rafmacalaba/datause-encoder-data huggingface dataset:
**_So what?_** One of the benefits of failing to recognize the importance of the limited financial sustainability of the sector under a wide range of circumstances is that we now understand better these circumstances. This has led to a number of solutions mitigating some of the issues raised so far. Some of the options are not under the control of the sector, some target accountability and processes, while others are ideas for new financing instruments designed to tackle the limited fiscal or financial ability of the operators or governments.

The first solution is outside the control of regulators as it depends on countries’ macroeconomic trends. Income growth is, indeed, part of the solution. As income increases, the scope to improve cost recovery increases. A survey by Nauges and Whittington (2010) of estimates of own-price elasticity for water from private connections in developing countries suggests that it is in the range from 0.3 to 0.6. Income elasticity is in the range 0.1 to 0.3. This implies that for middle income users, improving cost recovery can increase revenues and somewhat reduce consumption. It also suggests that, as countries move to middle income status, demand for investment should increase but that this will be easier to finance within the sector.

The second solution is also largely not under the control of the sector regulators and policy makers, although they can influence it. The specific design of decentralization may influence the financing of the sector at the subnational level. Decentralized financing decisions may make it easier for some of the cities to develop their own financing sources and the development of municipal bond markets provides evidence of the effectiveness of this solution.<sup>36</sup> Many cities have grown and are continuing to grow and their economies are increasingly diversified. This is improving their potential capacity to service debt on long-lived projects with debt servicing costs comparable to the cash flows that project finances are expected to generate. This raises equity issues again. Not all cities represent the same level of opportunities to rely on subnational bonds (i.e. the same level of risks). This is driven by different dimensions: diversification of the economy, average state of the economy, average age of the population, average wage of the population. Moreover, their borrowing capacity is often subject to some restrictions limiting their access to the main traditional sources of financing for the sector. For instance, most international donor agencies can hardly provide any or significant guarantees to cities. All these dimensions matter to the ability of any given city to raise revenue to repay debt or to pay the bills to the infrastructure providers. Unless creditworthiness concerns can be addressed, decentralization may penalize some cities more than others and a new financial gap is likely to emerge.

> 36 See for instance ADB (2011) for a discussion of how Asian cities have been mobilizing sub-sovereign finance through effective approaches and mechanisms consistent with better local fiscal structures.

31
streams are based on past values of observed variables that are likely to be correlated with crop revenues, but not necessarily in line with their production capabilities.

The IVs that we rely on originated from outside the IHS2 database, and are namely (i) the tobacco-maize relative land suitability index for the 9x9 km grid that each respective IHS2 EA is associated with and that stems from the Global Agro-Ecological Zones (GAEZ) database<sup>18</sup> , and (ii) the 1997/1998 district-level estimate of the number of tobacco farmers originating from the First Integrated Household Survey (IHS1) data. Both IVs are mapped in figure 2.

Our first IV, the GAEZ tobacco-maize relative land suitability index, is constructed as the difference between tobacco and maize land suitability indices. The suitability index is modeled using data on average climate conditions, soil and terrain characteristics and an assumption of farm management type. Input climate parameters derived from rainfall and temperature time-series include the timing, length and quality of the growing period as well as water availability. Soil characteristics relevant to plant production, including nutrient availability and retention capacity, rooting conditions, oxygen availability, salinity and toxicity, and management constraints are derived from the Harmonized World Soil Database. Terrain variables, elevation and degree of slope or slope class, are incorporated as limiting factors on production of different crops, as well as expected yield reduction factors due to erosion. All input layers are combined with an assessment of cropspecific requirements to produce estimates of land suitability for tobacco and maize production, on a continuous scale from 0 to 100. For the purposes of this paper, intermediate input level, rain-fed tobacco and maize production is assumed.<sup>19</sup> While we use an average suitability index value derived for each IHS2 EA, the GAEZ model outputs are produced at a spatial resolution of 5 arc minutes (approximately 9 _x_ 9 km), corresponding to an area larger than most EAs. As a result, although the GAEZ suitability index clearly has subdistrict specificity, it may not capture all variation at or within EA-level. It is also important to note that the suitability index is based on average climate conditions calculated over a baseline period of 1960-1990. Inter-annual climate variability is incorporated to some extent, as a yield-reduction factor, but recent climate trends and prior year conditions are not reflected in this IV. Changes over

> 18For additional information on the GAEZ dataset, see gaez.fao.org. Following the GAEZ instruction, we applied a 0.01 scalar to the suitability index values.

> 19The intermediate input level assumes an improved management system. Production is for subsistence as well as commercial sale through manual labor with some mechanization. Improved seeds and some fertilizer and pesticides are also used. Our results are robust, however, to the use of suitability index values derived under low-input scenarios.

13
An increase in 7r reduces the amount of investment that takes place, since it lowers the expected return to foreign investors.

Condition (10) establishes that the level of investment will be dependent on the investors' beliefs about the government's type. This means that the government's welfare function will now depend on ir, since welfare can be written as **WV(ro,r 1,ZO(r70**<sup>**,T,7r),Z**</sup> **1**<sup>**(r7-**</sup> **0**<sup>**,rr))**</sup> -Wi(r<sup>o,r7r).</sup> 0<sup>From (4), aW/8Z,</sup> = - Z,R'Z > 0 when evaluated at _-r_ = 0. Utilizing (1 1), this means that aWF(o,0,7)/81r < 0, since a greater likelihood that the government is protectionist reduces the amount of investment, which is undesirable for a free trade government. Free trade governments will find it costly if they are confused with protectionist governments, since it will make it more difficult for them to attract foreign investment.

# B. Equilibrium in the Signalling Game

We assume that the game between the governments and the foreign investors proceeds as follows. Governments, who have private information about their types, move first and announce a trade policy prior to period 0. Foreign investors then choose their level of investment, and the international agency makes a transfer of T if the country has chosen a free trade policy. In period 1, the governments choose a value of T, and foreign investors then make their choice of Z,.

An important aspect of this problem is that the foreign investment decision depends on the beliefs about the government's type. Since the government moves first and the government is the agent with the private information, this can be analyzed as a signalling game because the foreign investors may able to infer something about the government's type from the government's choice of _ro._ We will adopt the concept of sequential equilibrium to analyze this game, as this is a widely accepted technique for 16
24 _Table 12: Results for log out-of-pocket medical expenditures_

|VARIABLE|_log _<br>|_out-of-pock_<br>|_et medical e_<br>|_xpenditure_<br>|_s_<br>|
|---|---|---|---|---|---|
||(A)|(B)|(C)|(D)|(E)|
|SAH_CHANGE|**0.088**||**0.114**|**0.084**|**0.084**|
||**7.020**||**6.480**|**5.760**|**5.780**|
|SAH_POS_CH_LGE||**-0.182**||||
|||**-2.400**||||
|SAH_POS_CH_SML||**-0.082**||||
|||**-2.460**||||
|SAH_NEG_CH_SML||**0.068**||||
|||**2.190**||||
|SAH_NEG_CH_LGE||**0.240**||||
|||**3.670**||||
|POOR X SAH_CHANGE|||**-0.058**|||
||||**-2.100**|||
|HI X SAH_CHANGE||||0.017||
|||||0.540||
|URB_HI X SAH_CHANGE|||||0.041|
||||||1.110|
|RUR_HI X SAH_CHANGE|||||-0.032|
||||||-0.630|
|**_LINEAR COMBINATIONS_**||||||
|_SAH_NEGCHANGE - SAH_POSCHANGE_|-0.008|||||
||-0.179|||||
|_SAH_POS_LGE - 2 * SAH_NEG_SML_||-0.018||||
|||-0.198||||
|_SAH_NEG_LGE - 2 * SAH_NEG_SML_||0.103||||
|||1.261||||
|_SAH_CHANGE + POOR X SAH_CHANGE_|||**0.056**|||
||||**2.833**|||
|_SAH_CHANGE + HI X SAH_CHANGE_||||**0.101**||
|||||**3.691**||
|_SAH_CHANGE + URB_HI X SAH_CHANGE_|||||**0.125**|
||||||**3.735**|
|_SAH_CHANGE + RUR_HI X SAH_CHANGE_|||||0.052|
||||||1.092|
|N|7,486|7,486|7,486|7,486|7,486|

_Note: Results estimated with household-level fixed effects. Coefficients on controls (household mean age (level, squared, cubed), household size (level, squared, cubed), and wave dummies are not reported. t- statistics are reported under each coefficient. Coefficients in bold are significant at the 10% level (twosided). The test for symmetry between positive and negative shocks is based on a regression where positive (SAH_POSCHANGE) and negative (SAH_NEGCHANGE) health change are included as separate variables, ranging from one to three. Results are not reported_
- Ferreyra, M. M., Dinarte, L., Urzúa, S., and Bassi, M. (2021). _The Fast Track to New Skills: Short-Cycle Higher Education Programs in Latin America and the Caribbean_ . World Bank, Washington, DC. Accepted: 2021-05-19T15:16:32Z.

- González-Velosa, C., Graciana, R., Sarzosa, M., and Urzúa, S. (2015). Returns to Higher Education in Chile and Colombia \textbar Publications. _IDB WORKING PAPER SERIES_ , IDB-WP-587.

- Heckman, J. J., Urzua, S., and Vytlacil, E. (2006). Understanding Instrumental Variables in Models with Essential Heterogeneity. _The Review of Economics and Statistics_ , 88(3):389–432.

- Heckman, J. J. and Urzúa, S. (2010). Comparing IV with structural models: What simple IV can and cannot identify. _Journal of Econometrics_ , 156(1):27–37.

- Hull, P. (2018). IsoLATEing: Identifying Counterfactual-Specific Treatment Effects with Cross-Stratum Comparisons. SSRN Scholarly Paper ID 2705108, Social Science Research Network, Rochester, NY.

- Jepsen, C., Troske, K., and Coomes, P. (2014). The Labor-Market Returns to Community College Degrees, Diplomas, and Certificates. _Journal of Labor Economics_ , 32(1):95– 121.

- Kane, T. J. and Rouse, C. E. (1999). The Community College: Educating Students at the Margin between College and Work. _Journal of Economic Perspectives_ , 13(1):63–84.

- Kirkeboen, L. J., Leuven, E., and Mogstad, M. (2016). Field of Study, Earnings, and Self-Selection. _The Quarterly Journal of Economics_ , 131(3):1057–1111.

- Kline, P. and Walters, C. R. (2016). Evaluating Public Programs with Close Substitutes: The Case of Head Start. _The Quarterly Journal of Economics_ , 131(4):1795–1848.

- Leigh, D. E. and Gill, A. M. (2003). Do community colleges really divert students from earning bachelor’s degrees? _Economics of Education Review_ , 22(1):23–30.

- Levesque, E. M. (2018). Improving community college completion rates by addressing structural and motivational barriers. _The Brookings Institution_ .

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22
increases fastest at lower levels of wealth, and then stabilizes at around 18 percent at the wealth level of those in the third and fourth quintiles, and then decreases slightly to 16 percent at the richest level of wealth evaluated. In Eastern and Southern Africa while the associations are significant, they are typically not as large. The percentage who use no modem care falls from 36 to 32 percent for the average wealth of the poorest to richest quintiles. It is only the use of private medical facilities that increases appreciably with wealth: from 14 percent for those with the wealth of the poorest quintile to 19 percent to those with the wealth of the richest quintile.

**Table 13. Average predicted probabilities from Multinomial Logit Estimation of Advice/Treatment sought as a result of a child** **_under_ 3 having a fever in the past two weeks**

||_No/ Self_<br>_treatment_|_Public,_<br>_higher_<br>_level_|_Public,_<br>_lower_<br>_level_|_Private,_<br>_medical_|_Private,_<br>_commercial_|_Total_|
|---|---|---|---|---|---|---|
|_WesternandCentralAfrica_|||||||
|Probabilities evaluatedat:|||||||
|Meanwealthinthepoorestquintile|53.4|9.2|12.0|10.7|14.7|100|
|Meanwealthinquintile2|46.1|9.4|16.0|10.3|18.1|100|
|Mean wealthinquintile3|41.0|10.7|18.2|10.5|19.6|100|
|Meanwealthinquintile4|38.0|13.8|18.1|11.2|18.9|100|
|Mean wealthintherichestquintile|36.6|19.1|15.6|12.4|16.3|100|
|_EasternandSouthern Africa_|||||||
|Probabilities evaluatedat:|||||||
|Meanwealth inthepoorestquintile|36.0|8.8|30.9|14.3|10.0|100|
|Meanwealthinquintile2|35.9|8.8|30.8|14.4|10.1|100|
|Meanwealthinquintile3|34.9|9.5|29.6|14.9|11.1|100|
|Meanwealthinquintile4|34.1|10.0|28.6|15.6|11.7|100|
|Mean wealthin therichestquintile|32.0|11.0|26.0|19.1|11.8|100|

_Source:_ Author's calculations from pooled and weighted DHS data. See Table 10 for definition of categories. Predictions are the average probability averaging over all individuals with their observed characteristics but substituting all observations to have the average wealth of the specified quintile.

The MNL results conform to the expectation formed by the bivariate analysis in Table 11. A comparison between Table 11 (which doesn't control for background characteristics) and Table 12 (which does) implies that although a substantial amount of the rich-poor differential is explained by characteristics other than household wealth, wealth still plays a substantial role in determining whether to treat using modem methods versus home or no care, as well as treatment choice. Although the predicted differential is reduced after controlling for other characteristics it still exists. For example, the average predicted probability of seeking no modem sector care ranges from 53 percent for the poorest quintile to 37 the richest quintile in Western and Central Africa and from 36 to 32 percent in Eastern and Southern Afrca. The unadjusted ranges are 64 to 23 percent and 41 to 21 percent in each region respectively. Clearly other factors (including subregional fixed attributes) go a long way in explaining wealth differentials.

_26_
- 15 -

# Reaional/Sectoral Profiles of Poverty

Tables 2 and 3 gives the FGT poverty measures for a<sup>-</sup> 0.1.2 for each sector and state. The Appendix outlines how these poverty measures are calculated from the published grouped data. The measures<sup>are evaluated</sup> at two poverty lines, namely monthly per capita expenditures of Rs 76.65 (Table 2) and Rs 89.00 (Table 3) at 1983 all-India rural<sup>prices. The lower</sup> of the two poverty lines corresponds to the widely used Dandekar-Rath poverty line of Rs 15 per capita at 1960-61 all-India rural<sup>prices updated</sup> by the all-India CPIAL for 1983. The higher poverty line<sup>is obtained by</sup> updating the Sixth Plan poverty line of Rs 49.09 at 1973-74 rural<sup>prices by</sup> the consumer price index for middle rural population, developed<sup>by Minhas</sup> et al., (1987).

The motivation for using two poverty lines is not to compare the relative merits of alternative poverty lines or consumer price<sup>indices, but</sup> simply to examine whether the conclusions of our analysis are<sup>sensitive to</sup> the exact cut-off point used to define poverty. Our range<sup>of poverty lines</sup> appears to safely encompass the range of opinion on this issue.<sup>11</sup> The following observations can be made on the results in Tables 2 and 3: i) About 33 percent of the national population are deemed<sup>to have</sup> been poor in 1983 using the lower poverty line. This<sup>rises to about 44</sup> percent using the higher line. For the lower poverty line, the<sup>1983</sup> poverty gap per capita (P1) represents 8.1 percent of the<sup>poverty line,</sup> equivalent to Rs 6.20 per person in India per month, or about<sup>5.4 percent</sup> of India's mean consumption per capita in that year. The poverty<sup>gap rises</sup>
course, a set of countries which make up the source of more than 50 percent of its goods imports in some years.<sup>12</sup> The figure reveals that by 2011, more than 6 percent of Turkey’s imports from PTA non-partners were subject to a Turkish TTB, whereas less than 1 percent of Turkey’s imports from PTA partners were subject to a TTB. While the differential pre-dates the implementation of the customs union, the fact that this differential has grown since PTA implementation raises the concern that Turkey’s TTBs may be being used to further deepen the already existing preferences found within its PTAs.

Given the increased prevalence of both PTAs and country-specific TTB use through instruments such as antidumping in particular, what does existing empirical research suggest is the relationship between the two? Prusa and Teh (2010) provide a cross-country study using data since the 1980s which contains the most systematic empirical work to date. Their first compelling piece of evidence comes from the raw data, which is aggregated and normalized across all PTAs. They reveal that in the ten years prior to implementation of PTAs, the aggregate count of antidumping filings between (eventual) PTA partners is nearly six cases higher per year than in the years after PTA implementation. I.e., antidumping activity between (eventual PTA) partners falls substantially after PTA implementation. In subsequent econometric analysis they estimate a negative binomial regression model of determinants of the counts of bilateral antidumping filings, paying particular attention to potential differential treatment between PTA partners and non-partners, both before and after the implementation of the PTA. Their research is conducted at the relatively aggregate level, and thus also includes a number of macroeconomic control variables (domestic and foreign real GDP growth, real exchange rates, bilateral trade flows) that the associated literature examining the impact of business cycles on import protection has found to affect antidumping filing behavior.

Prusa and Teh (2010) find that the overall (net) effect of PTA implementation on antidumping activity is small. Nevertheless, they also present strong evidence of a compositional impact across the

> 12 As explored in more detail in Bown (forthcoming) and Karacaovali (2011), there are few legal constraints in the Turkey-EU customs union that affect the imposition by one side of TTBs on imports deriving from the other. This is quite different, of course, from intra-European Union trade in which EU rules prevent one EU member country from imposing a TTB on imports from another.

12
**Table A1.6: Base assumptions applied for operation and maintenance staff in generation**

|Generation technology|Operation staff|<br>units|Maintenance staff|units|Limit on staff|
|---|---|---|---|---|---|
||perunits||perunits|||
|Coal|25|1|35|2|No|
|Diesel|15|2|25|2|Yes|
|Gas - motor|15|2|25|2|Yes|
|Gas - turbine|20|1|30|2|Yes|
|Geothermal|20|1|30|2|Yes|
|HFO|20|1|30|2|Yes|
|Hydropower|10|2|20|2|Yes|
|Solar|0|1|10|1|Yes|

_Source_ : World Bank industry specialists.

# Transmission and distribution assumptions used in the overstaffing analysis

The benchmark number of employees for T&D is set by km of T&D lines with voltage greater than 1 kV and the number of customers. The total numbers of employees reported in the utilities’ annual reports are compared with the staff complement in utilities in three clusters of power utilities in Latin America with similar numbers of customers and km of T&D lines. Clusters were first identified in SSA, and then reference values were identified using utilities in Latin America with similar characteristics, as described below.

## **Step 1: Identify SSA Clusters**

Thirty-six utilities in SSA were grouped into three clusters with the characteristics shown in Table A1.7.

## **Table A1.7: Clusters in SSA**

|Cluster|Median customer size|Median km of T&D > 1kV|
|---|---|---|
|1|100,000|1,800|
|2|350,000|9,100|
|3|1,300,000|39,200|

_Source_ : World Bank staff analysis of utility financial statements and annual reports

## **Step 2: Identify reference values for employees in utilities in Latin America**

Fifteen state-owned utilities in Argentina, Brazil, Colombia, Paraguay, and Uruguay were used to derive reference values, each mapped to one of the three clusters (Table A1.8 and Table A1.9).

## **Table A1.8: Clusters in Latin America used to identify reference values for customers per employee**

|Cluster|Median customer size|Median km of T&D >1kV|Average customers per employee|
|---|---|---|---|
|1|100,000|2,621|297|
|2|330,000|8,601|440|
|3|1,300,000|24,123|571|

_Source_ : World Bank staff analysis of utility financial statements and annual reports 79
**Table C.1:** SURE Model: District-level characteristics and Rainfall z-scores

|Standardized outcome|(1)<br>Coefcient|(2)<br>SURE|
|---|---|---|
|variable at district level|_βdp_|p-value|
|HH head Age|.08|.46|
|HH head literate|-.23|.04|
|Rural|-.20|.07|
|HH head female|-.36|.00|
|HH size|.99|.00|
|Covid restrictions|.01|.95|
|Private water connection|-.04|.71|
|Electricity connection|.12|.27|
|Use clean fuel|-.12|.28|
|Distance to hospital|-.07|.51|
|Distance to PHC|.26|.02|
|School in community|.01|.89|

_Note_ : This table reports the coefficient from equation 5) from district-level regression of standardized district demographic variables on rainfall anomalies at the district-level. * Significant at 10%; ** Significant at 5%; *** Significant at 1%.

47