rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 6172

# **Abstract**

Farm-gate buying by small itinerant buyers is the dominant mode of primary marketing in Tanzania’s maize market. This paper estimates the effect of household distance to market on maize farm-gate prices, and the extent to which seasonally determined search costs can explain price variations between the lean and the harvest seasons using data from the most recent Tanzania Household Budget Survey (2007). The author observes that greater distance to market depresses farmgate prices but that it is a relatively modest effect, and that this effect is pro-cyclical in that it is stronger during the harvest season when prices are lowest. The paper discusses the latter result with reference to search costs as an explanatory factor. It also briefly places the findings in the context of Tanzania’s food security patterns, making a link between food insecurity and high search costs. The main policy conclusion is that coordinating mechanisms such as village market places (in parallel with farm-gate buying) may reduce transaction costs in rural markets.

This paper is a product of the Human Development Sector, 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 smahdi@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
The results are largely consistent with the _“Quid Pro Quo”_ hypothesis. As expected, banks with high level of public loans get more "business" from the government and lend out a significantly larger volume of public loans (by 13 percent as shown in columns 1 and 2 of Panel A). More interestingly, we find that these public loans are generally of better credit quality, as indicated by the significantly lower default rate (0.8 percent lower) in column 10. The results on other loan terms are not robust across different specifications, though column 6 of Panel A suggests significantly longer maturity for public loans made by banks with high level of public loans.

To sum up, our results suggest a specific channel of rewards in the form of government borrowing, with larger volume and better credit quality for banks that offer political rents and extend favorable lending to politically connected firms. Nevertheless, we should caution that the results here do not aim to establish the sequence of whether “the politicians offer rewards to the bank” or the “bank provides favorable lending to politically connected firms” comes first.

# **_V_ .E. Real Effects of the Political Lending**

In this section, we investigate whether the effects of political connections in the loan market can translate into effects on real outcomes for the borrowers. Starting from Bernanke (1983), much of the macroeconomic and finance literature examines how the credit channel can propagate shocks to lending institutions into outcomes in the real economy. However, the data limitations have made it difficult to identify the effect on the borrower’s economic outcomes with a few exceptions.<sup>32</sup> We study the impact of political lending on firms’ outcomes using a vast sample of matched bank-firm data that merges the loan database with balance sheet information of the borrower. The specification is presented in Equation 4: where ��,� corresponds to the firm-level information including assets, liabilities, revenue and employment. The variables are available
that poverty rates in denser urban areas, such as urban centers in the DOU approach and urban cores in the DB approach, are significantly lower than in other types of area. On the other hand, less dense urban areas, like urban clusters in the DOU approach and suburbs in the DB approach, tend to have poverty rates relatively close to rural areas.

The adoption of DOU and DB approaches leads to an increase in urban poverty rates as more rural households are reclassified as urban households. This finding is consistent with the study by Combes et al. (2023), which suggests that official national definitions of urban areas tend to underestimate levels of urbanization in African countries. Figure 5 compares urban poverty rates based on the official urban definitions (x-axis) and DOU or DB definitions (y-axis). Countries closer to the 45-degree line have similar poverty rates for both definitions. Several countries have higher urban poverty rates in the DOU or DB approaches than in the official urban definition, as they appear above the 45-degree line. For example, the urban poverty rate in Malawi (MWI) is more than 50 percent in the DOU and DB approaches, a significant increase from 20 percent with the official urban definition. Similar substantial increases are observed in a few other countries, such as Niger (NER) and Uganda (UGA). The comparison of urban poverty rates in the DOU and DB approaches in Panel C show that most countries have similar poverty rates between the two approaches, with a handful of countries (such as Malawi, Niger, Chad [TCD], etc.) having higher poverty rates with the DB approach.

While the negative cross-country correlation between poverty rates and GDP per capita is well known (see Panel A in Figure A1 in Appendix A), it is unknown whether such a correlation is observed for urban and rural poverty rates. Figure A8 demonstrates a linear relationship between urban and rural poverty rates observed across countries: countries with lower urban poverty rates also tend to have lower rural poverty rates
expenditure on pensions or assistance to the poor. Richer individuals are more likely to support hiking education spending but are less likely to support increasing expenditure on assistance to the poor. Individuals affiliated with a political party are more likely to support increasing expenditure on health care and lowering expenditure on pensions. Former members of the Communist Party are 20 percent more likely than others to support additional spending on health care. Membership in a religious organization is not correlated with policy preferences.

These findings are fairly homogenous across the countries covered in LITS II. Figure 3 shows the estimated coefficients associated with the age dummy variable “Older than 54 years”. In every country included in the sample (except for France), older individuals are less (more) likely to support increases in government expenditure on education (pensions). Accordingly, older individuals are more (less) likely to support increases in government spending on health care (assisting the poor) in almost every country, but the coefficients are mostly not statistically different from zero.

Table A2 shows the estimation results using a different definition of the dependent variable. In particular, we consider only the first (instead of the first and second) priority for government expenditure. The results shows that even though the point estimates are different in some cases, the signs and magnitudes of the age coefficients are very similar to those reported in Table 2. In other words, the results are not sensitive to this alternative definition of the dependent variable.

Based on the link between ageing and preferences over policy, the next step is to assess the potential for translating these preferences into actual policy through participation of different age groups in the political process. Because participation in elections tends to increase with age, as seems to be the case on the basis of the raw data, the preferences of older individuals are more likely to be reflected in policy making than those of their younger peers. To shed more light on this possibility, we interacted the age dummy with an indicator of participation in elections. The
<sup>7</sup> Four types of innovation outcomes ‐ product, process, marketing and organization ‐ and one innovation input – R&D ‐ were considered in both surveys. Following the Oslo manual, product innovation is defined as the introduction of new or significantly improved product (goods and/or services) and process innovation is disentangled into three components: methods of manufacturing goods or offering service (“methods” from now onwards); logistics, delivery, or distribution methods for inputs, products, or services (“logistics” from now onwards); supporting activities such as maintenance systems or operations for purchasing, accounting, or computing (“auxiliary” from now onwards). For product and process

> follow‐up questions, however, is slightly different as innovations questions are administered only to firms identified as innovators based on the ES.

> 6 We note that Enterprise Surveys cover manufacturing as well as services sectors but certain services such as education and health are not covered. Moreover, the primary sector, which encompasses agriculture, mining, forestry, etc., is also excluded from the survey.

> 7 The experiment of using show cards on half of the sample was conducted in Ghana, Kenya, Tanzania, Uganda, and Zambia.

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Armed with a rich data set at household and community level we study the targeting performance of this program. There are two stages of targeting. First economically backward areas are chosen by the center. Second, community groups-exploiting idiosyncratic local information-select participants within those areas. We address two questions:

- How much of the program's performance in reaching poor families was due to the

- center's efforts at reaching poor communities versus the efforts of those communities to reach their own poor?

- What factors influenced the center's targeting of communities, and the distributional

## outcomes within communities?

We begin with our theoretical model of benefit incidence for a decentralized program.

Section 3 outlines properties of our measure of targeting performance and then goes on to describe our data for Bangladesh and to present relevant descriptive results. Section 4 outlines our econometric methods for explaining distributional outcomes of the program.

Our results are discussed in section 5 and section 6 concludes.

# 2. A **model of benefit incidence for a decentralized social program**

A poverty reduction program exists with a fixed aggregate budget. The program is mn by a Project Office (PO) within the central (federal or provincial) government. The PO decides how to allocate the budget across "communities". People in each community decide how to allocate the PO's budget between the "poor" and "nonpoor" within that community.

We assume that the program does not generate spillover effects across communities, such as due to mobility between them.<sup>5</sup> Mobility-induced spillover effects can be ruled out by assuming that the community only makes allocations across long-standing members or that _5_ On the implications of mobility of the poor for decentralized social programs see Brown and Oates (1987) and Wildasin (1991). On how mobility might impact on the local political economy see Rose-Ackerman (1983).

_5_
we treat political influence as a latent concept. This paper uses an original, firm-level data set from 41 diverse economies (mainly in Europe, Central Asia, the Middle East, and North Africa) to understand how firms engage in interactions with public officials to gain influence. To do so, we draw upon data on several interactions between public officials and private firms. We do not start with a prior assumption of which interactions increase political influence, other than through political connections themselves, which we treat as indicative of influence. We use a Bayesian latent variable model, a form of item response theory (IRT), which allows for the aggregation of several indicators of firm political activity along with reported political connections as the reference category. By linking observed combinations of political interactions to the latent concept of influence—and several underlying predictors—we not only show how political connections interact with other engagements between firms and political decision-makers, but we also reveal how certain interactions may complement or temper the influence of those connections. The result is an index, estimated with error, of firm-level Political Influence, which aggregates diverse state-business relationships into a theoretically relevant measure.<sup>1</sup> In Section 2, we lay out a broad framework for understanding how firms may combine their interactions with public officials to garner political influence: we note that drawing political influence through these interactions carries a transactional cost and that the resulting influence is in competition with other

> 1To clarify for readers, we generally capitalize the estimated measure of Political Influence, as opposed to the underlying concept of influence.

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6 Sometimes this was because there was no estimate reported in the survey report or online tool. But often it was because indicator definitions can vary from one survey ‘family’ to another, and sometimes even within a survey family, either over time or between the survey report and the online tool. Although we have re-analyzed the raw microdata, the estimates we report are simply harmonized direct (re)calculations of the quantities reported in the survey reports and online tools. In line with the growing concerns about the use of modeling in global health data sets (AbouZahr et al. 2017; Boerma et al. 2018), we do not predict missing data – we do not produce forecasts for country-years where there is no survey.<sup>1</sup> The downside is that our data set is, as a result, full of gaps. The upside is that, insofar as the surveys we use are reliable, differences over time or across countries ought to reflect reality rather than modeling assumptions; conversely, when real changes occur on the ground, they ought to get reflected in our numbers, rather than being smoothed away by the modeling process. Our data set is freely downloadable, and a data visualization tool is also available.<sup>2</sup> To ensure our data are reproducible, and in line with GATHER (Guidelines for Accurate and Transparent Health Estimates Reporting) (Stevens et al. 2016), we document thoroughly our methods and highlight the differences between our definitions and others’, and provide the essential computer code that ought to make it possible for anyone trying to reproduce our results to do so.<sup>3</sup> The GATHER table showing pages where the various parts of the database construction process are recorded, is included as Table A2 in the Annex.

The HEFPI database can be used to analyze a variety of topics. With the database one can see not just how the population fares but also how different ‘wealth’ or income groups
**Similar results hold when using the head definition.** When restricting the sample to household heads, a slightly higher share of heads who are self-employed (20 - 21 percent) receive assistance compared to those who are wage earners (18 percent). All other kinds of assistance are nearly equally received by self-employed/family enterprises as compared to wage earners, with the latter being 1-2 percentage points more likely to sell assets, receive government assistance, or receive cash or in-kind support from a private contributor (Table 5). Asset sales are low, though 9 percent of wage-earning household heads and 8 percent of household heads owning an agricultural enterprise sold assets. In contrast, 6 percent of non-agricultural self-employed sold assets.

**The summary statistics suggest that households owning an enterprise, especially a non-farm enterprise, are more likely than non-enterprise owning households to experience income loss, and they depend more on external support to cope with shocks than households without enterprises.** These results are robust to the definition used to identify household enterprise. That said, the sample size does not allow us to test if the averages differ across groups. As the literature suggests, though, some sub-groups of household enterprise owners may be particularly susceptible to income losses or to using particular coping strategies. We further explore these correlations using regression analysis below.

# **4. Early Impacts of the Pandemic on Income and Use of Coping Strategies**

**In line with the analysis presented in section 3, in the first wave of the pandemic, households with enterprises, particularly non-farm enterprises, were more likely to report total income declines as compared to those without enterprises.** Table 6 shows the correlates of income declines from linear probability models when pooling data from the three countries with complete data: Ethiopia, Malawi, and Nigeria. The estimates suggest that, controlling for household welfare, owning a non-farm business increases the likelihood of total income decline by 26 percentage points in comparison to households not owning a business of any kind
from the World Bank Commodity Price Database, and the Boliviano/Dollar exchange rate from the IMF IFS dataset.

The choice of the ten products is based on their importance either in consumption or production structures of the economy. Bananas, for example, are the fruit that carries the largest weight in the CPI, and is among the top export products in the country, as well as sugar and cooking oil. Beef accounts for 5.24% of the CPI basket and together with wheat related products displays the largest contribution to the CPI. Chicken and related products account for 1.9% of the CPI basket (another of the top ten contributors to the index), as well as potatoes, and rice. We also included in the analysis milk and onions, although there is no international price, since the products are important for the consumption bundle of the poor. Finally, we include wheat flour where international trade is substantial and Bolivia is a net importer.

Table 2 provides some descriptive statistics on the monthly growth rates of these price series, and shows that for all products that are traded in world markets with the exception of bananas, growth rates of domestic prices are roughly similar to the growth rates of world prices expressed in Bolivianos. Domestic price changes tend to be less volatile than world price changes, which is reasonable given that the former include marketing and distribution margins that are less prone to volatility and that within domestic prices and also as expected, less processed products exhibited larger price volatility than more processed products.

To assess the extent of spatial integration, either between domestic and world prices, or between the prices in two given Bolivian cities we analyze how the price series under consideration co-move. First, we report correlation coefficients of the price levels and their changes. Second, we follow Engle and Granger (1987), and use a 2-step procedure. The first step consists of estimating a long-run static relation between every possible pair of price series pit and pjt, corresponding to regions i and j respectively
_Note_ : AEs = advanced economies; EAP = East Asia and Pacific; ECA = Europe and Central Asia; EMDEs = emerging market and developing economies; LAC = Latin America and the Caribbean; LIC = low-income country; MNA = Middle East and North Africa; PPP = purchasing power parity; SAR = South Asia; SSA = Sub-Saharan Africa. A.B. Panels show weighted averages (A) and simple averages (B), by country group, of the standard deviations of the residuals obtained from regressing four dependent variables—log differences of real primary expenditures, real government consumption, real revenues, and the change in primary balance (percent of GDP)—on real GDP growth. Weights used are the PPP GDP shares in the respective group’s total GDP. Annual data for 36 advanced economies and 148 EMDEs over 1990–2021.

C.D.E. Panels show the weighted averages of the standard deviations of the residuals obtained from regressing four variables—log differences of real primary expenditures, real government consumption, real revenues, and the change in primary balance (percent of GDP)—on real GDP growth. Weights used are the PPP GDP shares in the respective group’s total GDP. Annual data for 35 advanced economies and 142 EMDEs over 1980-2006, and 36 advanced economies and 144 EMDEs over 2007-20. Annual data for 22 LICs and 156 non-LICs.

F. Panel shows the average (unweighted) volatility in commodity exporters in each country group.

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borders as well as within the countries.<sup>46</sup> The World Bank has argued that an FTA would bring large welfare gains for consumers in Bangladesh, provided infrastructure and administrative capacity at customs borders is adequately expanded.<sup>47</sup> The conditions of land ports in Bangladesh are not satisfactory. It has also been found that ports in Bangladesh are plagued by labor problems, poor management, and lack of equipment; costs rise further with inadequate customs services, including delays in clearances and rent-seeking.

Poor trade logistics in Bangladesh affects the cost of export and import very significantly and is reflected in the Logistic Performance Index (LPI). The World Bank (2012) finds that the domestic logistics chain – including collection of products from producers, road haulage to the warehouse, containerization, haulage to the port, and customs at port- is still very underdeveloped. Bangladesh ranks 87 in the LPI, while its South Asian comparators like India and Pakistan rank 39 and 68 respectively.

The quality and performance of logistics services differ markedly between India and Bangladesh and also across their trade partners. These variations in time and cost stem from differences in the quality and cost of infrastructure services as well as differences in policies, procedures, and institutions. The quality of such services, policies, and procedures has a significant effect on trade competitiveness and market access. While there is strong anecdotal evidence that the lack of adequate trade infrastructure might have altered the bilateral trade potential due to trade costs, this study tries to assess the effect of trade facilitation/trade cost elements (e.g., transit and time) and logistics services on bilateral trade. It does so with the help of an augmented gravity model (AvW type), and then determines important trade remedies. The regressions are based on a cross-section pooled dataset for the years 2007 and 2008. A three-stage regression process is used here to understand the impact of trade facilitation, comprising several of the logistics and trade facilitation indicators and