rafmacalaba/datause-encoder-data huggingface dataset:
at 4.7 persons and this size is larger for poorer households. The first quintile (the poorest) spends about 12% of what the fifth quintile (the richer) spends on average. These extrapolations are not the exact figures available in macroeconomic statistics but they represent good approximations considering that they are derived from household data and a rather old data set.

**Table 5 – Baseline Population and Expenditure data by Quintile**

||**Population**<br>**(m)**|**Number of**<br>**households**<br>**(m)**|**Household**<br>**size**|**Total**<br>**expenditures**<br>**(m)**|**Total**<br>**expenditures**<br>**per capita**|**Total**<br>**expenditur**<br>**es per**<br>**household**|
|---|---|---|---|---|---|---|
|**Quintile 1**|6.66|0.98|6.8|34,789|5,223|35,362|
|**Quintile 2**|6.66|1.19|5.6|58,543|8,789|49,105|
|**Quintile 3**|6.66|1.36|4.9|82,599|12,398|60,946|
|**Quintile 4**|6.66|1.57|4.2|118,540|17,794|75,382|
|**Quintile 5**|6.66|1.99|3.3|285,699|42,913|143,304|
|**Total**|33.30|7.10|4.7|580,169|17,420|81,743|

Source: Authors’ estimations from household budget survey data

# **4. A distributional analysis of subsidies (October 2014)**

The analysis presented in this paper covers food products (sugar and flour), petroleum products (gasoline, diesel and liquefied petroleum gas – LPG), and electricity. Water is not considered to be subsidized in Morocco but the 2014 reforms included a change in the price structure for household consumers and this change has implications for household welfare. We therefore included simulation for water when we assessed the 2014 reforms.

The prices of subsidized products in October 2014 are described in Table 6 together with the unit subsidies and the unsubsidized prices. LPG, flour and electricity are the products with the highest subsidies relative to the unsubsidized price with LPG reaching 66.6% of the unsubsidized price. Households spend over 47 bn. MAD on subsidized products, which represents 8.1% of total household expenditure. The largest expenditure item among subsidized products is electricity (16 bn.). However, the largest subsidies are, by far, on LPG which alone costs the government about 11.8 bn. MAD. This is followed by electricity (6.4 bn.) and flour (2.4 bn.).

**Table 6 – Subsidized products (October, 1st 2014)**

||**Unit**|**Unit**<br>**price**|**Unit**<br>**subsidy**|**Unsubsidized**<br>**unit price**|**Unit**<br>**subsidy**<br>**(% of**<br>**unsub.**<br>**price)**|**HH Exp.**<br>**On Subs.**<br>**Products**<br>**(bn.**<br>**MAD)**|**Total**<br>**subsidies**<br>**(bn.**<br>**MAD)**|
|---|---|---|---|---|---|---|---|
|Gas LPG|Kg|3.330|6.654|9.984|66.6|5.9|11.8|
|Gasoline|L|12.800|0.000|12.800|0.0|2.8|0|
|Diesel|L|9.890|0.800|10.690|7.5|3.7|0.4|
|Sugar-piece|Kg|5.820|2.850|8.670|32.9|2.5|1.2|
|Sugar-cube|Kg|5.820|2.850|8.670|32.9|0.4|0.2|
|Sugar-<br>granul.|Kg|4.500|2.850|7.350|38.8|0.9|0.6|

9
# **5. Are returns consistent with risks?**

The relatively high costs of capital presented in section 4 would not have been a major issue if the associated returns had been at matching levels. The fact is, however, that private investment in infrastructure declined significantly during the 5 years that followed the Asian crisis. This implies that returns have probably not been consistent with the high levels of the cost of capital across regions. This would be relatively easy to demonstrate if estimates of the internal rate of return (IRR) for these projects were available for the companies in this sample. Indeed, the IRR summarizes in one single number both, the return earned by shareholders up to date and the return over the entire life of the project (when the residual value at the end of the project is taken into account). The data is however not available.<sup>14</sup> To get a sense of the (mis-)match between risks and return, we rely on a comparison of the cost of equity (one of the components of the cost of capital) and of the returns on equity (RoE).<sup>15</sup> We present the results per sector, per region and per income level groupings, respectively.

Starting with the estimates of the average returns per sector, Figure 6 shows that the RoE varied considerably across sectors but were lower than costs of equity in every case in the 5 years that followed the Asian crisis. The water and railroad sectors had negative average returns of –0.14% and –6.7% respectively, while the port and energy sectors had positive returns of 6–8%. The main point of Figure 6 however is that none of the sectors managed to generate a RoE consistent with the cost of equity. In other words, for no sectors were returns consistent with the risk assessed in the post crisis years.

**Figure 6:  Average returns by sectors (1998-2002)**

<!-- Start of picture text --> Cost of Equity vs. RoE<br>by Sector<br>20%<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>Energy Water Railways Ports<br>Cost of Equity RoE<br><!-- End of picture text -->

> 14 The interested reader could refer to Sirtaine et al (2004) who were able to collect the necessary data for a sample of Latin American infrastructure companies.

> 15 Contrary to the IRR, the RoE is exclusively an annual measure and missed out on the dynamic dimension of infrastructure projects, include the life cycle dimensions discussed earlier. The data is however not available for the sample.

13
11 The last type of government involvement in credit markets comes through the issuance of tax-exempt securities. The federal government has allowed securities issued by state and local governments to have tax-exempt status. The money raised from such issues has frequently been used for private purposes de ignated by the state and local govermments. Due to the tax-exempt status, the securities carry a lower interest rate. The subsidy element is the difference between the market interest rate and the cost of raising these tax-exempt funds. Table 4 shows the distribution by purpose of the tax-exempt securities issued in 1989. Unfortunately, the total outstanding issues are not available by sector, only in aggregate. In 1989, this total stood at $1046.2 billion, about 10% of the credit market debt owed by nonfinancial sectors.

TABLE **4**

|TAX EXEMPT SECUR|ITIES ISSUED IN 1989|Billions of_$_|
|---|---|---|
|**Purpose**|**New Issues**||
|Public organizations|77.0<br>(74%)||
|Private nonprofit organization<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|14.0<br>~(13%)||
|Industrial development bonds<br>_<br>_<br>_<br>_<br>_<br>**_**<br>_<br>_<br>_<br>**_**<br>_<br>_ _ _<br>_ _<br>_<br>_ _ _<br>_ _ _<br>_ _<br>_<br>_ _ _|5.8<br>**6 % )**||
|Housing bonds|_4.5_<br>(4%)||
|Student loans|1.8<br>(2%)||
|Pollution control<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_|1.2<br>(~~1%<br>)||
|TOTAL<br>.________________|104.4<br>___________<br>(100%)||

SOURCE: 'Special Analysis F," in Soecial Analyses: Budget of the United States Govemnment Fiscal Year 1989, Exeuive Office of the Pesident, Office of Management and Budget (Washingtun: U.S. Govement Printing Offcec, 1988).
**Table 17. Significance of legal and regulatory obstacles: Foreign investment limits**

|VARIABLES|(1)<br>OLS|(2)<br>OLS|(3)<br>OLS|(4)<br>OLS|(5)<br>Ologit|(6)<br>Ologit|(7)<br>Ologit|(8)<br>Ologit|
|---|---|---|---|---|---|---|---|---|
|Services|0.0311|0.0344|||0.0487|0.0553|||
||(0.0664)|(0.0658)|||(0.110)|(0.110)|||
|Export % of sales|-0.000806|-0.000889|-0.000576|-0.000601|-0.00116|-0.00139|-0.000805|-0.000923|
||(0.000859)|(0.000877)|(0.000852)|(0.000865)|(0.00142)|(0.00146)|(0.00145)|(0.00148)|
|Share of sourcing via imports|0.00294***|0.00235**|0.00324***|0.00259***|0.00463***|0.00375**|0.00524***|0.00433**|
||(0.000971)|(0.00102)|(0.000967)|(0.000997)|(0.00162)|(0.00171)|(0.00168)|(0.00174)|
|High-income source country|-0.0773|0.00779|-0.0690|0.0236|-0.123|0.0101|-0.108|0.0406|
||(0.0983)|(0.0994)|(0.0977)|(0.0987)|(0.166)|(0.169)|(0.170)|(0.173)|
|>250 employees|-0.0247|-0.0183|-0.0113|-0.00667|-0.0299|-0.0187|-0.0115|-0.00273|
||(0.0803)|(0.0819)|(0.0810)|(0.0829)|(0.132)|(0.137)|(0.139)|(0.146)|
|Cumulative inv. >US$10M|-0.0335|-0.0184|-0.0522|-0.0391|-0.0937|-0.0650|-0.119|-0.0909|
||(0.0786)|(0.0787)|(0.0779)|(0.0779)|(0.132)|(0.133)|(0.135)|(0.137)|
|Years in country|0.00234|0.00137|0.00232|0.00102|0.00438|0.00231|0.00417|0.00150|
||(0.00246)|(0.00248)|(0.00248)|(0.00248)|(0.00378)|(0.00391)|(0.00397)|(0.00409)|
|Level of foreign ownership (%)|-0.00207|-0.000470|-0.00213|-0.000273|-0.00339|-0.000864|-0.00361|-0.000663|
||(0.00138)|(0.00145)|(0.00135)|(0.00140)|(0.00228)|(0.00243)|(0.00228)|(0.00238)|
|Country fixed effects||X||X||X||X|
|Subsector fixed effects|||X|X|||X|X|
|Observations|2,424|2,424|2,424|2,424|2,424|2,424|2,424|2,424|
|R-squared|0.013|0.027|0.044|0.061|||||

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The dependent variable in all models is an ordered categorical variable corresponding to responses to the following question: "To what degree are the following factors an obstacle for your company to operate in this country? [Limit on amount of permissible foreign investment in your sector(s)]". Higher numbers denote more significant obstacles. Subsector fixed effects are at the ISIC A38 level.

Coefficients for constants (for OLS), cut points (for ordered logit), and fixed effects not shown in table.

34
# II. BACKGROUND AND DATA

We focus on the residential market for natural gas in Greater Buenos Aires. In late 2008, the national energy regulatory agency (ENARGAS) introduced the first increase in residential gas tariff since 2002. The revised schedule was composed of eight new tariff groups, each facing different variable fees per cubic meter; see Table I. This schedule introduced significantly higher unit prices for those consumers with higher levels of annual accumulated consumption. We exploit the resulting discontinuity in variable prices at the annual consumption level that divides categories R32 and R33 to examine consumption responses to price shocks.

# "Place Table I approximately here."

Our empirical analysis draws on administrative records from MetroGAS S.A., one of the largest residential gas distributors in Argentina. The company has a client base in Greater Buenos Aires of about 2.5 million residential households, who receive their gas bills every two months. The administration of MetroGAS agreed to provide us with data on a small share of its customer base around the discontinuity R32-R33.<sup>5</sup> Given this constraint, we have defined the sample with the view to optimize the implementation of the RD design for this discontinuity. Specifically, consumers were selected into the sample provided by the company if: (1) they had a residential bill issued in May 2009 with an annual accumulated consumption between 1480 and 1520 cubic meters; and (2) they had been customers for at least six bimonthly cycles by that month. The resulting estimation sample contains 7190 households.

This sample is composed of longitudinal records corresponding to the bills issued in May 2009, in the five previous bimonthly periods, and in the three subsequent ones. For each period, the data set comprises information on the amount billed, quantity of gas consumed (in cubic meters), type of reading (measured or imputed), category assigned to the consumer, and the exact dates of reading and issuance. It further contains information on the region and neighborhood of residence of each consumer.

# III. RESEARCH DESIGN

An ideal experiment designed to estimate the impact of a price shock on residential energy consumption would randomly assign some consumers to a treatment group, facing price PH, and other consumers to a control group, facing price PL. Unfortunately, a large-scale experiment of this kind has yet to be implemented, making the task of estimating this behavioral response rather difficult. To approximate such an ideal experiment, we exploit unique features of the price schedule described in Section II, along with the specific information set available to consumers.

In May 2009, households with annual accumulated consumption above 1500 cubic meters were assigned a unit price roughly 25% higher than those with an annual accumulated consumption barely below this level. This discontinuity of the unit price schedule makes it possible to apply a RD design in which the outcome variable corresponds to the consumption 4
**Hypothesis 3:** The security situation will be better in villages that have received a development program.

According to the “hearts and minds” theory, improved attitudes towards the government should decrease support for insurgents, which should in turn lead to a decrease in security incidents. Hypothesis 3 is also consistent with the “opportunity cost” theory, but is inconsistent with the “greed” theory and the “bargaining” theory, both of which predict that projects should increase insecurity, at least in the short run.

# **V. Data**

Data for the study come from three sources: a baseline survey, a follow-up survey, and events data on security incidents gathered by ISAF. The following sections provide further details on these data sources.

## **V.1. Baseline Survey**

Data from the baseline survey were collected during August and September 2007 and prior to the introduction of the development program in the 250 treatment villages. The survey consisted of four different instruments: (a) a male household questionnaire administered to ten randomly-selected male heads-of household in each village; (b) a male focus group questionnaire administered to a group of village leaders in each village; (c) a female focus group questionnaire administered to a group of important women who tended to overwhelmingly be wives or other relatives of the village leaders; and (d) a female individual questionnaire. In total, the survey covered 13,899 male and female villagers as well as village leaders across the 500 sample villages.

## **V.2. Follow-Up Survey**

Data from the follow-up survey were collected between May and October 2009. The follow-up survey was administered following CDC elections (which occurred between October 2007 and May 2008) and project selection (which occurred between November 2007 and August 2008), but before projects were completed.<sup>14</sup> The timing of the survey following project implementation but prior to the delivery of project benefits enables the isolation of effects of expectations. This is a particularly relevant

> 14 By that time only 18 percent of the projects were fully completed.

12
<!-- Start of picture text --> HB economics Jf Economics J political economy [jj Political Economy<br>0.00300%,<br>0.002802 _~<br>0.002402)wef PhS[7<br>0.00200% f~ \ po<br>0.0016020.001402) JS |,<br>0.00120%4<br>0.001002 /<br>0.000802 4 —|<br>0.00040% aiid a<br>SS \ —' a= ,<br>0.00000F855; Ten Te40 Teo aolTeE0 Ts00 TS20 Ts40 7360 TS80 000<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 14. Costs and revenues over time ($/kWh)<br>a. Unit costs b. Average revenues<br>0.30 0.30<br>Predominantly diesel<br>0.25 0.25<br>Predominantly hydro<br>0.20 0.20 Overall<br>Predominantly diesel<br>Predominantly hydro<br>0.15 Overall 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>2001 2002 2003 2004 2005<br>2001 2002 2003 2004 2005<br><!-- End of picture text --> _Source:_ Africa Infrastructure Country Diagnostic Power Tariff Database

# **e. Do IBT structures allow for costs to be recovered?**

As noted above, IBTs are premised on the notion that surcharges on higher volumes of consumption will compensate for discounts on lower volumes of consumption, so that the utility breaks even overall. However, this outcome is contingent on the block sizes and associated price levels being correctly calibrated. It is often the case that the surcharges apply (if at all) to very high levels of consumption that are rarely reached in practice. In the case of Africa, the answer is more promising than might be anticipated.

A minority of countries have residential tariffs that recover costs independent of consumption levels, meaning that even the lowest priced block is priced high enough to recover costs (category I in table 8). (This is ideal from a cost recovery perspective, but raises questions as to whether these IBTs are also performing the intended social function of providing subsidized power to small consumers.) A second batch of countries has IBTs that succeed in recovering operational costs (category II), but will not—or are not likely to—attain total cost recovery based on their historical average household consumption. The third group of countries (category III in table 8) is recovering costs within a consumption range that is close to the historic average. Finally, a fourth group of countries (category IV)—given current tariffs and the historical consumption patterns—will never attain operational (let alone total) cost recovery.

24
# **Performance-related Pay in the Public Sector: A Review of Theory and Evidence**

Zahid Hasnain<sup>1</sup> , Nick Manning<sup>2</sup> , and Jan Henryk Pierskalla<sup>3</sup>

## **JEL Codes: H11, H83, I18, I28**

_Acknowledgements_ : This paper has benefitted greatly from comments by Mike Stevens, Willy McCourt, Mariano Lafuente, Gary Reid, and Svetlana Proskurovska.

> 1 World Bank (zhasnain@worldbank.org)

> 2 World Bank (nmanning@worldbank.org)

> 3 Duke University (jhp5@duke.edu)
specification and sample. Two versions of the final model are reported: the difference between the two lies in the measurement of education -- dummy or splined variables. Finally, as a further test of validity, we analyzed the data using nonparametric techniques (see Appendix B). The results are discussed below.

# 5.1 Initial Specification

The initial model was given by: lnRL - **ao +** alLOCTION_HOME **+** a2LOCATION_FIXED **+** a3FIRM_AGE - ln(l + e **T)** where:

- TL - **bo +** bjEXPENSES **+** b2CAPITAL **+** b3STOCK **+** b,ln(LABOR) **+** b 5 TRAINING **+** b 6 SCHOOL_PRIMARY **+** b7 SCHOOL_SECONDARY **+** b 8 SCHOOL_POSTSEC **+** bgEXPERIENCE **+** b2OEXPERIENCE<sup>2</sup> (14)

The model was fitted to three subsamples of the data: Lima, other urban areas, and rural areas. All the first round point estimates of the parameters (obtained by a nonlinear least squares Gauss-Newton algorithm) had the correct signs and between 50 and 60 percent of the variance in the logarithm of revenues was explained.

# 5.2 **Preliminary Tests for Aggregation** and Pooling

The initial results showed similar patterns in the estimated parameters of the Lima and other urban areas models. Some parameters from the rural model were similar to their counterparts for Lima and for other urban areas. These results led to the hypothesis that an aggregate model based on a pooled sample could be estimated. At the same time it was necessary to determine whether we were justified in pooling data among female, male, and mixed enterprises. This question was particularly important given that one of our goals was to estimate 29
Taking these facts into consideration, it is reasonable to assume that if a woman has not carried out the option of aborting a pregnancy during the first trimester, it is most likely that her latent status is _B*_ . Based on this reasonable assumption, we can check the robustness of our findings by limiting the sample of women who miscarried to those who miscarried after the third month of pregnancy.

# **4. Estimation results**

# **4.1. Average impacts**

The estimations based on ENADID data confirm that women who become pregnant as teenagers indeed differ from their peers along a number of characteristics (Table 1). They are more likely to be indigenous and to come from less wealthy families, their parents tend to be younger; they are less likely to live in the same household as their fathers, and they begin sexual activity and forming unions at a significantly earlier ages. Most of these characteristics are “pre-treatment” in the sense that although they are measured after the event of interest occurred, they are unlikely to change as a result of the event (such as ethnicity or parental age). Table 1 clearly illustrates the inevitability of a selection bias in a simple comparison of outcomes among women who experienced a pregnancy as teenagers and those who did not. On the other hand, to validate our empirical strategy we need to demonstrate that among women who experienced pregnancy as teenagers those who miscarried are not significantly different from those who had a live birth. Table 4 presents the corresponding analysis. We fail to reject the hypothesis of equality of the means for all cases except parental ethnicity.

Table 5 presents the results of the analysis of the impact of early childbearing on the mother‟s outcomes. To illustrate the extent of the selection bias we present two types of estimates. First, we regress the outcomes of interest on an indicator for adolescent childbearing as in equation (1) on the sample of all ENADID respondents aged 19 to 30. The results are shown in columns (I) through (III). Second, we limit the sample to women who have experienced pregnancy as teenagers. Columns (IV) through (VI) present these results. In the former case, the group of controls comprises all women who were not rearing a child in their adolescence; in the latter one, the control group includes only women who experienced a pregnancy as adolescents, but did not bring up a child because their pregnancies terminated in a miscarriage.

10