rafmacalaba/datause-encoder-data huggingface dataset:
In the Appendix, we provide supplements to the survey, empirical exercises, and results in the main text. We begin with describing the sampling framework and weight construction for the Firm-level Adoption of Technology (FAT) Survey in Appendix A. Then, in Appendix B, we explain details on the construction of some of technology measures used in empirical exercises in the main analysis. It also provides additional figures on the structure of business functions and technologies. Finally, we provide the additional results in Appendix C, Appendix D, Appendix E, and Appendix F.

# **A FAT survey**

The Firm-level Adoption of Technologies (FAT) data are based on multi-country, multisector, and representative firm surveys to measure technologies adopted and used by firms. Table A.1 provides the number of technologies covered in the FAT survey as well as other existing surveys. Compared to existing surveys, the FAT survey covers a larger number of technologies and business functions. It also covers the agriculture sector in addition to the manufacturing and service sectors.

The survey also provides balance sheet information and selected information on business owners. Furthermore, the survey collects information on potential drivers of and barriers to technology adoption. The survey has been conducted by the World Bank in partnership with public or private local agencies across three countries: Brazil (the state of Cear ́a), Senegal, and Vietnam. The data were collected between June 2019 and March 2020.<sup>51</sup> Table A.1: Coverage of firm-level technology surveys

|Surveys|# of<br>Technologies|# of<br>Business Functions|Includes Firms<br>in Agriculture|
|---|---|---|---|
|Firm-level Adoption of Technology Survey|287|59|Yes|
|Survey of Advanced Technology (SAT)|57|3|No|
|Community Survey on ICT Usage and E-Commerce in Enterprises|9|0|No|
|Information & Communication Technology Survey (ICTS)|4|0|No|
|Annual Business Survey (ABS) 2019|5|0|No|

## **A.1 Sampling frame**

The sampling frames for the Brazilian state of Cear ́a, Senegal, and Vietnam were based on the most comprehensive and latest establishment census available from national statistical

> 51The survey is currently planned to be implemented in other countries including Bangladesh, India (the states of Tamil Nadu and Uttar Pradesh), Malawi, Kenya, the Philippines, Poland, and the Republic of Korea.

48
# _Listing_

Prior to the commencement of the listing operation, letters were sent to approximately 150 Nikkei associations with bases in the areas chosen, explaining the purpose of the survey, asking them to encourage their members to answer the survey, and providing a telephone number for any enquiries. A door-to-door listing exercise of the 75 census tracts was then carried out between 13 October 2006 and 29 October 2006. A census tract averaged 301 housing units. Listing used 42 interviewers in São Paulo and 24 interviewers in Parana. Interviewers went to each housing unit with a screening questionnaire, which asked whether or not the household had any members who were Nikkei, or Nikkei members currently in Japan. Households with Nikkei were then asked whether they had members who had returned from Japan, whether they had members currently in Japan, and the whether they had any members who were third or fourth generation Japanese. Three attempts were made to interview the household in the event that the first or second attempt yielded nobody at home. In the event that an interview could not be made due to refusal, no one at home, or the refusal of apartment building management to allow the survey, the Nikkei status of households was obtained through proxy-reporting from a neighbor or building manager.

Table 2 summarizes the results of the listing process. The listing covered 14,239 dwelling units in São Paulo state and 8,300 units in Parana state, for a total of 22,539 dwellings.<sup>7</sup> This was 21 percent more dwelling units than recorded for these census tracts in the 2000 Census, showing the extent of population growth and new construction over the six years since the Census. Among these 22,539 the listing detected 839 Nikkei households, 528 of which were interviewed in person, and 311 obtained by proxy-reporting. Proxy-reporting was more common in São Paulo state, particularly in São Paulo city, where household members were harder to find at home. Thus 3.7 percent of the dwelling units listed contained Nikkeis.

> 7 The listing exercise also listed empty dwellings, second-homes, and non-residential buildings so the total number of buildings/dwellings listed was 26,697.

16

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# **5.2 DETERMINANTS OF INDONESIA’S SUB-NATIONAL GROWTH**

## **Period OLS regressions**

We now broaden the model to incorporate a wide range of potential determinants of district growth from each of the categories outlined above. Table 6 shows our preferred model for each time period.

The results confirm the lack of income convergence, even in a relative sense, except during the immediate post-crisis period. However, measurement error in the GRDP data could give rise to a reduction in the size and significance of this variable. We also estimated the regression using mean per capita consumption expenditure as an instrument for per capita GRDP. Doing this increases the size of the income convergence effect in the 2001-05 period and it approaches statistical significance. We interpret this as weak evidence for conditional income convergence in the decentralization period. However, there no evidence from this estimation of spatial convergence even in the first period.

Surprisingly, having a population with a higher share of people who have completed junior secondary school appears to make very little difference to growth performance. This is in marked contrast both to the bilateral association between growth and education found above and to typical country-level growth regressions, which tend to show a strong association. We tested this finding using a variety of other measures for educational performance (e.g. primary schooling and senior secondary schooling) but the results were broadly similar.

Similarly, having a larger labor force and a higher urban share of the population does not significantly affect the growth rate except in the most recent period.<sup>21</sup> Between 2001 and 2005, districts with larger labor forces and those with more urban populations did have significantly higher growth rates, suggesting an urban concentration to the pattern of growth postdecentralization.

The inclusion of the sectoral profile of the local economy shows some sectoral biases to growth. Districts with a higher share of GRDP in manufacturing tended to have higher growth rates in the earlier two periods, although this effect has disappeared since 2001. By contrast, districts that were heavily dependent on oil and gas had slower growth rates prior to the crisis. Due to substantial multi-collinearity between sectoral shares (even after omitting one category), we do not estimate with all the sectoral shares. However, to obtain an impression of the effect of the concentration of economy activity on growth we constructed a Gini coefficient of sectoral shares of employment for each district. This shows a positive, but not statistically significant, relationship with growth. Similarly, in order to explore whether changes in the agricultural

> 21 We use labor force in preference to population size to avoid potential correlation with GRDP per capita.

19
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three (four) formal jobs pay 10% (20%) more than informal jobs. The light line shows that when we control for worker characteristics (including gender and education), the same trend holds but the line is shifted downwards implying that the initial wage advantage of informal work is even larger (17%). One explanation is that workers in the formal sector tend to be more educated (Conover et al., 2022). A similar pattern holds when we limit the sample to those who are continuously employed for at least three quarters (Appendix Figure A1), confirming results from IMSS data that fast wage growth is not primarily driven by selection.

Interestingly, the pattern looks different for the cohort of people aged 21 to 24 (Panel B). Starting hourly wages are higher in the formal sector and there is less wage growth over time. One possible explanation is that this older group is less likely to accumulate human capital on the job. The wage patterns for these older cohorts is more similar to aggregate wage differentials in other settings, where formal jobs pay more even after controlling for observable characteristics (e.g. Ulyssea (2010)).<sup>11</sup> However, evidence disaggregated by age and education levels shows that across many LMICs, youths with primary and secondary education earn more in informal than in formal employment (Shehu and Nilsson, 2014).

# **2.3 Characteristics of formal vs. informal employment**

What can explain this substantial wage growth for school leavers, and why is it limited to the formal sector? Table 2 compares job characteristics from employed youths in our study sample (described in more detail below) collected two years after graduating. Among these, 47% and 30% report that they received formal training on the job for formal employment with permanent and temporary contracts, respectively. By comparison, this figure is only 6.5% in the informal sector.<sup>12</sup> A very similar pattern holds for other forms of human capital accumulation: 8.5% of those informally employed report learning on the job compared to 45% (26%) among formally employed workers with permanent (temporary) contracts. These differences may in part reflect that the formally employed mainly work for firms (93%), while

> 11Some of this difference may be driven by unobservable differences.For example, Ulyssea (2018) finds that the formal sector wage benefit in Brazil completely disappears when controlling for firm fixed effects. We also find that for older workers the wage gap decreases (by about 30%) when we control for worker characteristics (Panel B).

> 12The average length of training workers report to receive is almost 60 hours. This excludes training received as part of the initial “on-boarding” process.

11
processing approach. This approach relies on the use of water and machinery such as cherry hoppers, which sort the coffee cherries based on quality, after which the sorted cherries are pulped using coffee pulping machines. After pulping, the cherries are further sorted by immersion in water for several hours (Macchiavello and Morjaria, 2021).

Coffee processed at these stations attracts a higher market premium than that processed through traditional methods by farmers. Estimates suggest that the prices of wet-processed coffee via the mills are 40% higher than that of dry-processed coffee (Macchiavello and Morjaria, 2015). This suggests that the introduction of coffee mills induced significant income shocks to coffee farmers in the country. In addition, the introduction of the mills also led to an increase in employment opportunities through wage employment, as labor<sup>1</sup> was hired to work in these mills (Sanin, 2021).

Thus, the extent to which the local economic shocks associated with the introduction of these coffee mills affect the cognitive development of children exposed to these shocks in early life is the focus of this paper. To this end, we combine unique administrative data on test scores of the universe of primary school students in Rwanda with data on the staggered rollout of the coffee mills to estimate the effects of early life exposure to the coffee mills on students’ performance in a high-stakes national exam.

To causally identify the impact of these shocks on students’ performance in the medium term, we leverage the staggered variations in the rollout of the coffee mills across various locations relative to the timing of birth. Using a 2.5km radius from a coffee mill as the catchment area for coffee mills, we measure early life exposure to coffee mills as children born in a catchment area after the opening of the coffee mill. Essentially, our identification strategy is a difference-in-difference design whereby we compare the educational outcomes of children in a catchment area born after the opening of a mill with those in the catchment area but born before the opening of a mill and those born in communities outside the catchment area. A key identifying assumption advanced here is that in the absence of these coffee mills, the educational outcomes of treated and control students would have evolved along a similar pattern. We present results from an event-study analysis to support this assumption, using the Borusyak et al. (2024) estimator which accounts for heterogenous treatment effects in analysis with staggered treatment. Another potential challenge to our identification strategy relates to the fact that children born just before the arrival of the coffee mills could also experience positive spillover effects, despite being classified as control as per our baseline approach. We address this by presenting additional analyses that estimate the cumulative years of exposure to coffee mills within the first five years of life on educational outcomes and find similar results.

> 1Mostly women

2
design based on a national sample frame developed from the 1999 Population and Housing Census. The sample is designed to be representative within urban and rural stratifications at the national and provincial levels and at the level of the country’s 69 districts. The total number of households interviewed was 13,212, and the questionnaire refers to the most recently completed agricultural season.

The 2001 Rwanda _Enquête Integrale sur les Conditions de Vie des Menages_ (EICV) was collected over 21 months from October 1999 to June 2001. The bulk of the data collection was done in the last 12 months, and therefore this paper limits the analysis to this time period (Rwanda Statistics Department, 2002). Data collection was done concurrently in all 12 prefectures of the country. The sample was selected using a two-stage stratified sample design and the sample is designed to be representative at the prefecture level. The total sample size was 5,739 rural and urban households, interviewed from July 2000 to June 2001, and the questionnaire refers to the most recently completed agricultural season.

In the analysis, we regress information on harvest sales (in kilograms and in local currency) and input use (fertilizer and hired labor) on time elapsed between the harvest and the date of the interview. We explore systematic under/over-reporting by length of recall. We examine whether reporting changes between interviews conducted further from the harvest and those completed close to harvest.

Agriculture is the dominant source of income for the rural households in these surveys. In Kenya, 82 percent of rural households were engaged in agriculture, as opposed to 22 percent that were engaged in non-farm enterprise and 31 percent where at least one member was engaged in wage labor. Differences are even larger for rural households in Malawi and Rwanda. In Malawi, 95 percent of rural households were involved in agriculture, with only 30 percent engaged in non-farm enterprises and 16 percent in wage labor. In Rwanda, 99 percent of households were engaged in agriculture, and only 13 percent in non-farm enterprises and 30 percent in wage labor. Most of the farming households are smallholders with plot sizes generally between two and three acres; Appendix Table 1 shows means for the sub-samples studied in this paper.

Both food staple and cash crops are analyzed for evidence of recall decay. Maize, a main staple crop, is examined in all three countries. Sorghum is also included for Rwanda as it is the most 8
data (1981-82 to 2017-18) on employment (total persons engaged) and real wages (based on total emoluments paid) from Annual Survey of Industries, Ministry of Statistics and Program Implementation, Government of India,<sup>27</sup> to compute the average labor supply elasticity, which turned out to be around 0.4. We introduced this labor supply equation in the model.

# **_c. Links between Production, Consumption and International Trade:_**

The sub-modules explained above are linked with each other. The percentage change in sector-level domestic consumption, _qdmk_ **_k,s_** , with a corresponding price change _pmk_ **_k,s_** , substitutes for imported subsector goods, _qimk_ **_k,s_** , with a corresponding price change _pimk_ **_k,s_** . The CES elasticity between these two variables is **_σD,k_** , and this substitution takes place based on their respective price differentials from the sector-level domestic prices _pdk_ **_k,s_** , as illustrated by equations (2) and (3):

# **For all** **_k_ in** **_SECT_ and** **_s in_ REG:**

**_qimk(k,s) = qdk(k,s) – σD(k) * [pimk(k,s) - pdk(k,s)]_**

**_........................... (2)_**

**_qdmk(k,s) = qdk(k,s) - σD(k) * [pmk(k,s) - pdk(k,s)]                              ........................... (3)_**

Domestic market and import price changes are aggregated to domestic price changes by weighting according to their respective shares. _VDK_ **_k,r_** is the total value of domestic consumption of goods corresponding to the sub-sector _k_ in the region _r_ , _VDMK_ **_k,r_** is the value of domestic consumption of goods produced by the domestic sector _k_ in the region _r_ and _VIMK_ **_k,s_** is the value of imports of goods produced by the sub-sector _k_ to the region _s_ .

# **For all** **_k_ in** **_SECT_ and** **_s in_ REG:**

**_pdkk,s =αDk,s *pmkk,s +αMk,s *pimkk,s_**

**_........................... (4)_**

where: **_αDk,s =VDMKk,s /VDKk,s and αMk,s = VIMKk,s /VDKk,s_** Finally, the total changes in supply and demand are equalized to ensure equilibrium, by equating the percentage change in total output _qok_ **_k,r_** with the share-weighted sum of exports and domestic consumption for all sectors _k_ in SECT and regions _r_ in REG. When the slack variable _tradslackk_ **_k,r,_** is exogenized, this equilibrium condition determines the change in market prices, _pmk_ **_k,r_** (output, _qok_ **_k,r_** ) is determined by Equation (5).

# **For all** **_k_ in** **_SECT_ and** **_r in_ REG:**

**qok** **_k,r_ = βD** **_k,r_ *qdmk** **_k,r_ +** **_Σs_ βM** **_k,r,s_ *qxsk** **_k,r,s_ + tradslackk** **_k,r ........................... (5)_**

# **<u>Figures</u>**

Notations in Figures 1 and 2 follow standard GTAP notations (Hertel, 1997) [22]. In general, the variables starting with: ‘ _q_ ’ represent changes in quantities, ‘ _t_ ’ represent tax/tariff changes and ‘ _p_ ’ represent changes in prices. Those ending with ‘ _k_ ’ are at disaggregate level. In the variable-names,

> 27 This is available from the following link (Accessed on Jan 15, 2020): http://mospi.nic.in/sites/default/files/asi_results/Table%201%20Annual%20Series%20upto%20201718%20For%20Principal%20Characteristics_p.xls.

27
_Figure 3. Number of state-owned firms identified by country and source of information_ <!-- Start of picture text --> ORBIS baseline<br><!-- End of picture text --> <!-- Start of picture text --> Overlap Orbis & External sources<br><!-- End of picture text --> <!-- Start of picture text --> External (supplementary sources)<br><!-- End of picture text --> <!-- Start of picture text --> ORBIS baseline Overlap Orbis & External sources External (supplementary sources)<br>The Gambia<br>Seychelles<br>Senegal<br>Sao Tome and Principe<br>Rwanda<br>Nigeria<br>Niger<br>Namibia<br>Mauritania<br>Mali<br>Malawi<br>Madagascar<br>Lesotho<br>Kenya<br>Eswatini<br>Côte d'Ivoire<br>Comoros<br>Chad<br>Cameroon<br>Cabo Verde<br>Botswana<br>Benin<br>Angola<br>Sri Lanka<br>Pakistan<br>Nepal<br>Maldives<br>Bhutan<br>Bangladesh<br>Afghanistan<br>Morocco<br>Egypt<br>Uruguay<br>Peru<br>Paraguay<br>Ecuador<br>Dominican Republic<br>Costa Rica<br>Colombia<br>Chile<br>Argentina<br>Uzbekistan<br>Slovenia<br>Serbia<br>Russia<br>Poland<br>North Macedonia<br>Montenegro<br>Lithuania<br>Latvia<br>Kosovo<br>Hungary<br>Estonia<br>Croatia<br>Bosnia and Herzegovina<br>Albania<br>Vietnam<br>Samoa<br>Philippines<br>Indonesia<br>Cambodia<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>SSA<br>SAR<br>MENA<br>LAC<br>ECA<br>EAP<br><!-- End of picture text --> <!-- Start of picture text --> 0%<br><!-- End of picture text --> <!-- Start of picture text --> 10%<br><!-- End of picture text --> <!-- Start of picture text --> 20%<br><!-- End of picture text --> <!-- Start of picture text --> 30%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br><!-- End of picture text --> <!-- Start of picture text --> 50%<br><!-- End of picture text --> <!-- Start of picture text --> 60%<br><!-- End of picture text --> <!-- Start of picture text --> 70%<br><!-- End of picture text --> <!-- Start of picture text --> 80%<br><!-- End of picture text --> <!-- Start of picture text --> 90%<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br><!-- End of picture text --> Note: Baseline information refers to the ORBIS first baseline using the algorithm developed without complementing any databases, overlap firms mean firms identified in ORBIS and external sources, and External Supplementary sources refer to the firms that were identified only through external sources.

Source: Authors’ elaboration based on ORBIS and Global BOS database.

On average, supplementary databases increased the number of firms identified with state participation by 47%, although with variation across regions. ORBIS is a limited source to provide a full landscape of the state footprint in the markets. Using supplementary databases (See Annex 3) beyond ORBIS as well as implementing a protocol to validate the information with government counterparts was key to overcome these limitations. For example, in the SSA region more than 60% of the firms with state participation were identified through complementary and official sources (Figure 3). In Moldova, Montenegro, and Serbia, 16
# **Tax Compliance in Romania**

Monica Robayo-Abril<sup>1</sup> Georgiana Balaban<sup>2</sup> Marcin Wronski<sup>3</sup>

> 1 World Bank

> 2 Romanian Ministry of  Finance

> 3 World Bank and SGH Warsaw School of Economics

## **ion:** H24, H26, D31, J31, J38

**Keywords:** tax compliance; labor market; minimum wage; tax data; EU-SILC; imputation, Romania

This paper was prepared as part of a collaboration between the World Bank and the Ministry of Finance in Romania to inform the tax compliance agenda in the country. The document was part of the Poverty Program for Romania in the World Bank's Global Poverty and Equity Practice. The authors are grateful for comments received from Madalina Avram (Romanian Ministry of Finance), Anne Brockmeyer (Senior Economist, World Bank), Reena Badiani (Senior Economist, World Bank) and George Stefan (The Bucharest University of Economic Studies).