rafmacalaba/datause-encoder-data huggingface dataset:
economic growth, though the specific component of inequality of opportunity –that inequality that correlates with circumstances at birth—does not show a robust effect by itself. One of the main contributions of that paper is the attempt to distinguish between income inequality and indicators of equity –in this case, inequality of opportunity. Along the same lines, Marrero and Rodriguez (2013), using the PSID database for the U.S. in 1970, 1980 and 1990, find a robust support for a negative relationship between inequality of opportunity and growth, and a positive relationship between inequality of effort and growth. Thus, the indicator of inequality used in the empirical work –based on different notions of what is the inequality that “matters”—may lead to different results. On the other hand, equity indicators –such as access to basic services and access to markets—may have a more delayed effect on growth, as they affect the long-term productivity of the poor or previously excluded groups.

_Revisiting equity-efficiency trade-offs_ As discussed in Kanbur (2013), there is no shortage of theoretical models showing why equity is associated with growth.<sup>4</sup> If the economy is “distorted enough” –producing within its full productive capacity, lifting constraints to productivity might actually improve both efficiency and equity. Political economy considerations might also lead to a situation where redistribution enhances efficiency (Levy and Walton, 2005).

> 4 There are diverging views on the link between economic growth and equity.  Aghion et al. (1999) present three arguments that support the view that wealth inequality should be growth-enhancing. The first argument is Kaldor's hypothesis that the marginal propensity to save of the rich is higher than that of the poor. Stiglitz (1969) and later Bourguignon (1981) formalized this argument and demonstrated that more unequal economies will grow faster. The second argument has to do with investment indivisibilities. In the absence of a well-functioning market for shares, wealth needs to be sufficiently concentrated in order for economic agents to be able to cover large sunk costs and thereby engage in entrepreneurial activities. The third argument is based on incentive considerations. In an economy where all agents have the same preferences, greater taxation reduces the return to savings and thus lowers the incentives to accumulate capital and hence the rate of growth (see for instance, Basu 2006).

5
<!-- Start of picture text --> Nearest Nearest + 20km Nearest + 50km<br>2.5<br>0.0<br>−2.5<br>2001 2006 2011 2016 2020 2001 2006 2011 2016 2020 2001 2006 2011 2016 2020<br>Year<br>Refugee Presence<br><!-- End of picture text --> Figure 4: Levels of refugee presence, parishes within 150km. The figure shows that for parishes within 150km of any refugee settlement, our three alternative refugee presence measures all increase after the December 2013 start of the South Sudanese civil war (red line).

were government funded (public) or non-government funded (private).<sup>13</sup> Intersecting these points with our baseline 2001 parish shapefile gives us the number of primary schools per parish-year (and by type). We normalize these values per 1,000 primary-school-age children for our measures of _Public Primary School Access_ and _Private Primary School Access_ .

Second, we use data on secondary schools provided by the Uganda Ministry of Education that was independently verified by the World Bank. For the more than 3,600 secondary schools, we have geographic coordinates, founding year, and whether the school is public or private. We similarly construct measures for _Public Secondary School Access_ and _Private Secondary School Access_ by normalizing the number of secondary schools (by type) per 1,000 secondary-school-age children. For more details on primary and secondary school data construction, see SI Sections S1.3 and S1.4.

Third, to measure _Health Access_ , we compiled a geocoded list of approximately 6,800 health facilities in Uganda by merging the 2006 health facility list from the Uganda Bureau of Statistics and the 2012 and 2017 master facilities lists from the MoH. For this outcome we have three time periods: 2006, 2011, and 2016. Uganda has five types of health facilities, ranging from HC-I (small clinic) to HC-V (hospital). A valid health access index should be able to capture three dimensions: (a) _distance_ to the nearest health facility, (b) how _crowded_ the facility is based on how

> 13Note that private does not mean students are necessarily paying tuition; it simply means the school is largely funded by non-government funds.

15
sets. The statistical matching techniques have been used by Robayo-Abril and Rude (2024) in the Romanian context.

**The data sources used in this research do not share common identifiers.** Therefore, we cannot apply record linkage and must use statistical matching techniques. We imputed tax income in the EU-SILC data set based on common characteristics. In tax data, employees' gross salary is reported by their employers. Our income concept is total gross labor income. If more than one employer employs one individual, we sum up the income from all employment contracts. The EU-SILC captures various income sources and distinguishes labor and non-labor income. In the EU-SILC data, we also use total gross labor income as our income concept. Both tax data and EU-SILC record the information on gender, year of birth, and region,<sup>8</sup> sector of the economy,<sup>9</sup> and employment status (full-time vs. part-time).   Although the list of the common variables is not long, it is not necessarily a limitation. Too many matching variables can generate unnecessary noise (Donatiello et al., 2016b).

**Among data fusion approaches, we utilize model-based methodologies.** Following the approach of Bacher and Prander (2018), we first estimate an empirical functional relationship between X and Z using the donor dataset, which in our case is the tax administrative data, as follows: The functional form of depends on the population parameters XX= ff(ZZ; θθ; δδ) and the imprecision with which Z might have been measured, denoted as An example of such a functional form could be a ff(ZZ; θθ; δδ) θθ linear regression: δδ. 0 1 1 pp pp XX= ββ + ββ ZZ + ⋯+ ββ ZZ + δδ

8 In the EU-SILC , the information is representative at the NUTS-2 level, while in the tax data, the information is at the NUTS3 level. More detailed classification in tax data was aggregated to match the classification in the EU-SILC.

9 In the EU-SILC , the information on the sector of the economy is provided using a simplified two-digit NACE classification. The tax data offers more detailed four-digit NACE classification. Similarly, as in the case of income, we aggregate the more detailed classification of sectors in the tax data to match the simpler classification in the EU-SILC.

15
We suppose that there is a uniform money demand function across all consumers, and we take the parameters of the equation from Qayyum, A. (2005).

# **Fiscal Parameters**

Tax rates for the benchmark case are taken from KPMG (2012). Levels of public expenditure are taken from Pakistan Statistical Yearbook (various issues), Table 10. We assume that the public sector produces current expenditure with a Cobb-Douglas function whose shares of capital and labor are given by the aggregate shares of these factors in private production, where the shares are taken from the Pakistan National Income Accounts. The public investment function, which determines public capital spending, is taken to have the same parameters as the private investment functions.

# **Appendix 3:  Consumer’s optimization problem**

Consumers maximize intertemporal utility functions, which have as arguments the levels of consumption, including imports, and leisure in each of the two periods. Formally, the consumer’s problem is then given by equation 5.<sup>15</sup>

> 15 See Feltenstein and Shamloo (2013) for a discussion of this modelling approach.

41
A final issue is that of weights. The outcome variables are means of household indicators taken at the regional level, although these are demeaned when predicting the evolution of the shock. When constructing these regional averages, we use household weights. Because the number of observations varies by region, country, and survey weight, it is appropriate to correct for heteroscedasticity. We therefore weight each region-wave observation by the number of households in the original phone survey data used to construct the average for that region.<sup>9</sup> This gives greater weight to regions for which the outcomes are measured with greater precision. These weights are applied both in model selection and in model estimation.

2.4 LASSO Model Selection To construct predictive models, we use LASSO to select which variables among the real-time indicators best predict changes in income, work status, food insecurity, behavioral changes, and educational disruptions. When predicting initial impacts, the dependent variable is the regional average, and when predicting the subsequent evolution of the crisis the dependent variable and the predictors are demeaned using the regional mean over multiple waves. The full set of real-time indicators are included as candidate variables. We use a variant of the LASSO that selects the penalty parameter lambda to minimize the Bayesian Information Criteria, following Zhang et al. (2010). When selecting models to predict in the first wave, both the LASSO and the resulting post-lasso estimates are clustered on country, and when predicting changes over time, the LASSO and the post-lasso estimates are clustered on country-wave.<sup>10</sup> It is not straightforward to apply Lasso to multiply imputed data (Du et al., 2020) and different approaches have been proposed. For the purpose of maintaining simplicity and to avoid overfitting, we select the model using only data from one arbitrarily selected imputation, and report that R<sup>2</sup> . In addition, however, we report the mean R<sup>2</sup> from 100 models, one for each imputation, each selected independently using LASSO and then estimated using OLS. The latter is our preferred measure of R<sup>2</sup> .

> 9 We do not give greater weight to more populous regions, conditional on sample size.

> 10 The BIC is calculated based on the unpenalized variables, which is appropriate since we are using LASSO to select the model rather than for post-estimation. The calculation of the BIC takes into account clustering, as described in Stata.

11
<!-- Start of picture text --> 50<br>45<br>40<br>35<br>By fee income share<br>30<br>By non-deposit funding share<br>25<br>20<br>15<br>10<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>20 Bins<br><!-- End of picture text -->

**Figure 8. Z-score by fee income and non-deposit funding shares.** _ROA_ + _CAR_ The Z score is defined as where ROA is return on assets, CAR represents capital assets ratio

# _SROA_

and SROA stands for standard deviation of return on assets. The fee income share is the share of non-interest income in total operating income. The non-deposit funding share is the share of non-deposit short-term funding in total deposits & short-term funding. This figure shows the Z-score by fee income and non-deposit funding shares. The horizontal axis represents the 20 bins of the fee income share (the non-deposit funding share), with each bin containing 5 percent of observations of one of these two variables in increasing order. The vertical axis shows the Z-score. Data are from Bankscope.

64
||**Table 8: Matern**|**ity leave and w**|**age rate intera**|**ction term res**|**ults**||
|---|---|---|---|---|---|---|
|Dependent variable:<br>Female workers|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Maternity leave (logs)*<br>Wage rate (real, logs)|**-2.001****<br>**(0.805)**|**-1.723****<br>**(0.680)**|**-1.747****<br>**(0.677)**|**-1.746*****<br>**(0.664)**|**-1.785****<br>**(0.686)**|**-1.292****<br>**(0.599)**|
|Maternity leave (logs)|20.015**|17.123***|17.406***|17.437***|17.515***|67.795|
||(7.755)|(6.508)|(6.442)|(6.330)|(6.435)|(44.700)|
|Wage rate (real, logs)|10.205***|7.776**|7.717**|7.648**|7.672**|5.815**|
||(3.719)|(3.139)|(3.127)|(3.072)|(3.161)|(2.659)|
|GDP per capita (logs)||4.484***|4.592***|4.428***|3.758***|35.549***|
|||(0.627)|(0.623)|(0.612)|(1.388)|(12.154)|
|Growth rate of GDP||0.564***|0.564***|0.564***|0.480***|0.437**|
|per capita (%, annual)||(0.189)|(0.180)|(0.180)|(0.168)|(0.178)|
|Females in adult||1.691***|1.746***|1.735***|1.630***|1.753***|
|population (%)||(0.476)|(0.472)|(0.468)|(0.458)|(0.468)|
|Number of workers||0.331|-0.160|-0.277|-0.226|-3.348**|
|(logs)||(0.262)|(0.256)|(0.261)|(0.255)|(1.354)|
|Female owner present||4.869***|4.801***|4.760***|4.359***|3.573|
|Y:1 N:0||(0.527)|(0.521)|(0.517)|(0.500)|(2.790)|
|Top manager is||13.684***|13.566***|13.556***|13.438***|13.616***|
|female Y:1 N:0||(0.801)|(0.795)|(0.795)|(0.780)|(0.763)|
|Age of firm (logs)|||-0.147|-0.114|-0.183|-0.127|
||||(0.388)|(0.394)|(0.393)|(0.392)|
|Exports (% of sales)|||0.037**|0.036**|0.030**|0.027**|
||||(0.014)|(0.014)|(0.014)|(0.014)|
|Foreign ownership|||0.031***|0.029***|0.029***|0.023**|
|(%)|||(0.010)|(0.010)|(0.009)|(0.009)|
|Firm offers formal|||2.493***|2.385***|2.148***|2.091***|
|training to workers Y:1|||(0.607)|(0.610)|(0.588)|(0.585)|
|N:0|||||||
|Inadequately|||-0.221|-0.164|-0.112|-0.058|
|educated workers:|||(0.207)|(0.222)|(0.218)|(0.220)|
|Obstacle severity (0-4<br>|||||||
|scale)|||||||
|<br>Firm registered when||||1.277|1.118|1.026|
|started operations Y:1 N|:0|||(0.811)|(0.760)|(0.739)|
|Firm experienced||||0.701|0.662|0.437|
|<br>losses due to crime Y:1||||(0.633)|(0.635)|(0.611)|
|N:0|||||||
|<br>Access to finance:||||-0.460**|-0.390*|-0.399*|
|Obstacle severity (0-4||||(0.228)|(0.215)|(0.208)|
|<br>scale)|||||||
|<br>Tax rates: Obstacle||||0.185|0.177|0.170|
|severity (0-4 scale)|||||||
|||||(0.237)|(0.226)|(0.222)|

69
<!-- Start of picture text --> Figure 5<br>Personnel (average)<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>ECA LAC EAP<br><!-- End of picture text --> Anticipating important differences in personnel, the questionnaire disaggregated total personnel in three categories: professionals, administrative and temporary staff. Professionals were subdivided in lawyers, accountants and economists. As shown in figure 6, administrative personnel weights heavily in EAP competition agencies, accounting for almost 65 percent of total personnel. Administrative personnel is also the most important category in the LAC region. Only in ECA there is more professional than administrative personnel. Lawyers and economists jointly account for 75, 45 and 30 percent of total personnel in ECA, LAC and EAP respectively.

10
<!-- Start of picture text --> Once these are accounted for, the multiplier is close to zero in this group of<br>countries as well.<br>In conclusion, in a Panel SVAR. analysis of the effects of government<br>consumption and taxes on output in several country groupings, we find results<br>on government expenditure that are similar to those in IMV: the multiplier<br>is substantially larger in high income countries than in developing countries,<br>in closed economies than in open economies, and under fixed- than under<br>flexible-exchange arrangements. We also re-affirm results from earlier SVAR<br>studies on the macroeconomic effects of taxes: the tax multiplier is close to<br>zero in high-income countries. We also report a new finding: tax policy is<br>more effective at stimulating short-run output in developing countries. The<br>impact multiplier there is approximately 0.3 and statistically different from<br>zero. The tax multiplier in the long run is estimated at 0.8.<br>5 Fiscal policy and debt dynamics<br>In the previous section we estimated fiscal multipliers for a broad range of<br>countries and found very heterogeneous responses to fiscal shocks. But in<br>some cases, the fiscal multiplier was found to be sizable. Active counter-<br>cyclical fiscal policy would seem to have some benefit in terms of stimulated<br>output in these cases. However, as recent experiences in Europe and many<br>past experiences in developing countries have made all too clear, active fiscal<br>management may have large costs as well. Increases in government expen-<br>ditures and tax cuts imply higher deficits, which in turn imply higher debt.<br>Will markets tolerate the higher debt levels caused by fiscal stimulus? Will<br>governments be forced to implement sharp and sudden fiscal reversals, as<br>appears to currently be the case in several European countries?<br>Recent studies have attempted to address these questions. Reinhart and<br>20<br><!-- End of picture text -->
on each loan received by each firm, including information on the loan amount, interest rate, and loan maturity. We complement the bank data with unique data on the credit program we analyze, including detailed information on loan applications by firms (such as the amount requested) as well as banks’ decisions (such as whether a loan request is approved or rejected and the approved amount). These data allow us to measure selection and to disentangle supply and demand factors in the allocation of credit across the whole economy.

Second, we use confidential administrative tax records from Chile’s tax authority ( _Servicio de Impuestos Internos_ ). These data sets contain monthly, firm-level information including sales, materials expenditure, value added, number of workers, wage bill, net worth, age, industry, and municipality. These data allow us to construct measures of pre-pandemic firm attributes (such as productivity, measured as value added per worker) as well as firm performance during the pandemic, which we do with monthly sales during 2020. Third, we work with publicly available firm-level data on firms that use the employment program, a policy of interest for comparison purposes. These data are published monthly by the employment authority ( _Direcci ́on del Trabajo_ ) and contain the dates that each firm uses the employment program and the number of workers in each firm that participates in the program.

We merge these data sets using unique anonymous tax identification codes (IDs) of workers and firms that are common across sources. To secure the privacy of workers and firms, the codes are run by staff at the central bank that have the security clearance to access the full data. Furthermore, the Chilean tax authority requires all the results that are extracted and used in this paper to be calculated using at least 25 tax IDs. In sum, the merged data set allows us to study real and financial aspects of both the credit and the employment program covering the universe of firms. For most of the analysis, we use the 2018–2020 period of these data sets.

To construct the sample of firms we use, we start from all legal and formally registered firms in the economy (602,874 firms) that have a tax ID, which we call _formal firms_ .<sup>5</sup> We then build several samples of the merged data that we use in different parts of the paper. Each sample has a different size and coverage (Table 1). The first sample, which we call _active firms_ , is constrained to include only firms with positive sales in 2019, which amounts to 449,615 firms. We use this sample to conduct the aggregate analysis of the paper and the

> 5We exclude natural persons who use their personal tax ID to borrow as a firm. For these natural persons, we do not have the same scope of information as we do for active firms, and we exclude 818,572 tax IDs for this reason. These natural persons are only included in our aggregate analysis when we report the total value of the program and in our estimate for total expected credit loss (Table 11).

7
detailed evidence on enterprise lifecycle dynamics, including factors determining their growth trajectories and survival rates (Banerjee and Duflo, 2014). This gap is particularly acute for informal enterprises, which constitute the majority of rural non-farm businesses but are often excluded from standard enterprise surveys. Additionally, the role of technology and digitalization in transforming rural non-farm opportunities remains understudied. While digital technologies are rapidly penetrating rural areas, we have limited understanding of how they affect non-farm employment opportunities and enterprise performance (Foster and Rosenzweig, 2022).

The human capital dimension of RNFE presents several interconnected challenges. Our understanding of skill requirements and returns to different types of skills in rural nonfarm employment remains incomplete. While education’s importance is well-documented, we lack detailed evidence on which specific skills are most valuable for different types of non-farm employment and how these skill requirements are evolving (Klasen, 2019). This knowledge gap extends to gender-specific challenges. While women’s lower participation in non-farm employment is well-documented, we lack comprehensive understanding of how household responsibilities, social norms, and local economic conditions interact to influence women’s participation decisions (Chatterjee et al., 2015). The broader economic context of RNFE also requires deeper examination, particularly regarding its relationship with agricultural transformation and migration patterns. While theoretical links between agricultural transformation and non-farm employment are well-established, empirical evidence on how changes in agricultural productivity and commercialization affect local non-farm employment opportunities remains limited (Barrett et al., 2017). Similarly, while rural-urban migration is extensively studied, we lack detailed understanding of how temporary migration affects household decisions regarding non-farm enterprise creation and employment (de Janvry and Sadoulet, 2019). Emerging challenges and policy effectiveness represent the final frontier in RNFE research. The impact of climate change on rural non-farm employment is particularly concerning. While climate change’s effects on agriculture are well-studied, its implications for rural non-farm activities and their role in climate adaptation strategies remain poorly understood (Carleton and Hsiang, 2016). Furthermore, while numerous programs aim to promote rural non-farm employment, evidence on their relative effectiveness and cost-efficiency remains limited (Mansuri and Rao, 2013), hampering our ability to design and implement successful interventions.

This research addresses several interconnected questions about rural non-farm employment in India. The primary inquiry examines the determinants of rural non-farm employment by investigating how individual characteristics interact with district-level factors, with particular attention to historically understudied complementarities between personal attributes 6