport costs may not be the right metric for assessing the size of the transfer in rural areas if the issue is ability to attend (access) rather than ability to get to school (transport). How

> 13 See for instance, Filmer, Hasan and Pritchett, 2006 and the references therein.

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