rafmacalaba/data-use-mentions-extended huggingface dataset:
good comparability between cases.<sup>117</sup> Likewise, the country sample presents relatively good variation with respect to the key variables over the period, allowing the research to better isolate key associations. Finally, the country selection will allow comparison at the sub-regional level, as key differences may emerge between the southern/eastern cluster versus the western cluster of African countries.

# **_Primary Variable/Indicator Construction_**

To perform these regressions, this research will rely on a customized data set that operationalizes the key variables, namely OGD, accountability, and service delivery provision and outcomes. All variables have indices that can be used to compare countries across the Sub-Saharan Africa region with respect to the levels of OGD and the resulting levels of accountability and service delivery. To address potential endogeneity issues, the direction of the relationship will be strengthened by the sequencing of data gathered, which would imply that OGD changes occur _prior_ to changes in accountability and service delivery.<sup>118</sup>

- **Open Government Data:** This index will draw upon the Open Data Barometer (ODB), produced on an annual basis by the World Wide Web Foundation, which scores countries on the openness of their data. In particular, the ODB analyzes global trends, and provides comparative data on countries and

> 114 In the future, more robust statistical techniques may be employed to the extent that data availability provides the necessary degrees of freedom to perform such analyses. The current limitations to doing so are discussed subsequently in the paper. 115 Twenty-five countries are included in the study, including: Benin, Botswana, Burkina Faso, Cameroon, Côte d’Ivoire, Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Malawi, Mali, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

> 116 While this number of data points allows for sufficiently significant and robust results, the
We test whether, in selecting the poorest, local leaders are more likely to rely on information about assets, likelihood of hunger, or subjective perception of immediate need. While assets are likely to capture long-term welfare, likelihood of hunger is a better proxy of current welfare, and perception of immediate need may reflect exposure to shocks. Third, we check whether local leaders rely on accurate information when making targeting decisions: i.e. whether what they know about household’s landholdings corresponds to landholdings as reported by the household.

Another contribution of our paper is in analyzing the effectiveness of targeting by local leaders in the specific context of a public works program. We focus on a program in the Lao People’s Democratic Republic where village heads played an important role in selecting program beneficiaries. Constraints on effective targeting are likely to differ by program type. For example, the likelihood of nepotism may be higher for programs where there are no implicit participation costs (e.g. unconditional cash transfers, food vouchers), compared to programs with strings attached, such as public works programs, especially if working conditions are difficult. In contrast to the workfare setting of our study, much of the existing targeting literature is focused on conditional cash transfers, including work focused on the targeting of health insurance (Alatas et al., 2012), food subsidies (Alatas et al., 2012 and Basurto, Dupas and Robinson, 2019), and fertilizer subsidies (Basurto, Dupas and Robinson, 2019).

We collect detailed welfare data on those who were identified as eligible to participate in the program by the village heads, as well as on a random sample of households from the same village. We also administer a series of ranking exercises to the village heads, aimed to elicit guiding principles for their selection, and to assess the quality of information they rely on. The paper is organized as follows: the next section describes the Road Maintenance Groups program, the process of beneficiary selection and provides 3
If both _z_<sup>X</sup> <30 and _z_<sup>M</sup> <30 then the sector is categorized as a two-way trade sector. In the sample, they are replicated, once as an import good for which _z_<sup>M</sup> is the relevant variable, and once as an export good for which _z_<sup>X</sup> is relevant. Thus, in the sample used to estimate (4), there are 94 observations, 41 for imports and 53 for exports.

Another study by Kee, Nicita and Olarreaga estimates U.S. import demand elasticities at the tariff line level using the GDP function approach.<sup>32</sup> Since the import demand elasticity variable consists of estimated values, we treat it as a variable measured with error, but with known measurement error variances (equal to the square of their standard error of estimate). A Fuller correction is applied to the variable before it is used as the denominator in the _z/_ e variable.<sup>33</sup> Corresponding estimates of export supply elasticities are unfortunately unavailable. Since most agricultural goods are relatively undifferentiated it is reasonable to assume that their export supplies are elastic. We therefore set the export supply elasticity equal to 2 for all goods.

# _Lobbying_

Agricultural PAC spending data for the three congressional election cycles between 1993-1998 were downloaded from the Federal Election Commission (FEC) website.<sup>34</sup> The main use to which the PAC spending data are put is to measure lobbying organization.<sup>35</sup> Whether a good i has political representation (Ii=1) or not (Ii=0) is determined on the basis of PAC spending summed over this period. Goods are ordered by PAC spending and sets of I are defined, each set corresponding to a percentile cutoff value. Models (4)-(6) are estimated using all
regressions are reported in table 3. The first-stage regression shows that the instrument is strongly and negatively correlated with 3G coverage (see appendix A, Table A.1). This makes sense given that the likelihood that households in a given EA are covered by 3G is expected to decline if the neighboring areas are distant from the closest 3G coverage areas. The two-stage least squares estimates show results that are similar to the results of the ordinary least squares regressions; the effects of 3G on total consumption are positive and statistically significant, and these effects are particularly pronounced in the case of nonfood consumption. As expected, the effects of 3G on poverty are also negative and statistically significant (though at the 0.10 level) in the two-stage least squares regressions.

The analysis also examined the results on various subsamples (Table 4). First, the sample was split into rural and urban areas. 3G coverage is significantly and positively correlated with consumption and negatively associated with poverty only for urban households (Table 4, panel a). Interestingly, this correlation is particularly pronounced for food consumption among urban households while 3G coverage is significantly and positively correlated with non-food consumption for rural households. The sample is also separated according to whether household consumption is below or above the median consumption level (in each survey). This exercise reveals that while the effect of 3G coverage is positive and significant for non-food consumption for poorer households, these effects lose

> 17 The point estimates do not change substantially if no weights are applied. According to our unweighted regressions, 3G coverage is associated with a 18 percent increase in total consumption and a 7 percent decrease in extreme poverty rate (see Table A.3 and A.4 in the appendix). To put this in context, similar work for Nigeria—integrating household panel data and historical coverage maps—finds an 11.1 percent increase in total consumption and a 7.9 percentage point decline in extreme poverty after three years of coverage (Bahia et al. 2020). Using
Policy Research Working Paper 5909

# **Abstract**

This working paper assesses cross-border economic integration in the Lower Congo region. It focuses on the Kinshasa–Brazzaville conurbation, which is projected to become Africa’s largest urban area by 2025, and is already serving as the gateway to large hinterlands. Despite their size and proximity, formal economic exchanges between the two cities are extremely limited. The volume of recorded passenger travel between Kinshasa and Brazzaville corresponds to about one-fifth of the volume of traffic between East and West Berlin during the time of the Berlin Wall, and formal trade volumes are derisorily small. As a consequence, the authors find evidence of statistically significant differences in retail prices, indicating unexploited scope for cross-river arbitrage. Through a survey of firms, they find that local traders perceive substantial scope for increasing crossborder economic activity if cross-river trade costs were reduced. Trade in locally produced goods and by small firms would especially benefit from such reductions. Existing high trade costs mainly result from a lack of competition in cross-river transport services, which are dominated by a duopoly of state-controlled operators. High administrative border costs, exacerbated by the presence of multiple government agencies at the border, act as a further obstacle. Liberalization of cross-river transport and customs reform could yield large economic benefits for local producers and consumers.

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 mhoppe@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
variation in the model. Table 8 reports the results, which show the share of the R<sup>2</sup> that is accounted for by ten different sets of variables. The sets are the Google mobility data, Google searches related to food (“Milk”, “Bread”, “Cheap food”), Money (“Money”), Jobs (“Jobs”, “Salary”, “Unemployment”, “Vacancy”), assistance (“Assistance”, “Free Food”, “Free Meal”, and “Food Bank”), exercise (“Exercise” and “Gym”), Religion (“Bible”, “Quran”, “Prayer”), and School (“Student”, “Online School”, “Online Education”). The final two sets of variables are the EVI (vegetation), and No2 (a measure of pollution), taken from satellites.

> 14 These figures for both wave 1 and the full sample are obtained from each multiple imputation replication.

21
costs. However, empirical studies and surveys of transport costs in Africa have shown that costs are inversely proportional to load size: the cost of transporting a unit of maize is lower the bigger the load (World Bank, 2007; Teravaninthorn and Raballand, 2009). Since large loads are easier to coordinate during the harvest season, the cost of transport per ton maize is minimized. So, standard transport costs are unlikely to be the explanation, indicating a role for search costs in explaining these findings through the channels discussed in the analytical framework.

_Other explanations and the strength of the proxy variable_ One of the challenges of using proxy indicators is the identification problem. Using distance to market as a proxy for search costs opens the possibility that our identifying variable measures factors other than search costs. If this is the case, then it would be difficult to attribute the results we describe in this paper to the effects of search and information costs associated with farm-gate buying. The question is whether there are other reasons besides buyer search costs and seller information costs that would cause the prices received by households that are distant from the market to be lower than those nearer the market. We identify two such factors.

The first is competition. On average, rural trading businesses face competition from only five other businesses, providing each one with a relatively high market share (World Bank, 2007). In this context, maize growing households that are distant from the market may be reached by fewer buyers, creating an opportunity for monopsony rents that push producer prices down as the distance from market increases. In addition, since remote households tend to be more vulnerable, their bargaining power will be further diminished. This will be the case if buyers tend to concentrate around the village and market centers and avoid covering long distances in search of maize.

Nonetheless, this effect is unlikely to be present. In this scenario, we would expect monopsonistic market power at the village level to be most concentrated during the lean season when buyers are already relatively
broadly.

In this paper, we exploit a set of real-time indicators from different sources to predict changes in welfare in developing countries during the COVID-19 crisis. Six main findings emerge:

1. The selected real-time indicators predict a significant share of the cross-regional variation in the initial impacts of the crisis. Across about 300 regions in 20 countries, eight Google search terms explain a combined 36 percent of the variation in the share of households reporting total income declines since the start of the crisis between April and August 2020. Meanwhile, during the same period, 10 Google search indicators explain a combined 34 percent of the variation in the share of respondents reporting job loss since the start of the crisis.

2. Compared with initial impacts, selected real-time indicators explain less of the variation in the prevalence of income declines and employment between April 2020 and 4
# **B DATA BASE CONSTRUCTION**

# **B. A Mayoral elections**

The database was constructed with original data from the Colombian electoral authority, . _Registradur ́ıa Nacional del Estado Civil_ Our database begins after the 2000 elections, since after that year the electoral data has information on the electoral roll and the total votes received for all candidates. To begin, we identified the political party of each candidate or the political movement that endorsed the candidate’s campaign<sup>20</sup> and then classified its ideology as left, center, or right following the methodology proposed by Fergusson et al. (2020).<sup>21</sup> The classification for each candidate’s ideology includes three steps.

1. Check party names, mottos, and slogans for words that identify the candidate’s party clearly as left-leaning or right-leaning (e.g., communist or socialist for leftwing, and conservative or Christian for right-wing).

2. In the event that the previous step did not work, we checked the party statutes (when available) for policy stances that clearly leaned either to left or right. We coded a party as left-wing if the party statutes included at least three of the following five leftist policy positions: (1) pro-peasant, (2) advocates greater market regulation, (3) thinks workers should be defended against exploitation, (4) advocates state-owned or communal property rights, and (5) anti-imperialist. We coded a party as right-wing if its statutes included at least three of the following five rightwing policy positions: (1) economic growth is emphasized over redistribution; (2) advocates free market, orthodox policies, and privatization; (3) believes that family and religion are the moral pillars of society; (4) appeals to patriotism and national-

> 20In Colombia, a mayoral candidate may register for the elections with the endorsement of a political movement or party, with legal status recognized by the National Electoral Council
CTT account for CTT operations; 21.25% to an account in the University President's office; 21.25% to an account for the inventor's laboratory or department; and 42.5% to the inventor personally. At any point in time, on an accrual accounting basis, the balance in the revolving fund account managed by the university CTT should always be "zero." As for funding of patents, CTTs in the United States expect the companies receiving the innovations for commercial application to pay for the patent application costs, as a "cost of doing business" or a "cost of goods sold," similar to labor costs or material costs. In the Top 25 Research Universities in the United States, there is much variance in the success of the institutions in receiving reimbursement of patent expenses, ranging from 16% of expenses funded by industry at the California Institute of Technology to 90% of expenses funded by industry at the University of Pennsylvania.

For patents funded "at risk" (without a company to reimburse the costs), there are a variety of sources for securing the funds. In a recent survey of the Top 25 Research Universities in the United States, 23 of the 25 institutions responded to the survey. One of the questions asked was: "What is the source of funding for patent applications prosecuted at risk (without a licensee to reimburse/fund expenses)?" Responses (23): 8 - Patent expenses funded from royalty income accumulated over time in the CTT.

14- Patent expenses funded from university accounts; university funds were (i) allocated to the TLO in an annual budget for TLO management; **<u>OR</u>** (ii) controlled by the Vice President for Research or other university official; **<u>OR</u>** (iii) provided by departments of faculties (colleges) for inventions arising from the applicable department or faculties (college); *
# Figure 2. Income gaps between various countries’ populations; early 21<sup>st</sup> century

# a. Denmark vs.  selected African countries   (ventiles)

b. US vs. India (percentiles) <!-- Start of picture text --> USA<br>Denmark<br>Uganda<br>Mali India<br>Tanzania<br>Mozambique<br>1 5 10 15 20 1 10 20 30 40 50 60 70 80 90 100<br>country ventile country percentile<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>1<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>1<br><!-- End of picture text --> Note: Based on national household surveys; adjusted for price differences between the countries using most recent PPPs. Year 2005.

10
Inflation in 2019/20 is predicted to be higher than the nominal GDP per capita growth, which makes the population worse off in real terms. The national poverty rate, measured at the 5.5 USD 2011 PPP daily poverty line used by the World Bank for upper middle-income countries, is forecasted to increase slightly from 10.7 percent in 2018/19 to 11.2 percent in 2019/20.

For simplicity we assumed that the GDP per capita growth rate has a one-to-one relationship with growth in household expenditure and is also distribution neutral. The main goal of the current analysis is not to get an accurate poverty rate, but rather to make sure we are using expenditures in the current prices. As such, the 2019/20 estimate is not a precise forecast but rather acts just as a baseline for analyzing the impact of the reforms in present-day Islamic Republic of Iran.

Figure 1. Headcount poverty rates in 2018/19 and forecasted poverty rate in 2019/20 using 5.5 USD 2011 PPP daily <u>poverty line</u> <!-- Start of picture text --> 12.0% 11.2%<br>10.7%<br>10.0%<br>8.0%<br>6.0%<br>4.0%<br>2.0%<br>0.0%<br>2018/19 Forecasted poverty,<br>2019/20<br>headcount poverty<br><!-- End of picture text --> Source: Authors’ calculation using HEIS 2018/19.

Note: Poverty is calculated using a spatially adjusted welfare aggregate to account for price differences across different regions and rural/urban areas following methodology described in Atamanov et al. 2016. The resulting poverty rate is therefore slightly different from the one reported in PovcalNet.

# 2.2 Reform scenario for simulation

On November 2019, NIOPDC announced a 50 percent price
43 and building connection charges in general tariffs of all—and especially high‐consumption consumers in all sectors—would help. In easing payment terms, attention should be paid to balancing tariffs and monthly installments for connection charges, because the combined effect could make monthly payments unaffordable.

- Utilities can do more to stamp out corruption and eliminate bribe payments demanded by their employees. In the two surveys where grid‐connected households were asked if they had paid a bribe to get connection, many replied “yes.” Bribes for connection further encourage multiple connections.

- Lifeline rates for blocks in the vicinity of 30–50 kWh a month may help strike a balance between affordability on the one hand and utility financial viability on the other. The first block of 25 kWh or less per month in some countries seems too small. If the country has a single schedule of a single block, progressive tariffs may be considered. For full benefits to be reaped, however, households need to be individually and accurately metered.

- Prepaid meters help low‐income households. The ability to pay in small increments when cash is earned, and not having to face the prospect of disconnection and a high reconnection charge, help especially the poor. However, electricity services are unreliable—in every one of the seven countries in which households were asked about service quality, blackouts were common—and this could mean that prepaid customers pay in advance for electricity they cannot get when needed. For this reason, it may be unfair to mandate prepaid metering on all customers.

- Consideration may be given to basing lifeline rates on a moving average of consumption over several months. Volume‐differentiated tariffs with a large increase in unit price from the first to the second block in particular may pose financial hardships for the poor if they are applied strictly on monthly consumption. To address this concern, in Cape Town, South Africa, free electricity is based on monthly consumption averaged over the past 12 months. Using moving averages and issuing warning messages in