rafmacalaba/datause-encoder-data huggingface dataset:
African electricity regulators. The median age was just under 5 years. However, 5 (29%) were 10 years old or more and accounts for 42% of the total number of sample years with an autonomous regulator. The over 13 year-old group of autonomous electricity regulatory agencies comprises Costa Rica, Philippines and Trinidad and Tobago.

# **4.4 Ministry or Autonomous Regulator and Per Capita GDP**

Figure 3 shows, very interestingly, that – at least within this sample - there is little relationship between the existence of an autonomous regulator and per capita GDP. Both autonomous and Ministry regulators are scattered through the income range.

The mean income for countries with an autonomous electricity regulator was $3,500. For those with a ministry regulator it was $3,300. The difference was not significant. However, low income countries with an autonomous regulator have younger regulators e.g.. the two Sub-Saharan African regulators established since 1998 (Kenya and Uganda).

# **Figure 3: Type of Regulator and Per Capita Income (in Real $ 1995)**

<!-- Start of picture text --> Independent regulators and National Income per<br>capita<br>Independent<br>regulator<br>Ministry<br>18000.00<br>16000.00<br>14000.00<br>12000.00<br>10000.00<br>8000.00<br>6000.00<br>4000.00<br>2000.00<br>0.00<br>Rank by GDP<br>GDP per capita<br>1 4 7 10 13 16 19 22 25 28<br><!-- End of picture text --> Source: Domah 2001 survey, supplemented and updated by authors

# **4.1.5 Generation Capacity and per Capita Income**

Figures 4 and 5 plot generation capacity by real GDP for our 28 country panel at the start and end of the period. The plots show an upward sloping but by no means either uniform or linear relationship.

21
# Dynamics and Synchronization of Global Equilibrium Interest Rates*

**Robert C. M. Beyer Lazar Milivojevic** The World Bank Goethe University Frankfurt

**Keywords:** equilibrium interest rate, stochastic volatility, Bayesian inference, synchronization

**JEL Classification:** E52, E43, C32

_________________________________________________________________

* This paper has benefited greatly from comments by Hans Timmer, Daniel Buncic, Martin Iseringhausen, Mátyás Farkas, Maik Wolters, and participants of the Mathematical and Statistical Methods for Actuarial Sciences and Finance (eMAF) Conference (Ca' Foscari University of Venice).
vector (r, -1). Finally, for the case where the largest root in the SURP, process exceeds unity but is less than 1+r (1< A < (1+r)), (35) holds if and only if SURP, and B,1 are cointegrated. In this case, the cointegrating vector need not be (r,- 1). The later is called the "Trehan-Waish cointegration test" below.

Trehan and Walsh (1991) applied their cointegration test to U.S. debt and primary deficits over the period 1964-1984, the same period employed in Hamilton and Flavin (1984). Their statistical tests indicate that SURP, is stationary (i.e., 0 < X < 1), but B, is not. Therefore they conclude that the government budget process is not sustainable. This result is consistent with the conclusion in Kremer's (1988) reconsideration of the Hamilton-Flavin analysis.

The government temporal budget constraint (3) depends only on SURP, not its decomposition into, say, non-interest government spending G and tax revenue T. By decomposing SURP in this way, however, it is possible to determine the restriction that the PVC imposes on the time series properties of government spending and revenue. Hakkio and Rush (1991) propose testing the PVC in (34) by checking whether the government expenditure _inclusive_ of interest payment G, + rtBt, is cointegrated with T, and whether the cointegration vector is (1,-1). The validity of their test depends on several auxiliary assumptions: (i) rt is stationary witl. unconditional mean r and (ii) both G, + (r,-r)B,-l and T, follow unit root processes (i.e. the A = I case above).

Hakkio and Rush apply their test to U.S. data for the period 1950:11 to 1988:IV as well as the sub-samples: 1964:I-1988:IV and 1976:1II-1988:IV. For the later two periods, they conclude there is no cointegration between T, and G, + rB,-,. Like Trehan and Walsh and Kremers, therefore, they reach the conclusion that recent US fiscal policy is not sustainable once the evidence from the 1980s is included in the dataset. The similarity in the 26
# **_Table 1: Regression Baseline Results_**

|VARIABLES|(1)|(2)<br>logof GDPpe|(3)<br>r capita(in PPP)|(4)|
|---|---|---|---|---|
|Persistence|0.781***|0.784***|0.726***|0.746***|
||(0.0569)|(0.0563)|(0.0491)|(0.0392)|
|ln(exch rate)|-0.0640|-0.0622|-0.0553*|-0.0172|
||(0.0404)|(0.0392)|(0.0332)|(0.0355)|
|ln(schooling)|0.0178|0.0445|0.0104|-0.0266|
||(0.0503)|(0.0502)|(0.0463)|(0.0452)|
|ln(credit/GDP)|0.0743**|0.0542*|0.0432*|0.0238|
||(0.0311)|(0.0304)|(0.0221)|(0.0245)|
|ln(trade/GDP)|0.0824|0.0609|0.0916***|0.0968|
||(0.0502)|(0.0490)|(0.0350)|(0.0584)|
|ln(govt C)|-0.262***|-0.259***|-0.215***|-0.127|
||(0.0442)|(0.0423)|(0.0359)|(0.0810)|
|ln(tele lines)|0.141***|0.129***|0.0769***|0.0816***|
||(0.0309)|(0.0297)|(0.0216)|(0.0261)|
|ln(inflation)|-0.0113|-0.0145|-0.00523|-0.0128|
||(0.0118)|(0.0110)|(0.00886)|(0.0112)|
|Δln(TOT change)|0.118***|0.123***|0.116***|0.110***|
||(0.0286)|(0.0277)|(0.0264)|(0.0339)|
|ln(bank crisis)|-0.0399|-0.0430|-0.0414|-0.0461*|
||(0.0317)|(0.0314)|(0.0259)|(0.0236)|
|Δln(commodity prices)|10.48***|11.11***|7.507***|6.963|
||(2.686)|(2.546)|(2.391)|(4.943)|
|ln(institutions)|-0.00265||0.00190|-0.00549|
||(0.0330)||(0.0247)|(0.0255)|
|Constant|2.502***|2.829***|3.203***|2.469***|
||(0.708)|(0.465)|(0.600)|(0.453)|
|Observations|464|502|464|464|
|Number of countries|126|141|126|126|
|Estimation|SysGMM|SysGMM|SysGMM|FE|
|Note:|Baseline|w/o Polity2|lags 1-3 as|baseline|
||||<br>instruments|as FE|
|No of instruments|153|166|171||
|AB(1)|0.023|0.024|0.033||
|AB(2)|0.102|0.045|0.062||
|Sargan test|0.131|0.017|0.001||

Note: Based on Brueckner (2013). Standard errors in parentheses. ***, **, and * indicate statistical significance on the 1, 5, and 10 percent level, respectively. AB(1) and AB(2) is the p-value of the Arellano and Bond test for first and second order autocorrelation, respectively. Sargan test reports p-values.

11
her husband looks for a job). This disaggregation emerged from field interviews as being the most relevant way to split the household into its component groups.

Consumption expenditures are recorded in several parts: first all common expenditures are collected (housing, electricity bills, food, etc.). Food expenditures are compiled based on a detailed account of who shares which meal and how much money is specifically used to prepare the meal. These are the “DQ” or “dépenses quotidiennes” ─ the name the Senegalese give to the amount of money a woman has at her disposal to buy fresh ingredients for the meals of the day. Next, individual consumption is collected at the cell level (e.g. expenditures on clothing, mobile phones, transportation, and food outside the home). Finally, expenditures that are shared between several cells but not the whole household are collected.

A measure of per capita consumption can then be constructed at the cell level allowing us to identify unequal consumption levels within households. Subgroups also emerge that take some or all of their meals separately (in 17% of households), thus widening the possibility for differences in nutritional intake among household members. The data allow us to construct a relatively individualized measure of consumption that we use to assess women’s individual economic welfare.

The analysis presented in this paper is mainly based on the sample of ever married adult women (15 years of age and older), without age limit, from the 2006 PSF1 database. This sample is presented by marital status in Table 1.

A second source of data is the DHSs of 2005 and 2010 which we draw on for comparison purposes and to complement the analysis using PSF. Specifically, the DHSs assemble information on aspects of women’s well-being, decision making and resource constraints that are not represented in PSF.

# **4. Marital status in Senegal**

Given the complexity of marital trajectories, computing divorce and widowhood rates from cross-sectional data is complicated, even when surveys contain recall of past marital history data. As noted in the Introduction, PSF1 identifies 18.5% of ever married women aged 15 and older as ever-widowed. As can be seen in Table 1, the rates are similar in urban and rural areas. Regarding divorces, the overall average of 13.2% hides a higher incidence in urban (16.7%) than in rural areas (10%). However, these numbers are a lower bound on the share of women who experience either widowhood or divorce. Nearly 7% of ever-married women have had more than one dissolution. We have no information on how the union that preceded the 8
# **1. Introduction**

There is a growing body of work that highlights gender disparities along various dimensions and the resulting consequences on economic outcomes. The present study contributes to this strand of the literature by analyzing how mandating a non-discrimination clause in hiring practices along gender lines (henceforth, non-discrimination<sup>1</sup> ) may influence the employment of women relative to men (henceforth, women’s employment) in the manufacturing sector of 58 developing countries. We find a large positive association between non-discrimination and women’s employment, an association that is much stronger among the relatively smaller firms and among countries that are relatively richer and those with a larger proportion of women in total population.

Gender disparity in various economic and social dimensions is now being recognized as a pervasive phenomenon across the world (Hausmann et al. 2006). However, research work in the area is severely hampered by lack of relevant data especially for developing countries. One exception is the recently compiled data by the World Bank’s Women, Business and the Law (World Bank, 2011). These data record any differential treatment in the laws between men and women in a way that is comparable across a large number of developing and developed countries. For example, these data provide information on whether or not a country has mandated a non-discrimination law in hiring practices along gender lines (non-discrimination). We combine this information with data on women’s employment at the firm-level obtained from Enterprise Surveys to better understand the association between non-discrimination and women’s employment.

> 1 We would like to mention here again that issue of non-discrimination discussed throughout the paper refers specifically to non-discrimination in hiring practices and not non-discrimination in the more general sense.

2
variables (called the instruments) dependent on localization and urbanization proxies but independent on sector productivity, and then use a two-stage estimations approach to first project localization and urbanization variables on the space formed by the instruments, and then used these projections on a second regression on the sector productivity variable. However, it is not obvious to find good instruments since most of the socioeconomic variables can easily be determined by one another. In our case, we don’t have good instrument candidates in the available set of variables.

Another econometric device is the Granger-causality test, which tries to determine the predetermined variables, that is, the variables whose lags help to predict the other one. Mathematically, let us consider two variables _X_ and _Y_ . We will say that _X_ Granger-causes _Y_ if _Yt_ = α 1 _Yt_ − 1 + α 2 _Yt_ − 2 + ... + β _X t_ − 1 + ε _t_ , and similarly, that _Y_ Granger-causes X if _X t_ = α 1 _X t_ − 1 + α 2 _X t_ − 2 + ... + β _Yt_ − 1 + ε _t_ . Since we have a panel database covering the period 1980-2000 for a set of variables, we can use this approach to have some insights on the causation relation between productivity and localization and urbanization variables. Table 8 summarizes the results.

Table 8: Granger-causality between sectoral productivity and localization and urbanization

|**Y**|**Produ**|**ctivity**|**Localization **|**Urbanization **|
|---|---|---|---|---|
|**X**|**Localization **|**Urbanization **|**Produ**|**ctivity**|
|Food,Beverages andTobacco|yes|yes|no|yes|
|Textile,WearingApparelandLeather Industries|no|yes|no|yes|
|Wood andWoodProducts,IncludingFurniture|no|yes|no|yes|
|Paper and Paper Products, Printing and Publishing|yes|yes|no|no|
|Chemicals and Chemical,Petroleum, Coal...Products|no|yes|yes|yes|
|Non-MetallicMineral Products|no|yes|no|yes|
|BasicMetal Industries|yes|no|no|no|
|FabricatedMetal Products,Machinery andEquipment|yes|yes|yes|no|

Source: Authors’ estimations.

Table 8 suggests that the causation between sector productivity and localization is such that localization effects Granger-cause the productivity in the Food and Beverages, Paper products and Basic Metal products sector. Urbanization effects also appear to Grangercause the productivity of Paper products and Fabricated Metal products sectors. Except for these clear causations, Table 8 indicates generally that productivity and urbanization Granger-cause each other, which suggest that both factors constitute a self-sustaining system.

# <u>4) Policy implications</u>

Given these two-directional causations, the policy implications discussed in the next section should be taken with caution. In addition, the database used for the estimations does not cover the period 2001 onwards that has been characterized by the financial crisis 20
been consistently above a threshold for three years; or, if a country exceeds the threshold by X percent; or a combination. The advantages of either system are that they provide a clear early warning of a likely change, but of course they also introduce a lag in reclassification. Such a system already exists in the operational guidelines, based on a three-year period.

Other options have been proposed to manage exchange rate volatility. One is to use longer averaging periods. At some point prior to 1983, for example, the Atlas method used a seven-year average. But a three-year period was felt to offer the best compromise between sensitivity to change and smoothing (World Bank, 1983). This still appears to be the case, and overall there does not seem to be a compelling reason to change current practice.

It should also be noted that GNI per capita estimates can be affected by revisions in the estimate of both GNI and the total resident population, caused, for example, by new data from economic and population surveys and censuses, or other sources.

# **_5.3 Market exchange rates versus Purchasing Power Parity (PPP) conversion factors_**

Another well-known issue concerns the conceptual underpinning of the use of market exchange rates as conversion factors. What one US dollar buys in the United States does not necessarily correspond to the amount of goods and services that one US dollar would buy in another country, when converted to that country’s national currency. The implication is that, while the use of exchange rates may be useful for some purposes—such as measuring countries’ relative spending power on the world market—they are not the most appropriate choice for the international comparison of income; they do not fully adjust for the differences in price levels between countries and therefore do not provide a measure of the relative sizes of the volume of goods and services they produce. Furthermore, exchange rate based conversions are likely to result in a systematic downward bias in the measurement of the GNI or GDP of lower income countries, since they tend to have relatively lower wages than more developed economies, and thus lower prices on their non-traded goods and services. For that reason, the GNI (and GDP) of lower income countries will typically be under-estimated when exchange rates are used to compare their value with those of high income countries.

The solution to this problem is also well known and has been proposed in many previous discussions about the use of GNI per capita: the alternative conversion factor that addresses some 20
of the extension of the school day, school building and teacher training was practically universal in participating schools. Other components, like additional teacher training and increased community participation were voluntary. These two characteristics of the sub-components of the program will be relevant when trying to assess their impacts, as they prevent the robust causal identi...cation of the e¤ects of separate interventions.

# 3 Data

In the analysis, we use three sources of data: the National Evaluation of Learning Achievements for the Sixth Grade of Primary School, the National Census of Schools and speci...c program information gathered from ANEP.

## 3.1 National Evaluation of Learning Achievements for the Sixth Grade

The National Evaluation of Learning Achievements for the Sixth Grade of Primary School<sup>2</sup> (hereafter "tests") was carried out in 1996, 1999 and 2002 in the context of the Program for the Improvement of Quality in Primary Education (MECAEP hereafter), which was ...nanced by the World Bank. It consisted of Math and Spanish assessments, and it was supposedly applied to a census of primary schools in 1996 and nationally representative samples in 1999, 2002 and 2005 (the 2005 data are not yet available). The 1996, 1999 and 2002 tests are directly comparable between years. The 2005 used item-response theory for the ...st time, which may limit the comparison in the future. While the 1996 test was meant to be a census, it excluded very small schools with fewer than 6 students in 6th grade. As a result, about 1,200 schools (approximately half of the total number of schools) were not included in the sample, but they likely represent a minority of students. (Cf. Table 1) The achievement tests consist of two sets of 24 questions each in mathematics and language. The mathematics test comprises 7 questions on comprehension of mathematical concepts, 12 questions that involve the resolution of a problem, and 5 questions involving an algorithm. The language test comprises 8 questions about language, 8 argumentative questions and 8 narrative questions. All questions are multiple choice with 4 possible options. The overall score is the number of correctly answered questions out of a possible 24. There is no penalty for giving the wrong answer, so that a child who guesses on every single question of the test is expected to obtain 6 points out of 24 on average.

Information about FTS was gathered both from administrative data and data collected

> 2 Evaluación Nacional de Aprendizajes, 6to año Enseñanza Primaria, Gerencia de Investigación y Evaluación ANEP-CODICEN

4
The results indicate that lack of secure property rights and enforcement of the law is a fundamental cause of civil war. In particular, an improvement in institutions from the median value in the sample to the 75th percentile is associated with a 38 percentage points’ reduction in the incidence of civil wars. Moreover, once institutions are included in the regression analysis, income does not have any direct effect on civil war. This suggests that the direct effect of per capita income found in previous literature may have simply captured the effect of institutions. Finally, we find that per capita income does not have an indirect effect on civil war through worsening institutions once we include historical factors as instrument of institutions. This result is in line with the recent findings in Acemoglu et al (2006), where they test the relationship between income and democracy. They show that controlling for factors that simultaneously affect both variables removes the statistical association between income per capita and various measures of democracy. Our findings undermine the emphasis on poverty as a determinant of civil war and indicate that research may concentrate more on institutions than on economic development if we wish to understand the causes of civil war.

Section 2 describes the data. Section 3 details the empirical strategy. Section 4 develops an instrumental variables approach to study the causes of civil wars. Sections 5 and 6 provide robustness tests. Section 7 concludes.

# **2. Data**

Data on civil wars come from the Armed Conflict Dataset, a joint project between the Department of Peace and Conflict Studies, Uppsala University and the Center for the Study of Civil War at the International Peace Research Institute, Oslo. An armed conflict is defined as a contested incompatibility that concerns government and/or territory where the use of armed force between 4
|Weight-<br>foreign<br>index|Weight -<br>domestic<br>index|Score|Restriction|
|---|---|---|---|
|0.0500|0.0500||Fee setting|
|||1.00|Mandatoryminimumormaximumfees|
|||0.50|Restrictions for some groups or activities|
|||0.00|No restrictions|
|0.0200|||Licensing requirements on management|
||||Alldirectors/mamagersoratleastamajorityofthemmustbenationals|
|||1.00|<br>or residents|
|||0.75|At least one director/managers must be nationals orresidents|
|||0.50|Directors and managers mustbe locallylicensed|
|||0.25|Directors and managers mustbe domiciled|
|||0.00|No restrictions|
|0.0200|||Otherrestrictions(scoresadditive)|
|||0.33|<br>Restrictions onhirmg professionals|
|||0.33|Restrictionsontheuseof<sup>finm's international names</sup>|
|||0.33|<br>Government procurement - restrictionstowards foreigners|
|||0.00|No restrictions|
|0.0100||1.00|Movement ofpeople - Temporary<br>No temporaryentry ofexecutives, seniormanagers or specialists|
|||0.75|Temporary entry ofexecutives, senior managers or specialists up to<br>30 days|
|||0.50|Temporary entry ofexecutives, senior managers or specialists up to<br>60 days|
|||0.25|Temporary entry ofexecutives, senior managers or specialists up to<br>90 days|
||||Temporary entry ofexecutives. senior managers or specialists over 90|
|||0.00|days|
|1.0000|0.3800||Total|
|Source:Ng|uyen-Hong(|2000).||