rafmacalaba/datause-encoder-data huggingface dataset:
- 12 -

used for tax purposes. Yet it is reasonable to assume that they are much less likely to under-report expenditure data because it is gathered by asking many questions on specific items and thus usually does not trigger fears of tax increases. Thus, the empirical parc of this paper examines expenditure levels rather than income levels.

Up to this point the discussion has assumed that individuals do not live together in households, but of course they do. The easiest approach to take here is to divide total consumption by household size and use this as a measure of each individual's welfare. This is not completely satisfactory because expenditures may not be divided up equally among household members and in addition there are likely to be economies to scale so that larger households tend to have higher welfare levels than are indicated by per capita consumption figures. Unfortunately, the former problem is almost impossible to solve with most household data sets since they usually do not have information on individual consumption. The latter problem can be resolved only by estimating equivalence scales, which is often a risky venture (cf. Pollak and Wales, 1979). In order to concentrate on the issue of targeting we will assume throughout the paper that per capita expenditures are a valid measure of each household member's welfare.

One final issue must be settled before empirical work can begin: Which index of poverty should be used? There is increasing support for the Foster-Greer-Thorbecke (FGT) class of measures since they are group decomposable and include some commonly used measures as special cases. In addition, this family of measures is not known to have any obvious disadvantages. Hence it is convenient to use them here. The family of
# Rural Nonfarn Qrowth and Rural Wages

In Table 2, we saw that more than half of the commercial bank credit outstanding in rural and semi-urban branches goes to sectors other than agriculture and food processing. Confining the investigation of the effects of the rural financial system to its effect on agriculture would be to ignore its potential positive effects on the rural nonfarm sector. Any positive effect on nonfarm output and employment could mitigate the negative effect on farm employment. Unfortunately, data on rural nonfarm output does not exist.

We are, therefore, using rural nonagricultural employment, which is available in the decennial censuses. In the census, both farm and nonfarm employment are measured by primary occupational status. Individuals are asked whether they worked in agriculture or nonagriculture for at least 183 days during the previous year. Random effects regression similar to the ones in Table 5 for crop output are run to explain the employment growth between the two population censuses of 1970 and 1980, in the sample of districts under investigation (for details see Khandker 1989). As rural wage data are also not available, data on agricultural wages published by the Ministry of Agriculture are used as a proxy . The wage rate for rural field workers is used.<sup>2</sup> ' The impacts on nonagricultural employment of commercial bank expansion and of overall rural credit are large, the estimated are close at 0.24 and 0.29. Not surprisingly, 2/ Where **data for field workers was further disaggregated, thedata for the ploughmen was used.**

**30**
**Table 3. Second Stage Regressions: Linear trend and Salaried Workers Depend Variable Cyclical RTS**

||**_Panel_**<br>|**_A: Current Ex_**<br>|**_ports_**<br>|||**_Panel B: Lag_**<br>|**_ged Exports_**<br>|||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Exports/ Exports(-1)<br>PanelA / Panel B|0.138|0.112|0.126|0.152|0.133|0.153|0.152|0.131|0.154|
||_(0.067)**_|_(0.064)* _|_(0.066)* _|_(0.073)**_|_(0.071)* _|_(0.072)**_|_(0.073)**_|_(0.072)* _|_(0.073)**_|
|Ext. Financ|-0.301|-0.293|-0.316|-0.327|-0.327|-0.353|-0.286|-0.271|-0.315|
||_(0.103)***_|_(0.094)***_|_(0.109)***_|_(0.112)***_|_(0.102)***_|_(0.119)***_|_(0.113)**_|_(0.101)***_|_(0.122)**_|
|CPI|0.009|0.019|0.009|0.011|0.022|0.010||||
||_(0.003)***_|_(0.008)**_|_(0.003)**_|_(0.004)***_|_(0.009)**_|_(0.004)***_||||
|CPI(-1)|||||||0.009|0.018|0.009|
||||||||_(0.004)***_|_(0.011)_|_(0.004)**_|
|Dom. Financ||||||||||
|Lending rate|-0.036|||-0.029|||-0.027|||
||_(0.026)_|||_(0.026)_|||_(0.026)_|||
|Deposit rate||0.005|||0.004|||0.004||
|||_(0.002)**_|||_(0.002)*_|||_(0.003)_||
|Real rate|||-0.036|||-0.041|||-0.041|
||||_(0.023)_|||_(0.026)_|||_(0.026)_|
|Obs|165|172|165|151|156|151|151|156|151|
|R-square|0.22|0.20|0.22|0.23|0.22|0.23|0.22|0.21|0.23|

All regressions include country and year dummy variables and no intercept Levels of Sig: 1% ***, 5%**, 10%* 26
<u>Food Insecurity: The analysis estimates that the net PAF impact on incidence of food</u> insecurity (as defined as self-reported food sufficiency for six months or less) is a decline of 19 percentage points when using a combined IV with difference-and-difference method, and a decline of 22 percentage points when using IV on post-treatment data only. The effect is stronger for disadvantaged households, such as those from _dalit_ and _janajati_ communities<sup>8</sup> . The results are significant since chronic food insecurity is a particularly important concern in Nepal and substantial amounts are spent per year on public works programs aimed at alleviating hardship for food insecure households. Sustained food price inflation remains a concern and an estimated 3.7 million people are currently food insecure. The impact of high prices and food insecurity is most severe on economically, geographically and socially marginalized communities. Since Nepal’s poorest households spend more than 75% of their income on food, high prices will continue to affect poverty alleviation efforts. The strong impact demonstrated by this evaluation suggests that the PAF is an effective program to address food insecurity.

<u>School Enrollment:</u> School participation among 6-15 year old children also appears to have been significantly affected by PAF interventions. The impact estimate is an increase of 14 percentage points in school enrollment for _all_ children in the given age-group but girls appear to have benefited even more, with 21 percentage points net increase. The effects are as strong among children from disadvantaged caste/ethnic groups. These are all notable and statistically significant impacts. While child education is not a direct outcome associated with PAF intervention, one can think of at least two ways by which a treated household would change its behavior in relation to this outcome. First, actual or perceived positive change in income/consumption for a household is more likely to reduce potential constraints of sending a child to school. Second, community mobilization and social-networking, as part of the PAF community organization (CO), may produce spillover effects such as motivating households towards child schooling.

<u>Child Underweight:</u> Close to 60% of children from sampled villages are underweight (annex table 1)<sup>9</sup> . We find that there is no statistically significant impact on child underweight rates. However, one can take this as a step in the right direction as shown by the negative sign of the coefficient, a pointer to possible effects in the coming years. Child malnutrition is a serious problem in the country, particularly among children from the poorest communities.

> 8 Separate estimate for households belonging to Dalit, Janajatis and other caste/ethnic groups (other than Brahmin, Chhetris and Newars) shows a higher decline in food insecurity (24 percentage points). 9 Data from Nepal Demographic and Health Survey (DHS) indicates similar incidence of underweight in Midwestern development region.

10
52

# Data Sources

IPA (1984): Energy Balances of Developing Countries, Intemraonal Energy Agency, Paris.

Lew G. and GowenM (1987): Household Energy Handbookl An interim Guide and Reference Manual, World Bank Technical PaperNo. 67, Washington,D.C.

World Bmk (1983): Indonesa Selected Issues of Energy Pricing, Report No. 4285-IND, Programs Department East Asia and Pacific Regional Office, v-1, Washington, D.C.

World Bank (1987): Indonesia: Strategy for Economic Recovety, Report No. 6694, Statistical Annex1, Washng D.C.

Wodd Bank (1990): Indonesia: Urban Household Energy Strategy, Report No. 107A/90, ESMAP, Wuhna, D.C.

Woodd Bnlk (1992): World Development Report, **Washington,** D.C.
# B Complementary Quantitative Exercises

## B.1 Micro-Level Adjustments in Benchmark Reforms

The dierential response of the economy response to reforms that remove entry and idiosyncratic distortions can be understood by exploring the micro-level adjustments, particularly the response in the distribution of innovation eorts across rms over time and the evolution of the productivity distribution. We illustrate these objects in gure B.1. The top two panels depict the response of innovation probabilities as a function of the rms' underlying physical productivity ( _TFPQ_ ) at a number of representative points of the transition: the initial steady-state ( _SS_ 0) _,_ the terminal steady-state ( _SST_ ) , and periods 1 and 10 ( _t_ = 1 _, t_ = 10) . Similarly, the bottom two panels depict the productivity distribution of rms at the same instances of the transition path.

Figure B.1 shows that both entry and idiosyncratic distortions depress innovation incentives, but idiosyncratic distortions have a disproportionate eect among the most productive rms in the economy (lines labeled _SS_ 0 in the top gures). This distinguishing feature of idiosyncratic distortions manifests in the properties of the productivity distribution. The share of rms at the top of the distribution declines sharply under idiosyncratic distortions (line _SS_ 0 of bottom left gure) whereas it shows almost no change under entry distortions (line _SS_ 0 of bottom right gure). As a result, when reforms are implemented and innovation intensities recover, aggregate innovation expenses increases strongly in the case of entry distortions, where there is a high share of highly productive rms to take advantage of the improved incentives to innovation, while it does so by about half as much in the case of idiosyncratic distortions, where there share of such rms is signicantly smaller.

The sharper increase in the rate of innovation helps rationalize the stronger decline and the speedier recovery of _TFP_ when removing entry distortions. As the economy expands innovation eorts and increases entry, it reallocates labor towards to innovation and rm creation, both of which are not capitalized in national income and product accounts<sup>21</sup> . Therefore, aggregate productivity declines on im21The Bureau of Economic Analysis in the US has started to incorporate some forms if intangible investment, such as software and entertainment, into the National Income and Product Accounts. However, as argued by Corrado et al. (2006) the majority of intangible investment still goes unmeasured in national accounts. Thus, we take the approach of treating payments to labor that go into intangible capital accumulation, which in the model corresponds to payments to labor devoted to innovation, as an expense rather than an investment. Furthermore, these adjustments are not done in the national accounts of the countries and periods under study.

42
# **AUCTIONS WITH ENDOGENOUS PARTICIPATION AND QUALITY THRESHOLDS: EVIDENCE FROM ODA INFRASTRUCTURE PROCUREMENT**

Antonio Estache Université Libre de Bruxelles Atsushi Iimi<sup>¶</sup> World Bank (FEU) Key words: Public procurement; auction theory; infrastructure development; governance. JEL classification: D44, H54, H57, C21.

> ¶ We express our special thanks to Japan International Cooperation Agency (former Japan Bank for International Cooperation (JBIC) ODA Operations) for their collaboration in data collection and their insightful comments on an earlier version of this paper. We are grateful to Decio Coviello, Marisela Montoliu Munoz, Tomás Serebrisky and Tina Søreide for their various suggestions and comments. We also thank many seminar participants in the Chaire Bernard Van Ommeslaghe Conference at the Université Libre de Bruxelles.
percent are: Westem/Central Africa, South Asia, Central America and Caribbean, South America, Eastern/Southern Africa, East Asia, and Central Asia / North Africa / Europe.

_B) Constructing an "asset index"_ The DHS do not ask about household income or consumption expenditures, but the DHS [E and III survey instruments do include two sets of questions related to the economic status of the household. First, households are asked about their ownership of various assets, such as whether any member owns a radio, a television, a refrigerator, a bicycle, a motorcycle, or a car. Second, they are asked about characteristics of their housing, namely whether electricity is used, the source of drinking water, the type of toilet facilities, how many rooms there are for sleeping, and the type of materials are used in the construction of the dwelling. There is substantial overlap in the questions asked in each country, but the precise list varies. The number of variables constructed from these questions is usually 15 or 16 but varies from 12 to 21 (last column of Table 1).

In order to use these variables to rank households by their economic status, they need to be aggregated into an index and of course the main problem in constructing such an index is choosing appropriate weights.<sup>5</sup> We use the statistical technique of principal components to derive weights. Principal components is a technique for summarizing the information contained in a set of variables to a smaller number by creating a set of mutually orthogonal components of the data. Intuitively, the first principal component is that linear index of the underlying variables that captures the most common variation among them.

If these assets were only to be used to used examine the impact of some other factor (e.g., maternal education) as a "control" for wealth in a multivariate regression we would not need to aggregate the variables (Montgomery, Burke, Paredes, and Zaidi, 1997) 6
cho et al. 2014), so that in 2005-2006, the financing of these services represented 1/3 of the budget of the program.

Employment and training programs are of particular interest due to their potential effects on individuals’ labor market outcomes and the long term earnings profile of program participants. As a result of the increase in funds provided to social services, the coverage of the potential demand for employment programs among CS beneficiaries increased from 24% in 2004 to 100% in 2007. We obtained access to individual records of participation in these programs from the provider of employment services for the years of 2004 and 2007. Two types of programs are available: those exclusively for individuals in CS families and programs where CS individuals where given preferential access. We study the effect of CS on the take-up of this last type of programs. To understand the impact of the supply expansion due to CS we disaggregate our analysis by cohorts of entry, depending on whether families entered the program before or after 2004.

# **3 Empirical Strategy**

Our goal is to estimate β from the following equation: where _Yi_ is the outcome of interest for family _i_ , _CSi_ is a dummy variable indicating whether the family participated in Chile Solidario, _Xi_ is a vector of controls (entering through function _f_ ( _._ )), and ε _i_ is an unobservable. β is the impact of the program on _Y_ which, in principle, can vary across individuals. Even if β does not vary across individuals, its estimation by ordinary least squares (OLS) may be biased if participants and non participants differ in terms of their observable and unobservable characteristics. Participants in CS are indigent, and therefore they are on average much poorer than those who do not participate in CS. It could also happen that, among the eligible, those who participate are the ones more eager to improve their situation. In order to estimate the causal effect of the program, we use a regression discontinuity design, exploiting the fact that the program eligibility rules imply that the probability that a household participates in CS is a discontinuous function of its CAS score. That is, in each municipality and time period, we compare outcomes of families just below (just eligible for entry at time t) and just above their respective cutoffs (see, for example, Hahn et al., 2001; Imbens and Lemieux, 2008; Lee and Lemieux, 2010).

**Selection of families and coverage** The CAS score is constructed from an instrument used to select families to several social programs in Chile, the Ficha CAS (see section 4 for details about Ficha CAS). The number of families to serve in each municipality was assigned in proportion of

9
19 property rights in the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPs), and standards in the TBT and the SPS agreements. Preferential Trade Agreements (PTAs), which have proliferated in recent years particularly as multilateral negotiations stagnate, increasingly cover non-tariff measures in addition to provide preferential market access. Horn, Mavroidis and Sapir (2010) divide PTA provisions in “WTO +”, those that relate to areas covered by the WTO agreements but where PTAs contain more stringent commitments, and “WTO extra” or “WTO x”, those that are covered by PTA rules but not by the WTO. They also identify those provisions that are legally enforceable based on the language used in the agreement. Following this classification, WTO (2011) surveys 100 PTAs signed between 1958 and 2011 by a group of countries broadly representative of WTO membership and that collectively cover over 90 percent of world trade. As shown in Figure 7, many NTMs that are regulated by the WTO are also regulated by PTAs, with more stringent provision that are generally legally enforceable. Areas include standards (TBT and SPS), anti-dumping and countervailing duties and intellectual property rights. In addition, a majority of PTAs cover several WTO-X areas, such as investment and competition policy.
**Table 4: Simulation of social tariffs for water**

||**Rea**<br>No. of<br>beneficiaries|**lity**<br>Total value<br>of subsidy|**Simu**<br>No. of<br>beneficiaries|**lation**<br>Total value<br>of subsidy|**Error of**<br>**inclusion**|**Error of**<br>**exclusion**|**Concentration**<br>**coefficient**|
|---|---|---|---|---|---|---|---|
|Metro area|10,000|4,000,000<sup>*</sup>||||||
|Federal<br>District|||794,579|19,074,216|73%|0%|+0.28|
|Buenos Aires|||369,916|11,239,721|10%|68%|-0.63|
|Mendoza|26,000|1,400,000|153,839|3,692,136|40%|48%|-0.18|
|Santa Fe|||147,965|6,214,530|40%|7%|-0.18|
|Chaco|7,000|41,500|69,244|2,077,320|1%|49%|-0.78|
|Salta|20,000|1,200,000|56,223|2,039,892|3%|57%|-0.68|
|Santiago|9,500|50,000|20,546|289,970|10%|73%|-0.38|
|Formosa|7,300|240,000|10,410|348,526|0%|81%|-0.80|
|Tucumán|9,500|250,000|8,879|594,277|29%|96%|-0.14|
|**Total**|**89,300**<sup>******</sup>|**7,181,500**<sup>******</sup>|**1,638,209**|**46,884,000**|**44%**|**73%**|**-0.15**|

_Notes_ :<sup>*</sup> Not yet fully utilized;<sup>**</sup> Excludes Santa Fe _Source:_ OPSM, May 2002

# **ELECTRICITY**

# **Provinces**

4.14 A national Tariff Compensation Fund has been established for some years in the electricity sector. It is financed from 60% of the revenues generated by a surcharge of US$0.024/Kwh on all electricity traded through the national wholesale market, which amounted to US$98 million in 2002. The Federal Electricity Council distributes these resources to the provinces on the basis of a formula that seeks to compensate for differences in the cost of electricity production across jurisdictions. The underlying principle is hence one of horizontal equity, which seeks to equalize the electricity tariff across the country.

4.15 Notwithstanding substantial divergence in income levels around the country, the fund is not designed to compensate for differences in ability to pay across provinces. It is therefore not surprising to find that the proportion of funds transferred to each province bares little relationship with the proportion of the country’s poor resident in each province (Figure 8). The majority of smaller provinces receive a much higher proportion of transfers than would be warranted with the extent of poverty in their jurisdictions. On the other hand, the three largest provinces receive scant resources in comparison with the extent of poverty that they contain. The most striking example is that of Buenos Aires 23
conscientiousness-related behaviors and experiences should eventually lead to more habitual behaviors and experiences, and personality change through bottom-up processes. Demonstrating more conscientiousness-related behaviors in the workplace, such as punctuality, responsibility, or orderliness, is desirable from both the employer’s and the individual’s perspective. A more punctual and responsible worker is more likely to be retained by the company and receive a higher wage. This may in turn lead workers to feel that they are living up to the expectations placed on them, promoting even more conscientiousness-related behaviors and experiences in the future.

Figure 1: Timeline of intervention <!-- Start of picture text --> Baseline sample: 386 Conscientiousness<br>Baseline survey intervention<br>• Treatment: 193<br>• Control: 193 April 2019 · F2F August 2019 · F2F<br>End-line survey Mid-line survey Eight weekly phone call<br>(371) (344) reminders<br>May 2020 · Phone January 2020 · F2F August–October 2019 · Phone<br><!-- End of picture text --> _Notes_ : This figure shows the timeline of the intervention for the treatment group. The number of respondents is in parenthesis. _F2F_ indicates the survey or intervention was done face-to-face, while phone surveys are coded with _Phone_ . See Appendix Figure A.1 for a more detailed version.

Figure 1 summarizes the timeline of the intervention and surveys. Appendix Table B.2 shows some basic characteristics of the sample for treatment and control workers. The average age of participants in our study was 36-37 years.<sup>18</sup> The vast majority were male and only around one-fifth had completed middle school. For most, this was not their first formal job and about one-tenth of the sample were recent migrants. We do not find any statistically significant difference in baseline measures of the Big 5 personality traits or household-level variables across the treatment and control groups with the exception of worker age: treatment workers were slightly younger than control workers but this difference is only marginally statistically significant at conventional levels. Applying a correction for multiple hypothesis testing eliminates any statistical significance in group differences.

Following Cumming (2014), we also report Cohen’s _d_ measures and their 95% confidence intervals in the last columns of Appendix Table B.2 for the differences between treatment and control groups. We find that Cohen’s _d_ are all smaller than 0.2<sup>19</sup> and that the confidence intervals include zero, suggesting that the differences between groups are not significantly

> 18Our sample is slightly older than the general workforce in Dakar. This is due to the company recruiting workers with some prior experience.

> 19This is the conventional cut-off value for Cohen’s _d_ below which effects are considered small or very small (Sawilowsky, 2009).

8