rafmacalaba/datause-encoder-data huggingface dataset:
# **6: Conclusions – An Agenda for Future Research**

This paper has shown that with disaggregated micro data on public expenditures, it is straightforward for fiscal economists to monitor the details and changing drivers of public spending trends. We have presented measures of fiscal force and momentum as measures through which to achieve this detailed monitoring. Furthermore we have demonstrated that the force measure has some relevance in signaling future fiscal episodes, suggesting that tracking force and understanding its components and drivers would be a worthwhile exercise for practitioners and World Bank macro-fiscal economists. By presenting micro force measures for four country cases, and linking them to the concept of expenditure “rigidity”, we demonstrated the practical utility of such disaggregated measures could have for policy makers seeking to identify expenditure programs in need of reform.

Finally, we believe that as more BOOST data becomes available, researchers can deepen our understanding of the drivers of (impetus, persistent force, and acceleration), and the limits to safe spending growth. In time it could be possible to consider thresholds for force which act as “speed limits” for countries given their macroeconomic conditions and characteristics. Unlike stop-go traffic lights, these speed limits would be prudent bands for expenditure growth and acceleration based upon the likelihood of expendituredriven crises and upon the type of spending under analysis (the likely shape of the road ahead, and whether the driver will slow down on their own).
# **I. Introduction**

**Rising energy prices over recent years demonstrate that many households are vulnerable to energy poverty.** The recent energy crisis in Europe raised concerns about energy affordability, especially among low-income households. Households with lower disposable income might spend a higher share of their overall income on energy. Therefore, they might be more affected by the energy crisis or rising energy prices, more generally speaking, than high-income households. The fact that poor people spend a larger share of their income on energy and might even decrease their energy usage in response to rising prices is often described by the concept of energy poverty. Energy poverty has negative welfare implications for poorer households. Energy poverty might result in severe health deterioration, especially in cold climates and during winter months. According to data by Eurostat, nearly one-tenth of the population in Europe, a total of 35 million people, is affected by energy poverty (European Commission, 2023).<sup>2</sup>

**Tackling energy poverty is one of the commitments of the European Union (EU), but identifying the energy poor is challenging, partly because of data limitations.** The EU targets both the mitigation and reduction of energy poverty (European Commission, 2023). To achieve this goal, policymakers apply a variety of tools, such as increasing energy efficiency, decarbonization strategies, and clean energy transitions. In addition, to facilitate knowledge exchange and good practices on energy poverty, the EU has launched the Energy Poverty Advisory Hub, the Horizon 2020 Energy Efficiency Calls, the LIFE Clean Energy Transition Program, and other initiatives. Still, identifying those who are most vulnerable to energy poverty remains challenging, both due to a lack of common definitions and reliable data sources (Robayo-Abril and Rude, 2023a). One important limitation is that monetary measures of energy poverty rely mainly on data gathered via household budget surveys (HBS), which often consist of small sample sizes and are only representative at high geographic levels. In addition, these surveys gather limited information on individual and household characteristics. Consequently, it is difficult to

> 2 In this case, energy poverty is the share of households unable to keep their homes warm, which is a non-monetary measure of energy poverty.

2
**Figure 9**

# **EM Assets co-movement**

This figure presents median R-squared from estimations of individual country's assets log returns (Equity/Spreads/FX) against the PC1 of the log returns in these EM. Source: Bloomberg, MSCI, GEM World Bank.

<!-- Start of picture text --> 67%<br>52%<br><!-- End of picture text --> <!-- Start of picture text --> 68% 67%<br>62%<br>52%<br>36%<br><!-- End of picture text --> <!-- Start of picture text --> 37%<br>25%<br><!-- End of picture text -->
# **Agreeing on Robust Decisions: New Processes for Decision Making Under Deep Uncertainty**

Nidhi Kalra, Stéphane Hallegatte, Robert Lempert, Casey Brown, Adrian Fozzard, Stuart Gill, Ankur Shah

**Keyword:** decision-making under uncertainty, investment, climate change, adaptation

**JEL:** D81, H54, O22, O18, Q54

Nidhi Kalra<sup>1,2</sup> , Stéphane Hallegatte<sup>2</sup> , Robert Lempert<sup>1</sup> , Casey Brown<sup>3</sup> , Adrian Fozzard<sup>4</sup> , Stuart Gill<sup>5</sup> , Ankur Shah<sup>6</sup> 1 RAND Corporation 2 The World Bank, Climate Change Group, Office of the Chief Economist 3 University of Massachusetts 4 The World Bank, Poverty reduction and economic management network 5 The Global Facility for Disaster Risk Reduction, The World Bank 6 Centre International de Recherche sur l’Environnement et le Développement The authors would like to thank Roberto Aiello, Arturo Ardila, Ken Chomitz, Leo Dobes, Patrice Dumas, Marianne Fay, Erick Fernandez, Nagaraja Harshadeep, Abas Jha, Todd Johnson, Marcelino Madrigal, Shomik Mehndiratta, John Nash, Niels Holm-Nielsen, Apurva Sanghi, Pascale Scandizzo, Tomas Serebrinsky, and Xiaoping Wang for their time, insights, and contributions. Parts of this article are based on Hallegatte et al. (2012).
gap, Pakistan, it was 4.0 percent. In all cases but Zambia, the poverty gap was larger in rural than in urban areas.

# **4 Experiments**

## **_4.1 Impacts of 10 price increases_**

In the first experiment, we consider two analyses in a small, open-economy setting. The first examines the direct impact of hypothetical 10 percent increases in the prices of individual products on both the poverty headcount and the poverty gap—the gap between the incomes of the poor and their poverty-line income. The second considers the impact of the same changes, taking into account their effects on wage rates for unskilled workers.

The specification of 10 percent price increases in experiments one and two do not imply that food prices are expected to rise by 10 percent (or any other amount). Rather, we examine the impact of a uniform change in the prices of these goods to get an indication of the direction and relative order of magnitude of effects for particular commodities. Correctly interpreted, the results of these experiments provide vitally important information because they summarize all the relevant information in our household survey database on the sources of income and the patterns of expenditure for each household. If, for instance, a large number of poor people in a particular country are net sellers of wheat, this experiment will tell us how much a 10 percent increase in the price of wheat will raise their incomes, and whether the gains to these households are large enough to offset losses to poor net-buying households so that national (and rural/urban) poverty rates fall.

In these initial analyses, we are able to sidestep the vexed question of the extent to which domestic and imported goods are substitutes, at least for the direct impacts through commodity prices. When we are examining the direct impact of a 10 percent change in the domestic price, it does not matter whether the domestic and imported goods are perfect or imperfect substitutes. The results without wage impacts from these experiments may be more 9
<!-- Start of picture text --> Returns to Schooling and<br><!-- End of picture text --> Aslam. M. , G. Kingdon, A. De, and R. Kumar (2010), Economic Returns to Schooling and Skills: An Analysis of India and Pakistan, Working Paper.

<!-- Start of picture text --> Meritocracy and Economic and Economic Economic Inequality,<br><!-- End of picture text --> <!-- Start of picture text --> Princeton<br><!-- End of picture text --> Arrow, K, S. Bowles, S. Durlauf (2000). Meritocracy and Economic and Economic Economic Inequality, Princeton University Press.

<!-- Start of picture text --> Intergenerational Income Mobility in in Britain”,<br><!-- End of picture text --> <!-- Start of picture text --> Journal of of Post<br><!-- End of picture text --> Atkinson, A (1981). “On Intergenerational Income Mobility in in Britain”, Journal of of Post Keynesian Economics, 3, 194-18.

<!-- Start of picture text --> “op Indian Incomes,<br><!-- End of picture text --> <!-- Start of picture text --> World Bank<br><!-- End of picture text --> Banerjee, Abhijit, and Thomas Piketty (2005). “op Indian Incomes, 1922-2000.” World Bank Economic Review, 19(1): 1-20. Bardhan, P (2010).

<!-- Start of picture text --> Bardhan, P (2010).<br><!-- End of picture text -->
|Table of Contents|
|---|
|1. Introduction ......................................................................................................................................................... 3|
|2. Preliminary Estimates Based on Official Trade Data............................................................................................. 7|
|3. Estimates Based on Fieldwork ........................................................................................................................... 10|
|Estimates of Informal Trade with Libya ......................................................................................................... 10|
|Estimates of Informal Trade with Algeria ...................................................................................................... 18|
|Extrapolation of Estimates of Informal Trade in Fuel from Algeria ............................................................... 21|
|Estimates Based on Unit Values .................................................................................................................... 21|
|4. The Economic and Social Impacts of Cross-Border Trade in Terms of Income and Job Creation<br>among Border Populations .................................................................................................................................... 22|
|5. Loss of State Revenues Linked to Informal Trade and Customs Responses ....................................................... 27|
|Loss of State Revenues Linked to Informal Trade .......................................................................................... 27|
|Customs Responses Against Informal Trade.................................................................................................. 27|
|6. Conclusion .......................................................................................................................................................... 27|
|7. Bibliography ....................................................................................................................................................... 30|
|Annexes ......................................................................................................................................................... 32|
|Annex 1: Products showing the largest statistical difference with mirror statistics ..................................... 32|
|Annex 2: Summary of observations based on fieldwork and international statistical data .......................... 33|

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Table 2: Percent of firms with FTM by sector

||Percent of frms<br>with FTMs (%)|Observations|
|---|---|---|
|Leather and footwear|54.01|188|
|Food processing|33.48|1,501|
|Health services|32.61|653|
|Financial services|31.59|452|
|Accommodation|26.17|605|
|Wearing apparel|20.97|934|
|Wholesale or retail|20.53|2,243|
|Agriculture - Crops|18.19|912|
|Pharmaceuticals|17.20|285|
|Other services|17.13|3,160|
|Other manufacturing|17.10|2,071|
|Land transportation|14.99|758|
|Livestock|13.35|385|
|Motor vehicles|2.40|288|
|Total|19.44|14,435|

Note: This table shows the percents of firms with FTMs across industries.Shares are weighted by sampling weights. Number of observations are unweighted. Data from the FAT surveys in 11 countries including Brazil, Burkina Faso, Chile, Ethiopia, Georgia, Ghana, Kenya, Korea, Rep., Poland, Senegal, and Viet Nam. FTM = Female Top Manager.

# **2.3 Productivity and other covariates**

To measure firm performance, we primarily focus on labor productivity, measured as the log of value added per worker. We take the log of the ratio of the difference between sales and the cost of intermediate goods and the number of employees. We convert values to US dollars using the Purchasing Power Parity (PPP) conversion from the World Bank.<sup>6</sup> In addition to labor productivity, the survey provides information on several firm and manager characteristics, including exporting and multinational status, management practices, firms’ sector and region, and managers’ education and experience in the sector (see Table B1 for summary statistics).

We convert some of firm and manager characteristics into three indexes, namely the _managerial quality index_ , the _management human capital index_ , and the _innovation and skill index_ . First, the managerial quality index aggregates information on firms’ managerial

> 6Due to confidentiality, information on labor productivity is not available for Chile and Poland in the FAT survey. Also, in the case of Burkina Faso, only 12 out of 80 firms with FTM provided information on both sales and intermediate costs. Therefore, our productivity analysis focuses on a smaller sub-sample excluding these three economies.

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GDP is lower in Chile (1.6 percent) than in Bolivia (2 percent), the redistributive gains are as much as 2.7 times larger in the former. Moreover, Chile achieves the same redistributive gains than Brazil (5.5 percent) with a significantly lower volume of direct transfers relative to GDP (Figure 12). Yet, Chile’s redistributive effect of direct transfers is well below the effect observed in Uruguay (7.3 percent), and in all Eastern European countries shown in Figure 13 for which the comparison is possible.

**Figure 12: Redistributive effects and social spending on direct transfers in selected Latin American countries**

<!-- Start of picture text --> Colombia Bolivia Guatemala Dom. Rep. Costa Rica Mexico Uruguay<br>(2010) (2009) Peru (2009) (2011) (2013) (2010) (2010) Chile (2013)Brazil (2009) (2009) Average<br>0% 4.5%<br>-1% 4.2% 4.0%<br>-1.4% 3.5%<br>-2%<br>-2.0% -2.0%<br>-2.3% -2.3% 3.0%<br>-3%<br>2.5%<br>-4% -3.7% -3.6%<br>1.6% 2.3% 2.0%<br>-5% 2.0% -4.5%<br>1.5%<br>-6% -5.4% -5.5% 1.4%<br>1.0%<br>1.2%<br>-7% 0.8% 1.0% 0.5%<br>-8% 0.5% 0.4% 0.4% -7.3% 0.0%<br>Percent change in Gini (from market income plus pensions to disposable income) Direct transfers (share of GDP)<br>Redistributive effect<br>Direct transfers (share of GDP)<br><!-- End of picture text --> _Source_ : Author’s elaboration based on the following works: Bolivia (Paz Arauco et al., 2014); Brazil (Higgins and Pereira, 2014); Chile (Martinez-Aguilar and Ortiz-Juarez, 2016); Colombia (Melendez and Martinez, 2015); Costa Rica (Sauma and Trejos, 2014); Dominican Republic (Aristy-Escuder et al., 2016); Guatemala (Cabrera and Moran, 2016); Mexico (Scott, 2014); Peru (Jaramillo, 2014); and Uruguay (Bucheli et al., 2014). _Notes_ : The year for which the country analysis was conducted is shown in parentheses in each bar of the graph. The average is the simple mean of the percent changes by country. The figures shown in the graph may slightly differ from those originally published in the works cited due to recent updates of the CEQ methodology.

**Figure 13: Inequality dynamics in Chile and selected countries in Eastern Europe**

<!-- Start of picture text --> Armenia (2011) Chile (2013) Georgia (2013) Poland (2014) Russia (2010)<br>0.55<br>0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>Market income plus pensions Disposable income Consumable income Final income<br><!-- End of picture text --> _Source_ : Author’s elaboration based on the following works: Armenia (Younger and Khachatryan, forthcoming); Chile (Martinez-Aguilar and Ortiz-Juarez, 2016); Georgia (Cancho and Bondarenko, forthcoming); Poland (Goraus and Inchauste, 2016); and Russia (LopezCalva et al., forthcoming). _Note_ : The year for which the country analysis was conducted is shown in parentheses in each country label of the graph.

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# **Table 7(f): Parsimonious/Reduced VECM – Closed Economy:**

``` MOD(62) Estimating the model by FIML The dataset is: C:\EGYMDJAN20151965.in7 The estimation sample is: 1959 - 2013 ``` ``` Equation for: Drm2 ``` ``` Coefficient Std.Error t-value t-prob Drm2_1 0.678687 0.08360 8.12 0.0000 CIa_1 -0.0160414 0.006776 -2.37 0.0237 dumm2008 -0.115021 0.04550 -2.53 0.0163 ``` ``` sigma = 0.0584738 ``` ``` Equation for: Drgdp ``` ``` Coefficient Std.Error t-value t-prob DINFL_1 -0.00456193 0.0008713 -5.24 0.0000 DInterestrt_1 -0.0221933 0.005650 -3.93 0.0004 Drm2_2 0.459160 0.06025 7.62 0.0000 CIa_1 -0.0166841 0.004290 -3.89 0.0004 dumm1986 -0.0604373 0.02730 -2.21 0.0336 dumm2013 0.126706 0.02703 4.69 0.0000 ``` ``` sigma = 0.0347738 ``` ``` Equation for: DINFL ```

|`Coefficient`|`Std.Error t-value t-prob`|
|---|---|
|`Drgdp_1 32.5405`|`10.29 3.16 0.0033`|
|`DINFL_1 -0.377184`|`0.1063 -3.55 0.0012`|
|`Drm2_2 15.4206`|`7.047 2.19 0.0356`|
|`CIa_1 2.19206`|`0.4763 4.60 0.0001`|
|`dumm1986 7.19174`|`3.288 2.19 0.0357`|
|`dumm2008 7.16930`|`3.263 2.20 0.0349`|

``` sigma = 3.54329 ``` ``` Equation for: DInterestrt ```

||`Coefficient Std.Error t-value t-prob`|
|---|---|
|`Drm2_1`|`2.68178 1.574 1.70 0.0975`|
|`Drgdp_1`|`-4.20947 2.275 -1.85 0.0729`|
|`DINFL_1`|`0.0292526 0.01732 1.69 0.1004`|
|`DDISC_1`|`0.550912 0.09303 5.92 0.0000`|
|`Drm2_2`|`-3.90089 1.442 -2.70 0.0106`|
|`DInterestrt_2`|`0.183695 0.1110 1.65 0.1072`|
|`CIa_1`|`-0.497069 0.1093 -4.55 0.0001`|
|`dumm1992`|`-5.41473 0.9314 -5.81 0.0000`|
|`dumm2009`|`-2.67578 0.6491 -4.12 0.0002`|
|`dumm2010`|`0.991003 0.5750 1.72 0.0939`|
|`dumm2013`|`2.82036 0.6527 4.32 0.0001`|

``` sigma = 0.502194 ``` ``` log-likelihood 31.986552 -T/2log|Omega| 344.153029
no. of observations 55 no. of parameters 26
```

52
# Taking Stock of the Impact of Power Utility Reform in Developing Countries: A Literature Review<sup>1</sup>

## Robert Bacon

**Keywords** : Electric utilities; privatization; restructuring; regulation; competition; developing countries; emerging markets

**JEL codes** : L32, L33, L43, L94

> 1  This paper is a product of the “Rethinking Power Sector Reform” knowledge program of the Energy & Extractives Global  Practice of the World Bank. Any views presented here are the author’s alone and should not be attributed to the World Bank or  any other person or institution. The authors are very grateful for financial support from the Energy Sector Management  Assistance Program (ESMAP) and the Public Private Infrastructure Advisory Facility (PPIAF). Thanks are due to Vivien Foster, Elvira Morella, and Fan Zhang who acted as peer reviewers. Any shortcoming is the sole responsibility of the author.
# **Table 3. Cross-sectional regression of CDBP on selected Financial Soundness Indicators**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES||Consolidated|Distance to Br|eak Point(CD|BP) (%points)||
|Regulatory Tier 1 Capital to Risk-Weighted Assets (%)|0.517|||||0.525|
||(0.354)|||||(0.396)|
|Non-performing Loans to Total Gross Loans (%)||-0.050||||-0.577**|
|||(0.121)||||(0.228)|
|Net Open Position in Foreign Exchange to Capital (%)|||0.118|||0.154|
||||(0.105)|||(0.122)|
|Liquid Assets to Short Term Liabilities (%)||||0.156*||0.213*|
|||||(0.083)||(0.114)|
|Interest Margin to Gross Income (%)|||||-0.087**|-0.028|
||||||(0.034)|(0.076)|
|Constant|3.710|12.000***|10.276***|7.100***|17.078***|1.698|
||(5.800)|(1.466)|(1.139)|(2.305)|(2.550)|(7.090)|
|Observations|43|42|40|44|44|35|
|R-squared|0.060|0.002|0.113|0.073|0.053|0.311|

Source: Own calculations using data from Fitch (up to third quarter 2020), and IMF Financial Soundness Indicators available at https://data.imf.org/?sk=51B096FA-2CD2-40C2-8D09-0699CC1764DA.

Note: Data on the selected core FSI indicators is as per the third quarter of 2020. Robust standard errors appear in parentheses.

Figure 3 provides information on the relevance of considering bank profits as a first absorbing buffer for increases in loan provisioning. Overall, the two approaches provide a similar assessment as denoted by the large pairwise correlation coefficient between the two indicators (0.953).

# **Figure 3: Testing for the inclusion of profits in the computation of the Distance from Break Point (CDBP)**

<!-- Start of picture text --> Upper middle income Lower middle income Low income<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>0 20 40 60<br>Consolidated Distance to Break Point (CDBP) including profits (in percentage points)<br>(in percentage points)<br>Consolidated Distance to Break Point (CDBP)<br><!-- End of picture text --> Source: Own calculations using data from Fitch (up to third quarter 2020).

Note: The figures report the percentage point increase in the non-performing loan ratio at the country level (the Consolidated Distance from Break Point - CDBP) that wipes out capital buffers for banks representing at least 20 percent of banking system assets (Y axis). Higher values denote higher capacity to absorb NPLs increases. The X axis reports the CDBP adjusted using equation (6) to incorporate bank profits as a buffer to absorb increases in loan provisioning. For each bank, we plug in the minimum value of a bank profit over a 10-year period to take into consideration that profitability may be low when a bank is hit by a credit shock.

11