rafmacalaba/datause-encoder-data huggingface dataset:
“entrepreneurial spirit,” are less likely to be internal or return (international) migrants, come from wealthier households, and work longer hours. Like De Mel, McKenzie and Woodruff (2008), Grimm, Knorringa and Lay (2011) find no evidence that successful and unsuccessful entrepreneurs differ in their aversion to risk.

# **III. Data**

Like De Mel, et al (2010) and Grimm, et al (2011), we measure the proportion of own account workers who have characteristics similar to employers. Like Grimm, Knorringa and Lay (2011), we measure the proportion of unsuccessful self-employed who have a high potential to be successful, based on selected observable characteristics. Our measures of success, however, are different from that used by Grimm, Knorringa and Lay (2011). Grimm, Knorringa and Lay (2011) use a two-part measure of success based on reported capital and profit. In contrast, we use two alternative measures success: (1) whether the self-employed worker is an employer (vs. an own account worker) and (2) whether the self-employed worker belongs to a family with per capita consumption above the $2/day poverty line. Although the latter is a meaningful measure of economic position of the household, it overstates the percentage of enterprises that have the potential to grow and create jobs. Attributing household poverty to an individual member’s enterprise is challenging, and a substantial proportion of enterprises with little potential for growth or job creation are likely to be run by households that have escaped poverty due to the presence of a wage earner or non-wage income. Therefore, we consider the second measure of success as a robustness test of our results, while the first measure is our primary measure of success.

The data that we use comes from micro-level household surveys collected by the Development Economics Group (DEC) of the World Bank, the International Income Distribution Database (I2D2). This data base consists of already existing data sets that have been collected and standardized. Most original country datasets are labor force surveys, budget surveys or living standards measurement surveys, and all are nationally representative. The data are an updated version of the dataset described in Montenegro and Hirn (2009).<sup>2</sup> These data include four sets of consistently defined and coded variables: (i) demographic variables, (ii) education variables, (iii) labor force variables, and (iv) household per capita consumption.

Not all variables are available in all countries and years. In our analysis, we only use surveys where we can identify whether the worker is an own account worker, owner or wage and salary employee. Most countries datasets are available for multiple years from the period 1984 to 2010. We only use the most recently available survey in each country in this analysis. We

> 2 The datasets for India and Sri Lanka in the I2D2 did not allow us to separate own account workers from employers.  We therefore used labor force survey data from India and Sri Lanka to supplement the I2D2 data.

6
Table 14: Plant level labor share and profit rate.

||(1)<br>Labor share|(2)<br>Proft rate|
|---|---|---|
|ETR _×_ secondary|-0.051**<br>(0.017)|0.073<br>(0.041)|
|Observations|53,517|53,517|
|R-squared|0.500|0.244|
|Plant FE|yes|yes|
|Industry-year FE|yes|yes|
|Other technologies|yes|yes|

The table presents OLS estimates of the relationship between plants’ exposure to robots, labor share in value added and gross operating surplus over revenue. Secondary is the 2006 share of plants’ employment with secondary education. Other technologies are capture by an index of innovation activities in 2006, interacted with year fixed effects. Standard errors are clustered at the 2-digit industry- and year-level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

Where _yv,t_ is real wages, **_X_** _v,t_ is a vector including the number of years of experience of worker _v_ at year _t_ (defined as years of schooling minus the worker’s age), its squared term and a gender dummy, _Educv,t_ is the years of schooling, and _ETRr,t_ is the regency _r_ exposure, defined as in (7). We also control for regency-time fixed effect as _αr,t_ .

The coefficient _γ_ 2 captures the impact of automation on schooling wage premia (a measure of wage inequality across skill levels). In particular it measures the difference in the change in return to schooling between a worker in a regency highly-exposed to automation versus a worker in a low-exposed regency. A positive coefficient indicates that automation is associated with increased wage inequality. As the main regressor varies at the district-year level, standard errors are clustered at that level too. As usual we instrument the exposure variable and its interaction terms through the same variables but replacing Indonesia’s robot imports with OECD countries’ imports.

Table 15 presents the results only of the coefficient of interest _γ_ 2, which suggests a significant inequality-inducing effect of automation, both for all sectors (odd columns) and for manufacturing only (even columns). The result applies whether using monthly wages (columns 1-2) or hourly wages (columns 3-4).<sup>36</sup> These results confirm the emerging evidence that automation is associated with a rise in economic inequality both through a reduction in the labor share in value added and an increase in the skill wage premia.

> 36The results are very similar if we do not instrument the exposure variable and if measure the automatability of occupations using the FO instead of GM probabilities (tables available upon request).

37
|P162621|2018|2018|10/16/2017|1 Active|Mozambique|
|---|---|---|---|---|---|
|P162621|2018|2018|4/20/2018|2 Active|Mozambique|
|P162621|2018|2019|11/9/2018|3 Active|Mozambique|
|P162621|2018|2019|6/10/2019|4 Active|Mozambique|
|P162621|2018|2020|2/6/2020|5 Active|Mozambique|
|P162621|2018|2021|7/21/2020|6 Active|Mozambique|
|P162623|2019|2020|8/15/2019|1 Active|China|
|P162623|2019|2020|2/26/2020|2 Active|China|
|P162623|2019|2021|7/22/2020|3 Active|China|
|P162635|2018|2019|8/31/2018|1 Active|Kyrgyz Republic|
|P162635|2018|2019|3/4/2019|2 Active|Kyrgyz Republic|
|P162635|2018|2020|9/7/2019|3 Active|Kyrgyz Republic|
|P162635|2018|2020|3/12/2020|4 Active|Kyrgyz Republic|
|P162637|2019|2019|5/22/2019|1 Active|Tajikistan|
|P162637|2019|2020|12/16/2019|2 Active|Tajikistan|
|P162637|2019|2020|6/24/2020|3 Active|Tajikistan|
|P162646|2018|2018|12/21/2017|1 Active|Rwanda|
|P162646|2018|2018|6/26/2018|2 Active|Rwanda|
|P162646|2018|2019|12/21/2018|3 Active|Rwanda|
|P162646|2018|2019|6/23/2019|4 Active|Rwanda|
|P162646|2018|2020|11/20/2019|5 Active|Rwanda|
|P162646|2018|2020|5/29/2020|6 Active|Rwanda|
|P162646|2018|2021|7/23/2020|7 Active|Rwanda|
|P162659|2018|2018|11/15/2017|1 Active|Yemen,Republic of|
|P162659|2018|2018|5/14/2018|2 Active|Yemen,Republic of|
|P162659|2018|2019|11/5/2018|3 Active|Yemen,Republic of|
|P162659|2018|2019|5/13/2019|4 Active|Yemen,Republic of|
|P162659|2018|2020|11/22/2019|5 Active|Yemen,Republic of|
|P162659|2018|2020|5/27/2020|6 Active|Yemen,Republic of|
|P162666|2017|2019|3/6/2019|1 Active|Rwanda|
|P162666|2017|2020|4/2/2020|2 Active|Rwanda|
|P162671|2018|2018|6/11/2018|1 Closed|Rwanda|
|P162671|2018|2019|1/11/2019|2 Closed|Rwanda|
|P162675|2019|2020|7/31/2019|1 Active|Cambodia|
|P162675|2019|2020|3/31/2020|2 Active|Cambodia|
|P162679|2020|2020|3/28/2020|1 Active|India|
|P162727|2018|2019|11/30/2018|1 Active|Turkey|
|P162727|2018|2019|6/10/2019|2 Active|Turkey|
|P162727|2018|2020|1/23/2020|3 Active|Turkey|
|P162727|2018|2021|7/23/2020|4 Active|Turkey|
|P162737|2018|2018|6/25/2018|1 Closed|Solomon Islands|
|P162737|2018|2019|1/16/2019|2 Closed|Solomon Islands|
|P162737|2018|2019|6/14/2019|3 Closed|Solomon Islands|
|P162743|2017|2018|12/7/2017|1 Closed|Djibouti|
|P162760|2017|2018|10/25/2017|1 Active|Zambia|
|P162760|2017|2018|6/22/2018|2 Active|Zambia|
|P162760|2017|2019|12/28/2018|3 Active|Zambia|
technology embodied in imported capital equipement goods.

**Preferences.** The representative household allocates consumption from among the range of differentiated varieties of final goods _ω_ . Consumer preferences are assumed to take the Constant Elasticity _<u>σ σ−</u>_ <u>1</u> _σ−_ 1 of Substitution (CES) utility function: _U_ = _ω∈_ Ω<sup>_q_(</sup><sup>_ω_)</sup> _σ dω_ , where _σ >_ 1 is the elasticity of �� � substitution between two varieties and Ω the set of available varieties. The optimal demand function for _−σ_ each differentiated variety is given by: _q_ ( _ω_ ) = _Q_ _<u>p</u>_ <u>(</u> _Pω_ <u>)</u> , where _Q ≡ U_ is the aggregate consumption � � of available varieties, _P_ the price index and _p_ ( _ω_ ) the price set by a firm. _R_ = _PQ,_ aggregate revenue. <u>1</u> The price index dual to the CES utility function is _P_ = �� _ω∈_ Ω<sup>_p_(</sup><sup>_ω_)1</sup><sup>_−σdω_</sup> � 1 _−σ ._

# _Production_

There are two sectors in the economy. One sector produces a homogeneous domestic constant-returnto-scale intermediate-input _xd_ with one unit of labor requirement under perfect competition. Labor is inelastically supplied and the wage is used as a numeraire. This homogeneous intermediate goods sector is characterized by perfect competition, so that the price of domestic inputs equals the marginal cost of producing the input: _px_ = _w_ = 1. Similar to previous works on heterogeneous firms and imported intermediate goods (Kasahara and Lapham (2013) and Amiti and Davis (2012)), we assume that intermediate goods are available in the country in fixed measure exogenously determined.<sup>13</sup> This sector also produces domestic capital equipment goods _kd_ under perfect competition and constant-returnto-scale using one unit of labor requirement. The price of domestic capital goods is then equal to one.

The other sector produces a continuum of differentiated final goods under monopolistic competition. In this sector, there is a continuum of firms, which are all different in terms of their initial productivity level _φ_ . Each firm produces a distinct horizontally-differentiated variety of final good in a monopolistic competition market structure. The production of each variety of final good _q_ involves a fixed production 13This assumption of a fixed measure of intermediate goods allows us to focus on the cost-reduction channel of intermediate goods trade in a tractable way avoiding the possible multiple equilibriums of models like Venables (1996).

14
**Figure 14** : **Demographics and growth in the mean income**

<!-- Start of picture text --> BRA PER<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percentiles Percentiles<br>Growth incidence curve Growth rate in mean Growth incidence curve Growth rate in mean<br>Growth rate at median Growth rate at median<br>90 165<br>80 160<br>155<br>70<br>150<br>60 145<br>140<br>50<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

**Figure 15: Decrease in the rural–urban premium for unskilled workers**

<!-- Start of picture text --> BRA PER<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percentiles Percentiles<br>Growth incidence curve Growth rate in mean Growth incidence curve Growth rate in mean<br>Growth rate at median Growth rate at median<br>120<br>170<br>100<br>160<br>80<br>150<br>60 140<br>40 130<br><!-- End of picture text --> _Source:_ World Bank staff calculations.

**Figure 16: Decrease in the unskilled- skilled premium for rural and urban workers**

<!-- Start of picture text --> BRA PER<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Percentiles Percentiles<br>Growth incidence curve Growth rate in mean Growth incidence curve Growth rate in mean<br>Growth rate at median Growth rate at median<br>90 220<br>85 200<br>80<br>180<br>75<br>160<br>70<br>65 140<br><!-- End of picture text --> _Source:_ World Bank staff calculations. Note: ARG-Argentina, BRA-Brazil, CAC-Central America & Caribbean, CHL-Chile, CHN-China, COL-Colombia, ECA-Europe & Central Asia, EUR-EU27 & EFTA, MEX-Mexico, MNA-Middle East & North Africa, PER-Peru, SAS-South Asia, SSA-Sub-Saharan Africa, USA-United States, XEA-Rest of East Asia, XHY-Rest of high-income, XLC-Rest of Latin America 32
Blattman, C., Gonwa, T., Jamison, J., Rodrigues, K., Sheridan, M. (2016). Measuring the measurement error: A method to qualitatively validate survey data. Journal of Development Economics, 120, 99–112 Brunton-Smith, I., Sturgis, P., and Leckie, G. (2017). Detecting and understanding interviewer effects on survey data by using cross-classified mixed effects location-scale model. Journal of the Royal Statistical Society A, vol. 180, Issue 2, 551–568 Catania, J. A., Binson, D., Canchola, J., Pollack, L.M., and Huack, W. (1996). Effects of Interviewer Gender, Interviewer Choice, and Item Wording on Responses to Questions Concerning Sexual Behavior. Public Opinion Quarterly. 60(3): 345-375.

Carlson, E. (2016). Identifying and interpreting the sensitivity of ethnic voting in Africa. Public Opinion Quarterly, vol.80, Issue 4, (1), 837–857 Couper, M.P. and Grove, R.M. (1992). The role of the interviewer in survey participation. Survey Methodology 18(2): 263-278 Davis, D. W. and Silver, B, D. (2003). Stereotype threat and race of interviewer effects in a survey on political knowledge. American Journal of Political Science, vol.47(1): 33-45.

Davis, D. W. (1997). The direction of race of interviewer effects among African Americans: Donning the Black Mask. American Journal of Political Science 41:309 – 322 de Mel, S., D. McKenzie, and C. Woodruff (2009). Measuring microenterprise profits: Must we ask how the sausage is made? Journal of Development Economics 88, 19–31 Fiala, N., and Premand, P. (2018). Social accountability and service delivery: Evidence from a large-scale experiment in Uganda. Mimeo Flores Macias, F., and Lawson,C. (2008). Effects of interviewer gender on survey responses: Findings from a household survey in Mexico. International Journal of Public Opinion Research, vol. 20 (1), 101-109.

Hatchett, S., and Schuman, H. (1975). White respondents and Race-of-Interviewer effects. Public Opinion Quarterly, vol.39 (4), 523-528 Himelein, K. (2016). Interviewer effects in subjective survey questions: Evidence from Timor-Leste. International Journal of Public Opinion Research, vol. 28 (4): 511-533 Huddy, L., Billig, J., Bracciodieta, J., Hoeffler, L., Moynihan, P.J., Pugliani, P. (1997). The effect of interviewer gender on the survey response. Political Behavior, vol. 19: 197-220 Jæger, M.M. (2016). Hello beautiful? The effect of the interviewer physical attractiveness on interview success. Sociological Methods & Research, forthcoming 22
districts in which this study takes place,<sup>6</sup> the schools that fell in the 90th percentile in terms of unutilized SMC funds had PKR 77,500 and PKR 62,000, respectively, each equivalent to at least three years of accumulated funds.<sup>7</sup> Private schools are virtually non-existent in Sindh. Ethnic and caste fragmentation means that, despite the large number of public schools, competition between schools is also not a viable source of pressure for improvement. Hence, there is no market-based _exit_ option available to parents (Hirschman, 1970). In the absence of accountability through school choice, and in an environment marked by low democratic pressure on service providers, the only option for dissatisfied parents is to directly _voice_ concerns with the educators (ibid). This makes Sindh a suitable context for exploring alternative approaches to community engagement.

# **2.2 Sample**

Sampling for the study was done in accordance to the districts: (1) being representative of the overall education profile of rural Sindh; (2) having a sufficient number of schools to meet the statistical requirements of our analysis; and (3) not posing excessive security risk for field teams. Using the PSLM survey, we ranked the 28 districts of Sindh by: i) proportion of adults who ever attended school, and ii) net enrollment rates of primary-age children (5-12 years). Also, using the

> 6 The districts in question were Matiari and Mirpurkhas. See section 2.2 for details of selected study districts. 7 We report cost estimates from School Improvement Plans prepared by SMC members during capacity building under ELECTIONS-CAPACITY; participants were instructed to prepare SIPs to spend all accumulated reserves (Figure A3, in Appendix A). In 2013, as the result of initial findings from the baseline survey and pilot activites, the Government of Sindh revamped the scope of the use of SIP Grants, specifically requiring all SMCs in the province to submit bank statements and members’ composition details to be eligible to receive annual funds. The idea was to stop the accumulation of SIP funds in delinquent accounts and only provide grants to schools with active SMC members and functional accounts. This accumulation of funds at the time of intervention might have contributed to expenditures on classrooms, which cannot be constructed alone from the annual SIP grants.

14
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O.A.M.D.G.

for the approaches for Boyes et al (1989) and Greene (1992). This is because the variables in the SISBEN sample are not the same as in _Familias,_ and very rudimentary.

We estimate the equation for z in the basic Heckman model explained above, taking ‘migration out of the SISBEN population’ as dependent variable. Given the limitations of the SISBEN register, we are somewhat restricted in the choice of possible outmigration determinants. Including household level variables would reduce the sample too much, so that we have to rely on municipality level characteristics alone. We include pre2000 data on the number of guerrilla fighters, landmine incidents, kidnappings and the homicide rate as right hand side variables. We also include the sum of all bank assets per head as an attempt to proxy for wealth at the municipality level. We include a squared term of each variable in order to be less restrictive in the functional form. The results are given below.

24
||**Table**<br>|**4.2: Project tolls and othe**<br>|**r relevant parameters,**<br>|<sup>**1992-1998**</sup><br>||**Sbidi f th**|
|---|---|---|---|---|---|---|
|**Sector**|**Project**|**Estimated average**<br>**daily traffic 1996**<br>**(veh/day)**|**Tolls for cars (US$**<br>**at tolls and**<br>**exchange rate of**<br>**12/96)**|**Tolls per km.**<br>**(USO/km)**<br>|**Transfers to the State (US$**<br>**millions)a**<br>|**uses rom e**<br>**State (US$ millions)b**|
|Inter-city|El Melon Tunnel|6,300|8.37<br>|119.52<br>|4.36 per year||
||La Madera Road|1,200|4.98<br>|4.15<br>|---|5.23 total|
||North Access to Concepci6n|3,000|4.77|6.35|||
||Santiago-San Antonio Highway|6,000|2.21|2.13<br>|20 payments of 6.13||
||Nogales-Puchuncavi Road|1,000|1.49|5.52<br>|-<br>||
||Route, 5Talca-Chillan|9000|2.41|2.49<br>|12.45*(1.05)(T-4)<br>||
||Route 5, Santiago-Los Vilos|9200|2.74|2.58<br>|10.90 (T=4)<br>9.34*(1.05)(1-**4**)<br>(T> )<br>||
||Santiago-Los Andes Highway|5,200|3.56<br>|4.95<br>|0.62*(1.05)(1-6 )||
||Route 5, Los Vilos-La Serena|2,500|3.12<br>|2.73<br>||10.90*(105)1-D5)<br>|
||Route 5, Chillan-Collipulli|5,900|2.74|3.18<br>|---<br>|4.67*(1.05)(1-5)<br>|
||Route 5, Temuco-Rio BuenoC|3500|2.74<br>|2.75<br>|2.01 (T=5)<br>|7.78*(1.05)1()<br>|
||Route 5, Rio Bueno-Puerto MonttC|5,800<br>|2.74<br>|2.28<br>|8.14 (T=5)<br>|9.34*(1.05)(1-I5)|
||Route 5, Collipulli-Temucod|5,700|2.74<br>|2.89<br>|18.22 (T=5)<br>3.1 1*(1.05)(T-5)||
||Santiago-Valparaiso-Vifla del Mar<br>Highway|12,600|4.27<br>|3.89<br>|||
||Route 5, Santiago-Talcad|18,000|2.73|2.87|124.5 (T=5)<br>10.9*(1.05)(T-5|)|
|Urban|Access to AMB airport|11,700|0.50|29.13|||
|Airports|Diego Aracena, Iquique|1,070<br>|3.41<br>||||
||El Tepual, Puerto Montt|1,006<br>|6.05<br>|---|---|...|
||La Florida, La Serena|1,478<br>|3.13<br>|---|.||
||El Loa, Calama|2,650<br>|5.62<br>|---|---|,---|
||AMB, Santiago|600|1.44<br>e|---|---||
||Carriel Sur, Concepcion|3,500|7.42t||||

# Source: MOP

3 The 'T' in some of the transfer/subsidy formulas stands for the concession year. The transfers in this<sup>column include those contained in the original tendering documents as part of the profitability</sup> enhancement of the concession as originally designed by MOP<sup>plus transfers as a result of the bidding competition.</sup> The 'T' in some of the transfer/subsidy formula **s** tands for the<sup>concession year.</sup> In this concession, subsidies were included in the bidding documents.<sup>However, because several firms presented an offer at the minimum toll level, the contract was awarded to the company whose</sup> sponsors offered the highest payment to the State. Therefore,<sup>this company has subsidies and transfers. However, the subsidies accrue to the concession company, while the transfers must be paid by</sup> sponsors directly.

_d_ In this case there were transfers to the govemment already built-in<sup>to the original design. In addition, the concession was awarded to the company whose sponsors offered the highest payment to the</sup> State.

'Toll at 30/6/97.

Toll at 31/8/98.

23