rafmacalaba/data-use-mentions-extended huggingface dataset:
Figure 28. Cumulative public and private investment per capita, 2004-2014 <!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Non-resource-rich<br>Resource-rich<br>GFCF (public) GFCF (private)<br>Constant 2015 thousand US$<br>Equatorial Guinea Gabon Botswana Angola Congo, Rep Tanzania Cameroon Uganda Ghana Chad Mali Madagascar Niger Togo Guinea Sierra Leone Congo, Dem Rep Seychelles Mauritius South Africa Cabo Verde Namibia Eswatini Senegal Comoros Benin Zimbabwe Eritrea Ethiopia Guinea-Bissau Burundi<br><!-- End of picture text --> _Notes: Government and private cumulative gross fixed capital formation during the boom divided by population in constant million 2015 US$. Source: authors’ calculations using World Bank national accounts._ Similarly, oil-rich countries show a higher accumulation of capital stock, including economic infrastructure, such as roads and airports, and social infrastructure, as in hospitals and schools. Oil-rich Equatorial Guinea and Gabon lead the list while the rest of resource-rich countries, mainly mineral-rich, - such as Guinea and the DRC - had just a small fraction of capital stock per person, as in most non-resource-rich African countries (Figure 29).

39
of firms in any economy – to sell essential supplies to the authorities (SMEs that do not already participate in PP may be able to retool relatively quickly if it is clear there is demand for their output). But this may also go the other way if the fixed costs associated with registration, certification and approval of new firms to bid to provide medical supplies are significant and take time to complete.

Openness of procurement regimes is proxied by a binary variable indicating GPA membership ( _GPAj_ ) and by the number of deep procurement agreements (DPAs) signed by each country with trading partners ( _Num_DPAj_ ). Membership of the GPA and the number of DPAs signed by a country implies more open PP regimes, which may be associated with a lower likelihood of imposing trade-restrictive measures. Conversely, transparency, due process and nondiscrimination commitments made in these agreements may impede rapid responses in procurement and induce governments to resort to export controls in the initial phase of the crisis.

> 12 We organize the data as a cross-section instead of a panel because the dependent variable is available by country on a weekly basis but all explanatory variables, except those on the incidence of COVID-19, are only available for each country annually in the year 2018 or before. The annual variation in our variables of interest will thus not explain the weekly or monthly variation in the count of trade policy measures imposed during the pandemic. This said, we account for the time dimension of the trade policy measures by computing the duration of the measures (where a removal date is reported in the GTA data set) and replacing the dependent count variables in equations (1) and (2) with their respective duration. Results are reported in Table 4.

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high EDD/NEDD ratio countries, fall under the control of NEDDs. Rent extraction involving the passage of legislation, the abuse of parliamentary allowances, illicit payments to elected officials by lobbyists etc. are clearly the preserve of elected officials. Conversely, the demand for petty bribes to undertake routine administrative tasks, administrative malpractice (at the local level) etc., are the preserve of bureaucrats who are, at least in high-income democracies, far removed from the direct oversight of elected officials<sup>37</sup> .

Of course, on the margin, many activities can be undertaken by either bureaucrats or elected officials. Thus, laws may be passed by the legislature but formulated primarily by elected officials or bureaucrats (as determined by the EDD/NEDD ratio). Therefore, rents perceived to be associated with ‘political life’ (broadly defined to include macro-level governance, e.g. the making of legislation or ‘grand political corruption’) should be determined by the EDD/NEDD ratio, since a greater control of these activities by elected versus unelected officials affects the magnitude of career concerned rent minimization. Conversely, even if oversight of the bureaucracy is substantial, many everyday occurrences of ‘petty’ (family or personal) rent extraction are only very indirectly associated with elected officials. It is, therefore, irrational to expect voters to attach much significance to the competency of elected officials if lower level bureaucrats, not directly or easily accountable to elected officials, are held responsible for the actions of such actors.

Using the data from the Global Corruption Barometer (Transparency International, 2011) it is possible to examine whether voters’ perceptions of rent extraction in different policy domains does in fact conform to these predictions. The specific questions of interest are: (1) ‘Corruption is a significant problem in political life’ (yes/no/don’t know) and (2) ‘Corruption is a significant problem in personal/family life’ (yes/no/don’t know
Policy Research Working Paper 10236

# **Abstract**

This paper investigates the effects of pollution on labor productivity in Chile. Data on fine particulate matter pollution in Chile were collected and matched to sectoral labor productivity at the city level. The endogeneity between labor productivity and pollution is controlled for by instrumenting on the presence of coal and diesel power plants. The paper finds that pollution reduces labor productivity. A series of robustness checks demonstrate that pollution has a statistically significant effect on productivity when the analysis controls for labor costs and entry rates. The paper provides extensive evidence to support a causal interpretation of this finding. The identification strategy is based on a stylized macroeconomic model. The pollution elasticity of labor productivity is used to demonstrate how the co-benefits of reducing pollution can be incorporated into mitigation policies in a general equilibrium framework.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at cjooste@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
control villages over time are necessary to capture changes in road conditions before and after the road investment.

This paper uses household-level panel data collected by BIDS with reference to two projects — the Rural Development Project (RDP) and the Rural Roads and Markets Improvement and Maintenance Project (RRMIMP) — supported by the World Bank. The data provide information on both project and control villages at the household level. We use a household fixed-effects technique to estimate the returns to road investment in terms of its impact on household per capita consumption (a measure of household welfare), labor supply, school participation rate of boys and girls among school age children, aggregate crop output and price indices, agricultural wages, fertilizer prices, and household transport expenses. We find that households do benefit in a variety of ways from road investment by paving an earthen road.

The results suggest that the savings of household transport expenses are quite substantial, averaging about 36 percent in RDP villages and 38 percent in RRMIMP villages. Road improvement also has a significant impact on men’s agricultural wage (increases by 27 percent in RDP villages), fertilizer price (falls by about 5 percent in RDP and RRMIMP areas) and aggregate crop indices (price indices increase by about 4 percent in both project samples, while output indices rise significantly by about 38 percent in RDP and 30 percent in RRMIMP project villages). The road effects are substantial for adult labor supply in RDP project villages, and schooling of both boys and girls. The overall effect of road improvement on household per capita annual consumption is also 11 percent in both project areas.

The distribution of benefits is not, however, independent of household resource endowments and location specific factors. We employ panel quantile regression techniques that show that while in RDP, project returns for per capita consumption average about 12-16 percent across different per capita expenditure quantiles (with the highest return for households in the 50<sup>th</sup> percentile), in RRMIMP the consumption benefits from rural road investments accrue
What really sets our dataset apart from the earlier survey based work, however, is that our data has bilateral contract information, allowing us to control both for buyer and supplier firm characteristics and analyze both sides of the buyer-seller transaction, whereas earlier work only analyzed one side of the buyer-seller transaction. An equally important distinction with previously used survey data is that we have multiple contracts for the same buyer and supplier firms, rather than a firm-average response. This allows us to include firm fixed effects in our empirical analysis, thereby correcting for time-invariant firm characteristics that might determine the choice of credit terms.

The limitations of our data set are that the number of buyers is small (a total of 56), which limits our analysis of buyer characteristics, and that we have relatively little information about the sellers in our sample. For example, we do not have information on the industry of the seller. Unfortunately, we have no way of getting more data. Because our buyers are mostly large firms we are less likely to pick up the financing motive as strongly as in earlier work (although we do have a mix of investment and non-investment grade buyers), but this allows us to focus on other non-financing motives.

We start by summarizing typical trade credit contracts terms, such as number of discount days, discount rate, and net days, and analyze how they relate to buyer and seller characteristics. We then turn to analysis. We argue that while it seems hard to conclude that trade credit is primarily a cheap way for suppliers to provide buyers financing in our sample (most of our buyers are larger and better rated than our suppliers, and still get credit), non-financing motives seem to be operative. In particular, large, investment-grade buyers get long terms from small suppliers. We believe this is consistent with relatively untrusted suppliers extending longer terms to buyers to guarantee product quality. However, this leaves suppliers exposed to riskier credits. This is where discounts help. Riskier buyers are offered discounts to repay early so that
on the interaction effects and coefficients of the other constraints. In the face of uncertainty about these effects, Hausmann, Rodrik, and Velasco recommend a practical approach based on removing the most binding constraint, with the “most binding constraint” defined as the one with the largest effect where issues of second-best effects are likely to be minimal.

## **2.4 This Study’s Contribution to the Literature**

In this paper, based on the HRV approach, we investigate the most binding constraint on the growth of firms, with the “most binding constraint” defined as the one with the largest estimated coefficient across all models and across regions and sectors. Compared with studies using the World Business Environment Survey data set, our paper uses a much larger sample. And while other studies use subjective firm responses as measures of the business environment at the firm level, we also include objective measures, in part to deal with endogeneity and in part to avoid measurement errors of perceptions at the country level.

In exploring the relationship between the business environment and firm growth, we go beyond distinguishing effects by firm size. We look closely at the effect of financial access variables—loan, credit constraint, sales credit, and external investment finance—on firm growth by firm size and age in different sectors and regions. We combine multiple financial access variables in a single regression in addition to evaluating the effect of each variable on employment growth controlled for firm size, age, and other characteristics. This allows an understanding of the impact of each dimension of finance on firm growth as firm characteristics change.

Moreover, our paper emphasizes which element of the business environment matters most for firms, especially for small firms. And it analyzes how different financial access variables affect the firm size distribution across regions and sectors.

# **3. Data**

In this paper we use a newly available firm-level data set from the World Bank Enterprise Surveys. The surveys cover more than 100,000 firms across more than 120 economies
improvement was warranted. By September 2002, foreign exchange reserves had fallen to around $7 billion. During this period, the IMF noted that progress on structural reform, including on trade barriers had not moved ahead. It is also notable the CPIA ratings for debt policy in Nigeria during the period of 1999-2001 were the weakest, with Nigeria getting a 1, the lowest rating score for external debt management.

Poverty in Nigeria had always been a major concern, and the 2003/04 living standards measurement survey showed that 54% of the population was living below the 1 dollar a day poverty line.

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majority of countries across the world, especially developing countries, face challenges in collecting poverty data. High-quality consumption surveys that are comparable for forcibly displaced persons and their hosts are and will remain in limited supply, given the cost and challenges associated with these types of surveys. In the meantime, cross-survey imputation methods can provide a second-best alternative that can potentially save time and money.

<mark>This study combined</mark> survey and census-type data on refugees to estimate welfare for <mark>refugees in Chad. W</mark> e showed how different sets of variables as well as different sources of data fare in the identification of poo <mark>r households, in particular, how well the set of variables available in the ProGres database can predict poverty. In a second step, the paper estimated the accuracy of the current U</mark> NHCR/WFP/CNARR <mark>targeting strategy and compared it with the targeting strategy based on imputed consumption in the light of international evidence.</mark> The results suggest that the set of variables available in ProGres accurately predicts the welfare rates for different poverty lines. Adding variables related to asset and animal ownership provides predictions that are very close to the ones with only the variables available in the ProGres data set. These results are especially promising, as the UNHCR ProGres data are available in most refugee locations where the UNHCR runs the registration system, and thus these methods are replicable in many settings of forcibly displaced persons.

The <mark>current targeting strategy that is used for food, livelihoods, and cash-based assistance, despite its simplicity, is rather accurate when compared with the existing international evidence. Th</mark> e targeting errors resulting from the current <mark>UNHCR/WFP/CNARR targeting strategy for a poverty rate of 25% are in the same error range as other targeting methods around the world, as reported in Coady et al. (2004). We also showed that the existing targeting method can be improved
Policy Research Working Paper 8705

# **Abstract**

This study analyzes whether subjective well-being measures can explain variation in peaceful uprisings, in addition to the objective measures typically used in analyses of this type of events. Using data on uprisings and subjective well-being for 118 countries from 2007 to 2014—a period during which nonviolent conflict became increasingly prevalent—the study finds evidence of a positive effect of life dissatisfaction on the incidence of peaceful uprising, but not its violent counterpart. This effect does not depend on the type of political regime, nor the stage of development, - and, to a large extent, it reflects changes in perceived sat isfaction with living standards and the ability to have a purposeful and meaningful life.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean 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://www.worldbank.org/research. The authors may be contacted at cw.si@cbs.dk, mburger@ese.eur.nl, or eianchovichina@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
7

## 3.3 Econometric Issues

The estimation procedure needs to tackle three issues. First, rather than distort the available information by phase averaging using an arbitrary phase length (e.g., computing 5 or 10-year averages), we choose to work with the original annual data in order to retain all the information. This in turn means that we need to use a dynamic specification in order to allow for inertia, very likely to be present in the annual information. Inertia in saving rates can arise from lagged effects of the explanatory variables on saving. Thus, considering a dynamic specification allows us to discriminate between short- and long-run effects on saving.’? Second, some of the explanatory variables in the core specification above (e.g., the real interest rate, real income growth, etc.) are likely to be jointly determined with the saving rate; therefore, we must allow and control for the joint endogeneity of the explanatory variables. Third, we must also allow for the possible presence of unobserved country-specific effects correlated with the regressors.

_ To address these issues, our empirical analysis is based on Generalized-Method-ofMoments estimators applied to dynamic models using panel data. These estimators allow us to control for unobserved country-specific effects and potential endogeneity of the explanatory variables.° Before we proceed, we must clarify the extent to which we control for joint endogeneity. Our panel estimator controls for endogeneity by using “internal instruments,” that is, instruments based on lagged values of the explanatory variables. Through this method we can relax the assumption that the explanatory variables are strictly exogenous; however, we cannot allow for full endogeneity of the explanatory variables. To be precise, we must assume that the explanatory variables are “weakly exogenous,” which means that they can be affected by current and past realizations of the saving rate but must be uncorrelated with future realizations of the error term. Conceptually, weak exogeneity does not mean that future saving rates cannot be correlated with current realizations of variables such as
and other EMDEs to broadly similar degrees. To test for differential impacts on energy-exporting and other EMDEs, a dummy for energy exporter status and an interaction term between this dummy and the oil price collapse dummy are added ( Table 3 , last three columns ). The main coefficient on oil price collapses, which now reflects the output response of non-energy-exporting EMDEs, remains statistically significantly negative; the coefficient on the interaction term, which reflects any differential impact in energy-exporting EMDEs, is statistically insignificant until the last year. The overall response of EMDE energy exporters’ output to oil price collapses is statistically significantly negative (as the probability of the corresponding F-test shows in the last column of Table 3 ).

> 13 This is also consistent with Ramey and Zubairy (2018) who use four lags for quarterly data, i.e. also one year.

> 14 Note that the coefficients reported in different tables are cumulative impulse responses. Hence, they already take into account any rebound in prices and economic activity that has taken place during the forecast horizon.

9
# Appendix 1: Identification of Rate Cycles

Section II discusses the application of the BBQ algorithm in Harding and Pagan (2002) to identify local maxima and minima in policy interest rates and thereby define rate cycles. A simple application of this algorithm to the data on policy interest rates, however, can be problematic due to several characteristics of the interest rate data—particularly long windows when there is no change in it. To address this and more accurately identify the turning points of easing and tightening phases consistent with changes in central bank policy stances, we start with the dates identified in the BBQ algorithm, but then make several adjustments and add additional criteria for a month to qualify as a turning point. This appendix describes the process used to identify the easing and tightening phases in more detail.

First, we apply the BBQ algorithm in Harding and Pagan (2002) to the policy rate data, with one adjustment. Early in the sample, when the policy rate is market-determined in some countries, there are occasionally sharp spikes in the rate that appear to primarily reflect shifts in market sentiment, shifts that are quickly reversed. To avoid classifying these temporary market-based fluctuations as turning points in a cycle, we smooth across these spikes by replacing it with (it+1 + it−1)/2 if the policy rate changes by more than 500 basis points in one month.

Second, we set three parameters for the BBQ algorithm: (1) the window on each side of a local maxima and minima must be at least 18 months; (2) a full cycle (including both tightening and easing phases) must be at least 36 months; and (3) any individual phase of a cycle (either a tightening or easing phase) must last at least 7 months. The first two criteria require relatively long windows on each side of a turning point and for the full cycle in order to capture changes in policy which are not reversed quickly; this also avoids classifying changes in policy rates that largely