rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Summary Statistics**

||Median|Mean|Std.|Minimu|Maximu|
|---|---|---|---|---|---|
||||Error|m|m|
|**1. Consumption per capita**(2016 MWK,<br>thousands)||||||
|Total consumption without aid|133.1|192.3|4.46|0|113562|
|Total consumption without food aid|133.3|192.5|4.46|0|113562|
|Total food consumption without transfers|77.0|99.4|0.56|0|2952|
|Total food transfers|1.6|8.3|0.10|0|544|
|**2. Weather shocks**||||||
|Drought in growing season||0.4|0.01|0|4|
|Days with excess rainfall (Nov-Dec)||1.8|0.01|0|8|
|Days with excess rainfall (Jan-Feb)||4.6|0.01|0|13|
|Days with excess rainfall (Mar-Apr)||1.8|0.01|0|8|
|TA’s median Palmer Index<br>||-0.2|0.00|-2|1|
|TA’s 98th percentile of rainfall||37.8|0.07|20|94|
|**3. Self-reported shocks**||||||
|Job loss (dummy expressed in %)||2.5|0.08|0|100|
|Food price shock (dummy expressed in %)||43.0|0.26|0|100|
|Death (dummy expressed in %)||11.7|0.17|0|100|
|<br>Death of breadwinner<sup>a</sup>(dummy expressed in %)||2.7|0.09|0|100|
|Death of another member<sup>a</sup>(dummy expressed||9.8|0.16|0|100|
|<br>in %)||||||
|**4. Household head**||||||
|Age||42.9|0.08|13|110|
|Female (Dummy expressed in %)||21.6|0.22|0|100|
|Years of education||5.0|0.02|0|17|
|**5. Household**||||||
|% its members in services||12.7|0.14|0|100|
|% its members in agriculture||53.5|<br>0.23|0|100|
|<br>% its members in industry||<br>3.7|<br>0.08|0|100|
|No one employed (Dummy expressed in %)||2.3|0.08|0|100|
|Household size||5.5|0.01|1|27|
|Dependency ratio||1.3|0.01|0|11|
|Has electricity (Dummy expressed in %)||8.5|0.15|0|100|
|Owns toilets (Dummy expressed in %)<br>||3.0|0.09|0|100|
|Own more than 2 plots (Dummy expressed in||35.3|0.25|0|100|
|%)||||||
|**6. Aid received in the last year**||||||
|Food aid<sup>a</sup>(dummy expressed in %)||22.5|0.22|0|100|
|<br>MASAF aid<sup>a</sup>(dummy expressed in %)||<br>4.5|0.11|0|100|
|<br>Education aid<sup>a</sup>(dummy expressed in %)||<br>0.5|0.04|0|100|
|<br>MASAF or Education aid (dummy expressed in||<br>4.9|<br>0.11|0|100|
|<br>%)||||||
|**7. Geography**||||||
|<br>Rural Household (dummy expressed in %)||84.2|0.19|0|100|
|Distance to town(km)||51.3|0.29|0|860|
|Observations|35998|||||

Note:<sup>_a_</sup> These variables are not included in the main regression but summarized for the reader’s information. Some indicator variables take values of 0 or 1 but are expressed as percentage terms. The summary statistics are generated by pooling the three IHS rounds.

7
# **Table 1. Determinants of debt transparency in IDA countries**

|_Debt Recording Systems_|I|II|
|---|---|---|
|Comsec|0.694**|0.590**|
||(0.272)|(0.270)|
|Dmfas|0.475*|0.389|
||(0.266)|(0.261)|
|_Portfolio Composition_|||
|Has Eurobond|0.491**|0.337|
||(0.245)|(0.243)|
|Has collateralized debt|-0.164|-0.006|
||(0.281)|(0.285)|
|_External scrutiny_|||
|Has rating|0.510**|0.504**|
||(0.229)|(0.227)|
|_Capacity_|||
|Percentage of staff with college degree|0.012**|0.009*|
||(0.006)|(0.005)|
|_Legal requirements_|||
|Index (statistical bulletin, ABP, and Strategy)||0.747**|
|||(0.336)|
|_Other factors_|||
|Lower middle income|-0.220|-0.182|
||(0.253)|(0.249)|
|Upper middle income|0.332|0.390|
||(0.435)|(0.428)|
|High DSA risk|-0.751***|-0.758***|
||(0.227)|(0.226)|
|Fragile state|-0.522**|-0.567***|
||(0.211)|(0.207)|
|HIPC|0.180|0.425|
||(0.294)|(0.305)|
|N. of Obs.|74|74|
|R-squared|0.63|0.65|

Notes: Syria and Yemen are excluded from the analysis since they are currently facing armed conflicts. Standard errors in parentheses, * p<0.10, ** p<0.05, *** p<0.01.

Column 2 in Table 1 adds an index that captures the presence of laws that require the publication of debt statistics and key DM documents. While most effects remain robust, the estimated coefficient for Eurobond drops considerably and loses statistical significance. This suggests that both factors are aligned in terms of data disclosure requirements. Notice that the index on legal requirements only reflects the obligation to publish, but does not capture the depth of the mandate or level of enforceability.
# **Table 11. Descriptive Statistics: Use of Time**

||**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**Female**|**Difference**<br>**between means (t-**<br>**test)**|**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**Male**|**Difference**<br>**between means (t-**<br>**test)**|**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**All**|**Difference**<br>**between means (t-**<br>**test)**|
|---|---|---|---|---|---|---|---|---|---|
|% of time working inside the|0.053|0.084|***|0.212|0.256|***|0.131|0.168|***|
|household farm|(0.124)|(0.143)||(0.261)|(0.280)||(0.218)|(0.237)||
|% of time working in agricultural|0.013|0.016||0.254|0.215|***|0.131|0.114|***|
|activities in other household farm|(0.071)|(0.081)||(0.282)|(0.276)||(0.237)|(0.225)||
|% of time working in non-|0.015|0.023|**|0.046|0.053||0.030|0.038|**|
|agricultural activities in other<br>household farm|(0.083)|(0.104)||(0.152)|(0.166)||(0.123)|(0.139)||
|% of leisure time and other activities|0.407|0.376|***|0.413|0.410||0.410|0.392|***|
||(0.163)|(0.168)||(0.158)|(0.158)||(0.160)|(0.164)||
|% of time spent in domestic chores|0.512|0.501||0.075|0.066|**|0.297|0.288||
|and taking care of children and other|(0.188)|(0.204)||(0.122)|(0.122)||(0.270)|(0.275)||
|Number of Observations|1,878|1,433||1,812|1,378||3,690|2,811||

Standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1 Source: CLS-WILL: First wave – household questionnaire.

# **Table 12. Descriptive Statistics: Formal Labor Markets**

||**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**Female**|**Difference**<br>**between means (t-**<br>**test)**|**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**Male**|**Difference**<br>**between means (t-**<br>**test)**|**No Violent**<br>**Shock**|**Violent**<br>**Shock**<br>**All**|**Difference**<br>**between means (t-**<br>**test)**|
|---|---|---|---|---|---|---|---|---|---|
|Worked for a salary in the past 12|0.107|0.128|*|0.363|0.345||0.233|0.234||
|months (1 if he/she worked for a|(0.309)|(0.334)||(0.481)|(0.476)||(0.423)|(0.424)||
|salary)||||||||||
|Tried to find a job in the past 12|0.057|0.090|***|0.247|0.258||0.150|0.173|**|
|<br>months (1 if he/she tried to find a|(0.232)|(0.286)||(0.432)|(0.438)||(0.358)|(0.378)||
|job)||||||||||
|NumberofObservations|1,878|1,433||1,812|1,378||3,690|2,811||

Standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.1 Source: CLS-WILL: First wave – household questionnaire.

42
household tastes. This results in the common practice in most, if not all, household consumption surveys of constructing total household consumption as an aggregate of consumption of different items such as food, non-food (including clothing, education, and/ or health expenses), durable goods, and housing (Deaton and Zaidi, 2002).

For brevity, let _xj_ be a vector of characteristics that represent all these factors, where _j_ indicates the survey type. More generally, _j_ can indicate either another round of the same household expenditure survey, or a different survey (census), for _j_ = 1, 2.<sup>11</sup> Subject to data availability, _xj_ can include household variables such as the household head’s age, sex, education, ethnicity, religion, language (i.e., which can represent household tastes), occupation, and household assets or incomes. Occupation-related characteristics can generally include whether household heads work, the share of household members that work, the type of work that household members participate in, as well as context-specific variables such as the share of female household members that participate in the labor force.<sup>12</sup> Other community or regional variables can also be added since these can help control for different labor market conditions.

It follows that the following linear model is typically employed in empirical studies to project household consumption on household and other characteristics ( _x_ ) for household _i_ in survey _j_ , for _i_ = 1,..., N. Equation (1) thus provides a standard linear model that can be estimated using most available statistical packages.

# **II.3. Data Sources**

> 11 More generally, _j_ can indicate any type of relevant surveys that collect household data sufficiently relevant for imputation purposes such as labor force surveys or demographic and health surveys. To make notation less cluttered, we suppress the subscript for each household in the following equations.

> 12 Regional characteristics related to macroeconomic trends such as (un)employment rates or commodity prices can also be included if such data are available.

11
<!-- Start of picture text --> Annual Healthcare & Welfare graduation rate: per 1000 population over 1998-03<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0.07<br>0<br>Angola Central African Ethiopia Liberia Mali Namibia Togo Zambia El Salvador Panama Colombia Brunei Hong Kong Israel Lao, PDR Mongolia Korea, Rep. Viet Nam Belgium Denmark Greece Italy Malta Portugal Slovenia Ukraine Samoa<br><!-- End of picture text -->

# **_What is the solution_ ?**

The solution to the skill shortage and brain drain related problems in Zambia must go to the root of the problem. The main reason for the shortages in Zambia is the country’s low capacity to produce skilled professionals although migration of the skilled tends to aggravate the problem. We suggest two broad principles for the design of an optimal solution to the skill situation in the country.

# • **Increased investment in human capital**

The primary solution to the shortage lies in producing more skilled workers. Even in healthcare, emigration restrictions are unlikely to solve the problem though they may have some impact. For example, the estimated shortage of doctors to meet the WHO recommendations is about 1654 which is approximately 5.5 times the estimated number of Zambian doctors practicing abroad. As in most developing countries, higher education in Zambia depends critically on financial support from the government. Low incomes and credit constraints deter most students from seeking costly private education. For example, a recent survey conducted by the Government to assess the state of primary education in the country revealed that the majority of children who did not attend primary school cited financial difficulty as the main reason.<sup>2</sup> Investment in education must not be undermined by the cap imposed on public expenditure. Current public spending on education by the Zambian government is low even by African standards.

> 2 Zambia DHS EdData Survey 2002, Education Data for Decision Making.

17
|P062668|1999|2004|11/22/2003|15 Closed|St. Kitts and Nevis|
|---|---|---|---|---|---|
|P062668|1999|2020|12/20/2019|16 Closed|St. Kitts and Nevis|
|P062682|1999|1999||1 Closed|Kyrgyz Republic|
|P062682|1999|2000|4/2/2000|3 Closed|Kyrgyz Republic|
|P062682|1999|2001|1/11/2001|5 Closed|Kyrgyz Republic|
|P062682|1999|2002|1/16/2002|7 Closed|Kyrgyz Republic|
|P062682|1999|2003|5/21/2003|10 Closed|Kyrgyz Republic|
|P062682|1999|2004|3/19/2004|12 Closed|Kyrgyz Republic|
|P062714|2001|2001|3/8/2001|2 Closed|Yemen,Republic of|
|P062714|2001|2002|3/12/2002|4 Closed|Yemen,Republic of|
|P062714|2001|2003|4/9/2003|7 Closed|Yemen,Republic of|
|P062714|2001|2004|6/18/2004|11 Closed|Yemen,Republic of|
|P062714|2001|2005|4/27/2005|13 Closed|Yemen,Republic of|
|P062714|2001|2006|10/31/2005|14 Closed|Yemen,Republic of|
|P062714|2001|2006|12/22/2005|15 Closed|Yemen,Republic of|
|P062714|2001|2006|6/13/2006|16 Closed|Yemen,Republic of|
|P062714|2001|2006|6/29/2006|17 Closed|Yemen,Republic of|
|P062714|2001|2007|12/28/2006|18 Closed|Yemen,Republic of|
|P062714|2001|2007|6/21/2007|19 Closed|Yemen,Republic of|
|P062714|2001|2008|12/5/2007|20 Closed|Yemen,Republic of|
|P062714|2001|2008|6/17/2008|21 Closed|Yemen,Republic of|
|P062714|2001|2009|12/19/2008|22 Closed|Yemen,Republic of|
|P062748|2001|2001||0 Closed|Vietnam|
|P062748|2001|2002|5/10/2002|2 Closed|Vietnam|
|P062748|2001|2003|6/19/2003|5 Closed|Vietnam|
|P062748|2001|2004|6/15/2004|8 Closed|Vietnam|
|P062748|2001|2005|6/10/2005|10 Closed|Vietnam|
|P062748|2001|2006|12/22/2005|11 Closed|Vietnam|
|P062748|2001|2007|7/9/2006|12 Closed|Vietnam|
|P062748|2001|2008|7/12/2007|13 Closed|Vietnam|
|P062748|2001|2009|7/14/2008|14 Closed|Vietnam|
|P062748|2001|2009|6/30/2009|15 Closed|Vietnam|
|P062748|2001|2020|6/2/2020|16 Closed|Vietnam|
|P062790|1999|1999||1 Closed|Bolivia|
|P062790|1999|2000|6/29/2000|3 Closed|Bolivia|
|P062790|1999|2001|6/21/2001|6 Closed|Bolivia|
|P062790|1999|2002|5/30/2002|9 Closed|Bolivia|
|P062790|1999|2003|6/18/2003|11 Closed|Bolivia|
|P062790|1999|2004|6/21/2004|14 Closed|Bolivia|
|P062790|1999|2005|5/13/2005|16 Closed|Bolivia|
|P062790|1999|2005|6/30/2005|17 Closed|Bolivia|
|P062790|1999|2006|6/22/2006|18 Closed|Bolivia|
|P062790|1999|2007|12/29/2006|19 Closed|Bolivia|
|P062790|1999|2007|6/25/2007|20 Closed|Bolivia|
|P062790|1999|2019|5/7/2019|21 Closed|Bolivia|
|P062837|1999|1999||1 Closed|Yemen,Republic of|
|P062837|1999|2000|6/29/1999|1 Closed|Yemen,Republic of|
# **I.2 Quantitative results for Section 4.3.1**

Table I.2 shows the calibrated parameter values and the effects of fiscal decentralization on debt levels when governments internalize interest rate schedules.

**Table I.2:** Counterfactual Experiment <u>governments internalize interest rate schedules</u>

|||Calibrated V|alues||||
|---|---|---|---|---|---|---|
|Parameters|1988|1996|2006||||
| ̄_B_|0.0646|0.236|0.237||||
| ̄_D_|0.587|1.063|0.601||||
|_θ_|0.792|0.654|0.515||||
|_ε_|0.018|0.018*|0.018*||||
|||1988–19|96||1996–20|06|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Moments|1988|1996|Counterfactual<br>1996 (%)|1996|2006|Counterfactual<br>2006 (%)|
|Local debt|0.062|0.130|0.131 (102%)|0.130|0.122|0.115 (193%)|
|Central debt|0.338|0.615|0.380 (15%)|0.615|0.336|0.603 (4%)|
|Total government debt|0.400|0.745|0.511 (32%)|0.745|0.458|0.718 (9%)|

Note: Assuming governments internalize interest rate schedules and re-calibrating each year. Calibration strategy is identical to the baseline. Total government debt (also known as “general government debt”) is the sum of the local and central government debt. Counterfactual experiments use revenue ( _e_ and _f_ ) and spending responsibility share ( _θ_ ) values from end year, and keep all other parameters at their starting year values. Numbers in **parentheses** show the percent of debt changes that is explained by decentralization. All debt levels are normalized by national GDP. *We assume _ε_ for 1996 and 2006 is the same as 1988.

41
# **4. Ten Observations on Cost Recovery and Financial Viability of the Power Sector**

## **#1: Electricity tariffs are rarely high enough to cover the full costs of service delivery, even where cost of service is low**

21. The following analysis starts with results for the full cost of service from an economic perspective (C3). This perspective is in line with the recent literature, which has tended toward a more economic perspective on cost recovery when considering the broader implications of the under-recovery of costs, including economic distortions, climate change and pollution (Huenteler et al., 2017). However, this paper also provides results for cost recovery levels A1 and A2, which are the most immediate determinants of the financial and fiscal viability of electricity service.

22. Overall, the findings from the 17 case studies suggest that cost recovery remains an elusive goal for many power sectors. Governments in all studied cases made significant efforts to improve cost recovery, often along the lines of the standard model. Cost recovery can be achieved through cost reduction and/or tariff adjustments. However, cost reduction takes time since it requires significant changes in generation sources and efficiency improvement. The case studies suggest that tariff adjustments remain the most politically difficult power sector reform and their implementation is patchy despite continued pressure from investors and financiers. This is reflected in a continued pattern of underrecovery of costs in most cases.

23. On the country level, the analysis of post-reform cost recovery levels suggests that tariffs in only 2 out of 17 case studies cover the full cost of service delivery (C3). Tariffs in 7 out of 17 case studies do not even recover operating costs of electricity service. Ukraine (109 percent on average in 2010-17) and Colombia (104 percent) are the top performers in this sample. Egypt, Rajasthan and Andhra Pradesh are the lowest performers. In addition, in several cases, bill collection losses introduce additional financial burden on utilities. Bill collection losses are particularly high in Tajikistan and Egypt. These findings are broadly in line with recent studies (see Annex 1), suggesting that the sample of cases is representative of developing countries more broadly.

24. On the utility level, the analysis of post-reform cost recovery suggests that tariffs in only 3 out of 24 case studies cover the full cost of service delivery (C3). Tariffs in 10 out of 23 case studies do not even recover operating costs of electricity service. Ukraine (109 percent on average in 2010-17) and Colombia (104 percent) are the top performers in this sample. Egypt, Rajasthan (all 3 utilities) and Andhra Pradesh (both utilities) are the lowest performers.

11
non-affected (comparison) households. The standard assumption underlying the validity of our estimates is that differences between the treatment and comparison groups would have remained constant in the absence of Agatha. As discussed in more detail in the following section, we confirmed the validity of this assumption using two waves of household data spanning a period before the shock.

Considering the nature of the event analyzed in the paper, a key element of our research design is the treatment (i.e. shock) allocation mechanism to classify the units of analysis between affected and less- or non-affected households. Following applications in the climatology literature, we construct measures of standardized precipitation anomalies recorded in May 2010 for each weather station to identify areas that experienced extreme rainfall shocks due to Agatha (Heim 2002; Keyantash and Dracup 2002). These measures capture the number of standard deviations away from the long-term (1980-2010) mean for each station. For the base empirical models we define excessive rainfall shocks as standardized precipitation anomalies that are 2 or more standard deviations above the historical mean, a typical threshold used in the literature. The treatment status of the households is thus coded by a binary variable (Rain Shock = 1 for affected households, = 0 otherwise) determined by the standardized precipitation anomaly of the closest weather station. In the robustness section we also discuss the sensitivity of the results to definitions of precipitation anomalies that take as a reference the long-run median rather than the mean.

The base empirical models are estimated with the following specification: where Y _imt_ denotes the outcome of interest (for instance, household consumption or poverty status) for household _i_ living in municipality _m_ in period _t_ ; 2011 _t_ is a year fixed effect that controls for the average changes in the welfare outcome of households across all municipalities between 2006 and 2011; α _m_ are municipality fixed effects that control for time-invariant _mt_ municipality characteristics and Rain Shock is a binary variable that identifies households located in the most affected municipalities in 2011. All regressions also control for a vector of household-level characteristics X _imt_ that are not expected to be affected by the shock but are likely to influence household consumption and include age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Finally, ε _imt_ is a 9
_____________________________________________________________________________________________________________________

|Country|Year of||Productivity|premia of e|xport starters [|p-values]||No. of|No. of|
|---|---|---|---|---|---|---|---|---|---|
|____________|start<br>_____________|Sales/w<br>_________|orker<br> <br>___________|Value added<br>__________|/worker<br>___________|TF<br>__________|P<br>___________|observ.<br>_______________|starters<br>_________________|
|France|All Years|6.3||1.0||-1.3||29,428|4,033|
||1993|5.8|[0.00]|1.6|[0.12]|-0.6|[0.59]|2,678|522|
||1994|7.0|[0.00]|1.3|[0.25]|-1.1|[0.37]|2,579|476|
||1995|3.9|[0.04]|-1.2|[0.34]|-3.0|[0.05]|2,447|421|
||1996|2.9|[0.21]|1.1|[0.41]|-0.5|[0.72]|2,220|291|
||1997|4.7|[0.05]|-0.4|[0.80]|-1.6|[0.31]|2,291|284|
||1998|7.3|[0.00]|0.4|[0.78]|-1.2|[0.43]|2,370|304|
||1999|8.5|[0.00]|3.4|[0.01]|0.7|[0.57]|2,475|297|
||2000|7.8|[0.00]|2.4|[0.05]|-1.8|[0.19]|2,522|319|
||2001|8.8|[0.00]|3.2|[0.02]|0.4|[0.79]|2,483|295|
||2002|7.5|[0.00]|-1.0|[0.52]|-3.1|[0.06]|2,462|259|
||2003|6.7|[0.01]|-1.3|[0.43]|-3.9|[0.02]|2,443|278|
||2004|6.2|[0.01]|3.0|[0.06]|0.3|[0.89]|2,458|287|
|Republic of|All years|8.9|[0.00]|5.3|[0.09]|||3,337|269|
|Ireland||||||||||
||1994|13.6|[0.26]|15.5|[0.20]|||288|24|
||1995|0.3|[0.98]|10.2|[0.56]|||287|21|
||1996|1.8|[0.87]|-1.0|[0.94]|||302|30|
||1997|12.3|[0.31]|8.0|[0.42]|||306|32|
||1998|-5.1|[0.73]|-7.4|[0.48]|||305|22|
||1999|11.0|[0.18]|11.9|[0.23]|||317|29|
||2000|-8.5|<br>[0.26]|-21.3|<br>[0.11]|||311|29|
||2001|23.7|[0.10]|30.8|[0.04]|||309|30|
||2002|27.2|[0.06]|28.9|[0.04]|||312|18|
||2003|14.4|[0.25]|8.7|[0.43]|||320|25|
||2004|-2.6|[0.87]|-4.1|[0.74]|||280|9|

56