rafmacalaba/datause-encoder-data huggingface dataset:
22 preference for preserving species in "conflictu may reflect some underlying desire to pay attention to high-profile species.

B. Regression #3: The FWS Priority System Regression #3 is identical to Regression #2 except for the addition of PRIORrIY and CONFLICr and the subtraction of MONOTYPIC and SUBSPECIES from the list of regressors. MONOTYPIC and SUBSPECIES are dropped for statistical reasons because they are included as components of PRIORrrY.

REGRESSION #3 - THE SPENDING DECISION WITH FWS PRIORrES

|INTOTAL|I<br>Coef.|Std. Err.|t|P> ItI|
|---|---|---|---|---|
|MiAMMAL|1<br>.54<br>|.40|1.354|0.177|
|**BIRD**|1<br>.46|.34|1.342|0.181|
|**REPTILE**|I<br>-1.62<br>|.47|-3.470|0.000|
|**AMHIBAN**|1<br>-1.19|.62|-1.917|0.057|
|NCRANK|I<br>.80|.18|4.398|0.000|
|LNLENG|1<br>.85|.14|**5.944**|0.000|
|**PRIORITY**|-.10|.04|-2.716|0.007|
|**CONFLICT**|1<br>1.20|.29|4.177|0.000|
|CONSTANT|1<br>7.99|.47|17.126|0.000|

Dependent variable is LNTOTAL. Method of estimation is Tobit. 237 observations.

The coefficient on PRIORrrY is negative and statistically significant. Other things equal, high priority species, i.e. those with a low numerical PRIORITY, receive more spending than do low priority species. At first glance, this suggests successful implementation of the priority system. Such a conclusion is mitigated, however, by the size of the estimated coefficient on CONFLICT Recall that CONFLICT is intended to be the least important component of the priority system, as it acts only to break ties between species with the same priority number. In spite of this ostensibly small role, the estimated coefficient on CONFLICT is more than ten times the estimated doefficient on PRIORrTY, and its t-statisic is greater an 4. Since ten units of PRIORITY-moving up from 14 to 4
41

**Table 12. Hours Worked in Cambodia**

||Percent of wo|rkers work|ing more|than 40|hours a|week||||
|---|---|---|---|---|---|---|---|---|---|
||1996|1999|<br>2004|<br>20|07|2008|2009|2010|2011|
|Male|60.55|70.82|<br>63.98|<br>72|.59|75.09|65.15|70.41|74.04|
|Female|57.78|71.57|<br>68.37|<br>73|.78|77.39|68.90|73.48|77.19|
|Agriculture|50.08|66.46|60.58|<br>67|.93|78.23|50.27|58.00|62.2|
|T&G|64.89|91.24|95.06|<br>95|.57|95.19|89.81|93.5|95.1|
|All sectors|59.5|71.1|<br>65.8|<br>73|.01|75.97|66.73|71.7|75.4|
||Percent of children l<br>|ess than 14<br>|years old<br>|in the e<br>|mploye<br>|d populatio<br> <br>|n|||
||1996|1999|2004|2007|2008|<br>2009||2010|2011|
|Male|0.27|0.29|0.89|0.51|0.11|<br>0.99||0.73|0.46|
|Female|0.99|0.28|2.07|1.4|0.37|<br>1.32||1.3|1.08|
|Agriculture|0.5|0.35|4.42|2.92|0.68|<br>2.35||2.44|2.12|
|T&G|1.91|0.18|0.34|0.55|0.48|<br>0.87||0.78|0.89|
|All sectors|0.54|0.29|1.38|0.87|0.21|<br>1.13||0.97|0.74|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011. _Note:_ T&G = textiles and garments.

Table 13 shows that on average in Sri Lanka a smaller percentage of people were working more than 40 hours a week than in Cambodia—47 and 76 percent in 2008, respectively. In Cambodia this share remained roughly the same from 2008 to 2012, while in Sri Lanka it went up to 59 percent by 2012. There was a big difference between the share of men and women working overtime in Sri Lanka—67 and 44 percent in 2012, respectively. Similar to Cambodia, people who worked overtime worked on average 55 hours a week. Also, similar to Cambodia, more people worked overtime in the apparel industry than in the economy overall—75 and 59 percent in 2012, respectively. The percentage of workers less than 14 years old in Sri Lanka is lower than in Cambodia—0.08 and 0.74 percent in 2011, respectively. There were no workers under 14 years old in the textile and apparel industry in 2012. Moreover, the percentage of girls
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Table 10: Risk Factors and Investment Flows

||(1)<br>fragile peace|(2)<br>positive net investment fows|(3)<br>positive net investment fows|(4)<br>positive net investment fows|(5)<br>positive net investment fows|
|---|---|---|---|---|---|
|pop. excluded from access to power|0.810**|||||
||(0.351)|||||
|pop. discrimated against|1.401***|||||
||(0.519)|||||
|refugees per 1000 pop.|0.00549**|||||
||(0.00241)|||||
|no refugees|-0.563**|||||
||(0.243)|||||
|fragile peace (ftted values)||-5.999***|-6.107**|-3.885*|-4.927***|
|||(2.059)|(2.772)|(2.286)|(1.673)|
|frst 5 years of recovery||1.344**|1.863**|0.933|1.156**|
|||(0.623)|(0.746)|(0.685)|(0.461)|
|peace||0.780***|0.169|0.579***|0.545***|
|||(0.167)|(0.291)|(0.181)|(0.204)|
|Observations|658|5,834|6,405|8,021|6,865|
|Number of countries||211|214|189|195|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. The dependent variable in column (1) is the dummy “fragile peace”, which take a value of 1 if a year is peaceful, within five years since the last year of conflict, and revert back to conflict within the next 5 years. Data used in the first stage estimation (column (1)) is gathered from several sources: “share of the population that is excluded from political power” and “share of the population that is discriminated” variables are provided by GROWup dataset; information about “refugees” is provided by UNHCR dataset. In column (2)-(5) the dependent variable is “positive net investment flows”. Data is gathered from four different data sources: OECD data in column (2); Dutch investment data in column (3); UNCTAD in column (4) and World Bank in column (5). Fitted values of “fragile peace” is coming from a regression of “fragile peace” on “share of the population excluded from political power”, “share of the population that is discriminated” and “refugees per 1000 population”. “Peace” is a dummy that indicates when a country is not in conflict, i.e. violence intensity is above a threshold of 0.008 battle-related deaths per 1000 inhabitants. “First five years of recovery” is a dummy that indicates if the end of the most recent conflict has been less than five years ago. “No refugees” is a dummy that takes a value of 1 if the yearly stock of refugees in a given country is equal to zero.
there are still (relatively skilled) labor requirements at the local level in organizing projects and workers.<sup>7</sup> This burden may well be higher in poor areas, making it harder to afford and implement such a complex scheme.

This paper examines the performance thus far of MGNREGS in meeting the demand for work across states. We examine the evidence for India as a whole using the household-level data from the National Sample Survey for 2009/10. We also use these data to understand who gets rationed and how this affects the scheme‘s ability to reach India‘s rural poor and other identitybased groups, notably backward castes, tribes and women. We also discuss the role played by wage setting on the scheme, and how rationing might be influencing labor market responses. Finally, we take a closer look at women‘s participation and how this is influenced by the rationing of work under MGNREGS.

# **2. Performance in meeting the demand for work across states**

The participation rate ( _P_ ) in MGNREGS can be defined as the proportion of rural households who obtain work on the scheme. This can be thought of as the product of the ―demand rate‖ ( _D_ )—defined as the proportion of rural households who want work on the scheme—and one minus the ―rationing rate‖ ( _R_ )—defined as the proportion amongst those who wanted work who did not get it. Thus for state _i_ we have the following identity: Notice that the share of households who are rationed is the product of the rationing rate and the demand rate. We shall call this the excess demand ( _EDi_  _Ri Di_  _Di_  _Pi_ ).

As noted, if MGNREGS worked in practice the way it is designed there would be no unmet demand for work. This is, of course, an exacting standard. In practice there may be frictions in implementation leading to some unmet demand, such that those wanting work do not

> unemployment allowances from their own budget.

> 7 The scarcest manpower resource locally is the junior engineer or panchayat technical assistant who can prepare technical estimates and draw up engineering plans for the works. Andhra Pradesh (AP) has used information technology to reduce the need for such skilled local staff, by developing standardized and computerized templates for the engineering plans of common types of works. AP is the exception however. Most other states face shortages of such skilled local staff.

4
2

# **1. Introduction**

The size of the public sector and its effects on private labor markets has motivated a good deal of research on the determination of the sectoral (public/private) wage process for paid employees in developed countries. Many studies have indicated significant differences in the wage structure in both sectors, with conditional and unconditional rewards in the public sector often exceeding those in the private sector. Policy initiatives to contain the growth of public-sector wages have been motivated by wage differentials established in these studies.

Much less is known about this issue for developing countries as noted by Christofides and Pashardes (2002), where in a number of countries large fiscal deficits have drawn attention to the public sector wage bill, often the largest component of public expenditure. A related concern voiced by Terrell (1993) is the impact of a large public sector on wage setting in the absence of strict market forces and whether it is having a ‘spillover’ effect on the remaining formal private sector where government pay scales serve as a standard for wage earners in the private sector. Indeed, in a number of developing countries, the public sector comprises a major part of the wage employment.

So is the case of Djibouti which is the focus of this paper - a low-income country where the public sector employs a disproportionate amount of the formal labor force at roughly 56.4 percent of the labor force, compared with 17.6 in the formal private sector.<sup>3</sup> Throughout the 1990s, the annual average pay of a civil servant was about 10 times the country’s GNI per capita and the average monthly civil servant wage was nearly 7 times higher than the minimum wage. Furthermore, Djibouti’s public sector wages serve as a benchmark for private sector wages, which are higher than those earned in its neighboring countries. The public wage bill forms a significant component of the total government budget, averaging 50.8 percent of total expenditure in the 1990s. While some portion of the wage bill can be attributed to an oversized civil workforce, another component may be due to above competitive wage levels.

> 3 This estimate is based on a 1996 representative household survey of the sedentary population. The labor force includes public and private workers (both formal and informal). If only public and private formal workers are considered, the public share is 76 percent and the private share is 24 percent.
30 analytical framework. As indicated in the section on measurement issues, it is particularly important to devise and test sensitive and specific indicators of managerial, clinical, and service production efficiency. In addition, it is necessary to determine the internal and external correlates of these dimensions of performance.

Observational studies would make it possible to diagnose the most critical areas for the second type of research, namely, intervention studies that would introduce planned change in hospitals and would assess its consequences. It is fundamental that intervention studies be based on comparative designs. Indeed, a problem with evaluations of the effectiveness of specific interventions is the frequent lack of control groups, which makes it impossible to attribute any observed change to the intervention itself, rather than to another source of variation. Thie external validity Ofthese typesof studies is also often threatened by the choice of highly specific sites that make it very difficult to generalize the findings and to truly build a body of knowledge. If the ideal randomized trials cannot be achieved, then quasiexperimental designs with clear control groups should be used. These kind of studies should be complemented by cost/benefit and cost/effectiveness analyses of the interventions (Wortman 1983).

Turning to the priorities on the topics for research, it must be stated, at the outset, that any ranking of topics is doomed to seem arbitrary, unless it is based on some explicit method to poll the perceptions of large numbers of experts and consumers of research. Nevertheless, we will attempt to offer what we believe is a preliminary list of the most urgent areas for inquiry, particularly in developing countries.
However, this is not the case for the provision of electricity alone, preventing us from concluding whether the provision of electricity alone had an impact on either GVA or population.

A closer examination by the period of the electrification of municipalities already endowed with a highway (rows 2, 3, and 4 of Tables 3 and 4) indicates that the complementarity effect mainly occurred in the 1996-2000 period. In that period, providing electricity in places already supplied with a highway positively increased gross value added by 44 percent and population by 10 percent.

Table 4: DIDM estimates of infrastructure provision (Population)

||_Dep_|_endent va_|_riable: Pop_|_ulation (log)_|
|---|---|---|---|---|
|||N2E|N2H|H2B|
|1970-2000 (E)|coef.|-0.001|-0.025|0.073|
||s.e.|(0.025)|(0.033)|(0.066)|
|1970-1980|coef.|0.097<sup>_∗∗_</sup>|-0.083|0.388|
||s.e.|(0.041)|(0.063)|(0.342)|
|1980-1996|coef.|-0.076<sup>_∗_</sup>|0.016|0.008|
||s.e.|(0.041)|(0.044)|(0.073)|
|1996-2000|coef.|-0.006|0.002|0.098<sup>_∗∗∗_</sup>|
||s.e.|(0.023)|(0.031)|(0.022)|
|1970-2000 (D)|coef.|0.027|-0.058|0.121|
||s.e.|(0.055)|(0.062)|(0.116)|
|Placebo|coef.|-0.073<sup>_∗_</sup>|0.003|-0.044|
||s.e.|(0.041)|(0.057)|(0.067)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision. (D): estimated effect one period after the infrastructure provision._

# **4.2 Structural Transformation**

We now turn to the impact of infrastructure provision on the industry share in Gross Value Added. The findings (summarized in Table 5) point towards evidence of structural transformation. There are also noticeable heterogeneous effects associated with the separate and joint provision of electricity and highways.

Providing electricity (columns N2E) increases, on average, the share of industry by 5 percent as compared to municipalities that were not electrified.

15
other social networks, in greater civic engagement by the poor or in more pro-social norms of behavior.

There are several possible explanations for the discrepancy between the null results (ours and those of Beaman et. al.) and the positive ones. First, there is a fundamental difference in the comparisons in the various studies. Our treatment and the treatment of Beaman et. al, is at the community level: we compare the behavior of residents from treated communities to residents of control communities, so our treated and control groups contain both members and SHGs. The treatment in Feigenbaum et al. (2013) is the frequency of micro-credit group meetings, and they only study group members, not non-members. In their observational studies Deininger and Liu (2013a) and Casini and Vandewalle (2011) compare SHG members to non-members. Desai and Joshi (2013) did administer their treatment at the community level, so we suspect the difference between their results and ours stems from our different measures. They quiz respondents about their knowledge of authorities in their village and whether they have lodged complaints with those authorities. These are plausible measures of civic engagement, but they do not measure norms or social networks as we do. Feigenbaum et al. (2013) employ network measures that are a subset of the type we use. The other studies measure social capital via self-reported actions in retrospective surveys. Thus our use of behavioral laboratory activities represents a contribution to this literature and a departure from the earlier studies.

The paper is organized as follows: we first give some background information on the evaluated program. Thereafter, we outline our empirical strategy to measure economic outcomes and social capital, the sampling procedure, as well as the empirical model. We then present impacts of the program on economic outcomes such as savings and economic livelihoods, as well as on social capital as captured by pro-social behavior in lab activities, social networks, and self-reported survey measures. The last section concludes.

4
More recently, however, new data from the mid- to late-2000s and several anecdotal accounts suggest some reasons for optimism. Emerging economies have improved their macroeconomic performance, lowered inflation, and reduced fiscal deficits (Gourinchas and Obstfeld 2011). These policy achievements, together with high liquidity in international markets, have allowed emerging economies to issue long-term bonds in domestic markets, as foreign investors have expected further appreciations of local currency and entered local markets in search of higher yields. In addition, these economies weathered the storms of the recent global financial crisis relatively well, indicating the strength and resilience of their financial systems (Eichengreen 2009; Kose and Prasad 2010; Didier et al. 2012).

Even with some reasons for optimism, the path ahead will certainly be challenging, especially for policy makers. In particular, the old model of convergence to international standards is being questioned precisely because those standards are being revised in the wake of the 2008–09 global financial crisis. One example is the definition of the limits of regulation when banks and shadow banks are interconnected and when banks pose too high a systemic risk to be allowed to fail. A second example is the need to provide better services to savers and investors, while monitoring the degree of risk, given the prevalence of domestic and global shocks. A third example is the model of financial development, in terms of what role banks (domestic and foreign, private and public) and capital markets play.

The goal of this paper is to present a bird’s-eye view of the major trends in Asia’s financial development, compared to the trends observed in advanced and other emerging economies. The primary value of this exercise is to put in perspective the absolute and relative size and the evolution of different components of the financial system using traditional and new and disaggregate indicators of financial development. While we focus on the borrowers’ (firms and government) side, we also provide some evidence on the savers’ (households) side. In 3
Growth rates for individual countries were low, and the coefficient of variation was high, indicating that growth was highly erratic (table A1). Figure 3 shows that African economies have by far the least predictable growth globally, as measured by the coefficient of variation (CV). Countries with different levels of income (like South Africa and Malawi), geographical locations (like Mali and Senegal), resource endowments (like Nigeria and Ethiopia), and long-term GDP per capita growth patterns (like Gabon, Niger, Madagascar, and Swaziland) share a common characteristic, high growth volatility.

**Figure 3: GDP per capita growth - mean, standard deviation and coefficient of variation by region (GDP weighted data), 1975-2005**

<!-- Start of picture text --> 8<br>6<br>4<br>2<br>0<br>Mean<br>Sub-Saharan East Asia & Latin America Low & middle South Asia Middle East &<br>-2 Africa Pacific & Caribbean income North Africa SD<br>CV<br>-4<br>-6<br>-8<br>-10<br>-12<br><!-- End of picture text --> Table 2 decomposes the standard deviation of GDP per capita and its growth into within and between country components. Growth is highly unstable in individual countries; the ratio of the within-country SD to the total SD of growth rates is 94 percent. The Comoros (–22.6), Ethiopia (18.4), Guinea-Bissau (–11.9), Malawi (24.6), Mauritania (34.6), Namibia (19.8), Nigeria (18.7), and South Africa (20.6) are notable for their extremely high volatility, even by regional standards.

Only three economies – Botswana (0.5), Cape Verde (0.8), and Mauritius (0.4) – have coefficients of variation of less than 1.0. These three economies are also notable for 6
Accordingly, the coefficients of the explanatory variables of equation (6) can be interpreted as the marginal effects on the probability of depositing money in a dollar-denominated account.

# **3.1** Data and Estimation Results

The data for this study are drawn from several sources: the 1999 International Financial Statistics (IFS) published annually by the International Monetary Fund (IMF), Levine, Loayza and Beck (1998), Caprio and Klingebiel's (1999) data set, and several central banks. The countries included in the study are Argentina, Belize, Bolivia, Costa Rica, Dominica, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Paraguay, Peru and Uruguay. Due to data limitations we concentrate on the 1990-1998 period.<sup>5</sup> The resulting sample has 117 observations (13 countries, 9 years).

The variables used in the estimation of equation (6) were calculated as follows. The ratio of FCDs to domestic currency deposits measured at the end of the period was obtained from the IFS, from some central banks, and from the IMF Western Hemisphere Department.<sup>6</sup> The nominal GDP per capita was computed by dividing the nominal value of GDP by total population and then multiplying it by the end-of-period exchange rate.<sup>7</sup> The interest rates were proxied by the deposit rates reported by the IFS. Finally, the proxy used for expected appreciation was calculated by subtracting the U.S. gross rate of inflation from the domestic gross rate of inflation. The summary statistics for the sample are presented in Tables 1 and 2.

Although each country shows important peculiarities, some stylized facts can be derived. The first important thing to notice is that the dollarization ratio has been, in general, steadily increasing over time. While in 1990 the average ratio for these countries was 0.23, in 1998 rose to 0.37. A second interesting fact is that this increase comes in a period when the inflation rate has demonstrated a dramatic decrease. In 1990 the average inflation rate for the thirteen countries was 1,353% (315% without counting the hyperinflation episodes of Argentina, Nicaragua and Peru); in 1998 this figure was 10%. This fact supports the idea that the dollarization process in Latin America follows a hysteresis process, since the optimal portfolio theory would predict a return to the domestic currencies once their expected value increases with lower inflation differentials. In fact, Figure 1 shows that the inflation differential

> 5Although information on most monetary and macroeconomic variables is available since 1970 for all countries, we could only obtain data on FCDs for all these countries for the nine years. 6FCD include all dollar denominated bank accounts including domestic and foreign banks. 7These variables were obtained from the IFS.

8