rafmacalaba/data-use-mentions-extended huggingface dataset:
ernment supplier and identify their location using publicly available data from the Ministry of the Economy. We then document that approximately 25% of purchases are from suppliers located in the same municipality, but with wide variation across entities. Local purchases are less prevalent in smaller municipalities, which could be driven by the scarcity of local suppliers, but they are no more or less common when we compare competitive vs. non-competitive purchase modalities.

Second, we document the extent of delays in payments to suppliers across municipalities in the country and how it correlates with municipal characteristics. Payment delays are considered one of the key barriers to the participation of small and medium-sized enterprises (SMEs) in public procurement since they often have less access to working capital loans and are unable to wait for longer periods of time before being paid by their clients (Barrot and Nanda, 2020; Breza and Liberman, 2017; Barrot, 2016; Conti et al., 2021). By law, public entities in Brazil are mandated to pay their clients in less than 30 days as a rule.<sup>7</sup> However, late payments are common – we document that 15% of all payments to suppliers of goods and services in recent years are made in more than 30 days, and 20% of municipality-year observations have an average payment delay over 30 days. Payment timeliness is also systematically correlated with local per capita GDP, with higher-income municipalities paying faster on average. The granularity of the data allows us to show that aggregate quantities, such as the average payment delay, might hide a strong variability in payment timeliness across suppliers and types of purchases. This is an instance in which microdata is crucial for a proper assessment of the quality of the budget execution process of a municipality.

The remainder of this paper is organized as follows. In Section 2, we provide further institutional details on procurement and budget execution in Brazil and present some statistics from our datasets. We then proceed to validate the quality of our budget execution data in Section
# **References**

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- Acevedo, K. and Bornacelly, I. (2014). Panel Municipal del CEDE. _Documento Centro de Estudios sobre Desarrollo Económico_ , 2014-26.

- Acton, R. (2020). Effects of Reduced Community College Tuition on College Choices and Degree Completion. _Education Finance and Policy_ , pages 1–71.

- Barrera-Osorio, F. and Bayona-Rodríguez, H. (2019). Signaling or better human capital: Evidence from Colombia. _Economics of Education Review_ , 70:20–34.

- Bettinger, E. and Soliz, A. (2016). _Returns to Vocational Credentials: Evidence from Ohio’s Community and Technical Colleges. A CAPSEE Working Paper_ . Center for Analysis of Postsecondary Education and Employment.

- Blandhol, C., Bonney, J., Mogstad, M., and Torgovitsky, A. (2022). When is TSLS Actually LATE? Working Paper 29709, National Bureau of Economic Research. Series: Working Paper Series.

- Camacho, A., Messina, J., and Uribe Barrera, J. (2017). The Expansion of Higher Education in Colombia: Bad Students or Bad Programs? SSRN Scholarly Paper ID 2921965, Social Science Research Network, Rochester, NY.

- Card, D. (1995). Using geographic variation in college proximity to estimate the return to schooling, Aspects of labour market behaviour: essays in honour of John Vanderkamp. ed. _LN Christofides, EK Grant, and R. Swidinsky_ .

- Carranza, J. E. and Ferreyra, M. M. (2019). Increasing Higher Education Access: Supply, Sorting, and Outcomes in Colombia. _Journal of Human Capital_ , 13(1):95
The main strength of the DLM database is that it distinguishes between three levels of education for migrants. High-skilled migrants are those with postsecondary/tertiary education. Medium-skilled migrants are those with uppersecondary education completed. Low-skilled migrants are those with less than upper-secondary education, including those with lower-secondary and primary education or those who did not go to school. The main characteristics of the diaspora that we consider in this paper are the following:

- The bilateral migration flow for each skill group from origin country o to destination OECD country d is proxied by the change between 1990 and 2000 in the stock of migrants from o to d.

- The bilateral indicator of positive selection is proxied by the log-ratio of the number of high-skilled to low-skilled new migrants from o to d (we disregard medium-skilled migrants for this specification with no impact on the results)

- The bilateral indicator of relative concentration is the ’high-skill minus low-skill’ difference in the squared proportions of migrants from a given origin country o to the 30 possible destinations, following equation (4).

- The size of the existing diaspora is measured as the immigrant population born in country o and living in the OECD country d (= o) in 1990.

# 3.1 Individual vs aggregate data

The use of aggregate macro data has many advantages but also introduces certain complications. In this section, we discuss the pros and cons of using this dataset, and the main econometric issues triggered by our approach. An important distinction in the empirical analysis of migration data concerns the use of a individual (micro) data as opposed to aggregate (macro) one. Micro data are collected at the household and/or individual level to study the impact of networks on the propensity to migrate and the educational composition of the migrants (Massey, 1986; Munshi, 2003, McKenzie and Rapoport, 2010). These datasets include different measures of individual economic, demographic and social characteristics, such as age, income, occupation and education. This contrasts with more approaches the employ aggregate international migration
35%), and Latin America and the Caribbean (25%); thereby, making it more difficult to capture comparable impacts as the event of violence is relatively rare.<sup>21</sup> At the same time, we find a measurable decline in reported emotional non-partner domestic violence, i.e., any form of humiliation or insult perpetrated by victim’s family, her in-law’s family, or other relatives (shown in Table 5). Notably, the effect size is substantial largely due to methodological reasons. The effect of treatment on emotional NPDV is presented graphically in Figure 3. In Figure 3, linear approximations for both treatment and control observations show that emotional NPDV is negatively correlated with income – as income falls, emotional NPDV tends to increase. Counterfactual for the treated group is captured by the dashed red line, i.e., we assume that without treatment the linear trend observed for control observations would continue to increase with falls in income. Considering that the effect of the program is captured by the difference in intercepts of local regressions for beneficiaries and nonbeneficiaries as shown in Equation 2 (m-(h)-m+(h)), we find strong discontinuity at the threshold with a significant drop in reported emotional NPDV.

On investigating further, we find that the estimated reduction in NPDV is mainly confined to decline in violence perpetrated by sibling or sibling-in-law and not parents/parents-in-law or other relatives (shown in Table 5a). Interestingly, we find that household structure and co-location among household members

> 20 Refer to Calonico et al. 2014a for a description of the command.

> 21 GBV rates refer to lifetimes experience of intimate partner violence. Source: Global Database on Prevalence of Violence Against Women, The World Health Organization.

14
)||(0.005)|(0.007)||(0.002)|(0.004)|
|Number of dependents||0.004|-0.003||0.013*|0.026**||-0.000|0.005|
|||(0.005)|(0.014)||(0.008)|(0.012)||(0.002)|(0.005)|
|Constant|3.672***|3.445***|3.544***|4.309***|3.863***|4.134***|4.360***|3.840***|4.163***|
||(0.004)|(0.025)|(0.065)|(0.008)|(0.034)|(0.049)|(0.003)|(0.012)|(0.025)|
|Observations|26890|26890|2545|5314|5314|1867|44135|44135|9366|

_Note:_ Robust standard errors in parentheses. Sample is restricted to male _waged workers_ aged 20-59. Data source: Bangladesh HIES 2016-17; Nepal LFS 2017-18; Pakistan LFS 2014-15 and 2017-18. * p _>_ 0.1, ** p _>_ 0.05, *** p _>_ 0.01.
variability over the short-term. It may also be due to factors that remained unaccounted for by the model. Having said that, discrepancies for country-specific results derived from cross-country studies are not exceptional in the literature (see results for LAC countries in Araujo _et al_ . (2016), for Ethiopia in Moller and Wacker (2017), and for Tanzania in Haile (2016)).

# **5.2 Robustness**

The original model by Brueckner (2014) has already passed several robustness checks. For example, it was shown (by testing the relevant first-stage relationship) that the instruments in the GMM regression are sufficiently strong. The main results are also robust to taking 10-year non-overlapping panel data, balanced panel data, time-varying coefficients, or alternative specifications. Furthermore, unconditional models were estimated variable by variable as this limits the weak-instrument problem in the case where various instruments appear strong in isolation but are highly correlated so that they are weak when used together (Dollar and Kraay, 2003).

In addition, Moller and Wacker (2017) estimate three other specifications (see columns 1 to 4 in Table 1). Column (2) shows the results when the Polity IV variable, which had a counter-intuitive sign in the baseline specification, is excluded. The results are almost identical to the baseline model in column (1). Column (3) expands the instrument set by allowing for first _-,_ second-, and third _-_ order differences (as opposed to including only the first lag). The findings indicate that the baseline estimates do not likely suffer from too few instruments. The coefficient estimates are again similar to the baseline, though the infrastructure parameter is smaller but still significant and large. Column (4) reports fixed-effect (FE) estimates, again with similar results but a somewhat smaller (but positive and significant) infrastructure parameter. The coefficient on the lagged dependent variable is somewhat smaller than in the baseline. This confirms that our baseline model is well-specified because of the downward
:_|For sale|For own use|Total|
|For sale|16.0|13.6|29.6|
|For own use|34.3|36.1|70.4|
|Total|50.3|49.7|100 (N=169)|
|**Nigeria**|_End of season:_|||
|_Start of season:_|For sale|For own use|Total|
|For sale|20.5|9.5|30.0|
|For own use|20.8|49.2|70.0|
|Total|41.3|58.7|100(N=3492)|

_Notes:_ The estimates for Ghana refer to individuals whose main occupation is in agriculture at the start and at the end of the season. The estimates for Malawi and Nigeria refer to individuals who worked at least one hour in agriculture at the start and end of the season. Based on the Ghana and Malawi Agricultural Labor Surveys and Nigeria GHS Panel Wave 3.

Beyond these changes in aggregate employment, there is even more churning at the individual level. Almost a third of farmers in Ghana and Nigeria, and half of the farmers in Malawi, ‘update’ their reports about the intended use of output between the start and the end of the season, and these transitions go in both directions (see Table 2, cells shaded in grey). In other words, even though there is a general trend of farmers being more likely to report producing for sale at the end than at the beginning of the season, a significant

> 23 Due to this sample restriction, the number of observations is smaller in Table 2 than in Table 1.

15
, expenditure items included at purchase price.

(9) Questionnaire reports market equivalent values.

(10) Questionnaire reports market equivalent values; outliers and missing imputed using prediction from hedonic regressions.

(11) Some of the infrequent expenses have been kept, most notably legal expenses and insurance.

(12) Annual license fees (TV / VCR / dish antina etc.); Annual license fee for arms etc.

Accounting for meals eaten outside of the household can have huge implications on poverty rates. A study from Peru showed that including the consumption of food eaten outside home raises the extreme poverty rate by 18 percent (Farfan et al., 2015). Furthermore, most household surveys 10
NIGER’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 9. Evolution of seats and city pairs in Niger**

<!-- Start of picture text --> a. Seats b. City pairs<br>450,000 20<br>400,000 18<br>350,000 16<br>300,000 14<br>12<br>250,000<br>10<br>200,000<br>8<br>150,000<br>6<br>100,000 4<br>50,000 2<br>- 0<br>2001 2004 2007 2009 (Est) 2001 2004 2007 2009 (Est)<br>Total International Intercontinental Total International Intercontinental<br>City Pairs<br>Number of seats<br><!-- End of picture text --> _Source_ : Bofinger, 2009.Derived from AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note_ : As reported to international reservation systems.

The fleet servicing Niger has been renovated. Around 95 percent of the seat capacity is new, largely due to the international nature of the services. Four airlines—Air France, Royal Air Maroc, Afriqiyah Airways, and Air Senegal International—comprised over 80 percent of the regularly scheduled traffic in 2007. In 2009 Air Senegal International, with a market share of 14 percent, ceased operations.

**Table 8. Benchmarking air transport indicators for Niger and select other countries**

|Country|Niger|Mali|Chad|Nigeria|Burkina<br>Faso|Central<br>African<br>Republic|
|---|---|---|---|---|---|---|
|_Traffic (2007)_|||||||
|Domestic seats (seats per year)|n.a.|n.
and 2020 microdata from the PNAD-Contínua.

<mark>Our findings showed, first, that employment estimates for 2020 based on elasticities were reasonably accurate in 11 of the 15 countries. However, in the remaining four countries the projections considerably overestimated employment levels and therefore underestimated job loss due to the crisis. This bias tended to be larger for labor markets that were more disrupted by the pandemic according to the stringency of social distancing measures, changes in workplace mobility, and a measure of scale-up of cash transfer programs. We also projected employment levels using elasticity estimations from a previous recession and found that they generate smaller biases in the projection of employment levels. However, depending on the method used for</mark> estimating elasticities, the biases do not differ much from using a non-period crisis.

<mark>Second, microsimulations for five countries across different regions of the globe showed declines in the per capita household income or consumption across the entire income/consumption distribution. Overall, the 2020 pandemic resulted in a decline of the middle class and an increase in poverty in all five countries. However, the pattern of impacts across the distribution varied across countries, with no clear pattern. When using employment projections based on elasticities</mark> instead of actual employment data, the projected income declines were smaller.

27
14140|
|||**Mobil**|**ity Estimates f**|**romQuadratic**|**CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0_**|7.02|6.27||**_IGME0_**|0.53|0.62|
|**_ES5_**|9.50|9.15||**_IGME5_**|0.463|0.528|
|**_ES10_**|11.63|11.59||**_IGME10_**|0.394|0.441|
|**_ES16_**|13.73|13.94||**_IGME16_**|0.312|0.335|

Notes: (1) The data used are IHDS 2012 with children aged 18-30. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; standard errors clustered at district-level in square brackets; standard errors clustered at state-level in braces; *** p<0.01, ** p<0.05, * p<0.1.
17 Appendix: Sources of Population Data

|ountry,<br>conomy,<br>or|Last cublis<br>P|hed censu<br>opulation|s<br>Source for population|Source for fertility|Source for mortality|
|---|---|---|---|---|---|
|erritory|Date|(1000)*|(mid-1985)|(1985-90, unless indicated)|(1985-90,<br>unless indicated)|
|anistan|Jun 79|13051 F|Bank projection from census,<br>adding refugees, assumed<br>to be abroad temporarity|Bank est.|Bank est.|
|lkbania|Oct 60|1626 F|UNPVSR<br>4/87 (official<br>est.)|Based on UNPVSR<br>4/87|Based on UNPVSR<br>4/87|
|ria|Sep87|22971 J|U.N. 1984 assessment|Based on UNPVSR<br>4/87|BasedonU.N. 1984<br>assessment|
|anerican Sam|oa Apr 80|32 J|USBOC<br>1985|||
|%ndorra|Nov 54|6 F|UNPVSR<br>7/86|--|--|
|ingola|Dec 70|5646 F|U.N. 1988 revisiorn (prelim.)|U.N. 1988 revision (prelim.)|U.N. 1988 revision (prelim.)|
|nguilla|--|-- -|UNPVSR<br>1/88|||
|iguaand<br>arbuda|Apr70|66 F|UNPVSR<br>1/87 (official<br>est.)|1980-85, based on UNPVSR<br>7/86; 1985-90, Bank<br>projection|1980-85, based on UNPVSR<br>
household surveys, which refers to workers indicating that they have access to a pension through their job. Although this implies small differences in coverage and average wages (see supplementary online appendix table S1.3),<sup>23</sup> wage inequality trends are similar across the two data sources in the common time periods, once household surveys data are restrict to formal sector workers (see figure 8).<sup>24</sup> This paper decomposes wage inequality into firm and worker components using the additive worker and firm fixed-effects model first proposed by Abowd, Kramarz, and Margolis (1999), henceforth, AKM. The model is used to disentangle the contributions of unobserved worker and firm heterogeneity to changes in wage inequality. Given our focus on changes over time in the importance of each of these components of wage inequality, it follows Card, Heining, and Kline (2013) and Alvarez et al. (2018) and estimate the model separately by subperiod.<sup>25</sup> It focuses on log monthly wages at jobs held in the private sector. The estimated model is where _αi_ is the worker component; _φj_ ( _i,t_ ) is the firm component; and _εit_ is the error component.<sup>26</sup> Based on equation (3), this paper decomposes the variance of wages within each subperiod into the variance of the worker component, the variance of the firm component, the covariance between the worker and firm components, and the variance of the residual. Following AKM and Card, Heining, and Kilne (2013), the variance in the worker effect is interpreted as wage inequality generated by a combination of workers’ skills and other char-

> 23See also wage kernels comparing the two sources in supplementary online appendix S5.

> 24In addition to the focus on formality and the definition of formality, these two types of data differ in several ways. First, whereas household surveys collect information on a sample of workers,
parents have not paid “enough”fees but who comes to school can be sent home by the teacher and be told to tell her parents to pay fees before coming back to school. As a result, there is a strong correlation between school participation and the amount paid by the parents, and parents pay on an approximate fee-for-service basis.<sup>14</sup> Rural preschools usually have a low level of total funding, though this varies with the income level of the community and parents’ valuation of preschool education. Equipment is minimal; preschool teachers have few teaching materials and classes are often held under a tree for lack of classrooms. Being a preschool teacher is a semi-volunteer position. A 1997 survey of 100 preschools

> 11 Approximate numbers calculated from data reported in the Welfare Measurement Survey III (1997), Kenya Central Bureau of Statistics.

> 12 Total number of preschools is from Kenya Ministry of Education, Science and Technology (1999), data on organization are from Kipkorir and Njenga (1997).

> 13 For a description of how community fund-raisers work, see Miguel and Gugerty (2002)

> 14 For a given level of participation, the amount of fees collected varies substantially. Since preschool teachers are local women, they often know the families of the children attending their class. As a result, they have a pretty good idea of the economic background of the families, and it should be possible for them to price-discriminate between families. I will not discuss such price discrimination in this paper.

5
level of statistical significance.<sup>21</sup> In columns (5) to (8), we present an alternative specification that controls for survey fixed effects (which are country-year specific) rather than country and year fixed effects. These within survey estimates in essence remove country level omitted variable bias from both time-varying and time-invariant covariates. The qualitative results in columns (1) to (4), in terms of sign as well as relative magnitude between MSMEs, SMEs and large firms, carry through in these specifications.

As robustness, in appendix A3 we estimate columns (1) to (4) from Table 3 over the Orbis sample and obtain qualitatively identical results.<sup>22</sup> In all regressions in this table, we use OLS regressions with firm and year fixed effects with standard errors clustered at the firm-level. Overall, firms with access to a loan have an average annual employment growth rate that is 1 percentage point larger than that for firms that lack such financing in the Orbis sample. As before, we find the association to be strongest in the case of MSMEs with a point estimates more than four times higher than that of large firms. We confirm with a t-test that it is statistically higher at the 99% confidence level.

In columns (5) to (12), as robustness, we present results using two additional measures for the volume of financing – growth of finance and debt ratio. In columns (5) to (8), we find that a 1 standard deviation increase in the growth of finance correlates to an overall increase in employment growth by 0.81 percentage points. This relationship appears entirely driven by the high correlation among MSME firms. A 1 standard deviation increase in the growth of finance is associated an increase in employment growth by 0.89 percentage points among MSMEs and only

> 21 Micro firms with access to a loan increase employment growth at a rate of almost 5 percentage points larger than that of micro firms without