rafmacalaba/data-use-mentions-extended huggingface dataset:
April 2012 and January 2013.<sup>25</sup> Third, an endline survey was conducted in all schools from January 2015 to March 2015,<sup>26</sup> and in households between January and June 2017.<sup>2728</sup>

**<u>Figure 4</u>** <u>summarizes the experiment sampling frame and timeline.</u>

In both the baseline and endline, the school surveys collected detailed data on school-level variables such as pupil enrollment and attendance, school infrastructure, facilities and resources, the school SIP, and SMC functioning and membership. The questionnaire also collected data on the total number of teachers employed in school and a count of those present in school on the day of the visit. The visits were held on a day when schools were supposed to be open but were not announced in advance. Information on enrollments, teacher and student presence, and SMC members was collected from school records in interviews with the head teachers. In addition, for the endline round of school surveys, a random sample of 20 students from Grade 3 and 4 from each school were interviewed and given standardized tests for Math, English and Sindhi.

In addition, household surveys for both baseline and endline gathered information on household demographic and socioeconomic characteristics, household schooling choices and engagement

> 25 The initial plan was to collect all baseline data in 2012. However, due to heavy flooding in Sindh province, the data were collected in two rounds from April to June 2012, and November-December 2012. The flooding delayed implementation of the pilot activities but did not significantly disrupt data collection beyond this delay. We control for a flood dummy in all regression estimates.

> 26 The endline survey exercise included two phases: surprise visits to schools in January 2015, and announced visits made to schools in the sampled villages between January 2015 and March 2015.

27 Following analysis of the school endline survey data, the endline household survey was conducted for a
13 Adams and Cuecuecha, forthcoming). The argument here is that rain is closely correlated with agricultural production and income, and so too little rain in one or several years may cause people to migrate out of rural areas. For this reason, changes in historical rain are correlated with the formation of migrant networks and with the receipt of remittances, but changes in historical rain are not correlated with unobserved changes in consumption patterns. We obtained historical rainfall information at the meteorological station level in Indonesia from the IFLS data. We then calculated the average level of rainfall for the year 2000 and the average level of rainfall for the period 1995 to 1999 by district. From this information we estimated a model in which the level of rainfall for the year 2000 is related to the level of rainfall for the period 1995 to 1999. We then used the residuals from this model as the unexpected rainfall shock in 2000. Our argument here is that changes in migration patterns and the receipt of remittances are influenced by both the unexpected part of rainfall in 2000 as well as the actual level of rainfall for 1995 to 1999, since both variables are exogenous at the beginning of the decision process estimated with our data.

For the three instrumental variables, our claim is that conditional on the set of human capital, household and district characteristics included in our specification, the unobserved components in the expenditure equation of the households are uncorrelated with the three instruments.

The second stage of our model estimates equation (3) and (5) using a generalization of the Dubin and McFadden method, assuming a nested logit specification for the probabilities needed in estimation.

The third stage of our model consists in estimating the values of the fixed effects. To obtain the fixed effects we implement a search method using the following three steps. First, we
|7,276|6,819|7,276|7,276|
|R-squared|0.852|0.850|0.853|0.852|
|**Forward links**|||||
|_DVAR_|0.0888**|0.106***|0.0436|0.0777|
||(0.0437)|(0.0387)|(0.0630)|(0.0804)|
|_FVADP_|0.232***|0.247***|0.2313***|0.2329***|
||(0.0258)|(0.0256)|(0.0257)|(0.0257)|
|_DVAR*Schooling_|0.0152***<br>(0.0053)||||
|_DVAR*Secondary degree_||0.0020***<br>(0.0006)|||
|_DVAR*Educ. Quality_|||0.0387***<br>(0.0139)||
|_DVAR* Training_||||0.0319*<br>(0.0180)|
|_Constant_|4.974***|4.828***|4.9067***|5.0618***|
||(0.177)|(0.190)|(0.1821)|(0.1825)|
|Observations|7,284|6,822|7,284|7,284|
|R-squared|0.870|0.871|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_
# **2 Brazil’s sector and its labor force sugar**

Brazil is the world’s largest sugar producer and exporter. It produces around 28 percent of all sugar cane in the world and exports around 25 percent of the world’s total processed raw sugar (FAOSTAT).<sup>7</sup> One crucial feature of the sugar cane industry is the close relationship between harvesting and processing – the raw cane has to be transported to the mill fast, since the quality of sugar deteriorates rapidly following the cutting of cane. This prompts the mills to be located in the midst of the sugar cane area. There are two main sugar cane producing regions: Center-Southeast and North-Northeast. There are approximately 300 plants processing sugar cane (sugar mills), almost 75 percent of which are in the Center-South. Because of poorer soil quality, the costs of growing sugar cane are higher in the North. Processing costs are also higher in the North than in the Center-South. The state of S ̃ao Paulo in the Center-South is by far the biggest producer. It accounts for 75-80 percent of sugar cane production and 60-65 percent of sugar production (see Table 1). The North-Northeast (mainly the states of Pernambuco), however, accounts for 70-75 percent of exports (Bollings and Suarez, 2002). Almost 45 percent of all production is consumed domestically. Domestic food manufacturers account for approximately 35-45 percent of the total consumption, the rest being direct consumption.

Prior to sugar sector in the 1990s reforms the Brazilian sugar and alcohol industry was highly regulated, and the policies for sugar and ethanol were interconnected. Sugar mills and distilleries received credit guarantees and

> 7A bit more than half of Brazilian sugar cane is used for the production of ethanol (fuel alcohol). Brazil’s interest in ethanol production dates back to the first oil crisis. The ethanol content of gasoline is regulated by decree at around 20 to 24 percent.

5
list if all the products within the code can be unambiguously considered as intermediate goods, not finished products. Hence, approximately 440 parts and components are built into a filter to isolate parts and components trade in aggregate trade flows. This filter is applied to bilateral trade flow data obtained from WITS/UN COMTRADE.

The other main source of information comes from the World Bank’s Logistics Performance Index (LPI). The LPI is based on a worldwide survey of logistics service providers -namely freight forwarders and express carriers- who evaluate logistics “friendliness” (on a numeric scale, from 1 to 5) of countries where they are based, and those with which they trade. This index is available for 155 countries, for the years 2007 and 2010, and is based on over 5,000 single country evaluations made by approximately 1,000 logistics professionals.<sup>5</sup> The final index is a weighted average of six main components, covering the following policy areas pertaining to logistics performance: efficiency of the clearance process, quality of infrastructure, ease of arranging competitively priced shipments, competence and quality of logistics services, ability to track and trace consignments, and timeliness of shipments.

# **3.2 Preliminary Analysis**

The remainder of the paper develops a fully specified econometric model and uses it to test the hypothesis parts and components trade is more sensitive to logistics performance than trade in final goods. Before moving to that context, however, it is useful to conduct some preliminary analysis based on graphical methods and descriptive statistics.

Figure 1 (sourced from Arvis et al. 2010) shows that there is a clear positive association between logistics performance and the share of parts and components in total exports. Countries with superior logistics performance tend to be relatively specialized in that sector. This finding is exactly what we

> 5 We use LPI data for 2007 only due to the fact that many countries lag considerably in their provision of trade data.

8
prices and costs increased.<sup>2</sup> As oil prices increased on average from US$43/bbl in 2016 to US$100/bbl in 2022, subsidies covered an increasing share of the market price. Relative to the size of the economy, the total subsidy expenditure also increased consistently from 1.5 percent of GDP in 2019 to 3.7 percent in 2023.

Figure 5. Subsidy expenditure as a share of GDP in Angola, 2019-2023 <!-- Start of picture text --> 4% 3.8%<br>3.7%<br>0.3% 0.3%<br>3% 2.8% 0.9% 0.9%<br>0.5%<br>2% 1.7%<br>0.8%<br>1.5%<br>0.5%<br>0.2%<br>2.6% 2.6%<br>0.3%<br>1% 0.4%<br>1.4%<br>1.0% 0.8%<br>0%<br>2019 2020 2021 2022 2023<br>Diesel Gasoline LPG Kerosene<br><!-- End of picture text --> Source: authors’ estimations based on IGAPE and INE In June 2023, the Government of Angola raised gasoline prices, and in April 2024, it increased diesel prices. These price increases removed around 40 percent and 9 percent of the subsidy per liter, respectively. On June 1, 2023, a government decree laid out a multiphase approach to fuel subsidy reform, for the first time since 2016. Retail prices remained constant until May 2023, when the retail gasoline price stood at about US$0.31 per liter, the fourth lowest price in the world. Then, the government decided to implement an adjustment
(although not by much) in Zimbabwe.<sup>7</sup> In all the countries, networks of ethnic minority firms produce a large share of value added in manufacturing and control a large share of the upstream supplier industries.

**Table 1: Ethnic Distribution of SMEs (percent of firms)**

||**Kenya**|**Tanzania**|**Zambia**|**Zimbabwe**|
|---|---|---|---|---|
|**_African_**|39|73|61|40|
|**_Asian_**|60|25|26|16|
|**_European_**|0.5|0.0|11|41|
|**_Other_**|0.5|2|2|3|
|**_No. of firms_**|184|158|159|132|

_Source: Enterprise Surveys, 1990s. Regional Program on Enterprise Development, World Bank_ Tables 2 and 3 present the financial characteristics of SMEs in our sample. We see that a large percentage of SMEs receive supplier credit in Zimbabwe compared with the other countries. This is explained by the deeper financial system and more highly developed manufacturing sector in Zimbabwe in the 1990s. SMEs in Kenya have relatively more access to bank finance than in the other countries – 25 percent of SMEs received at least some bank credit to start their firms, compared with 8 to 11 percent elsewhere. However, as table 3 shows, these characteristics differ significantly across ethnic groups. In all countries, minorities have much greater access to finance and longer relationship with their suppliers compared to indigenous African entrepreneurs; Tanzania is the only country where these differences are not significant, except for title to property.

**Table 2: Finance Characteristics of SMEs**

||**Kenya**|**Tanzania**|**
program on pre-program and post-program test-taking in 2012 and 2013, as well on the likelihood of taking both tests, with Equation (1). In this context, Ygsj is the number of students who were present and took the test. Using demographic data and pre-program test scores, we also characterize the types of students who are test-takers in the treatment and control groups.

_Effect of the Program on Learning_ Our sample for the analysis on learning outcomes includes students who took both preprogram and post-program tests in either 2012 or 2013. We estimate the effect of the program on exams in 2012 and 2013, pooling all subjects—Hindi, English, and Math—with the following regression: (2) Yisjz � β0 � β1Tj � γ<sup>′</sup> Xisj � εisjz for individual _i_ , in school _s_ , village _j_ , on subject _z._ Learning, Yisjz, is measured with either normalized test scores or with an indicator for whether the student’s test score improved from pretreatment to post-treatment in a given year. We include subject and cohort fixed effects as well as controls for school size prior to program implementation, captured by γ<sup>′</sup> Xisj in the above equation. We cluster standard errors by village.

We run Equation (2) separately for pre-program and post-program test scores in each of 2012 and 2013. Recall that the pre-program test in 2012 was administered prior to the implementation of the learning component of the program and we would not expect any learning gains on this test. Pre-program tests in 2013 were administered prior to program implementation in 2013, but students in treatment schools who were enrolled in 2012 would have had exposure to the program. The results on 2013 pre-program tests indicate whether any learning gains experienced in year one were sustained into the beginning of year two.

15
long-term (five-years-ahead) growth forecasts, from Consensus Economics and the IMF’s World Economic Outlook database (annex D). These forecasts are based partly on models used by the analysts and partly on the analysts’ judgement. Judgment can play an important role during periods of major structural change, which models may not be well-equipped to capture.

Each approach comes with advantages and disadvantages (table 2). Even in data-poor environments, univariate filters are straightforward to implement. Multivariate filters utilize additional information that can ensure that the measure of potential output is better aligned with its determinants, as suggested by economic theory. In particular, the multivariate filter-based estimates can ensure that estimated output gaps in the short term are consistent with indicators of domestic demand pressures (such as inflation, unemployment, current account balances, and capacity utilization). All statistical filters, however, have drawbacks: in particular, they suffer from well-known “end-point” problems that tend to lead to large revisions as new data become available. The approach employed here includes forecasts of real GDP growth to minimize this problem. Since they capture high-frequency movements, measures of potential growth based on filtering techniques correlate strongly with actual output growth and with each other.

7
) fixed effects. Age groups are defined as individuals under 25, 25-34, 35-49, and 50 and XX above, approximately quartiles of the study sample. Robust standard errors are clustered at the household level, the level of the treatment. In additional analyses we test for heterogeneity of these differences by proxy status, gender, and age. In these analyses we use fully saturated regression models to test each group’s differences against a null hypothesis of no effect and report p-values for differences across groups.

# **4. Results**

> 14 As discussed in Section 2, we have taken this approach of only using the data from the module that was administered first in the final round to avoid concerns over the potential influence of asking 90-day recall questions just before annual recall questions (or vice versa). Appendix Table 2 shows that this ordering indeed has a substantial impact, particularly on the long recall reported values.
Slower medium to long-run GDP growth will adversely affect long-run debt dynamics. If the real growth rate falls below the average real interest rate on public debt, government will have to accumulate primary fiscal surpluses in order to stabilize the debt/GDP ratio. Moreover, lower real GDP growth will reduce the growth of the tax base and hence make it more difficult to achieve an increase in the primary fiscal balance. The possible consequences of demographic changes for long-term real GDP growth are discussed in Section 4.

Romania’s planned adoption of the Euro in 2015 will affect public debt management. All domestic currency denominated debt will have to be converted into foreign currency denominated debt, most probably Euro denominated debt. Once Romania enters the Euro Zone, the distinction between domestic and foreign currency denominated debt will disappear and hence the exchange rate risk attached to the former, for foreign investors, will no longer be applicable, which could help to ease borrowing costs.

To conclude this section, the data on general government deficits and public debt reviewed above do not, per se, indicate serious threats to fiscal sustainability in Romania. However, two caveats are necessary. First, the improvement in the structural balance equivalent to 5.7 percent of GDP between 2008 and 2011 has only been possible as a result of draconian cuts to public expenditure, including cuts in the public payroll, public sector wages and a range of social welfare benefits as well as increases in VAT rates and social contributions. Political support for these cuts and tax hikes may dissipate once the immediate fiscal crisis has passed; hence they may not be politically sustainable. The forthcoming elections in 2012 and 2014 will be a test of public support for fiscal consolidation. Second, there are threats to fiscal sustainability arising from public sector deficits which are not currently captured in general government fiscal data and from long-term demographic trends. These issues are addressed in the following two sections.

10
This stability suggests that our trends and results are not reflecting some systematic transition from a second sector. Moreover, the relative wage points for manufacturing are at the low end of the scale compared to other major activity types (comparable to agriculture/mining and substantially less than services and transportation). These earnings distributions, while not separating out owner earnings from wage workers, suggest that most of the induced entry from outside manufacturing that we are observing is likely to have come from unpaid domestic work or from shifts away from agriculture/mining. An appreciation of this margin is again helpful for evaluating potential channels behind these entry responses. See also the related analysis in Ghani, Kanbur, and O'Connell (2013).

# _Potential Channel: Reporting Bias_

Iyer et al. (2012) find that reported crimes against women increased after state-level implementations of political reservations. They credit this increase not to higher underlying crime rates, but instead greater confidence among women to report crimes. One possible channel for our findings is that a similar growth in confidence or security led women to be more likely to report their household-based establishments after the reservations were implemented.

We doubt that this type of phenomena is behind our work for two reasons. First, conversations with experts on Indian data and the NSSO surveying procedures have not raised flags about this concern. As discussed in Section 3, the structure of the NSSO sampling frames does not build upon features like tax payments or business registrations, instead being derived from the Economic Census and household enumeration. This NSSO design limits the potential for any structural response being captured by our data due to these reforms. As these firms are legally permitted to be unorganized, there is no incentive to evade surveys. We have also identified a field in the NSSO that allows the enumerator to indicate whether respondents appear to be non-responsive or evasive. While the variable is only modestly populated, we have not identified any patterns to suggest behavioral changes of female-owned establishments around the reforms.

(especially female, household-based entrepreneurs) is
In correcting for selective reporting, the propensity scores (p-scores hereafter) are estimated using a probit regression for whether SP spending is data for a given country and year. In addition to (log) GDP per capita, the regressors are the log of the country population in that year and a variable measuring the availability of SP spending data for that country in prior years.<sup>29</sup> By including population size our reasoning is that public spending data have properties of a public good, such that lower average costs in more populous countries entail that the data are more likely to be available. The variable for past SP data is motivated by the assumption that <mark>past experience in providing SP spending data makes it less costly to currently provide such data.</mark> We then include a cubic function of the p-scores in the regressions for the SPEC as control functions for testing the selective reporting hypothesis.<sup>30</sup>

# **4.2 Results**

As can be seen from the “baseline” panel in Table 3, the slope coefficient in the simplest linear regression specification for the SPEC gives a semi-elasticity of 3.78 (implying a full elasticity of SP spending to GDP of 1.42, when evaluated at the mean SP share for 2019). This falls to a semi-elasticity of 1.37 (full elasticity in 2019 of 1.15) when the country effects are included. Qualitatively, this is what one would expect if the omitted country characteristics in the SPEC based on cross-country comparisons (such as Figure 1) tend to be jointly positively correlated with the SP share and GDP. Nonetheless, a positive and significant income effect on the SP share of national income remains when we allow for the latent country effects.

We still find a statistically significant non-linear income effect on the SP shares when we allow for country fixed effects in the PLREG estimator (the “baseline” panel in Table 3). Figure <u>4
representativeness not only at the national level, but also at the highest administrative division below it (WAEMU, 2020).

Of the 7,176 households sampled (3,588 per wave), 7,156 were validated and maintained in the final database. As a result, microdata from the EHCVM provides information from 7,156 households and 66,120 individuals distributed along urban and rural areas in the country. An advantage of using this survey for responding to the main question of this study is that it includes an in-depth labor module that covers social security contributions at the individual level and non-agricultural enterprise characteristics owned by household members. Therefore, the ECHVM provides information of both workers and firms, which can be exploited to obtain a better picture of labor informality.

The definition of labor informality used in this study considers individuals as informal if they do not contribute to social security (IPRES, FNR, retraite complémentaire) nor have a formal accounting system in their non-agricultural enterprise(s) (in case of owning one or more). This definition accounts not only workers without access to social security benefits, but also the long network of family businesses among the self-employed and employers that are beyond the scrutiny of the tax authorities. Two alternative definitions are also analyzed for robustness checks. The first one is a more stringent legal definition that considers as informal those individuals without contributions to social security or a formal accounting system transmitting to the Tax Authority in their non-agricultural enterprise(s). The second is a more relaxed definition, that classifies workers as informal if they do not contribute to social security nor have any non-agricultural enterprise(s) registered in the Commercial Register, or with a fiscal identification, or with a formal accounting system or with some employee registered at the Social Security.

4