rafmacalaba/datause-encoder-data huggingface dataset:
- 4 -

the equilibrium bid function is estimated by the Heckman (1979) and Lee’s (1978) two-step estimation method with a double selection process.

The remaining paper is organized as follows. Section II describes the traditional public procurement procedure and overviews the behavior of potential and actual bidders in our road procurement data. Section III develops the empirical methods and summarizes our used data. Section IV presents the main results and discusses some policy implications.

# **II. AN OVERVIEW OF PARTICIPATION DECISIONS BY BIDDERS AND AUCTIONEERS IN ROAD PROCUREMENT**

Public road projects are traditionally procured through the first-price sealed-bid competitive tenders. Notably, however, there are a variety of procurement systems involving different institutional elements. For instance, some auctions invite only domestic companies, while others accept international enterprises as well. But the most important difference in terms of auction design may be that some auctions adopt the strict lowest price criteria where only the price bids are compared, while others account for “quality,” which refers to anything the auctioneer cares about other than prices.<sup>3</sup> A typical example is prequalification. In largescale infrastructure projects, the technical evaluation prior to comparing prices is normally required in order to ensure the quality of the object or public work, the contract cost agreed, and the period designated.

From the auctioneer’s point of view, apparently, there is a tradeoff between price and quality if the latter is costly for firms to produce and attain; higher quality, higher prices. In addition, if quality is also costly to prepare before the auction, the optimal number of bidders that are allowed to enter would decline in the presence of increased entry costs, whence limiting

> 3 There are a certain amount of theoretical works on multidimensional auctions. They show that the two-stage bid evaluation system can implement the optimal mechanism maximizing auctioneer’s expected profits (Che, 1993; Cripps and Ireland, 1994). However, it is still far from applicable to practice.
ers in terms of developing their irrigation sub-sector (Riddell et al., 2006). At the same time, the use of drought-resistant crop varieties among smallholder farmers also remains low (Frenken, 2005).

All these factors heighten the risks of chronic food insecurity in drought-prone countries of Sub-Saharan Africa and raise the need for risk-management mechanisms, as drought shocks become common occurrences due to climate change. Yet, despite the strong correlation between drought risks and the prevalence of polygyny, as shown in Figure 2, there has been limited interest in analyzing this relationship more closely. This paper is the first to examine this issue formally.

# **3 Context, Data, and Descriptive Statistics**

We use Mali’s data to test our hypothesis that polygyny enhances households’ resilience after drought. Our data come from various sources, including the _Fourth General Census of Population and Housing (2009)_ , the _Enquête Agricole de Conjoncture Intégrée aux Conditions de Vie des Ménages (EAC-I)_ , and climatological data.

## **3.1 Context**

Mali is a landlocked, West African country located in the Sahel region, and along the polygyny belt that stretches from Senegal to Tanzania (Jacoby, 1995). It is bordered on the north by Algeria, on the east by Niger and Burkina Faso, on the south by Côte d’Ivoire and Guinea, and on the west by Senegal and Mauritania. Mali is among the hottest countries in the world. Its two main geographic areas, North and South, each face different climatic conditions for agricultural production. The northern area is more drought-prone and is in constant threat of desertification and population migration. The southern area, in contrast, is where the vast majority of its population is concentrated, deriving its livelihoods from rainfed subsistence agriculture and pastoralism.<sup>5</sup> About 65% of Mali’s land

> 5FAO, 2017.Socio-economic context and role of agriculture. Mali Country Fact Sheet. Available online at http://www.fao.org/3/a-i7617e.pdf.

10
9 The data used for the USSR are shown in the four tables of Appendix A. Appendix Table Al shows GNP of the USSR by sector of origin, based on the U.S. Congress JEC (or CIA) 1970-based constant price data for the 1960s and 1970s and their 1982-based constant prices for the 1980s. A distinction is made between market prices, also known as "established" prices, and txue factor costs, where the latter are arrived at by subtracting taxes, adding back subsidies, charging correct rates for depreciation and return on capital, and adding second economy provision of services. The analysis of service shares in total value added is based on these factor costs, which provide significantly higher service share estimatas. Specifically, in the most recent JdC calculation of GNP at factor costs in 1982 prices, the share of consumer services is nearly double that at market prices -- wore than half the difference being concentrated in housing services where subsidization is very high.

Appendix Table A2 shows USSR employment by sector, both including and excluding private agricultu.-e. The data are taken primarily from the U. S. Bureau of Census; and the estimates including agriculture are used in the comparison of the USSR and OECD countries. Appendix Table A3 shows employment by sector for each of the republics as well as for the USSR as a whole. These data were obtained from Goskomstat and the Center for Economic Forecasting of the Ministry of Economics for Easterly and Fischer 1992. They are the only employment data available for the republics and appear to exclude private agriculture, which would bias the Soviet data against the hypothesis of smaller service sectors. They al-e used in the comparison with the "world economies"."

> " These and accompanying data, including net material product in comparable prices, labor force, and capital stocks in comparable prices, by sector and by republic, 1970-90, are available from W. Easterly, World Bank, 1818 H St. NW, Washington DC 20433.
that would remain open were charging higher median fees relative to those that would eventually close in both 2010 and 2013.<sup>12</sup> In other settings, disagreement between different stakeholders contributed to the closure of project playgroups. “ _There was a conflict between the board members of the village education department and the village chief. The chief did not trust the board members and decided that the center would only receive support from the village government for one year, not indefinitely_ .” Thus, the end of the project presented considerable financial challenges to the playgroups. While many found ways to continue providing early childhood education, some struggled to find both the financial and human resources needed to sustain their programs. This situation was exacerbated by the fact that some teachers under the project received monthly honoraria (250,000 rupiah) that far exceeded typical wages in the villages for preschool teachers (50,000 rupiah). Others struggled to receive any regular compensation for their work at all.

# **IV. Methodology**

We examine five categories of predictors of sustainability for our main analytic sample of 245 project playgroups, which are summarized in Table 2 below.<sup>13</sup> These predictor variables were measured during the project period in 2010 and/or in 2013.

The first category of predictors is finance, measured using four variables: (i) amount of funding from the government, (ii) amount of funding from non-governmental sources, (iii) amount of funding from parents, and (iv) share of annual expenditure for teacher salaries.<sup>14</sup>

> 12 Median fees ranged from IDR 5,000 per month to IDR 10,000 per month. Rural households at the time reported a monthly wage of IDR 1.7 million as per Bureau of Labor Statistics (2016).

> 13 See Appendix Figures 2 to 4 for plots of distributions of some of these variables.

> 14 See Appendix Figure 5 for plots of distributions of teacher salaries.

9
fields. The model still leaves roughly 25 percent of the variation in oil output data unexplained. The error term in the model represents two primary possibilities. First, there exists some measurement errors as both oil output and RH data could not be reported or measured precisely. Second, the might be model specification errors whereby the relationship between oil output and RH are more complex than what is suggested by equation (2) as other factors (flare stack characteristics, wind conditions, etc.) might affect the RH-oil output relationship.

In Figure A.10 we plot the residuals of the estimation in Table A.8 Column 2 as an alternative validation of model. We provide additional plots for comparison. In the top panel, the linear model residuals are plotted against a normal distribution. The linear model residuals are estimated from a regression of oil output on radiant heat and gas to oil ratio in levels. In the bottom panel, the analogous residuals from the model in logs are plotted against a normal density. We observe that while the linear model residuals deviate substantially from the normal distribution, the nonlinear model residuals are a good approximation of the normal distribution. The normality of the nonlinear residuals supports the premise that the model of Table A.8 Column 2 is appropriate for calibrating radiant heat to oil production.

# **A.4.4 Inferring oil production**

To compute the level of oil production for each flare observation, we start with the results of the estimation of equation (2). We impute log production for 200 draws from the estimated joint distribution of _β_<sup>ˆ</sup> 1 and _β_<sup>ˆ</sup> 2. After converting to the level of oil production for each observation for each draw, we compute the daily sum of all draws for all Daesh flares. Last, we compute the expectation (average), 5th, and 95th percentiles of total Daesh production each day over the 200 draws. The implications of this method are notable in the results: confidence intervals are asymmetric with greater mean than median because the exponential of a uniform random variable is positively skewed.

42
||**Pr(F=1,M=1)**|**0.94**|**Pr(F=1,M=0)**|**0.03**|**Pr(F=0,M=1)**|**0.02**|
|---|---|---|---|---|---|---|
||**dy/dx**|**X**|**dy/dx**|**X**|**dy/dx**|**X**|
|_Violence_|||||||
|public order problem|-0.002|0.71|-0.007|0.71|0.007|0.71|
|guerrilla attack rate|0.003|1.46|-0.001|1.46|-0.002|1.46|
|homicide rate|-7.10E-05|51.81|7.56E-05|51.81|-2.08E-05|51.81|
|_Municipality controls_|||||||
|grouped settlement|0.048|0.37|-0.020|0.37|-0.015|0.37|
|rural sparse settlement|0.035|0.09|-0.011|0.09|-0.015|0.09|
|Oriental Region|-0.008|0.25|0.015|0.25|-0.007|0.25|
|Central Region|0.002|0.16|0.010|0.16|-0.010|0.16|
|Pacific Region|0.017|0.06|-0.004|0.06|-0.008|0.06|
|altitude|-5.43E-05|466.97|4.13E-05|466.97|-6.21E-07|466.97|
|altitude2|2.26E-08|633,344.00|-1.65E-08|633,344.00|-3.95E-10|633,344.00|
|percentage of people in lowest two strata|-0.017|0.64|0.017|0.64|-0.003|0.64|
|index of quality of life|-0.004|25.81|0.002|25.81|0.001|25.81|
|capture rate|-8.71E-07|458.61|2.78E-06|458.61|-1.97E-06|458.61|
|_Household/ head variables_|||||||
|age|0.005|45.38|-0.002|45.38|-0.002|45.38|
|age2|-3.44E-05|2,227.94|1.97E-05|2,227.94|5.59E-06|2,227.94|
|married|0.019|0.28|-0.015|0.28|0.001|0.28|
|female|-0.052|0.26|0.020|0.26|0.016|0.26|
|some or complete primary education|0.004|0.54|-0.005|0.54|0.002|0.54|
|some secondary or more education|0.023|0.14|-0.017|0.14|-2.11E-04|0.14|

Results-Table 5: Marginal effects after bi-variate Probit on holdings of fixed and mobile assets, definition (1) of fixed assets _Note: Table reports the marginal effects after the regression in the previous Table. Dependent variables are the participation in fixed assets, and mobile assets, respectively, using the asset definition (1) of Table 2._ _Default household education level is ‘none’, default employment ‘none or domestic worker’. Default region is Atlantic, default rurality degree is ‘urban’._ _For qualitative variables, dy/dx marks the percentage increase in participation after a discrete change from the default situation._ The dynamics are largely the same as observed previously, but we note some differences when the mere participation in assets, is considered. The public order dummy continues to have the sign predicted by our model, but stays below significance in every specification. Also the coefficient on the homicide rate behaves as predicted, but with varying significance. It reduces the participation in mobile assets (significant for definition (1)), and increases fixed participation (significant for definition (2)). The signs are robust to the asset definition, and can be shown robust to different combinations of control variables. All violence variables are jointly significant for both asset definitions.

The guerrilla attack rate now shows a new effect. More guerrilla attacks seem to make it more likely that people participate in both assets at all, independent of the asset definition. Given that the result is more pronounced for the broader definition, it could 39
different results from those reported above. While in Eastern Europe foreign bank lending growth was slower than that for domestic private banks during the crisis, in Latin America there is no strong evidence that foreign banks retrenched their lending significantly faster than domestic private banks. In contrast, we continue to observe that in Latin America, governmentowned bank loan growth exceeded that of other bank types during the crisis. This did not occur in Eastern Europe.

_Corporate, consumer, and residential mortgage loan growth_ Table 4 shows regressions for the growth rate of corporate loans among banks in Eastern Europe (columns 1-3) and Latin America (columns 4-6). In Eastern Europe, before the crisis, loan growth among foreign banks exceeded that for domestic banks (by between 5 and 12 percentage points, depending on the specification). But during the crisis, foreign bank corporate loan growth fell more sharply, by between 27 and 35 percentage points.

In the case of Latin America, there is generally no significant difference in corporate loan growth for foreign and domestic banks either before or during the crisis, except in model 6 which indicates that foreign banks’ lending growth was slower than for domestic banks in 2009. On the other hand, government bank lending in Latin America was slower than that for domestic banks before the crisis (by between 10 to 14 percentage points), but the reverse was true during the crisis, when government bank corporate loan growth exceeded that for domestic banks by between 22 and 35 percentage points, depending on the specification. In other words, government banks stepped up their lending to corporations in Latin America during the crisis, relative to both other banks and to their pre-crisis lending. No such pattern is visible in the case of government banks in Eastern Europe.

14
program was accompanied by the launch of a prospective impact evaluation designed to measure these impacts (Gibson and McKenzie, forthcoming).

Between October 2007 and April 2008 we conducted baseline surveys of households and communities in Tonga and Vanuatu before workers left to work in New Zealand. To construct a baseline, we worked with officials to identify households with RSE workers, households with members of the RSE work-ready pool who had not yet been selected, and then randomly selected households in the same villages which had no applicants to the program. In Tonga our sample had near national coverage given the wide geographic participation in the program, with the baseline survey covering 448 households containing 2,335 individuals in 46 villages. Vanuatu was a more challenging country to survey in, with the combination of rugged geography and high transportation costs making it infeasible to survey in all islands: we therefore limited the evaluation to three islands from which we believed there was a high chance of workers coming. Ultimately our baseline survey covered 456 households containing 2,173 individuals in 48 villages or communities.

We then re-interviewed these same households 6, 12 and 24 months later. The surveys collected detailed data on household income, expenditure, labor supply, subjective well-being, and the migration experience. Attrition was extremely low in Tonga, with every follow-up including at least 98 percent of the baseline households. Attrition was higher in Vanuatu, averaging 15-16 percent in each of the first two follow-ups, and 24 percent in the last follow-up. Bounding approaches are used to show robustness of our main results to this attrition.

To measure the impact of participating in the RSE on households, we use propensityscore matching to identify a group of households with RSE migrants and a group of households without RSE migrants that are similar in terms of a rich set of baseline characteristics. In particular, we are able to include a large number of characteristics that communities and employers are likely to have paid attention to when selecting workers, as well as characteristics of the household that may have determined the decision of whether to apply: this includes demographic variables, the health, education and labor market experience of male workers, household networks in New Zealand, household wealth, geographic location, and past income over the last two years. Using this information, we pre-screen the sample to identify comparable households.

21
39

# CHAPTER V PER CAPITA PRIVATE CONSUMPTION

The consumption of goods is the basis for economic welfare (Deaton, 1986).35 Therefore, at the individual level, per capita private consumption, rather than per capita GDP, is preferred as an indicator of welfare level. Glewe (1988)36 adds that using consumption rather than income data is also supported by the argument that the former is a better indicator of life-cycle welfare than the latter because while income may fluctuate over short periods of time, consumption is allocated more evenly ("smoothed") over time.

Private consumption is the market value of all goods and services purchased or received, including income in kind, by households and nonprofit organizations. It excludes purchases of dwellings but includes imputed rent for owner-occupied dwellings. Note, however, that per capita private consumption does not capture important non-consumption measures of welfare discussed earlier, such as life expectancy, infant mortality, and literacy. It is therefore important to look at both social indicators, particularly those of health and education, as well as private consumptions to gain a broader understanding of the standards of living of individuals and households.

> 35Angus Deaton, "The Measurement of Welfare: Theory and Practical Guidelines," ISMS Working Paper No. 7 (Washington, D.C.: World Bank, 1980).

36Paul Glewwe, "The Distribution of Welfare in C6te d'Ivoire in 1985," LSMS Working Paper No. 29 (Washington, D.C.: World Bank, 1988).
BPC transfers go to the first MIPP quintile. Also, unemployment benefits slightly favor the first quintile and then gradually decrease. 36F<sup>37</sup> There are, however, other programs that are more concentrated in the middle of the distribution. Salário Família, for example, is concentrated in the second and third quintiles (totaling 76% of the benefits), while Abono Salarial is concentrated in the third and fourth quintiles (receiving 68% of the benefits).

Preschool and primary education benefits tend to be concentrated in the bottom of the distribution. Over 60% of the benefits are received by the first two MIPP quintiles (Figure 10 panel b). Moreover, their benefits decrease as income increases and are very low for the wealthiest quintile. About 27% of upper secondary benefits are received by the first MIPP quintile, with the benefit share declining with income and reaching 6% in the top quintile. Tertiary education benefits stand in stark contrast. The share of benefits is positively correlated with income: about 35% of the benefits go to the top quintile and less than 10% to the lowest income quintile.

**Figure 10. Concentration of transfers by quintile of per capita market income plus pensions (% of total) a.Direct transfers b. In‐kind transfers**

<!-- Start of picture text --> 70<br>80<br>70 60<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10<br>0 0<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of per capita MIPP Quintiles of per capita MIPP<br>Education Primary Education Pre School<br>Bolsa Familia Rural Pension<br>Education Young Adult Education Upper Secondary<br>BPC Other<br>Health Benefits Education Tertiary<br>Unemployment Benefits Salario Familiar<br>Market Income Pensions<br>Abono Salarial Market Income Pensions<br>Concentration shares (% ) Concentration shares (% )<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

37 Given the high level of labor informality in Brazil and the strict unemployment benefit rules, one might be surprised by the fact that the quintile with the highest concentration share (26.2%) in terms of the unemployment insurance (UI) is the first one. It is important to keep in mind that the first MIPP quintile captures low-income individuals, including currently unemployed individuals that could have been formally or informally employed. Since the unemployment benefit is included in the disposable income concept, but not in the MIPP one, it is plausible to observe in the first MIPP quintile a (high-skilled) unemployed individual who is a UI recipient. When replicating the analysis of the concentration shares using the disposable income, we find that the quintile with the highest share in terms of the UI is the second one (23.24%), what reflects the more “intuitive” results of concentration of UI benefits in other quintiles of the distribution.

27
# Appendix 3 – Gender heterogeneity

_Table A3.1 Intention‐to‐treat effects of the original intervention on main socio‐economic outcomes by gender_

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||**ITT for**<br>|**males**<br>|**ITT for femal**<br>|**es**<br>|Coefficient<br>|
||(no controls)|(controls)|(no controls)|(controls)|equality (2)vs(4)|
|||_Main out_<br>|_comes – Socioeconomic_<br>|||
|Employment|0.034|0.020|0.080|0.084|0.064|
|index|(0.764)|(0.847)|(0.203)|(0.176)|(0.595)|
|Consumption<br>index|0.056|0.028|0.116*|0.110|0.082|
||(0.574)|(0.783)|(0.098)|(0.117)|(0.476)|
|Savings,|0.387***|0.349***|0.210***|0.209***|‐0.140|
|investment||||||
|and<br>debt||||||
|index|(0.000)|(0.000)|(0.001)|(0.001)|(0.167)|
|Business|0.082|0.090|‐0.022|‐0.025|‐0.114|
|skills index|(0.263)|(0.242)|(0.736)|(0.716)|(0.241)|
|Observations|555|547|968<br>948|||

_Note: P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, number of buildings at baseline, and exposure to conflict events between baseline and endline. Column (5) reports tests for coefficient equality between estimates from males and females in column (2) and (4). Displayed are differences of coefficient p‐values of the test in parenthesis._ 43