rafmacalaba/datause-encoder-data huggingface dataset:
# D. Capitalized Reported non-FDI Inconme

- = Flow of other investmlenit income: credit (IMF BoPY lines 15,17,19) / One-year US Treasury Bill rate (IFS line 60c..zf, US)

While the residual and Dooley methods differ greatly in conceptuial approach (one looks at flows, the other at stock figures), they u.se some of the sant'. measuires, or those closely linked through the balance-of paymenits identity. Under the residual method, capital flight equals A + B + F + H'. By the balance-of-payments identity, this equals - (C +D + E + G+ (LI-H')), the negative of private capital flows (shollterm, including banks and 'ionbanks, and long-term; C, D and E). errors **and** omissions, and the difference between World Bank and balance-of-payments reported versions of offici.0 capital. These last four items are those that Dooley uses to cal2ulate the total reported and unreported) assets held abroad. These annual changes in total (reported and unreported) assets held abroad, according to tne Dooley method, are simply annual capital-flight estimates according to the residual method.

These similarities also imply that the Dooly estimate can be made more easily by taking the World Bank residual flow measure and the subtracting the flow of capital corresponding to the series for the imputed stocks of reported assets. It is not possible however, to equah. the year-to-year difference in the imputed stock of reported assets with a flow, since they include both new flows froin the country, as well as interest earned abroad but not repatriated.<sup>8</sup> In principle, new flows and reinvested earnings should be reflected in the capital account, just as retained earnings on FDI enter the capital account. In Dooley, the new flows reported in the capital account are deemed unreliable. Why otherw:se impuite the stock of reported assets from the cu.rent-account and not measure it directly from the capital account'? And. in practice, earnings on nonFDI inivestments which are reinvested abroad are seldom reported &cujrately in the capital accoullt of developing coLlntries.

This implies that the year-to-year change **in** the irnputed stock of reported assets cannot be divided between a new flow from the country and earnings reinvested. An arbitrary assumption has to be made that none of the reinvested interest enters the capital accouint, that all earned interest is reinvested and, thus. that the year-to-year difference in stocks includes interest accrued.9 The flow on reported assets is therefore calculated as the difference between the stock this year miniuis the stock last year grossed up by one plus the interest rate. The Dooley measuire of the flow of capital flight is then obtained by subtracting this flow from thie World Bank residual measLure of the annual flow of capital flight.

8New flows from the countryn means new flows beyond those occurring through reinvestment of interest earnings.

9For some individual countries, better adjustine)its canl perhaps be made. See for example Eggerstedt et al. (1993) for Mexico.
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE In the water sector, the average tariff charged by SPEN, Niger’s water utility, stood at $0.55 per m<sup>3</sup> as of 2009, versus the estimated $0.82 per m<sup>3</sup> average costrecovery rate. The consequent macroeconomic burden of undercharging for water services, at 0.23 percent of GDP, is slightly lower than in other nonfragile LICs. As the tariffs currently charged by NIGELEC are enough to cover production costs, which are highly subsidized by Nigeria, at present there does not appear to be a cost-recovery problem in the Nigerien power sector (figure 22).

# **Figure 22. Underpricing of water in Niger is comparatively less burdensome**

Financial burden of underpricing, as percentage of GDP <!-- Start of picture text -->
1.5<br>1.3<br>1.0<br>0.5 0.3<br>0.2<br>0.0<br>0.0<br>Power Water<br>Niger LIC-NoFragile<br>Percenatage of GDP<br><!-- End of picture text -->

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ GDP = gross domestic product; LIC = low-income country.

Because of inequitable access to power and water services in Niger, subsidized tariffs are highly regressive. Close to 100 percent of those that have electricity or piped water connections belong to the top 20 percent of the expenditure distribution; such connections are nonexistent for poorer households (figure 23). Only the richest quintile has access to piped water; most of the poorest quintiles still rely on wells and boreholes for water supply.

**Figure 23. Consumption of infrastructure services in Niger varies by income quintile**

**a. Mode of water supply, by income quintile**

**b. Prevalence of connection to power grid among population, by income quintile**

<!-- Start of picture text --> 120% 40%<br>35%<br>100%<br>30%<br>80%<br>25%<br>60%<br>20%<br>40% 15%<br>20% 10%<br>5%<br>0%<br>0%<br>Q1 Q2 Q3 Q4 Q5<br>Q1 Q2 Q3 Q4 Q5<br>Piped water Stand posts<br>Power<br>Wells/boreholes Surface water<br>% population<br>% population<br><!-- End of picture text --> _Source_ : Banerjee and others 2009. _Note_ : Q1—first budget quintile, Q2—second budget quintile, and so on.

The inequitable distribution of water supply and electricity connections virtually guarantees that any subsidy to these services will be extremely regressive (figure 24). With a measure of distributional incidence of 0.27, Niger has one of the most regressive systems in Sub-Saharan Africa.

How expensive would utility bills become if cost-reflective tariffs were applied? With a cost-recovery tariff of $0.16 per kWh and a monthly subsistence consumption of 50 kWh for power and 10 m<sup>3</sup> per 39
# **1 Introduction**

What is the role of international remittances in the economic development of recipient countries? This important question is not completely new, and some answers have already been provided by the large literature on aid effectiveness: the positive or negative effects of remittances on economic development depend on how they are used.<sup>1</sup> For example, remittances are likely to have small effects on development if they are used primarily for the consumption of imported consumer goods. When remittances do not increase savings, they have little or no impact on domestic investment and thus do little to enhance the longer-term economic growth and may even create dependency. In general, however, very little hard evidence and firm data exist on how remittances are used and no hypotheses have been proved or discarded. An additional issue that has received relatively little attention is whether recipients adjust their labor supply in response to international remittance inflows. More specifically, do remittances cause an increase in reservation wages and thus induce reductions in the labor supply?

This paper investigates the latter issue for the case of Jamaica, a country that during the 1990s witnessed, on the one hand, a combination of persistently high unemployment rate (around 20%) and rising real wages and, on the other hand, a remarkable increase in international remittance inflows, reaching almost 20% of GDP in 2003. Working with Jamaican household surveys for the 1995-2002 period, Kim (2006) finds an empirical negative link between increased remittances and labor supply decisions of Jamaican households. Based on this finding, a computable general equilibrium (CGE) model of Jamaica with an endogenous labor supply calibrated to the elasticity estimates of Kim (2006) is constructed here to analyze three main empirical and policy relevant questions. First, we assess the potential economy-wide repercussions of a labor supply reduction due to an increase in remittances; the CGE model measures the macro effects on aggregate GDP, consumption, investment and government budget, and also estimates which sector or group of households will more likely be affected. Second, by altering the relative prices of tradables and non-tradables increasing external inflows normally cause real exchange rate appreciation and Dutch disease effects. These effects are accounted in our model which actually highlights that an endogenous labor supply that responds negatively to rising remittances exacerbates the real exchange rate appreciation, mainly by increasing wages more than in a case with an inelastic labor supply. Third, and perhaps this is the most important question, a numerical model can be used to simulate potential corrective policies that the government could implement to deal with the negative effects. In particular, we simulate policies

> 1 See, for example, Burnside and Dollar (2002) and Collier and Dollar (2002).

2
|**NABEUL**|7.4|0.800|0.108|8.1|0.517|0.064|
|---|---|---|---|---|---|---|
|**ZAGHOUAN**|12.1|1.311|0.108|13.7|0.605|0.044|
|**BIZERTE**|17.5|1.120|0.064|16.9|0.535|0.032|
|**BEJA**|32.0|1.848|0.058|26.4|0.641|0.024|
|**JENDOUBA**|22.4|1.215|0.054|21.5|0.858|0.040|
|**LE KEF**|34.2|1.459|0.043|33.1|0.843|0.025|
|**SILIANA**|27.7|1.787|0.064|24.7|1.007|0.041|
|**SOUSSE**|16.2|1.095|0.068|14.3|1.108|0.078|
|**MONASTIR**|8.3|1.149|0.138|7.7|0.426|0.055|
|**MAHDIA**|21.1|1.682|0.080|25.0|0.685|0.027|
|**SFAX**|5.8|0.661|0.114|6.3|0.400|0.063|
|**KAIROUAN**|34.9|1.330|0.038|29.3|0.354|0.012|
|**KASSERINE**|32.8|1.305|0.040|33.6|0.297|0.009|
|**SIDI BOUZIDE**|23.1|1.175|0.051|25.0|0.579|0.023|
|**GABES**|15.8|1.038|0.066|16.9|0.671|0.040|
|**MEDNINE**|21.6|1.179|0.055|18.5|0.733|0.040|
|**TATAOUINE**|15.0|0.995|0.066|17.8|0.815|0.046|
|**GAFSA**|18.0|1.085|0.060|19.0|0.862|0.045|
|**TOZEUR**|14.6|0.982|0.067|14.6|0.746|0.051|
|**KEBILI**|18.5|1.081|0.058|19.2|0.300|0.016|

Source: our elaborations from INS (2020).

For each governorate, Table 3 reports the minimum estimated value of the coefficient of variation (i.e. standard error over per capita consumption expenditure), the maximum value of the coefficient of variation, and the median value. From Table 3 we can see that in many cases per capita consumption estimates at cluster level are very precise, with an error of even less than 2% of the estimated mean. In the column “Median” we may conclude that in all the governorates at least 50% of cluster estimates have an error smaller than 10% of the estimated mean. Finally, in the column “Maximum” we can observe that in most of the cases the error is smaller than 50% of the estimated mean. In other words, there are very few cases (clusters) in which the estimated precision is not high and the estimates are not significative.

In order to reinforce the findings of Table 3, we have examined the 95% confidence intervals of per capita consumption at cluster level, reported in Figure 1. The graphs shows that, once PSUs have been ranked by per capita consumption expenditure (or poverty rate), confidence intervals among the richer clusters are disjoint with confidence intervals among clusters whose per capita consumption 13
**<mark>Table 2B – Impact of FD Law on States’ GDP and Employment Given Their Public Debt</mark>** This table reports OLS estimates of the impact of the FD Law on GDP and employment of states of varying ex ante public indebtedness. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postq_ is an indicator variable that equals one from 2016Q2 onwards. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels.  Detailed variable <u>definitions are provided in Table A1 in the Appendix.</u>

||GD|Ps,q|Empl<br>To|oyment<br>tals,q|Emplo<br>Prim|yment<br>arys,q|Emplo<br>Second|yment<br>arys,q|Empl<br>Terti|oyment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Postq|0.002||0.002||0.012||-0.010***||-0.002||
||(0.013)||(0.004)||(0.009)||(0.000)||(0.005)||
|Postq*DebtStates,16Q1|0.003***|0.003***|0.001|0.001***|0.002|0.002|0.007***|0.007***|0.003*|0.003|
||(0.001)|(0.001)|(0.001)|(0.000)|(0.007)|(0.009)|(0.002)|(0.001)|(0.002)|<br>(0.002)|
|Observations|480|480|420|420|420|420|420|420|420|420|
|R-squared|0.044|0.473|0.015|0.180|0.017|0.092|0.027|0.042|0.019|0.146|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

46
value and fail to meet the standard criteria for statistical significance. Per our prior expectation, we also find a large, negative, highly-significant impact for birth during the peak-catch period.

We find that female children have lower mortality rates than male children, ceteris paribus.

Mother’s education, family asset ownership and child size at birth do not have significant effects. Reported child deaths yield a higher mortality rate for the 2014 survey, but survey month has no impact on the results. In contrast, the birth-year results are strong and declining as public health services improve.

To assess their quantitative significance, we use our probit results to estimate differential mortality rates within a centrally-located grid square with typical population density (Figure 8, square outlined in light blue). We perform the prediction for a mother with no education who resides in a household whose asset count is zero; the mean child size at birth for this education and asset ownership group; survey year 2014; birth year 2012; the first six survey months (dummy values 0 for the survey months included in the result); and zero consumption preference for eggs and meat.

We produce four predictions: with and without fish consumption preference and peak-catch-season birth, respectively. Table 10 presents our results, which suggest substantial impacts. The estimated mortality rate is 72.3 per 1,000 for children born in this locale during the off-peak-catch season to mothers with no fish preference (beyond the structurally-determined factors, per model (1)). With no change in fish preference, the estimated mortality rate declines to 32.4 per 1,000 for a child born during the peakcatch season. In contrast, children born to mothers with fish preference have predicted mortality rates of 31.5 per 1,000 in the off-peak season and 12.3 in the peak-catch season.

In summary, our results suggest a remarkable 83% decrease in the child mortality rate (from 72.3 to 12.3) produced by two elements related to fish consumption. We believe that this should be viewed as an outer-bound estimate, because our interpretation depends on the following assumptions: (1) the fish consumption result reflects individual preferences, not other factors that cannot be observed; 21
To complement the figure, it is useful to point out that the two worst performing sectors in terms of RoE, water and railways, are also those which showed the highest level of volatility as indicated by standard deviations of RoE of respectively 0.23 ad 0.17.<sup>16</sup> These relatively high levels of volatility reflect the wide diversity of experiences covered by the sample. For ports and electricity, it is only 0.9 which is surprisingly low for such a diversified sample.

Figure 7 is intended to reveal any possible difference across regions on the gloom scenario emerging from the sectoral picture. The story is probably only robust for East Asia and Latin America where the number of countries covered by the sample is large enough (30 and 55 respectively) generating an average RoE of about 11% for East Asia and –0.8% for Latin America between 1998 and 2002. The negative returns in Latin America are consistent with the slow down observed in private investment in the region. However, East Asia was the only on region in which risk and returns were a match! The result for East Asia is in fact quite robust since the standard deviation around the mean is only 0.05. The Latin American experience showed much more volatility with a standard deviation of 0.19 around the mean.<sup>17</sup> The other results (RoE of almost 12% for South Asia, 3.7% for Eastern Europe and 0.6% for Africa) are however in general interesting to report because they suggest company returns varied significantly across regions and not just between East Asia and Latin America. In South Asia and Eastern Europe, the volatility of these RoE was small (0.07 and 0.09 respectively), while it was quite large in Africa (0.18).

**Figure 7:  Average returns by region (1998-2002)**

<!-- Start of picture text --> Cost of Equity vs. RoE<br>by region<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>EAP SA EAP LAC Af rica - Mena<br>Cost of Equity RoE<br><!-- End of picture text --> 16 Volatility is defined here as the grade of dispersion of the expected return in the sample and is usually measured as the annualized standard deviation. The standard deviation is a weighted average of the deviations from the expected value and it provides an idea of how far above or below the expected value the actual value is likely to be.

17 The differences in volatility between the two regions also reflects differences in approached to attract private investment. In East Asia, project finance approaches dominated, forcing well targeted ring fencing of risks at the project levels within sectors, while in Latin America, the dominating approach was the concessioning of services which were often quite encompassing at the sectoral level.

14
public health research in order to provide a cross-sectional picture of an evolving political economy of actors’ interaction.<sup>34</sup> Methodologically, stakeholder mapping relies on in-depth interviews with major stakeholders (therefore a large s/N sample to population proportion), but it can be used jointly with other quantitative or qualitative social science methods. It has also been found useful as a preparatory tool for micro-level household surveys and formal modeling.<sup>35</sup> For the analytical purposes of this paper, we do not present the complete stakeholder mapping and analysis. Instead, the empirical section uses a number of selected survey questions that tap exclusively into general questions of equity and efficiency assessments in relation to priority setting and litigation.<sup>36</sup> _Why not micro-level surveys with larger sample size for analyzing priority setting and health litigation?_ Priority setting and the Constitutionally mandated right to health are ultimately patientcentric issues, ideally addressed with micro-level data. However, stakeholder mapping and macro-level political economy analysis are more useful as initial analytical building blocks for several reasons: 1) the issues of health litigation and priority setting have been only recently emerging in developing countries; their salience is not yet high enough among the general public, and the pool of claimants is very small compared to the general population; 2) stakeholder analysis and mapping analyze _aggregated_ preferences of mobilized groups as opposed to _diffuse interests_ of citizens, therefore being more relevant to short term policy making dynamics; 3) patient focus groups can be a substitute for microlevel surveys especially as the problem is complex, vaguely specified and would benefit from interaction among participants; often, however, stakeholder analyses help identify the policy issue and preferences with greater precision, and guide the design of subsequent micro-level survey instruments whenever analytically necessary.

_Data collection_

> _34_ For an extensive methodological review, see: <u>Ruairí Brugha</u> and  Zsuzsa Varvasovszky. “Stakeholder Analysis: a Review,

> _“_ <u>Health Policy and Planning</u> (2000) 15 (3): 239-246. Examples of stakeholder analyses in the public health research: Blair et al. 1996; Reich 1994

> 35 <u>http://www1.worldbank.org/publicsector/anticorrupt/PoliticalEconomy/stakeholderreading.htm; Fadi El-Jardali, Jihad</u> Makhoul, Diana Jamal, Michael Kent Ranson, Nabil M Kronfol and Victoria Tchaghchagian. “Eliciting policymakers’ and stakeholders’ opinions to help shape health system research priorities in the Middle East and North Africa region.” <u>Health Policy and Planning</u> (2010) 25 (1): 15-27.

> 36 <u>Zsuzsa Varvasovszky</u> and Ruairí Brugha . “How to Do (or Not to Do)...: A Stakeholder Analysis _,”_ <u>Health Policy Plan.</u> _(2000)_ 15

> (3): 338-345.

17
# **1  Introduction**

Policy interventions in developing countries often aim to improve the employment prospects of low-income individuals and their families, particularly those facing severe labor market shocks or credit constraints. Some interventions specifically target women, under the view that women have an inequitable share of power in household decision-making, that women are more likely to face barriers to labor market entry, and/or that women spend resources more effectively than men, for example, on investment in children. Other policies, such as active labor market programs, do not explicitly target women but may affect men and women in different ways.

This paper examines the effectiveness of a variety of policy interventions that have been tried in developing and transition economies with the goal of improving women‟s employability and quality of work. The programs include active labor market programs, education and training programs, programs that facilitate work (such as childcare subsidies, parental leave programs and land titling programs), microfinance programs, entrepreneurship and leadership programs, and conditional cash transfer programs. The programs studied are heterogeneous, differing in scope, content, targeting and eligibility criteria, as well as in objectives. Common objectives are to reduce unemployment, increase wages, provide social protection, and/or increase women‟s empowerment in the household. The populations served by the programs are also heterogeneous in terms of demographics and labor markets. They reside in rural and urban settings in Latin America, Africa, Europe and Asia. Because the coverage of this survey is broad, we consider only a subset of relatively recent programs that have rigorous impact evaluations. Also, we focus on programs that aim to directly affect employment and earnings outcomes and do not consider programs oriented toward education or health, which may also influence labor market prospects. In reviewing the literature, we discuss the merits of alternative evaluation studies and attempt to synthesize results across multiple studies.

There are many questions that are potentially of interest in evaluating the effects of a social policy intervention. Foremost is the question of whether people affected by the policy or program intervention benefit from it, at least on average. Most of the evaluation literature, including most of the studies examined in this paper, focus on estimating the so-called average effect of treatment on the treated, which is the average program impact for people who were exposed to some treatment. The treatment may represent active participation in some program, such as a job training program, or passive exposure to some policy, such as being eligible for a 1