rafmacalaba/datause-encoder-data huggingface dataset:
relatively poor, young, unemployed or out of the workforce, uneducated, and not as religious as others, but more willing to sacrifice own life for his or her beliefs. These results may vary in magnitude and significance across countries and geographic regions, but are robust to changes in econometric techniques, model specifications, and a number of robustness checks, including tests dealing with the issue of non-responses, the inclusion of developed countries in the sample, various definitions of the dependent variable, and the inclusion of a variety of additional controls. Despite our efforts, given the fact that we rely on repeated cross-sectional data and therefore cannot deal with unobservable heterogeneity as well as fully address all simultaneity issues in our model, our results should be interpreted as conditional associations, rather than causal relationships.

As this paper explores attitudes towards terror acts, it is linked to two bodies of literature: one on the feelings of discontent and economic performance and another on the political economy of terrorism. Gurr (1971) argues that failure to realize gains during periods of modernization can lead to discontent among those with stagnant incomes, and therefore to political instability and uprisings. However, several case studies show that the occurrence of revolutions is more likely during times of improving economic conditions, underscoring the alternative possibility that improved conditions reduce frustration but enable revolutions due to increases in rebel resources, power, chances of winning, and consequently the expected utility of participation in their movement. Cross-country comparative studies on the link between political violence and economic performance produce mixed evidence, with some studies finding a negative association with per capita income (Parvin, 1973; Sigelman and Simpson, 1977) and others finding a positive association (Muller, 1985). More recently, Gurr and Moore (1997) explore the possibility that relative deprivation, not absolute income, drives discontent, while Fearon and Laitin (2003) use 4
The advantage of using this graph instead of calculating the errors of exclusion and inclusion directly is that one does not have to define beforehand who is and who is not in the target population. By presenting this information in graphical form one can read the errors of exclusion and inclusion for different definitions of the target population (e.g. households in the first two deciles of the income distribution or perhaps households in the first four deciles of the income distribution).

In summary, when studying the distributive impacts of policies it is preferable to present the relative benefit and cumulative beneficiary curves. Complementary to these graphs, it is recommended that some summary statistics, such as the Quasi-Gini coefficient and the errors of inclusion and exclusion for a given definition of the target population also be presented.

# **7. WHAT DOES THE EVIDENCE SAY?**

Up to this point the discussion has been for the most part conceptual. In this section we review the empirical evidence available regarding the performance of different transport subsidy schemes. However, we first present a review of the lessons learned from subsidies in the utility sectors. This review will help to put the evidence from the transport sector in perspective.

# **7.1 Lessons from the utility sectors**

Most of the arguments justifying affordability measures in transport are relevant for utility services. As in transport, utility subsidies are ubiquitous. A review of the literature shows there are many studies that analyze the distributive impact of subsidies in utilities, while very few case studies can be found for the transport sector. Therefore, it is important to draw the main lessons from the utilities case studies. In this section we summarize the results and lessons from a comprehensive international study of electricity and water subsidies undertaken by Komives, Foster, Halpern and Wodon (2005), although many other studies are consistent with their results.<sup>21</sup> The lessons are the following:

# _1) Quantity targeted subsidies in the utility sector are for the most part regressive_

Quantity based subsidies are distributed according to the consumption characteristics of the good or service in question. For example, a universal subsidy that decreases the price of electricity or water across all users and for all consumption levels is a quantity based subsidy. The amount of the subsidy received will depend on the units of the good consumed. More sophisticated consumption based subsidies are increasing block tariffs (IBT) or volume differentiated tariffs (VDT). The analogy with the transport sector is a subsidy that lowers transport fares across the board as with most supply side subsidies or untargeted demand subsidies.

> 21 See for example The World Bank (2000; 2002) and Foster and Araujo (2004) just to cite three examples.

31
- i -

# **Contents**

|**I.**<br>INTRODUCTION.<br>1|
|---|
|**II.**<br>**THE ROLE OF CONTRACTUAL SAVINGS**<br>**3**|
|CONTRACTUAL SAVINGS AND CAPITAL MARKETS DEVELOPMENT<br>4|
|_Increased market depth and liquidity_<br>_.4_|
|_Innovation, competition and efficiency_<br>_.5_|
|_Regulation.6_|
|_Corporate goverance_<br>_.6_|
|MITIGATION OF SOCIAL AND FINANCIAL RISKS<br>7|
|_Portfolio concentration risk_<br>_.7_|
|_Credit risk._<br>_7_|
|_Refinancing risk_<br>_.7_|
|_Term transformation risk_<br>_.7_|
|_Vulnerability to interest rate and demand shocks_<br>_.8_|
|_Financial markets volatility_<br>_.9_|
|_Economic resilience_<br>_9_|
|CONTRACTUAL<br>SAVINGS AND GROWTH<br>9|
|THE INTERNATIONAL EVIDENCE<br>10|
|**III. THE MODEL .17**|
|THE MONEY MARKET EQUILIBRIUM .17|
|THE QUASI-MONEY<br>MARKET EQUILIBRIUM.<br>1 8|
|THE STOCK MARKET EQUILIBRIUM .19|
|SIMULTANEOUS<br>ASSET MARKET EQUILIBRIUM<br>.21|
|COMPARATIVE<br>STATICS .23|
|_Contractual savings development .23_|
|_Non-life insurance development .23_|
|**IV. THE ESTIMATION .24**|
|THE EMPIRICAL MODEL .24|
|_Endogeneity issues .27_|
|_Market capitalization: alternative specifications .29_|
|CONTRACTUAL<br>SAVINGS AND VALUE TRADED..........................................30..............,.|
|**V.**<br>**CONCLUSIONS ....**<br>**32**|
|**VI. REFERENCES ..................................................................................................................................33**|
|**APPENDIX I ....**<br>**37**|
|THEDATA ....................<br>,...,....,....,.,.,....,,...,...37|
The map in Figure 5 shows a total number of deposit accounts per thousand adults using a combination of account numbers collected through Financial Access survey where available and the estimates generated through the above procedures. The deposit account penetration ratios represent all deposit accounts summed across the various categories (in most countries, commercial banks are the vast majority).

As the map shows, rates of deposit account ownership in formal institutions vary greatly around the world. Of the seven countries which have fewer than 100 bank accounts per 1000 adults, five are in Africa —Congo D.R., Burundi, Madagascar, Mauritania, and Ethiopia. High income countries exhibit the greatest levels of deposits penetration with an average of over 2000 accounts per 1000 adults.

Underlying the wide variation in rates of account ownership are large differences in the ability of poor households to access formal savings. In recent household surveys, Rwanda, Pakistan, Malawi, and Uganda<sup>15</sup> all reported less than 20 percent of households save through formal institutions, and Financial Access Survey data shows them to have fewer than 225 bank accounts per 1000 adults. In contrast, a recent study by the European Commission calculates that in Belgium and the Netherlands, greater than 98 percent of households have bank accounts and survey data show them to have over 1500 accounts per 1000 adults in commercial banks.<sup>16</sup> The trend is that higher rates of bank account ownership equate to more banked individuals in the population (see also section 3 above for the results of regressions of deposit penetration on reported numbers of banked adults in a country).

# **_4.3 The Number of Accounts Worldwide_**

Adding all the predicted and reported values puts global number of bank and non-bank accounts in the world at approximately 6.2 billion or more than 1 for each adult on the planet (and nearly 1 for each of the 6.7 billion people on the planet).<sup>17</sup> While there are more than enough accounts to

> 15 FinScope Rwanda (2008);  FinScope Pakistan (2009) ; FinScope Malawi (2008) ; FinScope Uganda (2006). 16 In Belgium 3,724 and the Netherlands 1,772 accounts per 1000 adults. European Commission (2008)

> 17 These numbers are necessarily rough and likely to have a wide margin of error. In the estimation for commercial bank accounts, the estimated margin of error is approximately 6%. The margins of error for the non-banks are likely

20
Ownership of a four‐wheel tractor is included in Models 1 and 2 to assess the importance of agricultural livelihood‐related assets in determining poverty. However, this variable was dropped in Model 3 due to the potential for excluding poor agricultural households that own a tractor. We also drop land phone and water pump variables in Model 3. Land phones are likely to become obsolete over time, and just 2 percent of households own water pumps, limiting the contribution of these variables as predictors of poverty. Other variables (see Table A1) were dropped from Model 3 because of their limited explanatory power. We also tested the inclusion of district and sector categorical variables in the model, but this did not alter the results significantly.<sup>11</sup>

# IV. Results

After several iterations of models testing the inclusion of different variables, we present three models that fit the HIES data well (Table 1). Model 2 differs from Model 1 only in the exclusion of household head variables. Model 3 is a restricted version of Model 2, retaining only highly significant coefficients, with partial sum of squares above particular thresholds (see section II above). Model 1 is the typical PMT model. If the program eligibility continues to be determined at the family level, however, Model 3 would be preferable, because it performs almost as well as Model 2 and requires less intensive data collection and verification.

All models generate high R‐squared statistics, with Model 3 having a slightly lower R‐squared as expected due to the lower number of variables. In Model 1, several of the household head characteristics are not significant contributors to the PMT score. However, widowhood of the head, age, and head working in a government, semi‐government or self‐employed job all affect the PMT score significantly. Across all models, categorical household size variables are the largest contributors to the PMT score, with the score decreasing as household size increases. This is typically the case in a PMT because welfare is defined in (log) per capita terms, which mechanically makes household size a crucial predictor. The remaining coefficient contributions to the PMT score are fairly straightforward, with asset ownership and housing quality contributing positively to the score.

> 11Because of their small impact, and the potential objections that could be raised from including such variables, they are omitted from the models presented here. Results from specifications including location variables are available from the authors upon request.

8
21

**Table 9.  Welfare loss associated with not having one additional** **<u>year of schooling, World</u>**

|Year|Inertia S|Enhanced S|Welfare<br>Lower bound|loss (%)<br>Upper bound|
|---|---|---|---|---|
|1900|0.5|1.5|17.5|35.0|
|1950|2.7|3.7|14.7|29.4|
|2010|8.0|9.0|14.7|29.4|
|2050|10.0|11.0|11.1|22.1|

Note: Enhanced S = Inertia S + 1

# **Regional estimates**

The years of schooling coefficient of fitting the basic Mincerian function (equation 12) within regional groups of countries is reported in Table 10. This is the upper bound of what countries might have lost in the period of known actual data by not having one more year of schooling. East Asia and the Pacific that includes many dynamic economies in the period 1950-2010 exhibit the highest loss, whereas Latin America and Sub-Saharan Africa the least. Such pattern might be due to two reasons. First, it is a well know fact that the Mincerian function underestimates the returns to primary education; that is, the level of education most relevant to these countries (Psacharopoulos 1994). Second, the inferior education quality in Latin America and Sub-Saharan Africa relative to other countries could be depressing returns in those regions. Also, it could be due to the fact that education is worth so much more in East Asia, especially the fast growing ―tigers‖, that any deviation from the optimum shows larger relative losses. The fact that income loss differs between regions could be due to differences regarding economic institutions or access to capital.

**Table 10.  The welfare loss of not having one more year of schooling,** **<u>by region, 1950-2010</u>**

|Country group|Welfare loss<br>(% per capita income)|
|---|---|
|Advanced economies|20.2|
|East Asia and Pacific|34.3|
|Europe and Central Asia|22.8|
|Latin America and Carr.|16.1|
|Middle East and N. Africa|14.5|
|South Asia|21.0|
|Sub-Saharan Africa|17.0|
|**World**|**25.8**|

Source: Equation (12) fitted within regional groups of countries Note: When multiplied by 100 the education coefficient measures the percent increase of per capita income associated with one extra year of schooling; all coefficients are statistically significant at the 1% probability level or better
21 beyond the poverty effect. This in done in steps. First, we evaluate the performance of children in female-headed households compared with all children with respect to childhood outcomes. Next, the portion of any performance difference due to the tendency to be in poverty is identified (e.g, the sensitivity of the outcome variables to household income), with the remainder of the difference attributed to an independent effect of female headship. Evidence of an independent effect would indicate that the consequences of poverty are more severe for children in female-headed households than for all children.

The PNAD data set allows the analysis of two potential outcomes for children: school attendance and labor-force participation. We interpret a lower rate of school attendance for children as an indicator that children are worse off. The interpretation of the labor-force participation of children and their well-being is more complex. It is not necessarily true that holding children out of the labor market will benefit society. For example, consider the case in which some families in the society are very poor and children in these families can find jobs that (1) will permit them to continue attending school; and (2) will pay them wages that are high enough to alleviate their poverty. In this case, it seems that society will be better off permitting children to work. However, if children work at the expense of school attendance, when basic reading, writing, math, and problem-solving skills are being taught, labor-force participation is clearly disadvantageous in the long run. An increase in the labor-force participation of children that leads to reducdons in school attendance probably leads to a reduction in society's welfare. By contrasdng the school attendance of children who live in female-headed households and who work with the attendance rate of all children in the labor force, we can investigate the extent to which children in female-headed households have mon. difficulty than other children in resolving the conflict between studying and working. We analyze school attendance separately for children ages 7-9 and ages 10-14. Due to the nature of the PNAD questionnaire,<sup>2</sup> ' we have to limit our study of labor-force participation to children aged 10 to 14 years.

**21** Labor market-related questions are applied only to persons 10 oi. more years old.
where the outcome of interest for a given applicant _i_ is the binary indicator for receiving a callback from the employer with the possibility of hiring, and the key variable of interest is , an indicator of whether applicant _i_ is female. Here, the main estimate of interest is , which represents ii FFE CCE the average treatment effect of being female on the employer callback rate—that is, the difference ββ in the callback rates for men and women solely due to their gender. Additional models included either _Major_ specific fixed effects (∑ ), or _Occupation_ nn specific fixed effects (∑ ). In more saturated models (not shown), controls for jj = 1 jj iijj γγ MMCCMME nn firm-level characteristics were also included. In all cases, the results remained the same. To follow jj = 1 jj iijj γγ OOC OOEECCO EEOO best practices, equation (1) was estimated using a linear probability model (Angrist and Pischke 2008), but the results were substantially identical if parametric estimations had been used. Similarly, as is standard, standard errors at the unique job ad level were clustered, thereby allowing errors to be arbitrarily correlated within each job advertisement.

Table 2 shows the results. On average, female applicants had a higher callback rate than male applicants. The difference—around 2.5 percentage points—is not substantially trivial because it represents more than a 10 percent increase over the average male callback rate. Indeed, this result is robust and is precisely and identically estimated across different econometric specifications.

**Table 2 Overall estimates by major and occupation**

||Employer<br>|callback<br>||
|---|---|---|---|
||(1)|(2)|(3)|
|Female applicant (β)|0.025<sup>**</sup><br>(0.011)|0.025<sup>**</sup><br>(0.011)|0.025<sup>**</sup><br>(0.011)|
|Observations|3,142|3,142|3,142|
|Clusters|1,571|1,571|1,571|
|R-squared|0.00|0.04|0.75|
|Outcome control mean|0.23|0.23|0.23|
|Major fixed effects|No|Yes|Yes|
|Occupation fixed effects|No|No|Yes|

_Note:_ Clustered standard errors by job advertisement appear in parentheses. * _p_ < 0.10 ** _p_ < 0.05 *** _p_ < 0.01.

At face value, this average result represents an encouraging finding when thinking about the potential role of discrimination against women in the labor market because it reveals instead slight 25
# **1. Introduction**

There is increasing concern in policy circles that developing countries that are rapidly decentralizing are exposed to the risk of macroeconomic instability due to growing fiscal deficits and soft budget constraints at subnational levels. While similar concerns exist for developed countries as well, the risk is perceived as exacerbated for developing countries because of lower potential revenue bases at local levels, higher dependence on federal transfers, and poor quality of legal institutions. The concern gains credence largely from individual case studies of sizable and persistent subnational deficits in federal countries like Argentina, Brazil, and India (Rodden, Eskeland, and Litvack 2001, World Bank 1999) and the theoretical and intuitive link between fiscal deficits and decentralization.

This link lies in a political economy perspective of fiscal federalism in which the geographical distribution of costs, benefits, and decisionmaking power over public expenditures leads to a “common pool” problem akin to the classic theory of distributive politics (Weingast, Shepsle, and Johnsen 1981, Inman and Fitts 1990, Aizenman 1998). If subnational governments make spending decisions, and are financed by transfers from the national government, which raises taxes, then the resulting amount of total government spending will be inefficient, because local authorities do not fully internalize the effects of their spending decisions on the consolidated government budget.

Within this framework, variations in political and institutional relations between the national and different subnational governments will have differential effects on the common pool problem, and hence on individual subnational fiscal policies. Jones, Sanguinetti, and Tommasi (2000) develop two hypotheses based on the common pool game for the impact of federal-provincial relations on spending by provincial governments in Argentina—one, provinces with higher federal transfers will have higher spending; and two, provinces where the governor is from the same party as the President will have lower spending. The first hypothesis is argued to result from the greater de-coupling of tax and spending decisions in provinces that are more favored by federal transfers. The second hypothesis follows the argument that the provincial governor who is politically affiliated with the national President will be more likely to internalize the effect of spending an additional unit of national resources due to internal party discipline. The empirical evidence from the Argentine provinces is consistent with both hypotheses. Similar evidence for the impact of intergovernmental transfers has been found for German provinces (Rodden 2000), the U.S. states (Rogers and Rogers 2000) and in cross-country analysis (Rodden 2002). This paper estimates the effect of political and fiscal relations between the national and provincial governments on provincial fiscal deficits in another large federation of the developing world, 1
responding to the increasing domestic traffic of Trinidad and Tobago, and The Bahamas, among their islands.

**Figure 7: Caribbean Air Traffic Capacity by Type of Route**

<!-- Start of picture text --> a. Composition in million seats, % of total b. Annual Growth Rates (%)<br>(2013)<br>4.0%<br>2.9% 3.0%<br>5.0 , 8.7% 3.0% 2.1% 1.8%2.0%<br>5.5 , 9.5% 2.0%<br>1.0% 0.6%<br>0.0%<br>-1.0% Domestic Caribbean Extra-Regional All<br>-2.0%<br>47.3 , -3.0%<br>81.8%<br>-4.0%<br>-3.8%<br>-5.0%<br>-6.0% -5.0%<br>Domestic Caribbean Extra-Regional<br>Growth 2001-13 Growth 2008-13<br><!-- End of picture text --> _Source: Authors’ calculation based on data by Diio (2014)._ In general, Caribbean connectivity can be described as having two components: large long-range routes bringing tourists into the Caribbean, and an inter-country network that contains many routes with very little volume. There are five important hubs: Puerto Rico, the Dominican Republic, Jamaica, The Bahamas in the north, and Martinique in the south.

In general, interlining between airlifts coming in from the outside and the domestic system is poor – LIAT, a dominant player in the Caribbean, has no interlining capabilities, meaning that passengers arriving on one of the airlines from the outside, e.g. American Airlines, would have to claim their baggage and re-check it at the LIAT check-in counter after having gone through customs. The networks are, in effect, separate, and have different purposes. Dominant routes in the Caribbean are MartiniqueGuadeloupe, Aruba-Curaçao-St. Eustacius, and the Dominican Republic-Puerto Rico-Virgin Islands.

# <u>Aggregates for the Caribbean Countries</u>

A little over half of all Caribbean air traffic (53 percent) can be traced to the Caribbean countries covered in this report. The traffic composition and patterns of the countries covered do not differ greatly from those seen in the rest of the Caribbean.

The air traffic of the countries covered is dominated by extra-regional flows coming from U.S.A. and Europe. Nearly 82 percent (approximately 25 million seats) of all capacity is on routes that connect the region with nations outside the countries covered. The rest of the traffic is evenly split between domestic traffic –about 10 percent (3 million seats) and intra-regional traffic within the group –about 9 percent or 2.7 million seats. In comparison to the whole Caribbean, among the Caribbean countries for this report domestic traffic has grown a little faster, and extra-regional traffic with the other Caribbean countries has grown somewhat more slowly.

30