rafmacalaba/data-use-mentions-extended huggingface dataset:
.920|14|
|MP|0.160|15|Odisha|0.381|15|UP|0.837|15|
|Chhattisgarh|0.125|16|UP|0.367|16|Bihar|0.696|16|
|Bihar|0.109|17|Bihar|0.301|17|Jharkhand|0.612|17|
|Jharkhand|0.097|18|Jharkhand|0.253|18|Andhra|0.462|18|
|CV|47.3|||39.5|||34.3||

Source: Author’s calculations. C.V- Coefficient of Variations

**_Social Infrastructure:_** Table 3 reports the score for the social infrastructure index and the relative ranking of the states. Like physical infrastructure, there is wide variation in social infrastructure across states, although the disparities have declined from 2010 to 2019. States such as Himachal Pradesh, Karnataka, and Assam are in the top ranks, whereas Uttar Pradesh, Jharkhand, and Bihar feature in the bottom positions. The relative positions of states have remained more or less constant, barring a few exceptions. For example, the relative position of Maharashtra has worsened (Rank 3 in 2005 to 11 in 2019), whereas Chhattisgarh and Assam have improved their respective ranks during the same period.

**Table 3: Relative Performances of States for Social Infrastructure Index**

||**20**|**05**||**2010**|||**2019**||
|---|---|---|---|---|---|---|---|---|
|**_States_**|**_Score_**|**_Rank_**|**_State_**|**_Score_**|
merged dataset with real sector variables available only at the Central Bank of Chile, we cannot use our baseline predicted default risk measure, which relies on sales, employment, and value added. Instead, we proxy firm risk using the loan spread, a forward-looking financial variable observed in the CMF data, though only for a subset of firms with prior borrowing.

While the triple interaction term is not statistically significant for either program, the previous results hold (Appendix Table 17, Panel A). That is, within firms under a lockdown, risk does not appear to influence participation in the credit or employment programs. Credit program take-up is broad and largely unaffected by lockdowns. In contrast, employment program take-up responds to real-side shocks (e.g., sales declines or closures), not credit risk, suggesting that lockdowns affect participation mainly through real disruptions. The triple interaction remains insignificant for firms near municipal borders (Appendix Table 17, Panel B). However, the lockdown effect for the employment program, though similar in size, loses statistical significance due to the limited number of firms with spread data.

# **5 on Firm Indebtedness**

We next study the effects of using the credit program on debt at the firm level. To do so, we estimate the following cross-sectional regression: ∆ _Debti_ is the growth in (net) outstanding bank debt during the entire 2020, normalized by sales in 2019. This ratio focuses on the change in indebtedness, holding constant sales and thus abstracting from the sales decline in 2020.<sup>52</sup> The dummy _Program Usei_ is defined as previously reported. _Riski_ corresponds to the fitted default probability value derived from the firm-level default regression estimates and used in Section 4.

Table 5 presents the results.<sup>53</sup> Firms with and without previous loans that use the credit program increase their indebtedness by 14.5 and 13.0 percentage points, respectively,

> 52We normalize the debt change by sales instead of
house characteristics, and household assets. The dependent variable is household per capita consumption.

W **e** train a machine learning model to predict household per capita consumption using an extreme gradient boosting model with optimal hyperparameters chosen via 5-fold crossvalidation. The parameter used in the models corresponds to the average of the selected parameters in each fold. Details on the range of parameters considered are shown in Table 4. Additional technical information regarding these parameters and other aspects of the extreme gradient boosting procedure can be found in Annex 3.

**Table 4.** Parameters for XGBoost models <u>to estimate benchmark welfare.</u>

|**Parameter**|**Range **|
|---|---|
|Maximum numberofboostingiterations|between50 and200|
|Maximumdepthofa tree|2or 4|
|Learningrate|0.1or0.3|
|Subsampleratio ofthe traininginstance|0.2,0.4, or0.6|
|Subsampleratio ofcolumns to construct eachtree|0.2,0.5, or0.7|

We estimate four models using different samples in the IHS: (1) The full sample (All districts-all households), (2) All households in the UBR districts (UBR districts-all households), (3) the poorest 50% of the households in all districts (All districts-poorest 50%), and (4) the poorest 50% of households in UBR districts (UBR districts-poorest 50%). Estimating four models allows us to assess the trade-offs along two dimensions: (1) using all districts in the sample rather than restricting to UBR districts, and (2) using all households rather than just the lower half. Annex 4 shows the R-squared for each model and the main explanatory variables in terms of the _gain_ measure.<sup>16</sup> Including all households (model 1) leads to higher R-squared, while limiting to the bottom half of UBR districts (model 4) leads to by far the lowest
expected loss, given that a loss occurs. In other words, Expected insured loss (y) = Emax[λμ-y,0] = Prob[y < λμ][λμ – E(y|y< λμ)].

Assuming one has an adequate representation of the yield density f(y), this is given by: This discussion demonstrates the importance of adequate measurement of the probability distribution function for yields. All key parameters of the insurance contract are related to such a measurement of yield risk. Though straightforward in theory, a number of complicating issues may arise when this is put into practice with actual yield data.

In order to illustrate these concepts, a set of 21 years of regional yield data for groundnuts in an important producing country was assembled. No time trend was found. The average yield is 751.3 kg/ha, the standard deviation is 304.2 kg/ha and the skewness is slightly negative (-0.14). Maximum likelihood procedures were used to estimate parameters of a beta density (the minimum possible yield was fixed at zero while the maximum possible yield was chosen to maximize the likelihood function). In addition, a normal distribution was used to model insurance contract parameters (using the mean and variance calculated from the 21 yield observations). Finally, nonparametric kernel density 17
as they were not in the initial pool of pre-listed beneficiaries to receive the transfer. We assume that these households are comparable to the treated households.

The Proxy Means Test has been used in previous studies as a criteria to identify treated and control groups of a cash transfer (for example see Stoeffler et al. (2020)). The challenge with this methodology of identification is that we do not have the true LPMT score of the control households. Using the CIMS data, it is possible to recreate a proxy LPMT score, but only partly. Indeed, as described in Appendix 1, the LPMT score is calculated as a sum of 10 different characteristics which include household characteristics, assets, work status and disability status. Out of the 10 characteristics, 3 are unavailable in the CIMS data, two of which we are able to proxy. We do not have any information on whether the household uses bottled or sachet water for drinking. We also do not have the information on (1) whether the head of the household is literate in English or Sierra Leonian language and (2) whether the head of the household has an informal contract. For these two characteristics, we use information on the education level and work with a formal contract of the respondent (as opposed to the household head) as proxies. Among the Round 1 beneficiaries, 32 percent are non-household heads and among the R2 beneficiaries, 45 percent are non-household-heads. We are therefore able to recreate an LPMT score that can take a total of 9 instead of 10, but with potential errors because of the two proxies.

The original LPMT was calculated using a simplified tool collected by program administrators.<sup>10</sup> The LPMT threshold used by NaCSA to determine the eligibility of a household for the ECT was 7 out of 10. As described in Figure 2, households that scored 7 or above have necessarily passed the original LPMT because these households have a true score of 7 to 10. However, there are potentially households that
Olsen, Mancur (1971). _The Logic of Collective Action: Public Goods and the Theory of Groups._ Cambridge: Harvard University Press.

Organization for Economic Cooperation and Development (2004), “OECD Principles of Corporate Governance.” PAHO (Pan American Health Organization) (2008). “The Essential Public Health Functions as a Strategy for Improving Overall Health Systems Performance: Trends and Challenges Since the Public Health in the Americas Initiative 2000-2007.” Working document.

PATHS (2008), _Celebrating Success. PATHS in Nigeria (2002-2008). Final Programme_ - _Report_ , Partnership for Transforming Health Systems. http://www.lath.com/Our <u>Projects/12</u> Reid, Gary J. (2009), “Actionable Governance Indicators – Concepts and Measurement,” AGI Initiative, Washington, DC: World Bank. Mimeo.

Reinikka, Ritva and Jakob Svensson (2006), “Using Micro-Surveys to Measure and Explain Corruption,” _World Development,_ 34(2): 359–370. February.

Roberts, Marc J., William Hsiao, Peter Berman, and Michael R. Reich (2004). _Getting Health Reform Right: A Guide to Improving Performance and Equity_ . New York: Oxford University Press.

Saltman, Richard and Odile Ferrousier-Davis (2000). “The concept of stewardship in health policy,” _Bulletin of the World Health Organization_ , 78(6):732-739.

Savedoff, William D. and Pablo Gottret, eds. (2008). _Governing Mandatory Health Insurance: Learning from Experience_ . Washington, DC: World Bank.

Savedoff, William D. ed. (1998). _Organization Matters: Agency Problems in Health and Education in Latin America_ . Washington, DC: Inter-American Development Bank.

Solon, Orville, Kimberley Woo, Stella A. Quimbo, Riti Shimkhada, Jhiedon Florentino, and John W. Peabody (2009). “A Novel Method for Measuring Health Care System Performance: Experience from QUIDS in the
?  Has such a shift led to an increase of the formal economies?

6. The following sections first look at the extent of undeclared work in the NM-EU countries (Section 2) and how – according to opinion polls – people in these countries perceive the role of taxation in undeclared work (Section 3). This is followed by a description of the various channels through which taxes may affect undeclared work (Section 4) and by cross-country comparisons of these potential sources for undeclared work, namely the overall tax burden (Section 5), the tax mix (Section 6), labor tax wedges (Section 7), and the design of taxation within the broad tax categories, which may encourage or discourage undeclared work (Sections 8 - 11). This is followed by an attempt to gain insight into the effectiveness of tax administration by comparing some input and output measures (Section 12). As non-tax factors can amplify the adverse effects of taxes on the labor market and reduce the effectiveness of tax reform, some of these other economic framework conditions are briefly discussed (Section 13). This paper concludes by refining the main results and possible best practices for tackling undeclared work (Section 14).

7. This paper argues that NM-EU countries have had mixed success tackling undeclared work. While taxation matters, other underlying conditions for formal sector activity are also important. Addressing the problem of undeclared work therefore requires a broad policy 2
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insurance. Access to social insurance in Argentina typically requires that one has a job in the formal segment of the labor market, which is less than half of the workforce, and relatively few of the poor (World Bank, 1999).

There is evidence that spending on social services has responded more to changes in national income than has social insurance. Wodon and Hicks (1999) study the effects of changes in GDP on targeted spending on social services (about 4% of total government spending). They find that the ratio of targeted public spending to the number of poor had a positive elasticity (of about three) to GDP; in recessions, there were more poor people, and less was spent on them.

The political regime is also likely to matter. For a sample of Latin American countries (including Argentina), Brown and Hunter (1999) find that democracies are more likely to protect social spending in a recession, but that authoritarian regimes are more inclined to expand social spending when the crisis is over.5 Argentina has been a democracy since 1982. The data series I will use start in 1980, and so it is reasonable to ignore the change in regime.

In this setting I examine how changes in the government's total budget affected the level and composition of social spending. Did budget cuts have similar effects to budget expansions? Were the categories of spending that are known to matter to the poor more protected than other types of spending?

Figure 1 offers a direct test for whether social spending has been protected from budget cuts. The figure plots the time series of changes in the log of total public spending ("Gasto Puiblico Consolidado") and the log of social spending (both in 1997 prices) as compiled by Government of Argentina (1999) and covering the period 1980-97.6 Unlike 5 This study does not, however, control for the level of total spending, so it is unclear whether the identified effects operate through the composition of spending or its aggregate level;
of 23 variables. The indicator for unlicensed at baseline is partialled out for the private facilities regressions. In Column 2, patient wealth index, years of education, health status, and distance from home are partialled out. HF = health facility; JHIC = Joint Health Inspection Checklist; OOP = out-of-pocket payments; PPP = purchasing power parity.

the JHIC score and (b) that there was at least some (perhaps substantial) information about this score available to consumers. As we will see in Section V, this is consistent with a diminished role for information as the mechanism for the improvements we observe. These patterns also suggest that regulating facilities at the low-end may be very costly given their high rates of churn and low patient loads, an observation we return to in the conclusion.

# **III.4 Design Integrity**

## **III.4.1 Balance, Attrition, and Accretion**

There are no systematic differences across treatment and control groups in baseline main outcome variables and key covariates with the exception of out-of-pocket payments at the facility level and the test of joint significance yields an F-stat of 1.020 (p=0.425) (Table A2 of the Appendix). Response rates were 93% at baseline and 97% at endline and non-response is balanced between treatment and control at endline with an estimated null difference (p-value = 0.974). At baseline there is a small 4 percentage point higher response rate among facilities in treatment markets 15
1

# **1. INTRODUCTION**

The economic literature on telecommunications reform is one of the largest on infrastructure reform (certainly larger than sanitation and any transport service and arguably in close competition with energy). This results partially from the relatively good quality of economic data on telecoms.<sup>1</sup> But the academic popularity of the sector also stems from three of its characteristics. First, telecoms’ impressive rate of technological change seems to have broken more bureaucratic impediments to change than reformers could have ever hoped for. Thus, it is important to get a sense of how much pro-active reforms added to the gains offered by the technological trend. Second, in spite of the high potential for competition in the sector, its complex regulatory dimensions seem to drive the size and distribution of the rent generated by the technological revolution. Third, the relative importance of the increased role of private operators in service delivery continues to be a source of argument.

The drivers of the impact of reforms and their distribution have been quite well documented in a plethora of country specific studies (with a very significant share on the US and the UK).<sup>2</sup> The impact of reforms has also been tested in cross-country studies but these are significantly scarcer than country specific ones. We review some relevant crosscountry studies later in some detail. Their country coverage varies from as little as 12 countries as in Fink et al. (2001), with a focus on Asian countries, to 200 countries as in Wallsten (2003), with both developed and developing countries. We do a cross-country analysis on the impact of reforms on telecom performance by using a database of 204 countries, 153 developing and 51 developed. The sample size is thus the largest so far— although only marginally so when compared to Wallsten’s.

When drawing general lessons for reformers, the main advantages of a large sample are that it reduces the risk of sample selection bias and allows a quick diagnosis of the
et al. 2011). It has been noted that International Labour Organization recommendations for measuring labor are likely to be inadequate in settings such as rural Tanzania, where the majority of labor is found in the informal self-employed and farm sectors (World Bank 2014).

A review of existing surveys that collect labor data in Africa shows that, in practice, the capture of labor market statistics in household surveys varies widely. The recall period, the sequencing of questions, the use of screening questions, the seasonal timing, the granularity of reporting requested, the unit over which labor is reported, and the choice of respondent can vary across surveys within and across countries. Inconsistencies in data collection methods hamper comparisons over time and space if survey methods differ, as has been shown in the context of the measurement of welfare, poverty, and hunger (Backiny-Yetna, Steele, and Djima 2014; Beegle et al. 2012, 2016; De Weerdt et al. 2016) and labor measurement (Bardasi et al. 2011).

National integrated or multitopic household surveys in Africa generally collect data on agricultural labor in two ways.<sup>4</sup> In one approach, general labor information, including agricultural labor, is collected in a labor module. In another, specific agricultural labor data are collected in an agriculture module, such as in the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA). In the former case, information on labor involving each household member above some specified age is collected in reference to the last seven days or, perhaps, the last 12 months (Anderson Schaffner 2000). The person’s labor input is not differentiated by plot, by crop, or by farm activity (such as weeding, harvesting, and so on). Instead, in the agricultural module outlined by Reardon and Glewwe (2000), the total days of labor at the household level over the last completed season are collected for each plot and by specific activity. An expanded agricultural module would
be reconciled with those. Using data from Texas, Mountjoy (2022) finds that the expansion margin share is about two-thirds, much higher than our 30%. One possible explanation for this discrepancy is that SC programs (typically taught at community colleges in the U.S.) are often viewed as an entry point into the higher education system, with students intending to transfer to bachelor’s programs. In Colombia, in contrast, SC degrees are viewed as terminal and rarely offer a pathway towards a bachelor’s program, as they mostly focus on facilitating the school-to-work transition. Another possible explanation is that many community colleges in the U.S. are located outside large cities, where they constitute the only higher education option for local students. In contrast, during our sample period SC programs in Colombia were mostly offered in large and medium cities that already other higher education options, leaving little room for newly entering students into the system. Further, Mountjoy (2022) finds that compliers along the expansion margin are more likely than those on the diversion margin to benefit in terms of average earnings and years of 19
value of that minimizes the mean squared error (MSE) in the validation sample and present the resulting estimates. We include information on 2019 GDP per capita, total population, and primary school enrollment rate as candidate covariates taken from the World λλ Development Indicators database.

Third, we obtained more detailed quarterly GDP data for a subset of countries that mainly consists of the OECD members.<sup>6</sup> In addition, since the quarterly data form a panel of countries, we employ group lasso to select features, grouping quarter fixed effects and country fixed effects.<sup>7</sup> The quarterly GDP growth data primarily come from high-income states, whereas the cross-sectional data set consists of countries with varying income statuses. One might expect that the predictive power of lasso-selected covariates is different across the samples, so we focus on these differences throughout the results below. There are 39 countries with non-missing data for quarterly GDP and the candidate features; a list is available in Table A2 of the appendix.

Some candidate features are likely to be strongly collinear. We thus use factor analysis to group the data as follows:

1. Two factors for food prices: apples, bananas, bread, cheese, eggs, lettuce, meat, chicken, onions, oranges, potatoes, milk, and rice (all relative to January of 2020)

> 5 https://www.worldpop.org/

> 6 These GDP statistics come from the OECD (2021).

> 7 In other words, lasso will either select all quarterly dummies or none of them (and the same for country dummies). We implement this using _gglasso_ in R.

6