rafmacalaba/datause-encoder-data huggingface dataset:
1/ Cukierman (19921 contains formal treatment of the now standard views on the advantages of central bank independence, which are not further reviewed In this paper. Persson (1 9881 has a broader discussion of time Inconsistency and the role of credibility in macroeconomic policy.

2/ In the case of Argentina the authorities chose to enhance monetary policy credibility by establishing a legally-binding monetary rule (requiring that the monetary base be backed by foreign reserves) rather than by effecting institutional changes. Chile is the one Latin American country where central bank independence was granted (in 1 989) after the bulk of financial sector reforms were completed.

.a Cukierman (1992) offers a somewhat different interpretation. He draws a distinction between party political instability (defined as political instability in countries with internal cohesion) and _regime_ political instability (where there is no internal political cohesion). He argues that central bank independence should be correlated positively with measures of party political instability and negatively with measures of regime political instability. These labels seem inappropriate as they seem to connote differences in political structure (party system _versus_ totalitarian regimes) rather than in degree of internal political cohesion.

4/ Brazil remains the only major Latin American country not to have constrained the government's control over monetary policy (see note 1). This is consistent with Cukierman's (1992) observation that widespread indexation (as is the case in Brazil) makes central bank independence less pressing (i.e., weakens FOI).

5/ Lago 1 991) provides a vivid example of such a process. From September 1988 to July 1989 the Peruvian central bank --one of the most independent in Latin America at the time-- stopped accommodating the bulk of the Treasury's requests for credit. This induced a 23% drcmtp in GDP and a 75% appreciation of the parallel exchange rate. The policy ended with the resignation of the President of the central bank, which signifies not so much that politics triumphed over monetary policy as much as the utter impossibility of running an independent monetary policy under the circumstances.

6/ Casual support for the claim that in Latin America monetary policy is dominated by fiscal policy is provided by the fact that what distinguishes successful stabilizations from unsuccessful ones in the region is that only the former addressed fundamental fiscal imbalances. This suggests that the crises were fiscal in origin.

7/ Empirical support for the correlation between inflation and central bank independence has been provided, among others, by Banaian, Laney and Willett (1983), Alesina (1988) and Cukierman, Webb and Neyapti (1992). Grilli,
Policy Research Working Paper 8131

# **Abstract**

In many parts of the developing world, access to electricity is uneven and inconsistent, characterized by frequent and long hours of power outages. Many countries now engage in systematic load shedding because of persistent power shortages. When and where electricity is provided can have important impacts on welfare and growth. But quantifying those impacts is difficult because utility-level data on power outages are rarely available and not always reliable. This paper introduces a new method of tracking power outages from outer space. This measure identifies outage-prone areas by detecting excess fluctuations in light outputs. To develop these measures, the study processed the complete historical archive of sub-orbital Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime imagery captured over South Asia on every night since 1993. The analysis computes annual estimates of the Power Supply Irregularity index for all 600,000 villages in India from 1993 to 2013. The Power Supply Irregularity index measures are consistent with ground-based measures of power supply reliability from the Indian Human Development Survey, and with feeder-level outage data from one of the largest utilities in India. The study’s methods open new opportunities to study the determinants of power outages as well as their impacts on welfare.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Page 6 Net flows, the difference between disbursements and principal repayments, averaged $2.5 billion per year in 1980-82. They fell sharply to an average of less than $300 million per year in 1983-88, for a cunig4itv surplus of only $1.7 billion over these last six years (Table 8).

Net flows stwung from a surplus of $800 million in 1983 to a deficit of $965 million in 1985. Although some recovery was registered in 1987 and 1988, at an average annual $400 million, the results remained significantly below the level of previous years, and once again are distorted by reporting for Cote d'Ivoire.

Looking at the most recent 1985-88 period, net flows over the four years resulted in a cumulative surplus of only $200 million for all of Africa, with isolated surpluseL heavily concentrated in seven countries. Besides C6te d'Ivoire's private sector, for which $1.7 billion in net flows are reported, only Congo, Ethiopia, Gabon, Ghana, Mozambique and Niger experienced positive flows, for an additional cumulative surplus of $1.6 billion. This implies that all other borrowers in the region experienced negative cumulative net flows of $3.1 billion in 1985-1988.

# Net Transfars

Net transfers, the difference between disbursements and both principal and interest payments, amounted to an aggregate cumulative net cash outflow of some $5.5 billion from Sub-Saharan Africa to the commercial banks from 1983 to 1988. The banks disbursed $14.5 billion and received $20 billion in total debt service payments.

In the more recent period 1985-1988, only Congo, Ethiopia and Gabon experienced positive net transfers, totalling a cumulative $1.1 billion (Table 9).

Interest payments, which had remained steady at average annual $1.4 billion between 1980-1985, declined by 1987 to $800 million as a result of arrears buildups and debt reschedulings. They recovered slightly to $1.1 billion in 1988 (Table 7).

Partly as a result of arrears, and partly of grace periods on principal repayments and lower interest margins extended to borrowing countries through London Club reschedulings, the region's total annual debt service payments to commercial banks, which peaked at $4.5 billion in 1984-1985, experienced a sharp decline to $2.5 billion in 1987-1988.

# 3. lic Sector Lone-term Borroving

The World Bank Debt Tables distinguish between African public borrowers and guarantors ("public and publicly guaranteed debt") on the one hand and private borrowers ("private non-guaranteed debt") on the other.

Lacking information on the actual apportionment of credit between commercial banks and suppliers within individual transactions, the WDTs assume that the commercial banks have been the sol source of long-term external lending to the
that is consistent with the notion of positive treatment effects. As graphs control neither for treatment intensity nor for other time-(in)variant factors, econometric analysis is needed.

The intensity of runoff and the amount of sediment dissolved as measures of soil erosion link directly to TBIWRD’s project objective. Simple field methods (Herweg 1996) suggest annual soil losses between 24 and 160 tons/ha and year (Tefera and Sterk 2010). To quantify these before and after project activities had been initiated, at five hydro stations (see figure 2) community members took daily measurements of the quantity of water (in m<sup>3</sup> /s) and sediment (in t/d) carried downstream at or close to the point of main water discharge from that micro-watershed. As table 3 illustrates, there was a declining trend in water flow and sediment load overall, though with some variation across stations.

# **4. Analytical Approach and Results**

Results from the DID approach suggest that project-supported SWC activities had significant effects on soil water content and photosynthetic activity, the size of which increases significantly over time. Estimated impacts vary by intervention type and season, although coefficients are difficult to interpret without more specific information on the nature, location, and timing of activities. Analysis of daily volume and sediment content of water flows at hydro-stations in the project area, though less rigorous, suggests that the impacts are not only statistically significant but economically meaningful.

# **_4.1 The Impact of SLM Activities on Vegetation_**

To assess impacts of SLM interventions on the outcomes of interest, we use a fixed-effects regression model on a geographic discontinuity design from treated micro-watersheds and controls in a 5-km radius of the TWIWRD critical watersheds (see figure 4). Using a panel data set incorporating a baseline period of preintervention data, estimates of program impact are based on a DID approach that cancels out any timeinvariant differences between treated and control areas. Indexing pixels by _i_ , micro-watersheds by _m_ , season-year combinations by _t_ , and years by _y_ , the specification is where the outcome variable _Yit_ , is the value of the NDVI, SAVI, and LSWI for pixel _i_ at time _t_ ; ����� is the treatment indicator, defined as the lagged cumulative share of total land affected by the three types of treatment discussed earlier; ��� denotes pixel-level rainfall; �� is a set of season/year fixed effects; �� a set of pixel fixed effects; and ��� an error term. The parameter of interest is �, the estimated impact of projectled SLM activities.

Results from estimating equation (1), shown in table 4, suggest that, controlling for pixel and time fixed effects, the three types of SWC investment have significant positive impacts on the vegetation indices 11
16 Table 14: Two Measures of Decapitalization PPWW/Disposable Cash (%) (IT + DT + PPWW)/Profit II

||1990|1991|1992<br>(1-6)||1990|1991|1992<br>(1-6)|
|---|---|---|---|---|---|---|---|
|AAA|9.8|18.5|7.9|AAA|43.4|63.9|58.2|
|AA|12.1|20.0|1.9|AA|50.2|195.1|189.7|
|A|7.1|_-11.5_|-4.0|A|52.0|-26.6|-25.3|

Notes: 1. Disposable cash is profit II - income tax - dividends + depreciation. 2. IT = income tax; DT = dividends. Also see Glossary.

These results clearly establish that there is no direct connection between incurring the PPWW and decapitalization. Low disposable cash could result from a high dividend tax burden, while big interest payments will directly reduce profit II. Table 15 on the ratio of dividend payments to profits after income tax demonstrates that there is no link between this tax and actual earnings, as would be the case in a market economy.

Table 15: Dividends/Profits After Income Tax (%)

||119<br>|90<br>1991|1992|
|---|---|---|---|
||<br>__<br>**_**__<br>___<br>___<br>___<br>__|___<br>___<br>___|(1-6)|
|| AAA|7.9|13.9|16.7|
|AA<br>|17.6|302.4|239.6|
||A|14.7|-18.9|-22.6|

Lastly, Table 17 shows that investments comfortably exceed depreciation for both AAA and AA fimrms in 1990 and 1991; but are lower for A firms, confirming decapitalization. However, Table 16 reinforces the conclusion that high PPWW payments are not the cause of decapitalization: PPWW payments were much lower than depreciation in all cases.

Table 16: PPWW/Depreciation (%)

|able 16: P|WW/Deprec|iation (%)||Table 17:<br> <br>|Investrnents<br>|/Depreciation<br>|
|---|---|---|---|---|---|---|
|__|1990<br>______________________|1991|1992<br>(1-6<br>)11|1<br>|11990<br>_<br>_<br>_<br>_<br>_|1991<br>I<br>_<br>_<br>_<br>_<br>_<br>_I|
|AAA<br>||<br>73.4<br>|<br>|<br>42.7<br>|17.3|AAA|147|144|
|AA<br> <br>|36.2<br> <br>|17.0<br> <br>|1.9<br> <br>|AA|125|134|
|A<br>||<br>35.9|<br>12.8|3.9<br>||A|82|80|
inequality increases have a much larger poverty-raising effect, and hence policy makers may be more willing to accept a modest growth decline in exchange for a reduction in inequality.

Another way to gauge the relative importance of growth and redistribution from the poverty reduction viewpoint is to measure the respective contributions of growth and inequality shocks to the observed variation in poverty. This is the approach followed by Kraay (2005). Under lognormality, it follows from (13) that the variance of poverty changes can be approximated as In the general case of nonzero covariance between growth and inequality changes, absent any information on their mutual causal precedence, we can use the simplifying assumption that half the covariance can be attributed to growth, and the other half to inequality changes. In such case the share of the total variance attributable to the growth component is: However, to implement (15) numerically we need three more ingredients, namely the variance of growth, the variance of inequality changes, and the covariance of growth with inequality changes. Table 8 reports these statistics computed on the basis of two alternative datasets: the one used thus far in this paper (i.e., Dollar and Kraay 2002), and the Povmonitor<sup>22</sup> database. Figure 3 presents the respective scatter plots. Apart from coverage (much more limited in Povmonitor), the main difference between both databases is that the Dollar and Kraay income data is based on National Accounts, whereas that in Povmonitor is based on household surveys.

Inspection of Table 8 suggests that the different coverage of the two databases is of little consequence for the volatility of changes in the (log) Gini coefficient: in both cases the standard deviation is about 0.05. However, the volatility of growth differs more markedly across the two databases: in the survey-based Povmonitor data, the standard deviation of growth (0.06) is slightly higher than that of inequality changes, while the opposite happens in the National Accounts-based Dollar and Kraay data, in which the standard deviation of growth (0.04) is lower than that of inequality changes. On the other hand, Table 8 also shows that growth and changes in inequality are nearly uncorrelated in both databases -- a conclusion confirmed by the scatter plots in Figure 3. Although the correlation coefficients in the table are of opposite signs (negative in the Dollar and Kraay data, and positive in the Povmonitor data), both are insignificantly different from zero.<sup>23</sup>

> 22 http://www.worldbank.org/povmonitor.

> 23 Similar evidence is provided by Deininger and Squire (1996), Chen and Ravallion (1997) and Easterly (1999).

-13-
<!-- Start of picture text --> alternative measures of export diversification and entry/trade costs, additional dependent variables,<br>changes in the country sample, and application of alternative estimators.<br>4.2 Alternative Measures of Diversification, Entry Costs, and Trade Costs<br>The results discussed above use /ines_cn2 as the dependent variable, i.e. a count of the number<br>of 8-digit product lines exported to the EU in each 2-digit sector. Table 5 shows that our results<br>are not sensitive to this choice. The first two columns use a more narrowly defined dependent<br>variable, in which 8-digit export flows are only considered to be non-zero if they are greater than<br>$100,000 or $1,000,000 respectively. The third column uses the original definition, but excludes<br>agriculture (chapters 1-24) from consideration. Although there are some small changes in coef-<br>ficient estimates as a result of these alterations to the dependent variable, Table 5 shows that the<br>substance of our results is unchanged: entry and export costs, as well as distance, exert a negative<br>and statistically significant impact on diversification, while the positive impact of own tariffs and<br>the negative impact of EU tariffs are statistically insignificant.<br>The final two columns of Table 5 take completely different approaches to measuring diversifi-<br>cation. Column 4 uses the relative variety measure of Feenstra and Kee (2006), which is closely<br>related to the measures used by Hummels and Klenow (2005) and Broda and Weinstein (2006).<br>Column 5, on the other hand, replaces /ines_cn2 with the Herfindahl index of concentration that is<br>frequently used as an inverse indicator of export diversification in policy circles (e.g., UNCTAD,<br>2006; and Amin Gutiérrez de Pifieres and Ferrantino, 1997). Both measures are calculated at the<br>2-digit sector level using 8 digit data, applying the formulae set out in section 2. As can be seen<br>from the table, both alternative measures of diversification produce qualitatively identical results to<br>those in our baseline formulation. The estimated signs are the same in all cases except EU tariffs,<br>while entry costs, export costs, distance, and GDP remain statistically significant at the 5% or 1%<br>level.<br>There are suggestions in the literature (e.g., Klinger and Lederman, 2006) that measures of the<br>number of administrative procedures required to enter the domestic market may be more accurate<br>25<br><!-- End of picture text -->
-2-

# _Fig. I_

<!-- Start of picture text --> XS'9~~~em<br>W lt-< Niger / rJ S Dji~~~~~~~~~~~~~~~obouti<br>Lifeexpectancytbirth in SuSudaharanAcan countries, abo<br>Guine><5 A | 47P N>r r;; ~~~~~ 44 _ / ~~~~~ 1 Ethiopia s<br>Sierran Africa of easte fv<br>tqot3al J dvlp mac Ken<br>Guinea develo contZaire In s p<br>A ub-Sahae nAwoeca iale h<br>SoMurcuni Golier T.J 8) p. 2.<br>Ango4AXJ~|I \s i /<br>yearsinz 190 wa esiae to be onl 49 yer intemd/9Tidead.ombia<br>lo igure islreydetNighifnpn 9 mb8~~~bw hidmraiyMO Madeor<br>VSou them w<br><!-- End of picture text -->

_Source: Goliber,_ T. J. (1985). p. 2.

Life expectancy at birth ipopulatian te African countries, about 40 years in 1960, was estimated to be only 49 years in the mid 1980s. This low figure is largely due to high infant and child mortality.

In many African countries, deaths of children under age five years account for more than palf tion is aual drathes, compared with to 3% in developed countries. Matemnal mortality rtes are also high: 200 to 600 deaths per I100,000 live births (perhaps an underestimate) -1I00 to 500 times the rate in developed countries. In some poor rural areas of Africa, by the time women complete their families S0% of the cohort is dead.

The distribution of the population in the region presents complex issues. About 80% of the population is rural; Africa is the least urbanized continent. In some parts, settlements are so sparse that governments find
The statistics for Africa are far worse: only 13 percent of people in the poorest income quintile benefit from social safety net programs<sup>4</sup> , well below the 41% share for the world. See Table 2. While 20 percent of all the beneficiaries of safety nets in Africa belong to the poorest quintile, that share is 30 percent for the world (World Bank 2012a). Evidence also shows that the fiscal policies of many developing countries are even less effective at reducing income inequality in practice than they are in principle, typically because transfers do not contribute much to inequality reduction and the scope for active redistribution is limited by low levels of revenue collection. Zambia‘s capacity to use its fiscal policy for redistributive purposes seems limited at best. Figure 1 below depicts a range of countries in terms of their GDP per capita and fiscal capacity, specifically tax revenues as a percentage of GDP. Zambia is in the low per capita income/low tax collection quadrant.

Table 2: Safety Nets in the World

|||Count<br>E|Po<br>ry-specific|orest 20%<br>wellbeing|distributio<br>Middl|n<sup>*</sup>||
|---|---|---|---|---|---|---|---|
|||uroe|||e|||
||Latin<br>America &<br>Caribbean|<br>&<br>Central<br>Asia|East<br>Asia &<br>Pacific|Africa|<br>East &<br>North<br>Africa|South<br>Asia|World|
|Social Assistance/Safetynets|51.9|51.1|63.3|13.2|15.6|33.2|41.6|
|Cash Transferprograms|5.9|9.2|16.4|2.9|3.8|0.9|5.7|
|Social Pensions|2.6|4.4|9.5|0.1|4.4|0.4|2.9|
|Other cash transfers||||||||
|programs: family, child or<br>disability allowances|1.9|32.0|1.2|0.1|5.6|0.1|5.0|
|Conditional Cash Transfer<br>program|29.6|0.0|0.0|0.3|0.0|0.4|5.1|
|In-kind foodprograms|7.1|8.8|7.0|5.1|0.5|27.1|15.4|
|Other social assistance<br>programs|18.5|11.7|35.6|7.3|2.8|9.1|15.0|

Source: World Bank (2008) Note: *Country-specific wellbeing distribution: Quintile distribution estimated for each country.

> 4 Safety nets include cash transfers (conditional or unconditional), social pensions, other cash transfers such as family or disability allowances, in-kind food programs and other social assistance programs such as school feeding, cash-for-work programs, for example. See World Bank (2008).

4
To ensure our results are robust to potential fluctuations in female LFP over time, we analyze seven rounds (years) of the LFS, including five years preceding the pandemic (i.e., 2015-2019) and the most recent four years since the pandemic started (i.e., 2020, 2021, 2022, and quarter 1 of 2023). In other words, we analyze female LFP during four post-pandemic years in comparison with those in the preceding five pre-pandemic years as the reference group.

We further estimate a variant of Equation (1) where we disaggregate the year dummy variables for the post-pandemic years into 13 quarter dummy variables. Doing this allows us to examine more closely whether, and to what extent the pandemic effects change in each quarter. Specifically, we estimate the following equation: i 0 1F F F i ∗(∑2023 ∑4 ∗Q 2 i i ii ii=2020 kk=1 ii kk ii ii ii ii yy = γγ + γγ YYF YY FFYYQQFFYY ) + γγ ZZ + PPYYP + YYF YY + σσ (2) where the post-pandemic interaction terms ∗Q include the quarters during 2020- ii kk 2022 and quarter 1 of 2023. The vector of control variables YYF YY FFYYQQFFYY i now include both i and the ii ii interaction terms F F F i , for the pre-pandemic years ZZ _t’_ = 2015, 2016, ..., 2019. XX ii′ ∗YYF YY Yet, while Equations (1) and (2) offer estimates on the changes with female LFP during the pandemic, they do not provide insights on the potential mechanisms through which these changes could happen. To further investigate these mechanisms, we hypothesize that the COVID-19related excess death rates could be a key mechanism that might affect women’s decision to stay in the labor force as discussed in Section 3. For example, if the pandemic excess death rate is exceedingly high in a certain province, it might result in the provincial government taking stronger action to implement lockdown measures such as closing down businesses and schools. Even in the absence of such state policies, families themselves may decide to avoid workplaces and schools where the risk of transmission is high. In principle, these policies measures and the COVID-19 15