rafmacalaba/datause-encoder-data huggingface dataset:
to contribute to this literature, this paper report findings from an impact evaluation of Bangladesh’s e- procurement system. It poses the research question: Does electronic procurement, in particular electronic tendering and contract award, improve value for money (i.e. economy), access to public tenders and administrative efficiency? If yes, under which conditions?

6. When the Government of Bangladesh (GOB) introduced e-procurement in 2011, the country provided both a promising and challenging context. Procurement-related violence frequently made the headlines of newspapers, as local “strongmen” connected to insider firms systematically used armed gangs to physically hinder competing bidders from submitting paper-based bids.6 Part of e-procurement’s promise was that it could cut through such practices and drastically increase access to public tenders. At the same time, limited administrative capacity and high staff turnover made introducing e-procurement challenging (World Bank, 2002).

7. Crucially to our claim of novel insights, the scope of e-procurement in Bangladesh goes beyond most if not all such systems in low and middle-income countries. It digitizes the entire contracting process, from advertisement to contract award, including buyer-bidder communication, bidder registration, bid submission, and bid evaluation, with no parallel “paper trail”. Hence, it is a fully functional transactional system. By contrast, most other e-procurement systems only provide for online advertisement and submission of tenders, but the “back-end” of tender evaluation and award remains largely paper-based (OECD, 2016).

8. This impact evaluation focuses on the adoption of e-procurement by the three government agencies that are jointly responsible for most7 of Bangladesh’s public works procurement: the Local Government Engineering Department (LGED), the Roads and Highways Department (RHD), and the Bangladesh Water Development Board (BWDB). These three agencies were also the first to introduce e-procurement in Bangladesh, starting in FY2012-13.

9. Our primary data source is administrative data on public procurement. We obtained detailed electronic procurement data for all electronic tenders of BWDB, LGED and RHD from Bangladesh’s Central Procurement Technical Unit between FY2011-12 and FY2017-18, a total of 69,240 tenders for 185 district-level procuring entities.8 To obtain equivalent data for paper-based tenders, we surveyed paper-

> 6 In 2014, Bangladesh ranked 145th out of 175 on TI’s Corruption Perception Index. Numerous high-profile, high-value corruption scandals in public procurement further support the perception that corruption is a widespread problem. 7 Based on the number and value of contracts.

> 8 Bangladesh has 64 districts, but each agency does not have an office in each of these districts. For example, BWDB does not have offices in districts without any significant rivers or canals.

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of a futures market. Of course, not all of the risk can be shifted as there is never a perfect relationship between cash and futures markets. There is always some _residual risk_ associated with the hedging process. This residual risk (the price risk that can not be shifted to futures markets) is commonly referred to as the basis risk. Put another way, the basis risk is the price variation in the physical cash market that can not be explained by futures price variation. A low level of variation in the basis implies that there is a strong price relationship between cash and futures markets and viable risk management strategies can be devised. A high level of basis variation indicates that the relationship between the physical cash market and the futures market is not strong and that not enough risk can be shifted to make the hedging operation worthwhile. This section examines the basis relationship between Polish wheat prices and prices of wheat on the CBOT and LIFFE.

# **3.1. Relationships between Polish Wheat Cash Prices and** CBOT **and** LIFFE **Wheat Futures Prices**

Ordinary least square regression was used to assess the correlation between cash prices in Poland and the corresponding futures prices on the CBOT and the LIFFE. The equation used is of the following form: s(t) = a + b* f(t) where: s(t) is the cash price for wheat in Poland at time t, and f(t) is the futures price for wheat in CBOT or LIFFE at time t. We used three forms of the variables. First, the variables were expressed in levels. Second, the variables were deseasonalized to assess the impact of seasonality on the relationship between futures and cash prices. The deseasonalized prices were created by regressing each of the prices on twelve monthly dummies plus a time trend. The estimated residuals from that regression are the deseasonalized prices. Third, the variables were differenced.

The adcdusted R squared can be viewed as one way of measuring the residual risk inherent in the basis. That is, the adjusted R squared indicates the percentage of the variability in cash prices that is not explained by the futures prices. The percentage of the cash price changes that is unexplained (1-R squared) is an estimate of the basis risk. The higher the unexplained variability, the lower the value of the adjusted R squared (the higher the basis risk), and the lower the usefulness of the particular wheat futures contract in hedging Polish wheat cash price risks.

The estimation of the R squared using the regression analysis described above was performed for the overall period 1990 to 1995 and for each individual year. R squares were computed for both calendar and crop years.

# **3.1.1. Correlation of Prices in** US **Dollar Terms**

Polish and LIFFE wheat prices were converted to US dollar equivalent prices. In addition to the correlations with prices in levels, the impact of seasonality on prices was assessed by using a deseasonalized data series as well as the first differences of the respective price series. Correlations See Labuszewski and Nyhoff(1988), and Smith, Smithson and Wilford (1989).

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earlier in the Northeastern states. While most regions have school participation rates of 15-17 year olds that exceed 80 percent, all Northeastern states show rates below 80 percent, with the exception of Bahia. Further, the share of 10-17 year olds working and studying exceeds 25 percent in several states in the Northeast (Piauí, Ceará, Maranhão), unlike in the rest of the country. This is not to say that youth in other states do not have close connections with institutions, as shown by the difficulty of labor force attachment by youth in the Southeast, where they both enter the workforce early (about 22 percent of 10-17 year olds are working) and they face the highest unemployment rates.

The North and Northeast do not fare poorly in all indicators, though, as they show much greater rates of political participation than do youth elsewhere. More than half of 16-17 year olds voted in the 2002 elections in four Northern (of 7) and three Northeastern (of 9) states. The voting rates rarely exceeded 35 percent in the other regions.

# _Brazilian Child-Youth Well-Being Index_

To capture how well each state in Brazil is preparing its youth of tomorrow, the YWI is expanded to include measures of investment in today’s children. This expansion addresses some of the methodological issues discussed above, such as additional variance in the health category, with the addition of two new indicators.

The Child-Youth Well-Being Index (C-YWI) closely tracks the YWI (Figure 4), suggesting that the well-being of youth in each state in the near future will continue to rank similarly to that of this generation (correlation coefficient of 0.91). The five states with the best YWI scores – Federal District, Santa Catarina, Goiás, São Paulo, and Minas Gerais – have the top C-YWI scores, though the ordering has slightly changed (Table 4). Similarly, the bottom seven scores – Pernambuco, Alagoas, Amapá, Maranhão, Amazonas, Piauí, Paraíba, Bahia, and Acre – have the lowest C-YWI scores, as well. While most of the rankings are similar, there are a few notable changes when including the well-being of children.

The biggest change is the state of Rio de Janeiro, which moves from a ranking of 14 to 6 when the additional variables are added (Table 4). The primary factors behind this movement are in the health and institutional connectedness categories. In health, the poor AIDS rates are off-set by low infant mortality and low neo-natal mortality rates.

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9 measure of the value of the opportunity set faced by people in that type. In other words, a hypothetical situation of equality of opportunity would require that: Using the superscript _k_ to indicate the type to which individual _i_ belongs, a typical element of the income vector **_y_** is denoted yik . The counterfactual distribution in which each individual is assigned the value of his or her type’s opportunity set is then simply the _smoothed distribution_ corresponding to the k 14 vector _y_ and the partition _П, i.e_ the distribution obtained by replacing yi with μk _,_ ∀i, k . Denoting that distribution as �μik� , Ferreira and Gignoux propose a very simple measure of inequality of opportunity, namely I��μik�� , where _I()_ is the mean logarithmic deviation, also known as the Theil (L) index. Among inequality indices that use the arithmetic mean as the reference income, this measure is the only one that satisfies the symmetry, transfer, scale invariance, population replication, additive decomposability and path-independent decomposability axioms (Foster and Shneyerov, 2000). This is the empirical measure of inequality of opportunity used in the income and expenditure survey sample in Section 5 below.

The mean log deviation is not, however, suitable for use in the Demographic and Health Survey sample. As discussed in the next section, the DHS surveys do not contain credible measures of income or consumption. It does however contain information on a number of assets and durable goods owned by the household, as well as dwelling and access to service characteristics. Following Filmer and Pritchett (2001), it has become standard practice to use a principal component of these variables as a proxy for household wealth. As a principal component, this wealth index has negative values, and its mean is zero by construction, so that the mean log deviation is not a suitable measure of its dispersion.

In our DHS sample, we therefore follow Ferreira et al. (2011) in using the variance of predicted wealth from an OLS regression of the asset index on all observed circumstances in **_C_** as our measure of inequality of opportunity. The essence of the rationale for this choice of measure is as follows.<sup>15</sup> We tend to think of advantage (in this case the wealth index _w_ ) as a function of circumstances, efforts, and possibly some random factor _u_ : Although circumstances are exogenous by definition (i.e. they are factors beyond the control of the individual and are hence determined outside the model), efforts can be influenced by circumstances: For the purposes of simply measuring inequality of opportunity (as opposed to identifying individual causal pathways), it suffices to estimate the reduced form of the system (2)-(3). Under the usual linearity assumption, this is given by:

> 14 See Foster and Shneyerov (2000).

> 15 This discussion draws heavily on Bourguignon et al. (2007a) and Ferreira and Gignoux (2011). Readers are referred to those papers for detail.
riches, no refinement, no comfort, and no benefit to those who may be possessed of wealth.” (Colquhoun, 1806, p.7).

The word “poverty” often appeared in both English and French literary works of the time. Measured by the incidence of references to “poverty” in English-language _Google Books_ , the peak of the First Poverty Enlightenment was 1787. There are 204 books listed as containing a reference to poverty in the database for 1787 used by the _Viewer_ —representing 0.005% of all words that year, which was the highest incidence in a single year over 1700-2000. The 204 books containing wide-ranging references to poverty, and no single volume dominates, though the largest count is found in Samuel Johnson collected works (published after his death in 1784). Dr Johnson wrote (often amusingly) about the foibles and hypocrisies found in how people (often rich people) think and talk about poverty. While an awareness of poverty is certainly evident in these writings, it is by-and-large an accepting and unchallenging awareness. While the first Poverty Enlightenment saw far greater attention to poverty in the literature, it was not associated with widespread empathy for poor people as fellow citizens on the part of those writing books, and it was not yet widely accepted that there was any public responsibility for poverty.

Nor did the economics of the 18th and 19th centuries offer any serious challenge to the view that poverty was to be largely accepted. Workers were often equated with “the poor.” Some economists saw poverty as an essential condition for economic development. No doubt it was agreed that rising real wage rates of the proletariat would reduce poverty, but it was argued that this would undermine wealth accumulation by reducing labor supply and (in the Mercantilist schema) making exports uncompetitive, and even corrupting the values of workers as they aspired to luxury goods.<sup>13</sup> Thomas Malthus famously saw ecological disaster ahead, with poverty and famine as the only check against rising population. It does not seem that even Adam Smith—widely seen at the time to be far more optimistic than Malthus about the scope for overall social progress—entertained much hope that the fruits of economic development would be equitably distributed:<sup>14</sup>

> 13 Muller (1993) provides an interesting discussion of these and other views held in the late 18th century. The Mercantilists saw foreign trade as a zero-sum game, so anything that reduced a country‟s trade balance would reduce its share of global wealth. 14

This is not to say, of course, that Smith cared little about poverty. Unlike some of his peers, Smith put weight on attainments of both the basic consumption needs of society as a whole in assessing national wealth, not just the luxuries available to the rich; see the discussion in Muller (1993, p.58). Also see Himmelfarb‟s (1973) discussion of Smith‟s views relative to others around the time, including Malthus.

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Policy Research Working Paper 6741

# **Abstract**

Following a decade-and-a-half stall, fertility in Rwanda dropped sharply between 2005 and 2010. Using a hierarchical age-period-cohort model, this paper finds that the drop in fertility is largely driven by cohort effects, with younger cohorts having substantially fewer children than older cohorts observed at the same age. An Oaxaca-Blinder decomposition is applied on two successive rounds of the Demographic and Health Survey. The findings show that improved female education levels account for the largest part of the fertility decline, with improving household living standards and the progressive move toward non-agricultural employment being important secondary drivers. The drop in fertility has been particularly salient for the younger cohorts, for whom the fertility decline can be fully explained by changes in underlying determinants, most notably the large increase in educational attainment between 2005 and 2010.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tbundervoet@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
High levels of political influence may determine the lower governance levels of the Board vis-àvis the CEO. Whereas the selection and appointment of the directors to the Board may not be based on strict meritocratic rules, the selection of CEOs is restricted in the majority of the cases to pre-determined criteria. This includes, but is not limited to, the need for a professional degree and a given level of sector expertise.

Similarly to other cases, T2 levels in both Board and CEO show more homogeneity than companies in T3. Companies such as ESSAP, ANDE, and EMCALI do not have a Board of Directors. Its decision-making body is individual, a President or Director appointed by political authorities.

<!-- Start of picture text --> Board Competitiveness CEO Competitiveness<br>Source: LAC SOEs Governance Database, The World Bank, 2009. Source: LAC SOEs Governance Database, The World Bank, 2009.<br>CEO BOARD<br>25 40<br>35<br>20<br>30<br>15 25<br>20<br>10<br>15<br>10<br>5<br>5<br>0 0<br>T1 T2 T3 T1 T2 T3<br>THRESHOLD LEVELS THRESHOLD LEVELS<br>NUMBER OF COMPANIES NUMBER OF COMPANIES<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2<br>.2<br>0<br>0<br>T1 C R PER T2 D R ECU BZL ARG BOL URU COL JAM T T MEX PAR T1 COL BOL ECU JAM URU MEX D R T2 PER T T ARG PAR C R BZL<br><!-- End of picture text -->

# **COMPONENT 3: MANAGEMENT/STAFF**

This index measures the composition and characteristics of the enterprise’s staff by levels of education, type of training, legal status, salary and benefit levels, hiring and incentives. Employees are a central part of SOEs of the infrastructure sector. They may become a filter to political decisions as a professional and well organized bureaucracy can oppose measures that hinder their career prospects.

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– on average, about 12% of the grid cells included in our sample experienced a drought, while approximately 6% recorded abundant rainfall conditions.

# 3 Empirical specification

3.1 Modelling the impact of extreme weather on water supply interruptions To analyze the relationship between water shortages and weather variability, we look at the impact of rainfall and temperature within a location defined by a 0.5-degree grid cell. However, average temperatures and precipitation levels may be endogenous given that firms may choose to locate in areas of moderate precipitation and temperature. Thus, rather than focusing on average rainfall, our main variables of interest capture the frequency of rainfall shocks, specifically, unanticipated deviations in annual amounts of rainfall from long-run local averages. While the relationship between firm outcomes and average weather patterns may be endogenous, shocks to such longrun averages should be exogenous. Before calculating rainfall shocks, the raw rainfall data are converted to the Standardized Precipitation Index (SPI), to account for the non-normal distribution of rainfall. Positive rainfall shocks are then defined as periods when the SPI has a value of 1.5 or higher; i.e., situations when the normalized rainfall data are 1.5 standard deviations above the longrun mean for that grid cell. Negative rainfall shocks are situations in which the SPI has a value of -1.5 or less. The +/-1.5 SPI thresholds were deliberately chosen to analyze how drought conditions impact water outages for firms; according to the climatology literature, an SPI value of -1.5 or lower is reflective of drought conditions (McKee et al., 1993). Two dummy variables are used to capture the impact of drought and of large positive rainfall shocks. The first model we estimate is outlined in equation (1) below: WaterOutage��� The dependent variable, WaterOutage��� is the number of water outages per day experienced by firm _i_ in location _r_ and sector _j,_ in a typical month. Large_Pos��� is a dummy variable equal to one if the SPI is above 1.5, Large_Neg��� is a dummy variable equal to 1 if firm _i_ experiences drought conditions, i.e., an SPI of -1.5 or less. The specification accounts for the following firm7
larger transmission of the wealth effect to the foreign country. Overall, trade liberalization leads to a higher volatility of output and to a lower volatility of consumption. The ratio between the standard deviation of consumption and the standard deviation of output goes from 0 _._ 4676 under trade frictions to 0 _._ 4167 with free trade. Figure 4 shows the sensitivity of the consumption volatility over that of output to a variation of the degree of trade integration. Deeper international trade integration improves consumption smoothing.

## **6.3** **_Sensitivity analysis_**

Hereafter, we analyze the sensitivity of the main results to the variation of the elasticity of substitution between domestic and foreign goods ( _σ_ ). As in Heathcote and Perri (2002), we try both _σ_ = 1 _._ 5 and _σ_ = 0 _._ 5. As we can see from tables 7 and 9 in the appendix, in front of a productivity shock to the Home country, the effects of capital account liberalization on the volatility of consumption and on the volatility of consumption relative to that of output are weaker in the high elasticity case than in the low elasticity case. As we can see from tables 8 and 10 in the appendix, also the effect of trade liberalization on the volatility of consumption relative to that of output is lower in the high elasticity case than in the low elasticity case. With a higher elasticity of substitution between domestic and foreign goods, prices are less reactive to the domestic productivity shock, this implies a lower volatility of the main variables of interest. However, qualitatively the results do not change.

Then, we discuss how the main results change when we modify the value of the parameter of labor disutility ( _η_ ). We first set _η_ = 1 _._ 5 which implies a higher Frisch elasticity of labor supply equal to 2 .Then we set _η_ = 2 _._ 5 which implies a lower Frisch elasticity of labor supply equal to 0 _._<sup> ̄</sup> 6. For a same productivity shock, the effects of trade and capital account liberalization on the volatility of consumption relative to that of output are slightly stronger for a less elastic labor supply. When labor is relatively more inelastic, it becomes more costly for the agents to adjust labor efforts to insure themselves against productivity shocks. Therefore, they might not be able to dampen fluctuations through labor movements. However, the main results do not seem to be very sensitive to this parameter, as we can see from Tables 11, 12, 13 and 14 in the appendix.

# **7 Conclusions**

In this paper, we use a 2 country, 2 goods, international real business cycle incomplete market model built upon Heathcote and Perri (2002) to study the relationship between trade integration, capital account liberalization and macroeconomic volatility in emerging markets. Capital liberalization is modeled trough the relaxing of a foreign borrowing constraint a’la Kiyotaki in the Home country whereas trade integration is modeled by a cut in quadratic iceberg trade costs.

In front of a productivity shock, an exogenous relaxing of the borrowing constraint implies that 15