rafmacalaba/datause-encoder-data huggingface dataset:
# Redistribution and Group Participation: Experimental Evidence from Africa and the UK<sup>∗</sup>

Marcel Fafchamps Ruth Vargas Hill Stanford University The World Bank

## Keywords: group membership, redistribution

## JEL codes: D03, O17

∗Marcel Fafchamps is a Professor at Freeman Spogli Institute for International Studies, Stanford University, 616 Serra Street, Stanford CA 94305. His email address is fafchamp@stanford.edu. Ruth Vargas Hill (corresponding author) is a Senior Economist in the Poverty and Equity Global Practice at the World Bank. Her email address is rhill@worldbank.org. The research for this article was mainly financed by the CGIAR Research Program on Policies, Institutions, and Markets (PIM). Supplemental funding for the 2014 UK sessions was provided by Stanford University. The opinions expressed here belong to the authors, and do not necessarily reflect those of PIM or the CGIAR. We benefitted from comments from Avner Greif, Pascaline Dupas, Edo Gallo, Eric Edmonds, Bruce Sacerdote, Erzo Luttmer, Stephane Straub, Eliana La Farrara, Alessandra Cassar, Bruce Wydick, Angelino Viceisza, and Eduardo Maruyama. We also received useful comments from conference participants at Oxford University and the Toulouse School of Economics and from seminar participants at the Nuffi eld College’s Centre for Experimental Social Science, Stanford University, Dartmouth University, The University of San Franciso, and the University of Auvergne. We thank Hee Youn Kwon for her assistance implementing the 2012 experiment in Oxford, and Nouhoum Traore for his assistance in implementing the experiment in Uganda and Nairobi. We are grateful to the lab at the Busara Center for Behavioural Economics, IPA, Nairobi for their assistance with the Kenya experiment. We thank John Jensenius for his assistance with the 2014 sessions at the CESS lab in Oxford. Part of this work was undertaken whilst Ruth Hill was at the International Food Policy Research Institute.
are introduced in the control set. Under the assumption that population density is positively autocorrelated, the present-day measures can serve as proxy controls for historical population. Under the additional assumptions that selection on population is positive and that the presence of mission stations increases population, it is possible to interpret the coefficients on Mission _ik_ from these regressions as lower bounds on the true causal effect.<sup>21</sup> The results from regressions that control for present-day population are reported in table 5.

In the first column, the specification includes fourth-order polynomial terms of population in 1995. In the second, it also includes a set of dummies for the presence of populated places of different sizes.<sup>22</sup> In the third column, the sample is restricted to cells containing a provincial capital, because these cities are likely to be large in terms of population and politically important. The regressions survive all three robustness tests. Estimates are still significant at least at the 99 percent level, and coefficients are comparable to those obtained previously (35 percent of the sample means).

# **2.5.3 Spatial Spillovers**

The baseline equation (1) does not account for the possibility that benefits from hosting a mission station in one cell could spill over to surrounding cells. On the one hand, this might induce an overestimation of the effect of missions on aid. In cases where a pair of neighboring cells both hosted missions but only one receives aid, the neighbor’s mission may be adding to the aid attraction. On the other hand, spatial spillover could induce an underestimation of the effect of missions on aid, because missionary presence in one cell might increase the probability of attracting aid to surrounding cells, even if those do not host any mission themselves. In both cases, failure to account for the presence of missions in surrounding cells could lead to omitted variable bias, in the first case with a positive sign and in the second case with a negative sign.

> 21See Angrist and Pischke (2008) for a discussion of this point, and Michalopoulos and Papaioannou (2013, footnote 13) for an example.

> 22These are dummies for the presence of at least one a) national capital, b) provincial capital, c) urban agglomeration of at least one million people or city with at least 500,000 people, d) urban agglomeration of at least 250,000 people or city with at least 100,000 people, e) urban agglomeration of at least 100,000 people or city with at least 50,000 people, f) places with at least 10,000 people, and g) places with at least 1,000 people.

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# **5 Simulated of a Carbon Tax**

The results of the vintage capital model indicate that energy-price induced improvements in capital stock can be significant in determining the future energy efficiency of production. Thus energy and climate policies that provide incentives for early investment in energy efficient capital stock may reduce future energy (including fossil fuel) input consumption. To illustrate the outcome of such policies we use the vintage capital model predictions to evaluate the effect of an unanticipated greenhouse gas emissions tax on energy consumption. Specifically, we utilize the model of equations (3), (7), (12), (14), and (15) to simulate the effect of a hypothetical carbon dioxide emissions tax implemented in 2006 (the first year after sample period) for two cases of the petrochemical and the electrical industries. Table 1 shows that both industries do respond to energy price increases in the short run. However, the longer term price-induced investment response is non-trivial in the former industry (as shown in Table 3, and Figure 2 above), while it is close to zero in the latter. To minimize the impact of cross-country differences for the purposes of illustration we are focus on a particular country - the United Kingdom.

We assume that all input prices, except for the energy price, and output remain at their 2005 levels (e.g. ∆ _Yi,t_ = ∆ _wi,t_<sup>_j_=</sup><sup>_k,l,m_</sup> = 0 _, t >_ 2005). In the counterfactual scenario, we assume there is a $ 30 tax per ton of emitted greenhouse gas. Using the data for the sector fuel consumption in the UK petrochemical and electrical industries, which we obtain from the UK Department of Energy and Climate Change publication _Energy Consumption in the United Kingdom_ , we find that one ton of the fuel mix emits, respectively, 2.44 and 2.2 tons of the CO2 in the petrochemical and electrical industries (computation details are available in Tables A.23 and A.24, technical appendix, section A.2).<sup>30</sup> We assume that energy-using capital stock in both manufacturing sectors is idiosyncratic in fuel mix, so no interfuel substitution is possible.<sup>31</sup> Then, a $ 30 tax per ton of greenhouse gas for these industries corresponds to an additional mark-up of $ 73 and $ 66 per _toe_ , or a 17 and 11 percent increase in energy input prices.

> 30The greenhouse emission coefficients per type of fuel (in million of British Thermal Units, BTU) are obtained from the US Department of Energy Voluntary Reporting of Greenhouse Gases Program website (http://www.eia.doe.gov/oiaf/1605/coefficients.html) and converted to tons of oil equivalent ( _toe_ , 1 _toe ≈_ 40 x 10<sup>6</sup> BTU).

> 31Steinbuks (2012) estimated econometric model of interfuel substitution for 15 energyintensive UK manufacturing sectors between 1990 and 2005, and found very small cross-price elasticities of fuel demand in both the short- and the long- run. This finding is consistent with earlier studies of interfuel substitution in manufacturing based on disaggregated data (Woodland 1993, Bjorner and Jensen 2002).

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|P082976|2004|2010|6/30/2010|15 Closed|Yemen,Republic of|
|---|---|---|---|---|---|
|P082976|2004|2011|1/4/2011|16 Closed|Yemen,Republic of|
|P082976|2004|2012|7/26/2011|17 Closed|Yemen,Republic of|
|P082976|2004|2012|1/3/2012|18 Closed|Yemen,Republic of|
|P082976|2004|2013|7/21/2012|19 Closed|Yemen,Republic of|
|P082976|2004|2020|6/17/2020|20 Closed|Yemen,Republic of|
|P082977|2004|2004|6/15/2004|1 Closed|Pakistan|
|P082977|2004|2005|6/3/2005|3 Closed|Pakistan|
|P082977|2004|2006|12/22/2005|4 Closed|Pakistan|
|P082977|2004|2006|6/23/2006|5 Closed|Pakistan|
|P082977|2004|2007|12/22/2006|6 Closed|Pakistan|
|P082977|2004|2007|6/1/2007|7 Closed|Pakistan|
|P082977|2004|2008|12/26/2007|8 Closed|Pakistan|
|P082977|2004|2008|6/14/2008|9 Closed|Pakistan|
|P082977|2004|2009|12/27/2008|10 Closed|Pakistan|
|P082977|2004|2009|5/27/2009|11 Closed|Pakistan|
|P082977|2004|2010|11/30/2009|12 Closed|Pakistan|
|P082977|2004|2010|5/26/2010|13 Closed|Pakistan|
|P082977|2004|2011|12/8/2010|14 Closed|Pakistan|
|P082992|2006|2006||0 Closed|China|
|P082992|2006|2007|5/22/2007|1 Closed|China|
|P082992|2006|2008|10/15/2007|2 Closed|China|
|P082992|2006|2009|9/20/2008|3 Closed|China|
|P082992|2006|2009|12/10/2008|4 Closed|China|
|P082992|2006|2010|6/28/2010|5 Closed|China|
|P082992|2006|2011|12/24/2010|6 Closed|China|
|P082992|2006|2012|9/22/2011|7 Closed|China|
|P082992|2006|2012|6/12/2012|8 Closed|China|
|P082993|2006|2006|5/23/2006|1 Closed|China|
|P082993|2006|2007|6/9/2007|2 Closed|China|
|P082993|2006|2008|10/12/2007|3 Closed|China|
|P082993|2006|2009|8/16/2008|4 Closed|China|
|P082993|2006|2009|12/10/2008|5 Closed|China|
|P082993|2006|2010|6/28/2010|6 Closed|China|
|P082993|2006|2011|1/5/2011|7 Closed|China|
|P082993|2006|2012|10/18/2011|8 Closed|China|
|P082993|2006|2012|12/21/2011|9 Closed|China|
|P082993|2006|2012|4/30/2012|10 Closed|China|
|P082993|2006|2013|10/24/2012|11 Closed|China|
|P082993|2006|2013|12/25/2012|12 Closed|China|
|P082996|2004|2004||0 Closed|Turkey|
|P082996|2004|2005|5/14/2005|2 Closed|Turkey|
|P082996|2004|2006|2/25/2006|3 Closed|Turkey|
|P082998|2005|2005|5/3/2005|2 Closed|Ethiopia|
|P082998|2005|2006|12/16/2005|3 Closed|Ethiopia|
|P082998|2005|2006|6/26/2006|4 Closed|Ethiopia|
|P082998|2005|2007|12/26/2006|5 Closed|Ethiopia|
||Balan|ce Sheet|Income Stateme|nt||Financial Ratio|s||
|---|---|---|---|---|---|---|---|---|
|Fiscal Year|Total<br>Capital|Total<br>As-<br>sets|Net<br>In-<br>come|Non-<br>Interest<br>Expense|Return on Equity|Return on Asset|s<br>Leverage|Expense|
||$ Mn|$ Mn|$ Mn|$ Mn|||||
|1989|1,582.6|4,006.1|196.5|76.8|12.41%|4.90%|2.531|1.92%|
|1990|1,864.1|5,606.3|157.0|105.9|8.42%|2.80%|3.008|1.89%|
|1991|2,104.0|6,648.2|165.9|119.7|7.88%|2.49%|3.160|1.80%|
|1992|2,389.3|8,132.7|180.2|139.7|7.54%|2.22%|3.404|1.72%|
|1993|2,702.4|8,913.4|141.7|163.2|5.25%|1.59%|3.298|1.83%|
|1994|3,198.1|14,722.8|258.2|175.2|8.07%<br>|1.75%<br>|4.604|1.19%<br>|
|1995|3,602.8|18,227.6|188.0|171.0|5.22%|1.03%|5.059|0.94%|
|1996|4,158.2|22,640.2|345.8|185.4|8.32%|1.53%|5.445|0.82%|
|1997|4,736.9|28,974.7|431.9|204.5|9.12%<br>|1.49%<br>|6.117|0.71%<br>|
|1998|5,084.3|31,620.7|245.8|218.0|4.83%|0.78%|6.219|0.69%|
|1999|5,344.2|33,456.2|249.3|258.0|4.66%|0.75%|6.260|0.77%|
|2000|5,733.0|38,719.0|380.0|265.0|6.63%|0.98%|6.754|0.68%|
|2001|6,095.0|26,170.0|345.0|304.0|5.66%|1.32%|4.294|1.16%|
|2002|6,304.0|27,739.0|215.0|327.0|3.41%|0.78%|4.400|1.18%|
|2003|6,789.0|31,543.0|487.0|332.0|7.17%|1.54%|4.646|1.05%|
|2004|7,782.0|32,361.0|993.0|360.0|12.76%|3.07%|4.158|1.11%|
|2005|9,798.0|39,560.0|2,015.0|403.0|20.57%|5.09%|4.038|1.02%|
|2006|11,076.0|38,420.0|1,264.0|436.0|11.41%|3.29%|3.469|1.13%|
|2007|14,017.0|40,599.0|2,490.0|482.0|17.76%<br>|6.13%<br>|2.896|1.19%<br>|
|2008|18,261.0|49,471.0|1,547.0|549.0|8.47%|3.13%|2.709|1.11%|
|2009|16,122.0|51,483.0|-151.0|582.0|-0.94%|-0.29%|3.193|1.13%|
|2010|18,359.0|61,075.0|1,746.0|664.0|9.51%|2.86%|3.327|1.09%|
|2011|20,279.0|68,490.0|1,579.0|700.0|7.79%|2.31%|3.377|1.02%|
|2012|20,580.0|75,761.0|1,328.0|798.0|6.45%|1.75%|3.681|1.05%|
|2013|22,275.0|77,525.0|1,018.0|845.0|4.57%|1.31%|3.480|1.09%|
|2014|23,990.0|84,130.0|1,483.0|888.0|6.18%|1.76%|3.507|1.06%|
|2015|24,426.0|87,548.0|445.0|901.0|1.82%|0.51%|3.584|1.03%|
|2016|22,766.0|90,434.0|-33.0|933.0|-0.14%|-0.04%|3.972|1.03%|
|2017|25,053.0|92,254.0|1,418.0|962.0|5.66%|1.54%|3.682|1.04%|
|2018|26,136.0|94,272.0|1,280.0|1,029.0|4.90%|1.36%|3.607|1.09%|
|2019|27,606.0|99,257.0|93.0|1,355.0|0.34%|0.09%|3.595|1.37%|

46
**Figure 12. Distribution of Urban Informal Firms by Firm Type**

<!-- Start of picture text --> Mozambique 78 2 21<br>Zambia 58 6 35<br>Zimbabwe 48 15 36<br>Avg. (Firm weighting) 72 4 25<br>Avg. (Equal-country weighting) 61 8 31<br>Low-educated Self-employed High-educated Self-employed Employer<br><!-- End of picture text --> Source: Own elaboration based on IES.

We consider two sets of variables to characterize urban informal firm types. The characteristics of the firms we measure are firm age, sector of activity, whether they operate outside household premises, the value of sales in the last month, the number of workers and productivity levels. We consider a set of variables related to financing: access to financial services and financial behavior including having a bank account, using banks or microfinance institutions for day-to-day operations, whether the accounts of the business are mixed with household expenses, and whether they use mobile money. We also separate “less vulnerable firms,” defined as those with productivity measures in the top 10 percent of the sample distribution, and “more vulnerable firms” to better understand the characteristics that are unique to higher productivity firms. We select the 10 percent cut-off following the informal firm heterogeneity analysis in the literature.<sup>56</sup> The productivity variable is calculated as the value of profits (sales minus total costs) per worker.

# **6.1 Distribution of Firm-Related Characteristics, by Informal Firm Type**

A minority of informal firms manage to survive and grow. Less than 3 percent of firms are more than 20 years old, with 43.3 percent being start-ups (up to one year) and nearly 30 percent in existence for 2 to 5 years (Figure 13). This reflects high entry (and probably exit) rates of informal firms. When comparing different firm types, we find that HE-SE are younger than other informal firm types, with nearly half being 2 to 5 years old but a disproportionately small share with six or more years. The difference is statistically significant and robust even when controlling for owner and other firm characteristics (Table

> 56 Grimm et al. (2012), Adoho et al. (2018), Karlen et al (2022) and Aga, Campos, Conconi, Davies & Geginat (2021).

28
rarely then been connected to employment growth relationships. In prior work, Ghani, Kerr and Tewari (2013) started down this path for Indian manufacturing. This earlier work measured specialization and diversity for the manufacturing sector and compared it to the United States. It found that specialization and diversity jointly enhanced manufacturing productivity—the subtle distinctions between the measures are described further below—and that initial manufacturing specialization in 1989 was a strong predictor of employment growth over the 1989 to 2010 period for Indian districts.<sup>1</sup> This study makes two central improvements on the earlier work. First, and most important, it provides a more comprehensive view of the local industrial sectors in India by bringing together manufacturing and services data, which in the Indian context also includes industries often grouped with retail trade or transportation. Services have been very important for India’s development, and notions of local specialization and diversity extend well beyond the manufacturing sector. The broader sector reach limits us to an analysis of employment growth since 2000, but this shorter time span is strongly compensated for by the more complete perspective on local specialization and diversity. In Sections 2 and 3 of this paper, we conduct many of the metrics and analyses carried out by Ghani, Kerr and Tewari (2013) to identify and convey how these patterns fit with those depicted earlier for the manufacturing sector.

We next use the developed metrics to consider employment growth at the district level across the 2000 to 2010 period. We conduct this analysis at many levels and with multiple decompositions in Section 4. We have two key findings. First, overall employment is strongly linked to broader district diversity. This pattern is distinct from our earlier paper that identifies manufacturing specialization in 1989-1994 as a key correlate of manufacturing growth across the 1989 to 2010 period, and we conduct several analyses to show their compatibility. This broader view of the local industrial base was important to discern the role of diversity and is highly robust. We also find that initial clusters of modern services experienced abnormally high

> 1 Some earlier studies have provided evidence through productivity estimations and related empirical work. Indian studies include Lall et al. (2004), Lall and Mengistae (2005), Deichmann et al. (2008), and Fernandes and Sharma (2011). Duranton and Puga (2004) and Rosenthal and Strange (2004) provide broad background to the agglomeration literature. Acemoglu and Zilibotti (1997) and Imbs and Wacziarg (2003) are examples of work on national diversification, and Kaulich (2012) provides an extensive survey and empirical evidence.

3
40 benefits of general goods, such as defense, police, and adniinistration, are typically<sup>allocated as</sup> if they are pro-poor when measured as a percentage of recipient income and favor<sup>urban</sup> inhabitants.

For developing countries, benefit incidence studies concentrate heavily on a few countries. Few studies have been done for countries outside of Central and South America--and we are<sup>not</sup> aware of any benefit incidence studies for Africar. countries. Given the scarcity<sup>of results and</sup> the World Bank's continued interest in distributional issues (e.g., Conable, 1990, p. 299),<sup>we</sup> believe that further benefit incidence research may yield laige benefits for<sup>policy makers. In</sup> addition, while survey costs for some earlier studies were high, increasing availability of household survey data may have substantially lowered the costs of undertaking research<sup>in this</sup> area (though in some cases minor modifications of the survey instruments may be required<sup>to</sup> generate the required data).

To make sure this research is of greatest possible value to policy makers, we make a number of recommendations on conducting benefit incidence analyses. First, aggregate results based on the zero-government counterfactual rely on very strong assumptions about fixed<sup>relative</sup> prices and incomes, government efficiency, and the relationship between marginal and total benefits. Also, those studies are often not designed to identify which types of public services benefit the poor. We believe that researchers should focus more on providing benefit incidence studies on specific government functions or programs that can help policy makers reach conclusions about proposed reallocations of resources among govemment programs.<sup>Second,</sup> benefit incidence should be assigned to households based on household survey information<sup>on</sup> usage rather than on ad hoc assumptions that assign benefits based on income or the number<sup>of</sup> members in the household. Third, improved annual cost measures for services need<sup>to be</sup> developed, particularly with respect to capital inputs. Fourth, researchers should group households by deciles and whenever possible should consider other groupings, including household income adjusted for household composition, age, location, and other relevant socioeconomic variables. Finally, careful attention to life-cycle benefits, benefit shifting, rent-seeking, out-of-pocket costs, displacement of private sector efforts, average versus marginal<sup>incidence, and</sup>
when the target behavior does not occur. CM techniques have been developed and tested in the context of clinical trials and settings, and are a clinically accepted tool in fields such as substance abuse, but have rarely (if ever) been implemented on a large scale in the manner of CCT programs.

As with CCTs, CM interventions have been tied to participation and the uptake of services in several domains, although risk behaviors are the important determinant for participant selection, rather than income constraints. CM has been shown to improve drug abuse outcomes (Rawson et al., 2002) and uptake rates of counseling sessions (Petry et al., 2001b); attendance at weight loss sessions; attendance at HIV drop-in center activities (Petry et al., 2001a) and antiviral medication adherence (Rosen et al., 2007); and attendance in smoking cessation clinics (e.g., see Higgins et al., 1994; Petry, 2000; and Emont and Cummings, 1992). Of particular interest, however, is the use of CM to elicit a complex behavioral change – usually, to discourage an unhealthy behavior by positively reinforcing the cessation of that activity (e.g. drug or alcohol abuse, smoking, or over-eating). The conceptual basis of CM and CCTs is thus largely similar, although advocates of CM impose no a priori assumptions about the effectiveness of the use of cash as the incentive or reinforcement device, and have experimented with a variety of reward mechanisms, including vouchers and prizes.<sup>2</sup> In addition, many CM studies are designed to explore effect differences due to variations in the value of the conditionality (known as the ―dose-response‖ curve), the frequency of monitoring and payments, and the length of time that the elicited behavior change is sustained after the program has ended.

The use of CM has been most intensively studied in relation to its efficacy in treating substance abuse. A landmark study by Higgins et al. (1994) demonstrated that incentives delivered contingent on submitting cocaine-free urine specimens significantly improved treatment outcomes in ambulatory cocaine-dependent patients. Over 50 percent in the treatment condition achieved at least two months of cocaine abstinence versus only 15 percent of the controls. Silverman et. al. (1996) showed that 47 percent of

> 2 The findings of studies reviewed for this paper suggested that cash is typically preferred by research subjects, and in some studies it has been shown to have a greater behavioral effect than the equivalent noncash reward (Kamb et al., 1998; Deren et al., 1994; Vandrey et al., 2007), although the findings are hardly conclusive.

9