rafmacalaba/data-use-mentions-extended huggingface dataset:
contracts (Parrado, Dahlström & Lapuente, 2018). Moreover, discretion can be used to many ends depending on incentive structures set by organizational management practices and norms, politicization of the bureaucracy, or informal power relations. Unfortunately, with the notable exception of (Rasul & Rogger, 2015) neither of these considerations are taken seriously bringing considerable theoretical opacity to the debate.

# _b) Evidence_

There is a good number of high-quality research papers comparing auctions and negotiated procedures as a proxy for rule-bound versus discretionary decision making in public procurement, some exploiting natural experiments some others explicitly modelling the selection of procedure type to better identify causal links. Unfortunately, only one paper could be identified which opened up the black box of bureaucratic decision making and management in order to address the discretion versus rule dichotomy at its core.

The literature on the effects of using negotiated procedure or auction is divided on their relative merits. For complex products, negotiations appear to produce better outcomes. Exploiting a discontinuity in the rules governing procedure type selection in Italy, negotiated procedures lead to a higher likelihood of the same firm winning repeatedly while projects are delivered faster and larger firms win more often (albeit the latter two effects are present in a smaller neighborhood around the discontinuity) (Coviello et al., 2018). While the number of bidders decrease this does not decrease discounts. Research on social housing construction in Paris, France, come to similar conclusions regarding fewer bidders invited in negotiated and nonformalized procedures, however the authors also find lower prices (Chever & Moore, 2012). While the analyzed construction contracts could be considered as complex, further evidence from France using tendering data on small and simple purchases point at the same direction (Chever et al., 2017). Contrary to these findings, the analysis of German passenger rail line auctions, come to the conclusion that auctions led to 16% more trains for 25% lower prices compared to lines awarded in negotiated procedures (Lalive & Schmutzler, 2011). What drives these differences is
sup> However, this finding may also be a function of the set of countries that have been analyzed, which in turn has been limited by the

- availability of firm-level datasets. In fact, there is also evidence that countries tend to

> 15 They are large, of course, to those who lose their jobs.

> 16 Explanations could include political economy (along Anderson, 1992, lines) or the fact that it ignores the fact that countries such as China are even more unskilled-labor abundant than the developing countries on which research has centered (e.g., such as Morocco, Mexico, Chile, Colombia) – see Wood (1997).

25
in the short-term. The suspension rate increased sharply from 25 percent in 2019 to over 40 percent in 2020. The large difference between exit and suspension rates warrants the need to empirically explore the causal impact of the pandemic and government lockdowns separately on permanent closures and temporary suspensions among HBs. We do so in the next section.

Table A.1 provides detailed summary statistics of the main HBTC analysis sample (commune-month level) (Panel A). There were, on average, 6.7 temporary business suspension filings and 0.5 permanent closure filings in a commune per month during January 2018-January 2020 (pre-COVID-19 period). For the statistics related to suspension filings, note that here we count all filings observed in the data, which captures both singular and multiple filings per firm-year, instead of the annual aggregation of the number of firms filed for suspension at least once a year reported in Table 1. By extension, the average dormancy duration of firms that filed for suspension was 2.8 months in 2018 and 2.5 months in 2019, respectively. Compared with the average number of regular annual filings per commune of 92.5 in January 2019 (s.d. is 169.45), the business suspension rate equates over 7 percent of all HBs each month, while the closure rate is over half a percent, as confirmed in Table 1. There are strong heterogeneities in both business closure and suspension rates across sectors and firm size. On average, HBs exiting from the wholesale and retails industry accounts for more than half of the total business suspension and closure filings. The data also suggest a decline in the suspension rate as operating revenue grows (the smallest firms by revenue are most likely to file for business suspension). But the largest firms by the revenue quartile (above 75th percentile) are the most likely to file for business closure.

# **2.3 The Labor Force Survey**

The Labor Force Survey (LFS) is a nationally representative survey that is conducted on
carbon emissions depends on the number and types of vehicles and the intensity of use.

## **3.4.1 Income and vehicle ownership**

**Fact 6: As countries get richer, more households acquire cars and each household might acquire more than one car.** Figure 9 uses data from the International Road Federationfor 2017 to plot the number of passenger cars per 1000 inhabitants against country GDP per capita. Except for the poorest countries, there is a log-linear relation between country income levels and car ownership.<sup>7</sup> Using similar data for motorcycles, we show that there is no similar relationship between motorcycle ownership and GDP per capita.

> 7The correlation coefficient between the number of passengers cars and the log of GDP per capita is 81%.

17
disaggregated sectors for each country. The classification into about 40 sectors (roughly the 2- digit level detail of ISIC Rev3) coincides with the OECD Structural Analysis (STAN) database.<sup>6</sup> The other set of indicators in the dataset concerns productivity and its components. The data sources used for the analysis of productivity differ from those used for firm demographics in many countries. For productivity measures, data are needed on output, employment and possibly other productive inputs such as intermediate materials and capital services. Using these source data, indicators are calculated on labor productivity by industry and year, and on the decomposition of productivity growth into within-firm and reallocation components (see below).

# **Box 1 Main indicators available in the firm-level database**

The use of annual data on firm dynamics implies a significant volatility in the resulting indicators. In order to limit the possible impact of measurement problems, it was decided to use definitions of continuing, entering and exiting firms on the basis of three (rather than the usual two) time periods. Thus, the tabulations of firm demographics contained the following variables:

**Entry** : The number of firms entering a given industry in a given year. Also tabulated, where available, was the number of employees in entering firms. Entrant firms (and their employees) were those observed as (out, in, in) the register in time (t – 1, t, t +1).

**Exit** : The number of firms that leave the register and the number of people employed in these firms. Exiting firms were those observed as (in, in, out) the register in time (t – 1, t, t +1).

**One-year firms** : The number of firms and employees in those firms that were present in the register for only one year. These firms were those observed as (out, in, out) the register in time (t –
. As a diagnostic and monitoring tool at the system level, _Teach_ helps governments identify bottlenecks in service delivery, monitor the effectiveness of their policies, and focus efforts to improve teacher practices. As a professional development tool, _<mark>Teach</mark>_ <mark>can be used to identify individual teachers’ strengths and weaknesses and coach teachers to improve their practice.</mark> In this paper, we made two contributions to the literature. The first contribution is primarily a methodological one. To the best of our knowledge, this is the first paper to use item response theory (IRT) for a classroom observation tool to go beyond reliability and factor analysis by assessing the information each element provides as it relates to teachers’ latent ability. The second contribution is empirical. This study validates the use of _Teach_ scores for system diagnostics and monitoring with data from Punjab, Pakistan.

This paper is organized as follows. Section 2 presents the framework to validate the _Teach_ scores for system diagnosis. Section 3 provides evidence on the tool’s content and cognition. Section 4 provides evidence of coherence and correlation. Section 5 concludes with a brief discussion of our findings.

3
# EXECUTIVE SUMMARY

Public infrastructure investment has the potential to open new markets, lower private production costs, reduce transaction costs, and increase competition. Recent studies document public infrastructure's stimulating impact on private investment. Along side this growing body of evidence substantiating the importance of public investment in infrastructure for development is an accumulation of evidence that infrastructure investment in LDCs is suboptimal. The primary objective of this study is to isolate the factors influencing public infrastructure investment. Pooled cross-national and time-series data covering 27 low and middle income economies over the 1980-1986 period are utilized to assess the relative influence of the internal features of an economy, government objectives, specifically the extent of commitment to poverty alleviation, and external assistance on observed infrastructure expenditures. Of particular relevance to Bank operations is the estimated influence of the level of foreign assistance and alternative forms of foreign assistance (official development assistance, commercial bank lending and direct foreign investment) on infrastructure expenditures.

The analytical framework guiding the estimations is a four-sector general equilibrium model consisting of urban households, urban producers, rural households and a single government. The model's solution determines the optimal level and mix of public expenditures on infrastructure, human resources and consumption public goods, and the optimal mix of taxes on wages, domestic output and property. The model generates a set of reduced form equations for several forms of government expenditures and taxes. The reduced form equation for infrastructure spending provides the departure point for the empirical analysis. The determinants of central government expenditures and consolidated government expenditures are estimated separately. The internal features of the economy considered include basic structural characteristics of the economy, including the existing stock of infrastructure, level of development, population density, urbanization, the ruralurban balance, the labor force participation rate, and institutional development; factors reflecting macro balance and strategy orientation including the size of the foreign sector, the government's budget balance, the external balance, debt obligations, and commitment to poverty redress; and external factors including terms of trade shifts, and foreign savings flows. Sensitivity
inequality increases have a much larger poverty-raising effect, and hence policy makers may be more willing to accept a modest growth decline in exchange for a reduction in inequality.

Another way to gauge the relative importance of growth and redistribution from the poverty reduction viewpoint is to measure the respective contributions of growth and inequality shocks to the observed variation in poverty. This is the approach followed by Kraay (2005). Under lognormality, it follows from (13) that the variance of poverty changes can be approximated as In the general case of nonzero covariance between growth and inequality changes, absent any information on their mutual causal precedence, we can use the simplifying assumption that half the covariance can be attributed to growth, and the other half to inequality changes. In such case the share of the total variance attributable to the growth component is: However, to implement (15) numerically we need three more ingredients, namely the variance of growth, the variance of inequality changes, and the covariance of growth with inequality changes. Table 8 reports these statistics computed on the basis of two alternative datasets: the one used thus far in this paper (i.e., Dollar and Kraay 2002), and the Povmonitor<sup>22</sup> database. Figure 3 presents the respective scatter plots. Apart from coverage (much more limited in Povmonitor), the main difference between both databases is that the Dollar and Kraay income data is based on National Accounts, whereas that in Povmonitor is based on household surveys.

Inspection of Table 8 suggests that the different coverage of the two databases is of little consequence for the volatility of changes in the (log) Gini coefficient: in both cases the standard deviation is about 0.05. However, the volatility of growth differs more markedly across the two databases: in the survey-based Povmonitor data, the standard deviation of growth (0.06) is slightly higher than that of inequality changes, while the opposite happens in the National Accounts-based Dollar and Kraay data, in which
Finally, we find that—also contrary to normal patterns in consumption data—food consumption as a share of total household expenditures is very low in the South, despite being the region with the highest official poverty rate. Even more surprisingly, it seems that Engel’s law<sup>6</sup> does not hold in rural areas, where the expected negative correlation between food consumption shares and expenditure levels is hardly detectable.

When looking at the causes of consumption underreporting, three elements emerge. The first is the preprinted food-item list used in the consumption questionnaires. The 2008/09 survey included a list of 38 food items, 18 for purchased food and 20 for consumption of household produce. This list seems to be too restrictive to adequately capture dietary diversity and consumption patterns across provinces. In addition, the enumerators’ apparent tendency to underreport food transactions (including food purchases, the bartering of household produce, or food received in exchange for work) could have been facilitated by an overly restrictive questionnaire, which may not have left enough room to record all food-related transactions and which did not provide extra room for reporting additional food items not included in the printed list. Also, the fact that the pre-typed list included only primary agricultural commodities, but not prepared or packaged food, could have prevented the comprehensive reporting of food consumed in urban areas, where households devote a larger share of consumption to packaged food. The 2002/03 survey was also affected by similar problems, and this appears to have biased earlier poverty figures, particularly for Southern Mozambique.

The second issue is poor management of data-collection activities. Food consumption data in the IAF<sup>7</sup> 2002/03 and IOF 2008/09 were collected in a different manner than initially planned. Food transactions should have been reported by the households themselves through the use of a diary, in which every two days they would record the daily food transactions that had occurred; and three enumerator visits to each household were initially envisaged.
# **Understanding the Geographical Distribution of Stunting in Tanzania: A Geospatial Analysis of the 2015–16 Demographic and Health Survey**

# **Background**

Stunting, defined as being more than two standard deviations below the World Health Organization (WHO) Child Growth Standards median,<sup>1</sup> arises from a range of factors including low breastfeeding rates and malnutrition among mothers, inadequate health care or poor access to safe water and sanitation leading to a range of acute or subclinical infections in early life. Globally, around 155 million children are estimated to be stunted, and international targets have been established to reduce global stunting by 40% by 2025<sup>2,3</sup> as one component of achieving the SDG goal 2.2 of eliminating all forms of malnutrition by 2030.<sup>4</sup> However, the current average annual rate of reduction in stunting of 2.3% falls short of the 4% required if this goal is to be met.<sup>5</sup> Recent World Bank estimates suggest that the per capita income penalty incurred among contemporary workforces as a result of failure to eliminate stunting in their childhood amounts, on average across all countries, to around 7% of gross domestic product (GDP) per capita. In Sub‐Saharan Africa and South Asia, these figures rise to around 9% to 10%.<sup>6</sup> Several studies have shown that stunted growth during childhood entraps the future of children in a vicious circle of recurrent diseases, reduced human development<sup>7,8,9</sup> and lower earnings.<sup>10,11</sup> This, in turn, can increase the likelihood of being poor and even lead to the transmission of poverty to the next generation.<sup>12,13</sup> Like many low‐income countries, stunting is widespread in Tanzania. An estimated 2
# **Appendix:   Calibrating DGE estimates using survey-based self-employment data**

This appendix explains the benchmarking of the DGE model of Elgin and Oztunali (2012) to survey-based self-employment data. In Elgin and Oztunali (2012), employment in the informal sector, NIt has the following form: After transforming equation (1), AIt can be expressed as follows: Following Loayza and Rigolini (2011) and Fiess et al. (2010), data on self-employment provided by Elgin et al. (2021) can be used to define NIt and calculate AIt using equation (2). Following the earlier literature, α is assumed to be equal to 0.36 while δ takes the country average of the depreciation rates reported in Penn World Tables 9.1 (PWT). Following Ihrig and Moe (2004), γ is assumed to be 0.425. Capital stock (Kt) and formal employment (NFt) are obtained from PWT 9.1 and updated using the _World Development Indicators_ . Assuming a balanced budget for the government, τt is obtained as government spending in percent of GDP reported in PWT 9.1.

Rewriting the production function of the informal sector (YIt) using equation (2), we find YItto be a function of AFt and NIt: ∂YIt ∂YFt which gives ~~>~~ 0. Since > 0, it is possible that the DGE estimates will move ∂AFt ∂AFt procyclically in the presence of large shocks in formal productivity. However, when other types of shocks occur at the same time, YIt may not move procyclically. For instance, if NIt 29
in the gains from trade across households is negatively correlated with the log of per capita GDP (Figure 9). This is because, in the HIT data, poorer countries tend to have more heterogeneous income and consumption household portfolios.<sup>9</sup> Pooling all countries in our database, we plot the household-level gains from agricultural trade against the initial level of per capita household expenditure using a kernel non-parametric regression. Figure 10 shows the result of this exercise. In line with the stylized facts above, we find that the kernel slope is positive and steeps upward until the top percentiles of the developing world distribution of income and then becomes negative. Still, richer households tend to gain more from liberalization than poorer households. But it is the upper-middle class that stands to gain the most from agricultural tariff liberalization.

To quantify the extent to which the effects of trade vary for the poor vis-`a-vis the rich, we calculate the pro-poor bias in agricultural trade policy. This is the difference between the average welfare effect for the poor (defined as the bottom 20% of the income distribution) and the average welfare effect for the rich (the top 20%).<sup>10</sup> On aggregate, agricultural tariff liberalization would be slightly pro-rich, with the richest households gaining 2.64 percent and the poor 2.20 percent (see columns 4-6 in Table 2). There are 29 of the 54 countries in which tariff liberalization would have a pro-rich bias. Using data from six countries which are also included in HIT data set, Nicita, Olarreaga and Porto (2014) show that tariff protection is pro-poor. In this study, however, we find that their results do not generalize to all developing countries. In 24 countries in the HIT data set tariff protection is pro-rich (and liberalization, pro-poor).

One simple way to assess how much we can gain by exploiting the heterogeneity in the household surveys is to compute the gains from trade using aggregate price changes
figure 5). The components that received the lowest scores

**Figure 5. Benin’s Logistics Performance Index is one of the highest in West Africa**

<!-- Start of picture text --> 3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Logistics Performance Index<br>Senegal Benin Guinea Togo Nigeria Niger Cote d'Ivoire Gambia, The Ghana Liberia Mali Burkina Faso Guinea-Bissau Sierra Leone SSA<br><!-- End of picture text --> _Source_ : World Bank 2010.

by operators in the country were the quality of trade- and transport-related infrastructure (ports, railroads, roads, information technology) and the efficiency of the clearance process (speed, simplicity, and predictability of formalities) by border control agencies.

The time required to trade across Benin’s borders is longer the average experienced by coastal countries in West Africa but better than the average for Sub-Saharan Africa. The time necessary to comply with all procedures required to export goods from Benin is 30 days, versus 24 days in other coastal countries in West Africa. On average 32 days are required to import goods into Benin, above the average for regional coastal countries, at 28 days. The cost to export or import to Benin, at around $1,300 per container, is comparable with the costs faced by Nigeria, but considerably higher than the costs of

> 1 The Logistics Performance Index is based on a worldwide survey of operators on the ground (global freight forwarders and express carriers), providing feedback on the logistics ―friendliness‖ of the countries in which they operate and those with which they trade. They combine in-depth knowledge of the countries in which they operate with informed assessments of the global logistics environment.

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32 , Table 8 National saving rate: Alternative estimators, full sample (Dependent variable: Gross national saving/GND))

||a||ae|a||
|---|---|---|---|---|---|
|Estimator<br>Regression<br>instruments|OLS-CS<br>Levels<br>-|OLS-Pool<br>Levels<br>-|Within<br>Devia.<br>from mean|GMN-Diff<br>Differences<br>Levels|GMM-Syst<br>Levs - Diffs<br>Diffs - Levs|
|Lagged national saving rate|-|0.802<br>(34.2)|0.572<br>(19.054)|0.378<br>(6.143)|0.384<br>(6.650)|
|Real per-capita GNDI*|0.043<br>(5.012)|0.008<br>(3.009)|0.064<br>(5.255)|0.070<br>(1.501)|0.102<br>(2.685)|
|Real growth rate of per-capita GNDI”|0.878<br>(3.216)|0.285<br>(9.459)|0.271<br>(10.07)|0.249<br>(2.573)|' 0.447<br>(4.831)|
|Real interest rate* °|0.023<br>(0.288)|0.0121<br>(0.669)|0.016<br>=<br>(0.649)|-0.333<br>(-2.503)|-0.136<