rafmacalaba/datause-encoder-data huggingface dataset:
15 The importance of cocoa in the Ivorian economy means that a partial equilibrium analysis can be misleading however. First, it excludes interactions between cocoa and other rural goods, and the industrial and service sectors. Second, the fiscal impact of price and tax policy for cocoa is large because of its share in present government income. In this section we investigate the economic effects of cocoa taxation and marketing policy with a computable general equilibrium (CGE) model'<sup>6</sup> .

The model begins with a modified social accounting matrix depicting the economy at the end of 1996 (summarized in Annex 1). In this model, an initial equilibrium exists where household income equals consumption plus savings plus net transfers; the value added from labor, land and capital in goods production is equal to the household endowments of those factors; exports equal imports plus net foreign savings (aid) minus debt service; and government revenue equals governnent consumption plus net foreign savings and net transfers to households minus debt service.

_Goods production._ There are 28 goods production sectors. Agricultural sectors are cocoa, coffee, forestry, food crops, other primary products (including livestock, fisheries, other perennial exports). Goods are produced by nested constant elasticity of substitution (CES) functions. Ignoring taxes for the moment, for any goods sector, Qi, the production function is: The term in square brackets is the nest for value added from primary factors; _L_ indicates labor ('u' for urban, 'r' for rural), K capital, N land, and V' is a parameter such that _i_ = **I/ca** - 1 where a is the elasticity of substitution among factors. The term _Gi,_ is the aggregate of intermediate inputs into production and the elasticity of substitution between G and factors is zero 17 Land is used only in rural goods. Both rural and urban labor produce rural goods because the value of agricultural output has two components. The first is on-farm output produced with land and rural labor. The second is the additional value of output between farm gate and FOB, which is produced by urban labor and capital.

> 16 A similar model has been used by Chia et al (1992) to investigate poverty issues in Cote d'Ivoire.

> **17** There are some differences between the present model and that of Chia et al (1992). In our model labor is not differentiated by skill level; our model imposes a constant elasticity of substitution among factors equal to 0.5 in all rural goods (coffee, cocoa, cotton, forestry, fisheries, other agricultural exports, food crops) and to 0.8 in all urban goods (the 21 sectors of industry and services), while the corresponding elasticity varies between 0.4 and 0.9 across sectors in Chia et al; our model is based on the 1995 input-output matrix of Cote d'lvoire and 1996 national accounts data, while that of Chia et al uses 1986 data. Both models assume zero substitution between primary factors and intermediate goods.
# **Indirect Effects of COVID-19 Nonpharmaceutical Interventions on Vaccine Acceptance**

Maurizio Bussolo, Nayantara Sarma and Iván Torre*

# **Declaration of Interest:** none

**Keywords:** COVID-19 **,** pandemic, vaccine hesitancy, vaccine acceptance, non-pharmacetutical interventions, trust, norms

**JEL:** I12,I18

*The authors are at the World Bank. This paper’s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Holly Krambeck, Gabriel Stefanini Vicente and the World Bank, the MIT, and Facebook-META teams of the development data partnership (https://datapartnership.org/) for making the data for this study available to us, and Adanna Chukwuma and Tania Dmytraczenko for useful comments.
31

**Estimates (Selection Corrected) for**

**Table 5.  Per Capita Household Expenditure Ghana, 2005/06**

|Variable|Receive no<br>remittances|Receive<br>internal<br>remittances<br>(from<br>Ghana)|Receive<br>international<br>remittances<br>(from African<br>or other<br>countries)|
|---|---|---|---|
|**Human Capital**||||
|Number of members over age 15 with primary<br>education|-0.054<br>(-0.66)|-0.174<br>(-1.120)|-0.237<br>(-0.58)|
|Number of members over age 15 with junior<br>secondary education|0.050<br>(1.40)|0.140**<br>(2.230)|-0.019<br>(-0.17)|
|Number of members over age 15 with senior<br>secondary education|0.276**<br>(2.38)|0.067<br>(0.590)|0.094<br>(0.37)|
|Number of members over age 15 with<br>university education|0.700***<br>(3.53)|1.485***<br>(3.400)|0.556<br>(0.48)|
|**Household Characteristics**<br>Age of household head|-0.093***<br>(-4.06)|0.013<br>(0.270)|0.098<br>(0.33)|
|Square of age of household head|0.001***<br>(4.26)|0.0002<br>(-0.500)|-0.001<br>(-0.38)|
|Household size|-0.121***<br>(-12.04)|-0.131***<br>(-9.200)|-0.134***<br>(-2.93)|
|Number of males over age 15|0.058<br>(1.54)|0.060<br>(1.050)|0.025<br>(0.21)|
|Number of children under age 5|-1.030***<br>(-10.00)|-0.918***<br>(-6.450)|-0.819<br>(-0.66)|
|**Migration Networks**||||
|Number of female-headed households receiving<br>internal remittances (from Ghana) in group in<br>1998/99|0.060<br>(0.22)|-1.020***<br>(-4.28)|0.028<br>(0.03)|
|Number of female-headed households receiving<br>international remittances (from African and<br>other countries) in group in 1998/99|1.036*<br>(1.78)|-1.077<br>(-0.94)|1.273<br>(0.4)|
|Square of internal migrants in group in 1998/99|-13.578<br>(-0.70)|-10.032<br>(-0.380)|-7.497<br>(-0.14)|
|**Ethno-religious group characteristic**<br>Square of income in ethno-religious group|3.5E-15<br>(1.17)|7.56E-15**<br>(2.29)|-2.6E-15<br>(-0.21)|
|Lambda (Selection control)|0.008<br>(0.03)|-0.272<br>(-0.79)|<br>-0.047<br>(-0.05)|
|Constant|17.917<br>(37.80)|17.813<br>(20.88)|15.534***<br>(2.03)|
|Adjusted R<sup>2</sup>|49.79|53.51|52.69|
|Implied residual standard error|.5693|.5441|.4842|
|N|2515|1159|210|
Policy Research Working Paper 8408

# **Abstract**

This paper provides evidence on how the provision of social infrastructure, such as reliable electricity, can be leveraged to increase taxation in developing countries, particularly in Sub-Saharan Africa. First, using comprehensive data from the latest round of the Afrobarometer survey, the paper uses the instrumental variable approach to estimate the effect of access to and reliability of electricity on the tax compliance attitudes of citizens in 36 countries in Sub-Saharan Africa. The evidence shows a significant positive effect of electrification on tax compliance attitudes, with potentially strong externalities. The analysis also finds that the reliability of supply is crucial in explaining the impact of electricity access on attitudes toward taxes. Second, the paper provides suggestive evidence on national identity as one channel driving this impact. Access to social amenities such as electricity induces a sense of national identity among citizens, thereby incentivizing them to contribute, through taxes, toward the functioning of the state. Third, using data from the most recent World Bank Enterprise Surveys and under conservative assumptions, the paper estimates that countries in the region could in total generate additional tax revenues of more than $9.5 billion (4.3 percent of total tax revenue) per year solely by resolving issues related to electricity shortages. Put together, the paper concludes that the financial returns associated with public investments toward improving access to and reliability of electricity are substantial and could be harnessed to augment the financing gap in the sector.

This paper is a product of the Office of the Chief Economist, 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://www.worldbank.org/research. The authors may be contacted at mblimpo@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
**Appendix Table A4: Cross-Country Regression Results, 1962-2007**

|||O|LS|||2S|LS||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Dependent Variable:|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-|Female Ed-<br>ucational<br>Attainment|Fertility<br>Rate|Fertility<br>Rate|Female<br>Labor<br>Force Par-|Female Ed-<br>ucational<br>Attainment|
||||ticipation||||ticipation||
|(Log) Female Labor|-0.57***|-0.56***|0.10|0.36**|-1.03***|-0.94***|0.21|0.19|
|Need of Exports|(0.132)|(0.127)|(0.159)|(0.181)|(0.233)|(0.222)|(0.266)|(0.294)|
|(FLNX)|||||||||
|(Log) Openness|-0.00|-0.06|-0.07|0.10|-0.01|-0.06|-0.07|0.10|
||(0.038)|(0.050)|(0.063)|(0.096)|(0.042)|(0.051)|(0.061)|(0.093)|
|(Log) GDP per capita|-0.37***|-0.36***|-0.08***|0.47***|-0.37***|-0.36***|-0.08***|0.47***|
||(0.021)|(0.022)|(0.028)|(0.042)|(0.021)|(0.021)|(0.028)|(0.042)|
|(Log) Population<br>38||-0.04*|-0.05*|-0.01||-0.04*|-0.05*|-0.01|
|||(0.023)|(0.028)|(0.032)||(0.023)|(0.027)|(0.032)|
|_R_<sup>2</sup>|0.651|0.659|0.091|0.591|||||
|||||||First|Stage||
|Dep. Var.: (Log)<br>FLNX|||||||||
|(Log) Predicted FLNX|||||1.548***|1.62***|1.62***|1.64***|
||||||(0.212)|(0.219)|(0.219)|(0.237)|
|F-test|||||20.19|15.95|15.95|14.01|
|_R_<sup>2</sup>|||||0.328|0.340|0.340|0.359|
|Observations|146|146|146|126|146|146|146|126|

Notes: Robust standard errors in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%. _Fertility Rate_ is (log) total births per woman; _Female Labor Force Participation_ is (log) female labor participation rate (% of female population ages 15+); _Female Educational Attainment_ is the (log of) Barro-Lee measure of female educational attainment (average years of schooling among female population ages 15+ at five-year intervals). All of the variables are averages over the period 1962-2007, except for _Female Labor Force Participation,_ which is averaged over the period 1980-2007. Variable definitions and sources are described in detail in the text.
# Distributive Concerns When Replacing A Pay-As-You-Go System with a Fully Funded System,

## by Salvador Valdes-Prieto<sup>2</sup>

> 1 The second version of this paper was a background paper for the World Bank project on oldage security. Comments from participants at a World Bank workshop, a referee, and the editors are acknowledged. The efficient research assistance of Rodrigo Cifuentes, M.S. in Economics at U. Cat6lica de Chile, was essential for the successful completion of this paper. 2 Professor of Economics, Instituto de Economia, U. Cat6lica, Santiago, Chile.
violence variable of 2007 to the enrollment data of 2005. Though this is not ideal, it is the best that can be done with the available data.

The results again do not show an effect. Among the 15 regressions, only one yields a statistically significant result. This result is for the effect of homicide rates on the enrollment of children ages 6-11 and, though significant, is extremely small (a reduction of the share of enrolled children of about 0.00002 for a municipality that suffers an increase in violence of the magnitude of that experienced in Mexico from 2007 to 2010). Furthermore, that result disappears when we use only data from 2005 and later.

The result that had been found to be more robust in terms of the number of high school students does not have its counterpoint in terms of an effect on the enrollment rates of 15-17 year olds. This pattern of results is consistent with a null effect on education enrollments, but with a small effect on migration away from increasingly violent areas. Interestingly, there is also no effect on the enrollment rates of college-age young adults (18-20 and 21-23).

These data allow us to separate regression results for male and female students. One of the hypotheses is that more crime attracts young men to participate in the lucrative but illicit activity, and could thus incentivize them to leave school. To the extent that crime does not attract young women in the same degree, we would expect the effect to exist for men but not for women. On the other hand, if it was the case that women are more vulnerable and feel that going to school exposes them to more risks, we could expect there to be an effect for female but not for male.

21
2008, Peru in 2010, Mauritania in 2011, Ghana and 2005, Niger in 2011, and Nigeria between 2011 and 2012, while in Indonesia between 1997 and 2003, and Mauritania in 2008, subsidy cuts generated opposition to the reform.

Most successful fuel subsidy reforms have been complemented by social measures to mitigate the effect of rising prices on households. In a recent article, Moayed, Guggenheim, and von Chamier (2021) examine 31 cases of energy subsidy reforms in developing countries over two decades (1991-2012). The authors find that all the subsidy reduction reforms that failed occurred when there was significant protest and no parallel offsetting transfers, while all the reforms that were complemented by cash compensation measures were successful or, at least, partially successful. In Indonesia, for instance, unlike other attempts to increase energy prices in 1998, 2000, and 2002, which led to large-scale, violent protests, the 2005 subsidy reform was successful as the government used part of the fiscal savings to offset the rising cost of living for poor households, through social assistance programs. Similarly, the energy subsidy reforms implemented in Iran in 2010 did not generate significant negative reactions, due in large part to the cash transfer program, unlike the 2019 reform, during which the government did not communicate in advance about the reform, nor compensate households for price increases. In addition, the 2010 measures also led to a significant reduction in national poverty. The two Bonogas programs in the Dominican Republic have also considerably reduced the cost of subsidies for the government, by ensuring the support of the public and the powerful unions in the transport sector.

- 5-

Figure 4. Unit subsidy as a share of market wholesale price in Angola, Jan 2019 to December 2023 <!-- Start of picture text --> 110%<br>90%<br>70%<br>50%<br>30%<br>10%<br>-10%<br>-30%<br>2019 2020 2021 2022 2023<br>Gasoline Diesel Kerosene LPG<br><!-- End of picture text --> Source: authors’ estimations based on IGAPE As in many oil-producing countries, Angola's government provides substantial subsidies for fuel consumption. In 2011, fuel prices in Angola were 67 percent lower than the average price in SubSaharan Africa, and among the lowest in the world, due to tax and duty exemptions on petroleum products. With the decline in oil revenues from 2015 onward, the government started a strong fiscal consolidation that left fuel subsidies as the only de-facto social spending, while spending on health education, social protection, and water infrastructure declined. In end-2015, the government removed gasoline and diesel subsidies by increasing retail prices, but subsidies reemerged as soon as market 6
Table 4: Average WTP

||Av<br>2013|erage WTP<br>2016|
|---|---|---|
|Stove Durability (1 year)|17 birr|54 birr ($2)|
|Time Reduction (10%)|47 birr|86 birr ($3)|
|Fuel Reduction (10%)|62 birr|142 birr ($5)|
|Smoke Reduction(10%)|45 birr|112 birr($4)|

Note: The average is calculated between all treatment households and long-term use treatment households. The conversation to $ (USD) is approximate (1 USD = 27.52 birr May 2018) The results support the view that the WTP estimates obtained from choice experiment surveys (for the control group) are stable, at least over a three-year period, and can therefore be used for costbenefit analysis and welfare calculations over time. The results also show that respondents in treatment groups, especially the respondents who continue to use the stoves long-term, are willing to pay significantly more for the individual benefits of the stoves than in 2013. Next, we discuss the policy implications of this in the conclusion.

# **6. Conclusions and Policy Implications**

There is a growing effort in the non-market valuation literature to better understand the stability and evolution of preferences over time. A small number of studies (Bliem et al. 2012, Brouwer and Bateman 2005, Mørkbak and Olsen 2014, Price et al. 2016) have used the test-retest method to analyze stability of preferences but have found mixed results. A combined repeated choice experiment and an RCT on stove adoption provide a unique opportunity to analyze both the stability and the evolution of preferences.

Respondents were given the exact same choice question in 2013 and 2016 and the treatment group in the RCT received improved firewood stoves, whereas the control group continued to use traditional stoves. This setting allows us to use a difference-in-difference framework to analyze the results from the choice experiment, the first instance of a difference-in-difference choice experiment analysis that we are aware of. Further, we exploit the heterogeneity in use in the treatment group to analyze the evolution of preferences in response to the effectiveness of the stoves.

We conduct a national survey in Ethiopia starting with 504 households in 36 communities in 2013, and we are able to conduct the study with 486 of the same households in 2016, giving an extremely low attrition rate of just 4%.

14
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 39 5. Using the bootstrap conditional residuals from step 4, along with the generated **_β_**<sup>_∗_</sup> in step 2, and making use of the values of the auxiliary variables for all the population units, generate bootstrap values of the response variable for all individuals in the population, as follows: This provides us with a complete census of the response variable, from which we calculate the true value of the indicator for area _d_ , 6. Repeat steps 1–5 for _r_ = 1 _, . . . , R_ , resulting in _R_ complete censuses. For each census _r_ , we compute the indicator of interest _δd_<sup>_∗_(</sup><sup>_r_)</sup> . The ELL estimator of _δd_ is then obtained by averaging the true values _δd_<sup>_∗_(</sup><sup>_r_)</sup> over the _R_ censuses, as follows: As an estimator of the MSE of the ELL estimator, the ELL method uses the bootstrap variance, given by Consider now that we wish to estimate the area mean _δd_ = _Y_<sup> ̄</sup> _d_ using the ELL bootstrap procedure, keeping **_β_**<sup>_∗_</sup> = **_β_**<sup>ˆ</sup> 0 fixed across the bootstrap replicates for simplicity. Let _ydci_<sup>_∗_(</sup><sup>_r_)</sup> be the values generated in step 5, in the _r_ -th bootstrap replicate of the ELL bootstrap procedure. According to the bootstrap generating process, for **_β_**<sup>_∗_</sup> = **_β_**<sup>ˆ</sup> 0, the bootstrapped value of the area mean in the _r_ -th replicate is
1977 it was about equal to the average SII wage, implying that the total of wages exempted from contribution must for this reason have increased strongly. A minimum of TL 40 per day, however, also applied, with the consequence that with inflation an increasing amount of wages must have become liable for contributions. With a fixed interval for contribution liability and the whole distribution of daily wages moving upwards with inflation, the result might be an increasing share of wages liable to contributions up to a point and after that a decreasing share. With ongoing hyperinflation and average daily wages according to SMI reaching TL 2,412.7 in 1984, the tendency for the share to decrease should tend to dominate, possibly already from the mid-1970s, allowing, of course, for later changes Iin the nominal limits (at this moment unknown to me). If thus reporting refer to wages paying contributions over all insured we would have an explanation of the increasing gap between reported SII per employe* wages and SMI wages/. Information about both the liability interval ater 1977 and SII reporting practices would have to be solicited directly from the SII. Needless to say, misleading reporting might also exist for. the census but there is no obviotvs reason why that should be the case.

Minimum wage legislation was introduced in the late 1960s under union pressure and was originally regionally differentiated at a level of 25-29 TL/day. In June 1974 it was unified at TL 40/day. Subsequent increases brought it up to TL 110 from the end 49 -

-
Only in Tajikistan did the summary results differ from the trends in the pooled regressions. In Table 8, columns 1 and 4 show that assignment to any hypothetical incentive is not significantly different from controls. Examining the results for each treatment arm (Table 8, columns 2 and 5) suggests that the effect reaches significance only for the first treatment arm. Including additional controls (Table 8, columns 3 and 6) does not meaningfully impact the estimated effect.

**Table 8: Main results table Tajikistan**

||OLS: A<br>|gree to Vaccin<br>|ate = 1<br>|Logit(odds r<br>|atio): Agree t<br>|o Vaccinate = 1<br>|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Treat|-0.015||-0.019|0.917||0.890|
||(0.023)||(0.022)|(0.123)||(0.121)|
|T-Arm1||-0.050*|||0.755*||
|||(0.030)|||(0.126)||
|T-Arm2||-0.011|||0.936||
|||(0.028)|||(0.157)||
|T-Arm3||-0.016|||0.911||
|||(0.029)|||(0.153)||
|T-Arm4||0.021|||1.144||
|||(0.028)|||(0.202)||
|Inc. Per Capita|||0.000*|||1.001*|
||||(0.000)|||(0.000)|
|Rural Location|||0.125***|||1.951***|
||||(0.024)|||(0.233)|
|Household Size|||0.005*|||1.033|
||||(0.003)|||(0.021)|
|Constant|0.791***|0.791***|0.651***|3.780***|3.780***|1.776***|
||(0.020)|(0.020)|(0.035)|(0.458)|(0.458)|(0.352)|
|Observations|2,267|2,267|2,267|2,267|2,267|2,267|
|R-squared|0.000|0.003|0.022||||

Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 `.` Logit model coefficients report odds ratios. Offers in each arm of the experiment include: (1) nothing – control group, (2) 50 somoni (T-Arm1), (3) 100 somoni (T-Arm2), (4) 200 somoni (T-Arm3), and (5) 400 somoni (T-Arm4).

As additional validation, all respondents were asked whether they “support giving people financial incentives for vaccination” following the experiment questions. The results are consistent with findings from the survey experiment. In Uzbekistan, 72 percent of respondents said they disagree with paying incentives for people to be vaccinated. In Kazakhstan, 58 percent said they disagreed with paying incentives. Only in Tajikistan, where the experiment failed to find any systematic effect on vaccination intentions, just 18 percent said they disagreed with paying incentives.

In table 9, using the same dependent variable, responses are disaggregated by reason for non-vaccination among those who stated they would not be vaccinated in a separate question during the same interview. These highlight that much of the negative response to incentive payments comes from respondents who stated they would be willing to be vaccinated changing their response to refusal in the case that they were offered an incentive. Among those who said they would not be vaccinated, very few changed their response following the offer of an incentive. However, small but significant differences in some cases were apparent with respect to suspicion of vaccines and their risks (table 9 columns 1, 3 and 4).

13