rafmacalaba/datause-encoder-data huggingface dataset:
# **6.5 The Role of Expectations: Evidence**

From the model presented in the previous section we expect changes in investments to be largest if _pi_ and _qi_ are large. The simplest possible way to calculate these two values is to calculate and for each country _i_ . We then interact the peace dummy in (9) with these values to see whether there is any heterogeneity with regard to the persistence of peace and war. Table 9 shows the result in columns (1) and (2). Indeed, transitions to peace are particularly beneficial in terms of investment flows if peace in the country is persistent, i.e. if _qi_ is high. Somewhat surprisingly, _pi_ is not associated with any heterogeneous effects. This pattern is fairly robust across the other FDI data sources (except for data from the UN) and exclusion of outliers like Israel. It is also economically meaningful. If we take the coefficient in column (1) we get that a change of persistence by 10 percentage points, from say 0.9 to close to 1 raises investment flows in peacetime by about 15 percent.

What drives this heterogeneity? Up until now we assumed _qi_ to be constant over time and both forward and backward looking. If investors have an understanding of the conditions in the respective country then this might not be the best model of expectations. Instead, it could be that investors have a model to forecast instability which is taking factors like the quality of a cease-fire agreement into account. Investors could know, for example, that the stability of peace ( _qit_ ) rises sharply with the duration of peace - from only 72 percent in the first year after conflict to about 90 percent five years later. This implies that the first five years after conflict feature a particularly high risk of relapsing, i.e. low _qit_ . Only about 40 percent of all recoveries last longer than five years. To explore this, we construct a dummy that takes a value of 1 in the first five years of recovery. In column (3) we show that the dummy takes on a negative sign but is insignificant. In light of Figure 18 this is not surprising. If investors would systematically wait with their investments we would expect slowly increasing flows in recovery which is not what we see.

However, if investors are able to distinguish situations which are stable from those that are not then we expect investment to be linked to actual future outcomes. In column (4) we test this by adding an additional dummy which takes a value of 1 if the country is during its first five years of recovery but will re-enter conflict within the next five years. This dummy captures the actual fragility of peace in the crucial first five years of recovery. Adding this leads to a striking result, inflows are significantly lower in situations which are fragile. Without fragility, the increase in inflows is now 80 percent instead of 66 percent. Inflows in years of fragile peace are 40 percent lower. Of course, reverse causality is a serious concern here: perhaps it is that investment inflows secured stable peace and not the other way around. However, given the interview 54
# 3: Power sector reform policies and their expected impacts

In attempting to assess whether power sector reform has been effective, it is necessary to start with a view of what reform steps are entailed. Different lists have been supplied by different authors, with the simplest list corresponding to the steps identified in most econometric studies assessing the impact of reform packages adopted by different countries. These various approaches to understanding what is meant by sector reform are discussed in section 3.1.

Based on such lists it is necessary to understand the role of each step in improving the performance of a country’s power sector. An important aspect of such analysis is the separation of the benefit of a step taken by itself (e.g. unbundling) with the benefit when combined with another step (e.g. privatization). If one policy must be put in place before gains in performance are to be expected from the implementation of a second policy, then the partial reform would be ineffective. Section 3.2 outlines the roles of unbundling and privatization in combining to achieve improvements in sector performance.

Section 3.3 discusses the types of indicators of performance that have been utilized in the various statistical studies, noting that some indicators relate directly to user welfare (prices, quality of supply), others refer to the internal performance of the firm (T&D losses, thermal efficiency) and yet others measure benefits external to the sector (GDP, HDI). The latter two types of indicator do not indicate whether users were better-off because of the performance improvement.

## 3.1 The components of energy sector reform policies

The components of a power sector reform strategy have been identified in various combinations by different studies. Those which are more analytically based (Joskow, 2008; Jamasb et al. 2005; Gratwick and Eberhard, 2008) tend to be more detailed and identify more components than statistical studies. Statistical studies that use an overall index of reform measured as the sum of components (ESMAP, 1999; Erdogdu, 2010; 2011b; Urpelainen, 2017) tend to use more components than studies estimating the impacts of individual reform steps (Ba and Gasmi, 2017). Table 3 indicates the reform components identified in various studies.

10
Finally, Table 9 reports the shares that energy subsidy benefits represent on total household spending. Consistent with previous results, LPG and electricity subsidies are the largest contributors to household expenditures, which in the case of the poorest quintile represent a substantive 8.8% of total expenditures. This share of subsidy benefits over total household spending decreases along with expenditure levels, up to 2.4% of total spending for households in the top quintile. Finally, gasoline and diesel do not represent any substantive share of total spending, yet they are larger for the richest rather than the poorest quintiles. For all households, these two sources of subsidies represent about 0.2% of the total 3.9% of household expenditures transferred from energy subsidies.

Table 9: Energy Subsidy Benefits Received by Households (as % of total household expenditure)

||Gasoline|LPG|Diesel|Electricity|Total|
|---|---|---|---|---|---|
|Quintile 1|0.0|4.8|0.0|4.0|8.8|
|Quintile 2|0.0|3.4|0.0|2.6|6.0|
|Quintile 3|0.1|2.7|0.1|2.1|5.0|
|Quintile 4|0.1|2.2|0.1|1.5|3.9|
|Quintile 5|0.3|1.1|0.1|0.8|2.4|
|Total|0.2|2.1|0.1|1.6|3.9|

_Source:_ WB staff calculations using SUBSIM.

# **5. Simulating the Distributional Impacts of a Subsidy-Reducing Reform**

# **5.1. The Proposed Reform**

The government of Tunisia recently announced its intention to remove all subsidies associated with gasoline, diesel and LPG, and to increase the prices of each tranche of electricity for residential consumers (Jomaa 2014).Yet a specific and detailed proposal on the timing, sequence, and compensatory measures is still being discussed internally and has yet to be announced at the time of this paper. In any case, this reform proposal raises the question of what the expected poverty and distributional effects of such changes might be. This section reports the estimated effects of the simulated subsidy reform, but first explains the methodology to estimate those effects. Discussion then turns to the additional distributional effects of expanding cash transfers using the fiscal savings generated by the subsidy reform.

# **5.2. A Methodological Note on Simulations**

Given the preliminary stage of the policy discussion, the estimations consider two effects. One is the direct effect of price increases following the (partial or full) removal of subsidies. Direct effects have unequivocal impacts on individual and household budgets proportional to the 16
**Figure 4**

# **Growth outperformance in EM relative to G7**

This figure shows EM growth and G7 growth. EM is the simple average of individual countries. Source: IMF WEO April 2011.

<!-- Start of picture text --> 12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>Forecast<br>-8%<br>Forecast G7 EM EM +- 1 SD<br><!-- End of picture text -->
|**_Ta_**|**_ble 2. El_**<br>**Gend**<br>**Hous**<br>**He**|**_ectricity_**<br>**er of**<br>**ehold**<br>**ad**|**_expenditures_**<br>**Educati**|**_share for ho_**<br>**on of Househ**|**_useholds usin_**<br>**old Head**|**_g electricity only for heati_**<br>**Employment of the Hou**<br>**Head**|**_ng_**<br>**sehold**|
|---|---|---|---|---|---|---|---|
||||**No school**|**Some**||||
|**Quintile**|**Male**|**Female**|**or primary**<br>**completed**|**secondary**<br>**school**|<br>**Secondary**<br>**+**|**Un-**<br>**employed**<br>**Employed**|**Retired**|
|**1**|11.2|11.4|11.0|11.2|11.7|11.9<br>10.1|11.4|
|**2**|7.3|7.4|7.3|7.4|7.1|8.0<br>6.9|6.9|
|**3**|6.9|5.4|6.5|6.7|6.7|3.0<br>6.3|7.1|
|**4**|5.4|8.4|5.3|5.4|5.9|3.9<br>5.5|5.5|
|**5**|4.2|4.5|3.2|4.3|4.3|3.1<br>4.3|3.8|
|**ALL**|**6.3**|**6.9**|**6.3**|**6.5**|**6.1**|**8.2**<br>**5.7**|**6.9**|
||**Kids**<br>**fam**|**in the**<br>**ily**|**Disabled**|**in family**|**Social**<br>**Assistance**|**Roma**<br>**Dw**|**elling**|
|||**No**||||||
|**Quintile**|**Kids**<br>**up to 5**|**kids**<br>**or**<br>**older**|**No**<br>**disabled**|**Disabled**<br>**in family**|**FMS**<br>**No**<br>**FMS**|**Roma**<br>**Non**<br>**Roma**<br>**House**|<br>**Apt**|
|**1**|9.3|11.6|10.8|13|12.9<br>9.8|6.9<br>11.3<br>9.9|11.8|
|**2**|8.1|7.0|7.4|6.7|7.6<br>7.2|n/o<br>7.3<br>7|7.4|
|**3**|8.7|6.3|6.4|9.5|8<br>6.5|5.5<br>6.7<br>6.8|6.6|
|**4**|5.4|5.6|5.5|6.3|4<br>5.6|4.4<br>5.6<br>5.8|5.4|
|**5**|4.4|4.2|4.2|3.7|n/o<br>4.2|n/o<br>4.2<br>4.5|3.9|
|**ALL**|**6.6**|**6.3**|**6.1**|**8.2**|**9.8**<br>**6.2**|**4.7**<br>**6.4**<br>**6.2**|**6.5**|

Source: calculated from 2004 ISSP Montenegro Household Survey.

6
# TABLE IV

HUMAN CAPITAL SHARE BY INCOME STATUS AND REGION

||Schooling<br>|<br>Schoo|<br>ling and Le|<br>arning|
|---|---|---|---|---|
||_w_= 0|_w_= .15|_w_= .2_0_|_w_= .25|
|Human Capital Share|||||
|Baseline|0.09|0.20|0.25|0.30|
|High Income|0.18|0.41|0.52|0.65|
|Upper Middle Income|0.29|0.54|0.65|0.79|
|Lower Middle Income|0.11|0.16|0.19|0.23|
|Low Income|0.07|0.11|0.13|0.16|
|East Asia & Pacific|0.19|0.32|0.38|0.45|
|Europe & Central Asia|0.31|0.49|0.57|0.67|
|Latin America & Caribbean|0.10|0.13|0.15|0.18|
|Middle East & North Africa|0.10|0.19|0.23|0.28|
|North America|0.51|0.76|0.86|0.97|
|South Asia|0.35|0.47|0.52|0.56|
|Sub-Saharan Africa|0.05|0.06|0.07|0.08|

_Notes:_ y: real output per worker (2000-2010); h: human capital based on school and learning estimates (2000-2010). We convert HLO units into standard deviations based on a cross-country standard deviation of 68 to correspond to the returns to schooling parameter value which is given per standard deviation. We assume rates of return based on the microeconomic literature. We take the value _r_ = .10 for the rate of return per school year, and _w =_ .20 per standard deviation increase in learning. The .20 value is based on U.S. data. However, we might expect that returns to skills will be higher in developing countries, where the supply of skills is lower, as is the case in the returns to schooling literature. Significant work has been done to identify this parameter value. For the purpose of this paper, our intention is not to provide a final result, but rather to motivate the use of the data for future use in the development accounting literature. To this end, we take parameter values as given and conduct sensitivity analyses with values _w_ = .15 and _w_ = .25. When _w = 0,_ our accounting de facto only includes schooling; for any value _w > 0_ , we include learning as well as !"#[%&'())] schooling. We include 131 countries in this development accounting exercise. We define the human capital share as . !"#[%&'(,)] _Source:_ Schooling data are from Barro-Lee (2013). GDP data are from PWT 9.0. Learning estimates are from our database. Literature estimates are derived from the referenced papers.

21
Third, the level of aggregation within occupational categories may introduce a bias. For example, in cases where the ISCO-08 code is only available at the 2-digit level (as it is the case in the analysis throughout this paper), we need to make assumptions about the distribution of workers across all the 4-digit occupations nested within a 2-digit one.<sup>22</sup> This paper makes the assumption that such nested distribution is identical to that of the U.S.<sup>23</sup> Table 9 analyzes—for the group of countries with 4-digit ISCO-08 data— the share of employment classified as green if one were to assume that such countries’ highest level of disaggregation is 1, 2, 3 or 4-digits. It shows that higher levels of aggregation lead to different estimates of the share of green jobs, in total and by type. At the same time, they lead to different country rankings. For example, Peru and Panama have 22 and 18 percent of jobs in green occupations according to the 4- digit classification, respectively, but such shares are 22 and 24 percent according to the 1-digit classification. To overcome this limitation, a potential extension of our methodology is to use the occupational structure of “similar” countries (instead of that of the U.S.) with 4-digit ISCO08 data to impute the green employment shares at the 2-digit level for countries without more granular occupational data.

> 22 Even though some countries in SEDLAC report the ISCO08 occupations at the 4-digit level, we decided to use the more aggregate 2-digit level since this paper is focused on cross-country comparisons. Thereby, using 4-digit classifications for some countries and 2-digit for others would introduce noise. However, the mapping of green occupations at the ISCO08 4-digit level that this paper develop is available for analyses at that level of disaggregation.

> 23 As mentioned in Section 2, employment shares are based on U.S. estimates, which are obtained from the 2018 Occupational Employment and Wage Statistics (OEWS) published by the U.S. Bureau of Labor Statistics.

32
inequality with a measure of the level of human capital (the average education of adult population in 1970, measured in years of schooling), an (inverse) measure of health conditions in the state (infant mortality rate in 1970) and a measure of “local empowerment” (the rate of unionization within the manufacturing sector). Measures of initial physical infrastructure stocks in each state, including paved road extension and railway lengths, were also included, but were insignificant in all specifications and were dropped. All of these variables come from the 1970 Census, and are thus measured fifteen years prior to the beginning of our study period, making exogeneity rather plausible. Table 8 gives the values of the initial conditions at state level.

As reported in Table 7, the coefficients on two out of the three initial conditions are significant, and all three have the expected signs in the headcount regression. A larger proportion of the population with at least some schooling, a lower rate of infant mortality and greater unionization (initially) were all associated with a greater subsequent elasticity of poverty reduction to industrial growth. For the poverty gap equation, only the unionization rate is significant (with the expected sign) but the other two variables have the expected sign. The effect of the changes around 1994 is roughly as before. Growth in the tertiary (services) sector remains more effective in reducing poverty than growth in either agriculture or manufacturing, in both sub-periods. The coefficient on agricultural growth in explaining changes in the headcount index is insignificant in both sub-periods, but the point estimate still falls (i.e. growth becomes less poverty-increasing) after 1994. The coefficient on inflation remains positive, large, and statistically significant. The coefficient on federal social security and social assistance transfers remains negative (i.e poverty-reducing) and statistically significant in both periods.

# **6. Conclusions**

Brazil’s disappointingly low rate of poverty reduction over the last two decades was not only due to its low rate of economic growth. It also reflected a low growth elasticity of poverty reduction, consistent with the country’s high level of inequality. The sectoral and geographic pattern of growth, and the limited initial ability of the poor to participate in, and to benefit from, that growth, have all helped attenuate the pace of poverty reduction, even when moderate growth was achieved. There were marked differences in the poverty-reducing effect of growth across different sectors, with growth in the services sector being consistently more pro-poor than either agriculture or industry. The lower growth rates in the services sector after 1994 (compared to the 23
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_— 30 —_
Table 3: Bilateral Tariffs and Domestic Value Added in Foreign Production: Censoring and Instrumental Variables Estimation in Non-RTA Sample

|Panel A: OLS vs. Tobit|||
|---|---|---|
|O|LS|Tobit|
|(1)|(2)|(3)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup><br>-0.17**<br>(0.068)|-0.24***<br>(0.079)|-0.77***<br>(0.23)|
|Observations<br>8,187|8,187|4,431|
|R-Squared<br>0.997|0.994||
|Panel B: Instrumental Variables (DVA|-in-Servic|es)|
|Line|ar-IV|Tobit-IV|
|(4)|(5)|(6)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup><br>-0.21***<br>(0.053)|-0.28***<br>(0.082)|-0.80***<br>(0.26)|
|Observations<br>8,187|8,187|4,431|
|R-Squared<br>0.997|0.994||

|Panel C: Instrumental|Va|riables (DV|A-in-1970|)|
|---|---|---|---|---|
|||Line|ar-IV|Tobit-IV|
|||(7)|(8)|(9)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>||-0.87***<br>(0.16)|-1.22***<br>(0.26)|-2.74***<br>(0.93)|
|Observations||6,055|6,055|3,280|
|R-Squared||0.997|0.992||
|Column Fixed Efects (all panel|s)||||
|Importer-Industry-Year-Decile||Y|N|N|
|Importer-Industry-Year||N|Y|Y|
|Exporter-Industry-Year||Y|Y|Y|

Note: The regression specification is based on Equation (16). The dependent variable in all columns is the applied bilateral tariff of country _i_ in industry _x_ against exporter _j_ at time _t_ : _t_<sup>_i_</sup> _xjt_<sup>.LogDVA(ln(</sup><sup>_DV Aj_</sup> _ijt_<sup>))</sup> is domestic value added from the importing country( _i_ ) embodied in final production in industry _x_ in the exporting country ( _j_ ). Sample includes only countries pairs and years with no reciprocal trade agreement in force. Standard errors (in parentheses) are clustered by importer-exporter pair. Significance levels: * _p < ._ 1 , ** _p < ._ 05, *** _p < ._ 01.

39
|'able5-|-1||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|I lie V''nrld at *'ork<br>|l<br>||||||||||||
|_Fstimated assharesof world working-age_|_population_||||||||||||
||Working age|Unemployed|Not in labor|Workers in|Workers in|Workers in|Wage|Non-wage|Wage|Non-wage|Wage|Non-wage|
|_<br>______________________|population||force|agriculture|industry|services|agriculture|agriculture|industry|industry|services|services|
|'World|100.0|3.4|30.1|30.4|13.6|22.6|2.4|27.9|8.0|5.6|14.6|R.0|
|IlizhIncminre econonmles_<br>_<br>|15.5|0.9|4.7|0.5|3.0|6.4|0.2|0.3|2.6|0.3|5.5|0.9|
|MliddleInconme econo,ndes<br>|29.1|1.3|10.5|<br>5.3|4.6|7.3|1.4|4.0|3.6|1.1|5.0|2.3|
|Low inconie econondes|55.4|1.2|14.9|<br>24.6|6.0|8.8|0.9|23.7|1.8|4.2|4.1|4.7|
|F.ast Asia and Pacific<br>|33.2|0.8|6.0|15.3|4.8|6.4|0.7|14.6|1.7|3.0|3.3|3.1|
|Eluropeand Central Asia<br>|9.1|0.5|2.4|1.5|2.2|2.5|0.3|1.2|1.8|0.4|2.0|0.5|
|I.atin America and Caribbean<br>|8.3|0.3|3.6|1.0|1.1|2.3|0,4|0.6|0.9|0.3|1.6|0.7|
|MliddleEast and North Africa<br>|4.4|0.3|2.1|<br>0.6|0.5|0.8|0.1|0.5|0.3|0.1|0.6|0.2|
|O.E.C.D.<br>|15.1|0.9|4.6|0.5|2.9|6.3|0.2|0.3|2.6|0.3|5.4|0.9|
|S,,tld Asia<br>|21.0|0.4|8.6|<br>7.5|1.8|2.8|0.3|7.2|0.5|1.2|1.2|1.6|
|Sub-Saharan Africa|8.9|0.4|2.8|<br>3.7|0.5|1.5|<br>0.3|3.3|0.2|0.3|0.6|0.9|

41