rafmacalaba/datause-encoder-data huggingface dataset:
determined by the tax administration without any form being filled by the business, this “offer” was not technically implemented. The advisors also left their contact information in case the _entreprenant_ had any complaints about future tax payments and inspections, and offered mediation services in case of a dispute between the firm and the tax administration.

Appendix 1 provides more detail on how these three packages were implemented.

# **3.2 Sample selection and study population’s characteristics**

A listing survey was conducted in Benin’s largest city of Cotonou in March and April 2014. This survey was designed in order to obtain a representative sample of all businesses operating in Cotonou, including Dantokpa market.<sup>6</sup> All businesses with fixed location, except international and nationwide companies and liberal professions, were targeted. Overall, 19,246 businesses were listed, of which a sample of 7,945 were surveyed. We then dropped businesses which were already formal, and which had very high or very low profits and sales to arrive at a sample of 3,596 for the study. Appendix 2 provides details on the sampling protocols and this selection process.

Table 1 provides descriptive statistics for businesses selected in the sample, and compares them to the overall set of informal businesses and to formal businesses. Businesses selected for the study have very similar characteristics to the whole population of informal businesses surveyed, and the overall study shows good external validity for the whole city of Cotonou. Formal businesses had on average 3.2 employees and monthly profits of around CFAF 210,000 (USD 352), while informal businesses had 1.1 employees and a monthly profit of CFAF 46,000 (USD 77). About 60% of businesses were connected to the electricity network, 55% of businesses were involved in trade activities, 26% worked in services, and 16% were craftsmen. 63% of businesses sampled for the study were owned by women. This reflects the high share of female owners in Dantokpa market. Approximately 30% of business owners never went to school, and less than 20% of the businesses were keeping some type of accounting. Formal businesses had higher access to the banking system: 80% of them owned a bank account, whereas only 20% of informal businesses did.

> 6 The largest market in Cotonou and one of the largest in West Africa.

9
- World Bank. 2016. _World Development Report 2016: Digital Dividends_ . The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0671-1 (21 August, 2020).

- ———. 2017. _World Development Report 2017: Governance and the Law_ . The World Bank. http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0950-7 (21 August, 2020).

Xiaojun, Yan, and Xin Ge. 2016. “Participatory Policy Making under Authoritarianism: The Pathways of Local Budgetary Reform in the People’s Republic of China.” _Policy & Politics_ 44(2): 215–34.

Zionts, Jacob. 2018. “‘Blockchain Democracy’ and the Misleading Promise of Moscow’s Active Citizen Program.” _ConsenSys Media_ . https://media.consensys.net/blockchain-democracy-andthe-misleading-promise-of-moscows-active-citizen-program-2eda7141c5b9 (31 May, 2020).

Zubarevich, Natalia. 2019. “Development in the Russian Regions and the 2019 Elections. Is There a Correlation Between Economy and Politics?” _PONARS Eurasia, Point & Counterpoint_ . http://www.ponarseurasia.org/point-counter/article/development-russian-regions-2019elections-correlation-economy-politics (31 May, 2020).

33
**Table 6: Hicksian Own Price Elasticities**

||**All**|**Ru**<br>|**ral**<br>|**Urba**<br>|**n**<br>|
|---|---|---|---|---|---|
|||Poor|Non-Poor|Poor|Non-Poor|
|Cereal|-2.968***|-3.221***|-4.113***|-2.518***|-2.293***|
||[0.036]|[0.089]|[0.083]|[0.033]|[0.038]|
|Meat/Fish|-1.613***|-2.857***|-1.683***|-1.259***|-1.221***|
||[0.011]|[0.059]|[0.015]|[0.011]|[0.007]|
|Oil|-0.844***|-1.056***|-0.768***|-0.856***|-0.568***|
||[0.001]|[0.012]|[0.002]|[0.003]|[0.012]|
|Tubers/Plantain|[0.006]|[0.023]|[0.018]|[0.005]|[0.008]|
||-1.596***|-1.563***|-1.314***|-1.779***|-2.593***|
|Other Foods|[0.008]|[0.018]|[0.008]|[0.008]|[0.023]|
||-7.662***|-0.896***|-5.526***|-7.521***|-6.992***|
||[0.084]|[0.003]|[0.117]|[0.193]|[0.253]|
|Observations|21,239|6,766|4,785|5,743|3,945|

Standard errors in brackets, 100 bootstrap replications *** p<0.01, ** p<0.05, * p<0.1 Source: 2012 1-2-3 Survey, DRC Table 6 shows compensated own price elasticities, which has been computed with the twostep approach presented above for expenditure elasticities. All the entries in the table are strictly negative, suggesting that the Slutsky matrix is likely to be negative definite. Own price elasticities vary by area of residence and poverty status. These compensated elasticities provide a means to assess the welfare implications of the change in relative prices induced by the VAT.

Price elasticity of Marshallian demand  _ij_ is related to the compensated elasticities  _ij_ with the Slutsky equation: where  _i_ is the expenditure elasticity for commodity i, and<sup>_s_</sup> _i_<sup>its budget share. Because income</sup> and substitution effects do not counteract, a first order or second order approximation of the welfare loss would respectively overestimate and underestimate the true change in welfare resulting from the introduction of VAT. We proceed to assess the welfare loss with alternative welfare measures.

# **6  Welfare Analysis**

# **6.1 Welfare Loss: Measurement and Estimates**

Equipped with the parameter estimates from the QUAIDS, we compute the compensating variation, by simulating the impact of price changes. The CV is a measure of welfare change, consistent with consumer theory. It is defined as the amount of income to be given to a household after a price change so that its members experience the utility level they had before the price change. Economic theory and recent econometric research suggest that the exact CV is a better metric for welfare analysis than its more popular competitors - the first and second order approximations (J. Banks et al., 1997, 1996, C. Nelson et al., 2011). The CV is computed with the following equation: 17
# **Addressing Access and Behavioral Constraints through Social**

# **Intermediation Services:**

# **A Review of Chile Solidario and Red Unidos**

Adriana Camacho Wendy Cunningham Jamele Rigolini Veronica Silva<sup>1</sup> U. de Los Andes World Bank World Bank World Bank JEL: D1; H5; I3

> 1. Contact author: Jamele Rigolini, The World Bank, 1818 H St. NW, Washington, DC 20433. Email: jrigolini@worldbank.org. We would like to thank Margaret Grosh for her comments;  Juliana Sanchez from ANSPE for providing administrative information about Unidos; and Rafael Calderón, who authored a background paper on behavioral economics and _Red Unidos_ .
Zhang, Y., Yang, Y., Yang, K., Tan, X., Sun, X., Leng, B., Zhou, C., Zhu, B. (2021). Non-linear wave attenuation quantification model improves the estimation of wave attenuation efficiency of mangroves. Estuarine, Coastal and Shelf Science 245.

Zhu, D., Hui, D., Wang, M. et al. (2021). Allometric growth and carbon storage in the mangrove Sonneratia apetala. Wetlands Ecol Manage 29, 129–141 35
52

# (a) Developing countries

<!-- Start of picture text --> 60<br>NRA ag tradables<br>40 NRA non-ag tradables<br>RRA<br>20<br>0<br>1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>-20<br>-40<br>-60<br>(b) High-income countries<br>80<br>NRA ag tradables<br>60<br>40<br>20<br>0<br>1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>-20<br>-40<br>-60<br>percent<br>percent<br><!-- End of picture text --> Figure 2. Developing and high-income countries’ NRAs to agricultural and non-agricultural tradable sectors, and RRAs, 1955–2010 (%). Calculations use farm production–weighted averages across countries. RRA is defined as 100*[(100+NRAag<sup>t</sup> )/(100+NRAnonag<sup>t</sup> )−1], where NRAag<sup>t</sup> and NRAnonag<sup>t</sup> , respectively, are the percentage NRAs for the tradable segments of the agricultural and non-agricultural sectors.

Source: Anderson 2009, Chap. 1, updated from estimates in Anderson and Nelgen 2012b.
**Table 3. OLS Regressions across countries for decision-making over large purchases comparing spouses’ responses**

||(1)|(2)|(3)|
|---|---|---|---|
||Wife takes power<br>vs. wife does not<br>have power at all|Wife takes power<br>vs. wife and<br>husband agree she<br>is DM, or DM is<br>joint|Wife takes power<br>vs. husband gives<br>power|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: bottom 20%|-0.054***|0.053***|-0.004|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: next-to-bottom 20%|-0.025***|0.027***|-0.007|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: second highest 20%|0.023***|-0.018**|0.012|
||(0.01)|(0.01)|(0.01)|
|Wealth quantile: top 20%|0.070***|-0.068***|-0.002|
||(0.01)|(0.01)|(0.01)|
|Constant|0.603***|0.716***|0.782***|
||(0.02)|(0.02)|(0.02)|
|Observations|34,797|37,280|33,827|
|R-squared|0.232|0.109|0.049|
|AdjustedR-squared<br>Notes: (1) Country fixed effects included; robust stan|0.23<br>dard errors in parentheses. *** p<|0.11<br>0.01, ** p<0.05, * p<0.1|0.05|

<u>Notes: (1) Country fixed effects included; robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1</u> Restricting to just cases of disagreement in column 3 gives the clearest case of this. Here we compare our two key scenarios and see that the wife assigning herself more decision-making power compared to what her husband assigns to her is positively related to a smaller age and education gap, higher labor force participation (including off-farm employment), earning more than her husband, and owning land. Interestingly, wives in rural areas are less likely to take power, versus being given power, which is consistent with women in rural areas having to face more entrenched social hierarchies and facing greater obstacles “taking” or assigning greater power to themselves (ILO, 2017). Taken together, these relationships suggest not only that discordance among spouses is nonrandom and systematically related to other measures of power, but that the act of taking power specifically isolates a key facet of bargaining power. This facet is related to proxies of women’s status in ways that uncontested power or discordance where the husband 19
21 The earlier urban data set presented in Appendix Table 3 of Ingram and Liu (1997) includes 35 cities, each with observations at two points in time. In the current urban crosssection analyses (see Appendix Tables 5 and 7 of the current paper), two cities (Beijing and Santiago), both with only one year observation, were added to the data set. The data for Beijing and Santiago are shown in Table A-2.

Table **A-2.** Data for Beijing **and** Santiago

||Beijing, 1994|Santiago, 1990|
|---|---|---|
|Urban population ('000)|_5,748_|**4,768**|
|Urban land area (kM <sup>2</sup>)|485|467|
|Per capita GNP in 1987 US$|807|1,960|
|Population density (persons/km2)|11,860|10,201|
|Total motor vehicles per 1,000 persons|54|84|
|Passenger cars per 1,000 persons|46|67|
|Road network density (km/kM<sup>2</sup>)|7.47|5.10|
|Per capita road length (m)|0.63|0.50|
|Motor vehicles per km of road|86|168|

Source: Data for Beijing are primarily from Li (1996); and for Santiago, from Kain and Liu (1994).

**Annex B. Deriving the Elasticity from a Quadratic Log-log Equation**

Regression estimated: logY = log a **+** b, log X **+** b2 (log X)<sup>2</sup> (1) Transforming: Taking derivatives of (2): dY/Y = (b,/X) dX + {(2 b2 log X)/X} dX = (b, **+** 2 b2 log X) dX/X _(5)_ Elasticity:

|E=(dY/dX) (X/Y)=b1 + 2b2 log X|(6)|
|---|---|
education to be in an automation-prone occupation is consistent with our model’s assumption. In all countries, post-secondary education appears to provide skills that enable individuals to be employed in non-automation-prone occupations. While the data are less unanimous on what these skills are, some important insights emerge. First, those with general rather than vocational upper secondary are more likely to be in a non-automation-prone occupation for those countries in which there is a statistically significant difference, with one exception. In the countries without a statistically significant difference, this at least suggests that vocational programs, which typically focus on technical skills and deemphasize cognitive and non-cognitive skills, offer no clear advantage to avoiding automation-prone occupations. Second, there is clear evidence that literacy achievement, as a measure of cognitive skills, tends to be positively associated with being in a non-automation-prone occupation. Third, we see some evidence that the non-cognitive skill of openness to experience is positively associated with being in a nonautomation-prone occupation. This is consistent with the automation literature’s finding that creativity is a determinant of being complementary to automation as openness to experience has been found to correlate with creativity in the psychology literature (Furnham 1999).

_Automation-prone occupations and stage of development_ Our model assumes that the proportion of labor in the automation-complementing sector (equivalent to high-skill labor) is higher for wealthier countries, and it predicts that wealthier countries will have lower proportions of informal sector (low-skill) labor. Our simulation predicts that the proportion of employment in the automation-substituting (middle-skill) labor peaks at the cusp of developed country status. Applying our definitions of automation-prone occupations to the IPUMS-I data shows that this pattern is generally correct.

Figure 4 presents estimates of the proportion of workers in informal agricultural occupations, automation-prone occupations and non-automation-prone occupations, plotted against real GDP per capita. The figure includes only countries’ latest data since 1990 and those data sets that use the ISCO-88 classification of occupations. Similar to the model’s simulation presented in Annex 1 Figure 1, the proportion of automation-prone occupations peaks just around the point where the informal sector disappears which corresponds closely to the high-income status threshold.

32
One last stylized feature of ELG growth is nicely captured by the modified model with trade extemalities. Williamson (1979), citing the Kim and Roemer (1979) study of Korea's industrialization, notes that, in contrast with most other developing countries, Korea's relative (to GDP) price index of investment goods declined by approximately 40 percent during the period 1962-1975. Table 6 shows that with the subsidy to imports of heavy manufacturing necessary to induce the acquisition of heavy manufacturing imports, there is a dramatic decline in the domestic purchaser's price index of heavy manufacturing products. The producer price index of heavy manufacturing iicreases between E3 and FA because of the more than doubling of the export subsidy to heavy manufacturing in E4 compared to E3. It is also interesting to note that the subsidy rates in experiments E3 and E4 are in line with the subsidies to infant industry exports described by Westphal (1982). The patterns of structural change in Table 6 are, of course, only suggestive, but they do capture the salient differences in structural change observed between ISI and ELG strategies.

In these experiments, the policy mix involves only subsidies to exporting manufactures and importing heavy manufactures. As with the small model, the total expense of the program is large. In the high elasticity externality experiment (E4), the cost of all subsidies equals 12 percent of gross sales and 27 percent of GDP. We did an additional experiment that started from E4, but added a uniform indirect tax rate (td) of 15 percent. The result is a similar pattem of subsidies, but essentially no net tax on households. It is certainly feasible to design a mix of indirect taxes and subsidies that exploit the externalities, with little impact on total net tax revenue. The implied sectoral tax and subsidy rates are also within observed ranges for semi-industrial countries.

-29-
A variety of factors explain these differences in growth outcomes, and it is beyond the scope of this paper to analyze all of them. Instead we emphasize the most common factors identified in the literature, but we also stress the difference in their initial endowment of human capital, which has not been sufficiently appreciated despite the large differences on this score between the two regions (table 2).<sup>5</sup> Table 2: Social Infrastructure Indicators 1870-1910

||Rail<br>(K|road<br>m)|Prim<br>Enrol|ary<br>lment|Literacy<br>Rate|
|---|---|---|---|---|---|
||1870|1910|1870|1910|1870-90|
|Denmark|770|3445|58.3|65.8|99|
|Finland|483|3356||26.4|89|
|Netherlands|1419|3190|59.1|70.3|97|
|Norway|359|2976|60.8|68.6|98|
|Sweden|1727|13829|56.9|66.9|98|
|UK|21558|32184|48.7|78.5|96|
|Australia|||69.6|89.2|97|
|Canada|4211|39799|75|88.2|90|
|New Zealand|||50|90.9||
|USA|85170|386714|72|97|88|
|Argentina|732|27713|20.9|37|46|
|Brazil|745|21326|5.8|10.8|14.8|
|Chile|732|5944|18.7|38.8|30.3|
|Colombia|0|988|5.9|20.8||
|Mexico|349|19748|16|24.8|22.2|
|Peru|669|2995||15.3||

Source: Railroad from Mitchell (1998a and b). Enrollment rates from Benavot and Riddle (1988). Literacy from O’Rourke and Williamson (1995), except Brazil, Chile, and Mexico whose rates were taken from Engerman, Mariscal, and Sokoloff (1997). The figures for Brazil and Chile correspond to 1890, and those for Mexico to 1900.

Many have argued that the reason for the success of the Scandinavian transformation lies in how open these economies were. O’Rourke and Williamson (1995) establish that most of Sweden’s catch-up was due to mass migration, international

> 5More recently, Maloney (2002) has studied in more detail this and other issues related to the capacity of resource-rich economies to absorb and develop technology.

6
# **Table 3: 2**<sup>**nd**</sup> **step estimates: School outcome**

|||Bahasa Indon<br>Score<br>|esian Score<br>_bin|Math<br>score<br>|Score<br>_mth|
|---|---|---|---|---|---|
|VARIABLES||coef|se|coef|se|
|D_standard2|Dummy, =1 if school is of Pilot National Standard|-2.793*|(1.674)|-0.386|(1.289)|
|R_admission|Ratio of application / admission|1.773|(1.497)|2.183*|(1.151)|
|T_enroll|Total enrollment|-0.0208**|(0.00820)|0.0240***|(0.00630)|
|bk05_kr07|Principal annual Income (Rp)|6.06e-07**|(2.56e-07)|3.61e-07*|(1.97e-07)|
|bk09_pj03b|Share of teachers receiving a written performance<br>evaluation from principal|3.028*|(1.584)|1.127|(1.218)|
|bk09_kg03xC|Share of teachers receiving training from district or<br>subdistrict Education Office|3.360|(2.682)|3.605*|(2.062)|
|bk09_kg03xD|Share of teachers receiving training from private foundation<br>|5.258*|(3.142)|5.873**|(2.423)|
|bk09_kg04E|Share of teachers receiving training in: Assessing the<br>performance of your students|1.563|(3.653)|4.918*|(2.819)|
|bk09_kr12|Share of teachers certified|6.739**|(2.884)|5.276**|(2.226)|
|exp_hat|Predicted value of school expenditure on students activities|1.91e-07|(1.16e-07)|2.35e-07***|(9.04e-08)|
|nonPNS_hat|Predicted value of non-PNS teachers|0.912|(0.616)|0.903*|(0.478)|
|T_attend_hat|Predicted value of teacher attendance|0.130*|(0.0733)|0.113**|(0.0564)|
|Dedu_parent|Dummy, =1 if parental education at least senior secondary|8.387***|(1.847)|6.258***|(1.419)|
|bk10_kr05|Parental annual income|1.57e-06***|(4.91e-07)|1.07e-07|(3.77e-07)|
|Constant||17.08*|(8.842)|4.352|(6.784)|
|Observations||377||377||
|R-squared||0.425||0.368||

23
# **FURTHER READING FOR PART IV**

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Dyon, Geoff. 2010 (Jan. 30). “China Shakes Up Land Seizure Rules after Outcry at Violence and Evictions,” Financial Times.

LaGrange, Adrienne. 2011 (November). _Reform of Legal and Regulatory Framework for Involuntary Resettlement in the Hong Kong SAR,_ Consultant Report for Asian Development Bank. Manila.

Lai, Weihang. 2011 (March 1). “Guangzhou Land Compensation Acquisition and Protection Standards”, www.chinesecivilization.org Mahalingam, Ashwin and Aditi Vayas. 2011. “Comparative Evaluation of Land Acquisition and Compensation Processes Across the World,” <u>Economic and Political Weekly,</u> Vol XV, No. 32. August.

Peterson, George. 2009. _Unlocking Land Values to Finance Urban Infrastructure._ World Bank. Washington, DC.

61
Becker, Stan. Couples and reproductive health: A review of couple studies. _Studies in Family Planning_ 27(6): 291–306.

<mark>Bernard, T., Dercon, S., Orkin, K. and Taffesse, A.S., 2014.</mark> _<mark>The Future in Mind: Aspirations and ForwardLooking Behaviour in Rural Ethiopia</mark>_ <mark>(No. 2014-16). Centre for the Study of African Economies, University of Oxford.</mark> Bertrand, Marianne, and Sendhil Mullainathan. 2001. "Do people mean what they say? Implications for subjective survey data", _American Economic Review Papers & Proceedings_ , 91(2): 67-72.

Blood, R., and Wolfe, D. (1960). Husbands and Wives: The Dynamics of Married Living. New York: Free Press.

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Bond, Timothy, and Kevin Lang. 2014. “The sad truth about happiness scales.” NBER Working Paper No. 19950.

Bowles, Samuel, Steven N. Durlauf, Karla Hoff (eds.) 2011. _Poverty Traps._ Princeton University Press and Russell Sage Foundation.

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Chirkov, Valery I., Richard M. Ryan, and Ken M. Sheldon (Eds.). 2011. _Human autonomy in cross-cultural context: Perspectives on the psychology of agency, freedom, and well-being_ . New York: Springer.

Cicchetti, Dante (Eds.). 2016. _Developmental psychopathology: Theory and method (Vol. 1, 3rd ed.)_ . New York: Wiley.

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de Brauw, Alan, Daniel O. Gilligan, John Hoddinott and Shalini Roy. “The Impact of Bolsa Familia on Women’s Decision-Making Power.” _World Development_ 59.

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