rafmacalaba/datause-encoder-data huggingface dataset:
directly estimating the elasticity of quitting with respect to unemployment benefits, usually an impossible task given the lack of temporal variations in these benefits.

Our empirical estimates of the determinants of labor flows from the salaried to the selfemployed sector strongly support the specification suggested by the quitting theory: the individual probability ofjob separation is decreasing in the formal-sector wage (the expected payoffto staying) and increasing in benefits to self-employment and the probability of finding a formal-sectorjob (the expected payoff to leaving). Moreover, the above mentioned symmetry property of the quit-rate function cannot be rejected. When the microeconomic estimates are used to calibrate the macroeconomic model, we find the long-run effects of macroeconomic shocks on wages, labor turnover, and (formal-sector) employmen **t** o be substantial. The strong employment response found here stands in stark contrast to the disappointingly small unemployment effects reported by Danthine and Donaldson (1990,1995) and Kimball (1994) who calibrate an efficiency wage model with shirking (Shapiro and Stiglitz 1984) to US unemployment data.<sup>6</sup> This paper can be interpreted as providing a two-stage "test" of the real world relevance of efficiency wage models with labor turnover. In the first stage, microeconomic data are used to estimate and test what we believe to lie at the heart of this type of efficiency wage model, namely the quit-rate function. If the coefficients are found to be significant and of the correct sign, in a second stage the estimated quit-rate function is incorporated into the macroeconomic model and the calibrated model economy is used to assess the quantitative importance of efficiency wages. This second-stage check is important since there seems to be little value in having a macroeconomic theory of unemployment (underemployment) which is supported by microeconomic data but implies an almost constant unemployment (underemployment) rate. In this paper we present one fully worked out example of this two-stage procedure in the hope that it will spur interest in further 6Danthine and Donaldson (1990, 1995) explicitly consider aggregate uncertainty by solving a stochastic dynamic general equilibrium model. Kimball (1994) studies the dynamic and steady state effects of macroeconomic shocks in a deterministic model, but his quantitative result on employment variations is obtained by comparing steady state equilibria.

3
# **3.2 Structural estimation**

We adapt the adaptation model of Kimball et al. (2015) and Galiani et al. (2018) to incorporate delay disappointment where both adaptation and disappointment are modelled by exponential decay as in Figure 1. In particular, we estimate via Nonlinear Least Square (NLS) the following equation where the second and third terms in parentheses capture hedonic adaptation and delay disappointment, respectively. Equation (2) is valid for _t > T_ , consistent with our post-intervention follow-up data.<sup>9</sup> We focus on satisfaction with overall house quality and satisfaction with quality of family life as outcome measures of SWB.<sup>10</sup> Table 2 reports the estimated parameters of equation (2). We estimate the full version of the model which includes delay disappointment, and a restricted version that ignores it, essentially estimating the equation in Galiani et al. (2018). The delay effect _βD_ is the only statistically significant parameter when satisfaction with overall house quality is used as a proxy for SWB. Recall that SWB would eventually decline by _βD_ if the program were never implemented. In contrast, when satisfaction with quality of family life is used, only the transitory effect _βT_ is statistically significant.

Following Kimball et al. (2015) and Galiani et al. (2018), we now consider the cumulative impact of the program (i.e. “area under the curve”) and measure the specific contributions of the permanent, transitory and disappointment effects, respectively. In particular, for an individual with gross interest rate _R_ (which includes an annual mortality risk _d_ and an interest rate _r_ ), the cumulative impact of the pro-

> 9The analogous expression when _Te ≤ t ≤ T_ is

> With only post-intervention follow-up data, we cannot test if _βP_ and _βT_ changed due to delays. We thus estimate _βD_ assuming that _βP_ and _βT_ are fixed. With pre-intervention data, one could estimate _βD_ directly and allow for different values of _βP_ and _βT_ depending on whether households experienced delays or not.

> 10We do not include the 28 municipality fixed effects in equation 2 because the nonlinear routine in STATA did not converge for either SWB measure when included. Appendix Table A6 replicates Table 1 without the municipality fixed effects and finds were similar results.

10
contribution to structural child labor below the average contribution across all sectors.<sup>17</sup> As shown, less than 50 percent of workers in Bolivia have, on average, worked in a sector with low contribution to child labor in the past decade.

Table 4: Percentage of workers who satisfy each JQI component, by year

|||||Components||||
|---|---|---|---|---|---|---|---|
||(1)<br>Social<br>Security<br>Benefts|(2)<br>Minimum<br>wage<br>compliance|(3)<br>Low incidence of<br>child labor|(4)<br>Low incidence of<br>relative<br>underpayment|(5)<br>Low incidence of<br>compensatory<br>moonlighting|(6)<br>Labor<br>stability|(7)<br>Low incidence of<br>overqualifcation|
|2007|14.6|63.6|46.3|60.9|59.4|56.1|66.5|
|2008|13.4|66.1|37.2|60|58|57.8|68.2|
|2009|15.6|67.7|39.5|60.5|57.5|57.4|67.2|
|2011|18.1|68.7|39.3|58.9|58.5|55.5|66.5|
|2012|18.8|68.2|41.3|61.9|60.2|56.5|64.4|
|2013|22.4|68.1|41.6|61.3|58.1|55.7|66.5|
|2014|16.8|62.8|47.1|59.3|57.2|56.5|66.3|
|2015|18.9|63.3|41.4|61.4|58.3|56.7|71.4|
|2016|19.2|62|46.5|61|53.3|57.1|67.5|
|2017|18.9|60.5|48.1|61.6|54.8|56.7|66.8|
|2018|20.4|62.1|41|61.4|54.3|55.8|68.1|
|2019|21.7|62.8|40.6|61.1|51|55.9|67.9|
|2020|21.1|58.5|44.3|60.2|53.1|55.5|67.3|
|2021|17.0|62.8|42.1|63.3|52.6|57.5|68.8|

_Notes:_ Based on data from EH 2007-2021.

Column (4) shows that approximately 60 percent of workers in Bolivia have an hourly income above the third decile of the income distribution of their respective cohorts. Moreover, the share of workers complying with this component has remained relatively stable over the decade, with a peak in 2021, reporting the highest percentage throughout the period. On the other hand, Columns (5) to (7) report the share of jobs for the components with higher success rates compared to other variables. In other words, these variables exhibit the lowest variance across workers. For instance, less than 50 percent of workers in Bolivia have been engaged in compensatory moonlighting, although this rate has increased since 2013. In addition, Column (6) shows that slightly more than 50 percent of the employed population in Bolivia has worked more years than their respective cohorts, serving as a proxy for tenure. Finally, Column (7) reports that, on average, one out of three workers in the country has been affected by overqualification, and this component has shown modest changes over the decade.

> 17As above-mentioned, we excluded the agriculture sector when calculating the average contribution in the economy to child labor.

13
# **1 Introduction**

Informal employment is commonplace in developing countries. Although since the term “informal employment” was coined in the early 1970s the literature has proposed various definitions, from the viewpoint of workers (as opposed to firms) the consensus seems to be that workers who are not covered by social protection, such as social security, or who work without a wage contract are considered to be informal (World Bank 2013, p. 64). In taking this definition to data from labor force or employment surveys, workers who are self-employed or salaried without social security benefits are considered to be informal. According to the World Bank (2013), existing global studies of the incidence of informal employment in developing countries suggest that informality rates range between 40 and 80 percent of the employed labor force. Similarly, in the data from Labor Force Surveys from Brazil and Mexico used in this paper, the informal-employment share exceeds 40 percent of the labor force in Brazil, and it is almost 60 percent in Mexico.

More importantly, the large informal employment shares in developing countries has inspired decades of research aiming to understand the causes of informality and more broadly the functioning of developing-country labor markets. One strand of the literature emerged in the late 1990s out of concerns about the role of international integration as a determinant of informality in developing economies.

Goldberg and Pavcnik (2003) examined the impact of trade liberalization implemented in the 1980s and 1990s in Brazil and Colombia. They found that trade reforms had no relationship with changes in informal employment in Brazil, and found weak evidence that trade reforms caused an increase in informal employment in Colombia. Bosch and Maloney (2010) studied labor market dynamics in three large Latin America countries, namely Argentina, Brazil and Mexico. They explored labor dynamics using statistical markov processes of individual workers’ trajectories across sectors from labor force surveys. The authors found evidence that 2
78 RENT CONTROL IN KUMASI, GHANA Table 5.4 presents those estimated parameters of housing expenditure functions for renters and owners. In general the results are remarkably consistent with results from developed countries (see Mayo, 1981). The regression fits were typical for this type of equation: typical R-squared statistics are in the 0.1 to 0.3 range (minimum is 0.06, maximum, 0.57). Fits were similar for owners and renters.

The median of all renters income elasticities was 0.49; developing country elasticities ranged from 0.31 (Pusan, Korea) to 0.88 (Davao, the Philippines). Most clustered between 0.4 and 0.6 with estimated U.S. elasticities lower than developing country estimates. The median of all point estimates of owner income elasticities was 0.46, with extremes of 0.17 in Cairo and 1.11 in Santa Ana, El Salvador. The majority of point estimates lie between 0.4 and 0.6. In 9 of 14 cases where comparison was possible, estimated developing country owner income elasticities were greater than those of renters. Comparing expenditure equations across countries revealed practically no systematic variation of income elasticities with country or city income level or population size, but considerable variation in dollar-adjusted intercepts, which were positively related to average city income. Rent-to-income ratios therefore declined systematically with income within cities, but increased with income across cities. Figure 5.4: Rent to Income Ratios, CrosCountry Model These relationships are shown Pent to income (Penters) graphically in Figure 5.4 for renters in 100* four representative cities . **Seovl** Relationships for owners are similar, 80% .. . .. although average rent-to-income ratios + cairo are invariably higher at every income %14 >evel for owners within given housing iarkets. 40 The relationships portrayed in 20% . ..... Figure 5.4 are very similar to the consumption patterns within and across 50 100 150 200 250 300 350 400 450 500 550 countries documented by Kuznets (see Income in 1981 U S Dollars Kuznet3, 1961 and other works citedSource Malpezzi and Mayo (t985) therein). Qualitatively, housing consumption is remarkably smaller at various income levels than are between-country differences at lifferent average income levels. Malpezzi and Mayo explored alternative theoretical explanations for these results and then tested a series of long run cross-country housing expenditure models. The simplest cross-country model parallels the log-linear within-country model, but with the addition of a price term, the relative price of housing which was constructed using data from Kravis, Heston and Summers (1982).

Defining R as rent, y as household income, and p. as the relative price of housing, Malpezzi and Mayo originally estimated the following models for renters and owners in developing countries:
certain countries, generating either missing values or prices drawn from unusual outlets; for example, Deaton and Heston (2009) point out that rice is hard to find in Ethiopia and teff (the staple in Ethiopia) is hard to find in Thailand, say. One could avoid this problem by choosing more representative country-specific bundles, but this would re-introduce the quality bias discussed above, which has plagued past ICP rounds. There is also a problem of “urban bias” in the ICP price surveys for some counties; the next section describes our methods of addressing this problem. As was argued in Ravallion et al. (1991), a further concern is that the weights attached to different commodities in the conventional PPP rate may not be appropriate for the poor; section 6 examines the sensitivity of our results to the use of alternative “PPPs for the poor” available for a subset of countries from Deaton and Dupriez (2009). Another limitation is that the PPP is a national average. Just as the cost of living tends to be lower in poorer countries, one expects it to be lower in poorer regions within one country, especially in rural areas. Ravallion et al. (2007) have allowed for urban-rural cost of living differences facing the poor, and provided an urban-rural breakdown of our prior global poverty measures using the 1993 PPP. We plan to update these estimates in future work.

Given that the bulk of the PPPs have risen for developing countries, the poverty count will tend to rise at any given poverty line in $PPPs. However, the same changes in the PPPs also alter the (endogenous) international poverty line, given that it is anchored to the national poverty lines in the poorest countries. Next we turn to the poverty lines.

# **3. National and international poverty lines**

In setting an international poverty line using the 2005 ICP we have aimed to follow the same definition used in our past work, namely that the line should be representative of the national lines found in the poorest countries—in the spirit of the original “$1 a day” line (RDV; World Bank, 1990). For this purpose, RCS have compiled a new set of national poverty lines for developing countries drawn from the World Bank’s country-specific _Poverty Assessments_ and the _Poverty Reduction Strategy Papers_ (PRSP) done by the governments of the countries concerned. These documents provide a rich source of data on poverty at the country level, and almost all include estimates of national poverty lines. The RCS dataset was compiled from the most recent poverty assessments and PRSPs over 1988–2005. In the source documents, each poverty line is given in the prices for a specific survey year (for which the subsequent poverty 8
Financial crises generally beget research analyzing the impact of the financial system on growth and welfare. In this regard, the recent financial crisis was no different. A number of papers, theoretical and empirical, have built on previous research studying on the one hand, the contributions of finance to growth and on the other, to volatility. The benefits of deep financial systems have long been recognized for consumption smoothing purposes and to support long term growth. More recently, research has focused on whether finance in all forms supports growth or whether some types of finance should be constrained (regulated) in its development because it is not productivity/ growth enhancing. Previous research has also addressed whether financial systems can increase volatility in an economy. New research reexamines this issue in the context of the changing global markets where credit creation has been linked closely to large cross border financial flows and financial markets have been progressively liberalized.

This paper builds on recent research that examines the link between financial system growth and economic growth and volatility. Most research indicates that financial system development enhances growth and allows consumption smoothing. Enhancing access to credit for firms and households is an important goal of policy around the world. The paper examines whether greater financial depth can increase output, or consumption volatility (instead of smoothing consumption per capita). Specifically, it uses panel data estimation covering 153 countries over the period 1964-2013, extending the period over which other papers have investigated this issue, using both 2SLS and GMM estimations and it is found that private credit is associated with lower volatility in GDP and consumption per capita, though the effect is nonlinear. At very high levels of financial system development as measured by the level of private sector credit to GDP or the value of financial assets to GDP, finance has a positive effect on economic volatility. This effect holds true even after accounting for trade and financial openness, institutions, the level of development among other things, and even after accounting for regional differences, including income levels. The paper also examines whether the speed of credit creation, rather than the size of financial development affects volatility; it does not find evidence that it is so.

The second part of the paper focuses on whether the composition of borrowershouseholds versus firms- matters in terms of how financial system size may affect economic growth and volatility. This section builds on recent research by Beck et al (2012) that concludes 2
Table <u>3. The effect of COVID-19 exposure on educational outcomes</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|_A. Dropout_|||||
|Warning index|0.008|0.007|0.007|0.007|
||(0.001)***<br>[0.001]|(0.001)***<br>[0.001]|(0.001)***<br>[0.001]|(0.001)***<br>[0.001]|
|Mean outcome|0.086|0.086|0.086|0.086|
|Adjusted R<sup>2</sup>|0.007|0.079|0.090|0.128|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_B. Effective promotion_|||||
|Warning index|-0.020|-0.019|-0.019|-0.019|
||(0.002)***|(0.002)***|(0.002)***|(0.002)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Mean outcome|0.802|0.802|0.802|0.802|
|Adjusted R<sup>2</sup>|0.022|0.068|0.095|0.118|
|Observations|13,337,281|13,301,355|13,337,055|13,301,145|
|_C. Switched to public school_|||||
|Warning index|0.001|0.001|0.001|0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Mean outcome|0.010|0.010|0.010|0.010|
|Adjusted R<sup>2</sup>|0.005|0.013|0.101|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|_D. Switched to private school_|||||
|Warning index|-0.002|-0.002|-0.002|-0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Mean outcome|0.032|0.032|0.032|0.032|
|Adjusted R<sup>2</sup>|0.003|0.053|0.066|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for details). Each column presents results from a separate regression for each outcome. Clustered standard errors by municipality are shown in parentheses. Columns 1 and 3 estimates Equation (1) with no controls, Columns (2) and (4) include controls for gender, urban residence, level of schooling, grade, and whether the school provides bilingual instruction. We present q-values that adjust for multiple hypothesis testing in brackets, calculated using the method by Benjamini and Hochberg (1995) that controls for the false discovery rate described in Anderson (2008). Significance levels: ***p<0.01; **p<0.05; *p<0.10.

18
# **Tables**

Table 1: Treatment effects on primary IPV outcomes

||(1)<br>Emotional IPV|(2)<br>Physical IPV|(3)<br>Sexual IPV|
|---|---|---|---|
|Treated couple|0.07**|0.05*|0.10***|
||(0.03)|(0.02)|(0.03)|
||[0.007]|[0.014]|[0.001]|
|Spillover couple|0.06***|0.11***|0.17***|
||(0.02)|(0.02)|(0.02)|
||[0.004]|[0.001]|[0.001]|
|Observations|1,974|1,960|1,972|
|Baseline controls|Yes|Yes|Yes|
|Control mean|0.66|0.33|0.42|
|Treated vs Spillover p-value|0.92|0.02|0.01|

_Note_ : IPV = intimate partner violence; OLS = ordinary least squares. Strata fixed effects included. Standard errors are clustered at village level, shown in (rounded brackets). Sharpened q-values are shown in [square brackets]. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1 Results show OLS estimates of direct treatment and spillover effects, and differences between effects on trained and spillover couples.

Sharpened q-values were calculated to adjust for multiple hypothesis testing using the Benjamini and Hochberg (1995) correction method. The unit of observation is individual level, and is calculated using a dummy aggregating whether a wife reported experiencing any of each of the forms of emotional, physical, or sexual violence, controlling for the baseline value of dependent variable (recoded to 0 if missing), as well as a dummy for whether the baseline response was missing. Control mean is the average value of the dependent variable in the control group.

35
||Reg<br>nlyfor non-<br>O|ressio<br>migra|n 2<br>nthouseholds|
|---|---|---|---|
|Number of household’s workers|-0.0<br>37||54)<br>(-0.|
|Average number of years of education (ref.: 0-4 years)||||
|4-8 years|0.553|***|(3.50)|
|8-10 years|0.819|**<br>*|.22)<br>(4|
|10 years or above|0.846|***|(4.15)|
|Nu<br>ndents<br>mber of depe|-<br>0.091|**|(-2.15)|
|Land area of the household|0.018|***|(3.13)|
|Percentage of paddy field of the village (/100)|-0.053||(-0.12)|
|Percentage of vegetable field of the village (/100)|1.440|***|(4.45)|
|Inverse Mills Ratio|-0.059||(-0.18)|
|Constant term|6.525|***|(32.94)|
|_R_<sup>2</sup>||0.172||
|Number of observations||468||

Note: t-statistics in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%.

# **7.2 Remittances, inequality and poverty**

We use the results of regression 2 to simulate the counterfactual of how household incomes, poverty and inequality would have been in the absence of migration for all the households. Table 4 shows the comparison between observed income and predicted income. In the absence of migration, household per capita income would have been 17.1% lower, while Gini would haven been 16.7% higher. In other words, participation in migration not only increases household income but also lowers inequality in rural areas. Poor households largely benefit from migration/remittances. Using the basic needs poverty line developed by Ravallion and Chen (2004) for rural areas, which is equal to 850 _yuan_ in 2002, we find that remittances lead to a decline in the incidence of household poverty (<sup>_P_</sup> 0<sup>) from 27.5% to 14.3%, in the depth of poverty</sup> (<sup>_P_</sup> 1<sup>) from 12.0% to 5.6%, and in the severity</sup> of poverty (<sup>_P_</sup> 2<sup>) from 7.1% to 3.1%. The strong</sup> impacts on depth of poverty suggests that migration reduces the income gap among the poor; and those on the severity of poverty, which assigns higher weights to the poorest of the poor, suggests that migration improves the well-being of the poorest disproportionately. In other words, the gains in poverty reduction due to migration go disproportionately to the poorest households (de Braud and Giles, 2008).

20