rafmacalaba/datause-encoder-data huggingface dataset:
Beneficiaries were selected based on a Proxy Means Test (PMT) (Del Ninno & Mills, 2014). The PMT formula was calculated from a regression of per capita consumption on basic household demographic and economic characteristics with data from a nationally representative survey (Katayama, 2010). A village-specific PMT eligibility threshold was then chosen so that 30% of the households were designated as beneficiaries in each village. Ex-post assessments of the targeting method show that the PPFS-CT targeting procedure is relatively efficient in reaching poor households (McBride, 2014). The transfers were delivered to a woman within selected households.<sup>10</sup> A distinct feature of the cash transfer pilot project is that it also promoted women’s participation in local saving groups known as _tontines_ . _Tontine_ members bring cash to a common pot each time they meet (daily, weekly or monthly). At each meeting, one member in rotation takes all the cash from the pot and invests it. _Tontines_ are an important vector for investments in rural Niger, and in Sub-Saharan Africa in general (Van den Brink & Chavas, 1997). The PPFS-CT encouraged beneficiaries receiving cash transfers to set-up _tontines_ , so that households could save and invest in productive assets. According to project managers, almost 90% of the beneficiaries took part to these _tontines_ during the program.<sup>11</sup> The pilot program has several other noteworthy features, which makes it particularly relevant to address knowledge gaps on the sustainability of transfer impacts on productive investments in low-income settings. First, the program targeted very poor, food insecure households in the rural Sahel, one of the poorest regions of the world where climatic shocks are recurrent.<sup>12</sup> Second, the program delivered small, regular, foreseeable, monthly transfers, which are distinct from the large transfers delivered as emergency response following seasonal shocks. Third, and most importantly, the transfers took place for 18 months between January 2011 and June 2012, and this limited duration was clearly communicated to beneficiaries at the beginning of the program. A follow-up survey collected 18 months after the end of the transfers (36 months after the baseline survey) provides a rare opportunity to assess long-term transfer impacts. Specifically, we can analyze the sustainability of program impacts after households have made investments,

> 10 In polygamous households, the recipient was typically the first wife.

> 11 _Tontines_ are often referred to as a rotating savings and credit association (ROSCA). Project monitoring data suggest that _tontine_ funds were mostly used by beneficiaries to purchase livestock.

> 12 Niger had the lowest Human Development Index (HDI) in the world in 2012 (see <u>http://hdr.undp.org/en/statistics/).</u>

10
|**Average estimated mean**<br>**squared error (x1,000)**|||||
|---|---|---|---|---|
|Random sample SAE|3.331|4.991|0.179|0.093|
|Full enumeration SAE|0.869|2.014|0.084|0.033|
|Full enumeration direct|0.240|0.510|0.043|0.045|
|**Coverage rate**|||||
|Random sample SAE|0.988|0.990|0.952|0.976|
|Full enumeration SAE|0.970|0.966|0.953|0.965|
|Full enumeration direct|0.543|0.538|0.537|0.547|
|**Estimated median relative**|||||
|**bias**|||||
|Random sample SAE|-0.622|-0.726|9.651|40.522|
|Full enumeration SAE|-0.491|-0.325|7.281|10.281|
|Full enumeration direct|-0.041|0.000|0.000|-0.186|
|**Estimated mean absolute error**|||||
|Random sample SAE|0.028|0.040|0.009|0.006|
|Full enumeration SAE|0.017|0.026|0.006|0.003|
|Full enumeration direct|0.019|0.030|0.006|0.004|
|**Estimated rank correlation**|||||
|Random sample SAE|0.541|0.750|0.497|0.264|
|Full enumeration SAE|0.819|0.866|0.805|0.741|
|Full enumeration direct|0.830|0.852|0.808|0.722|

Table 7 presents summary statistics comparing the sample SAE and full enumeration SAE, as well as the direct estimates using the full enumeration of sampled AGEBs. Panel A compares across all municipalities, while Panel B compares only across in-sample municipalities. Not surprisingly, both the small area estimates and the direct estimates using the full enumeration sample perform much better than the small area estimates using the smaller, more typical, sampling strategy. The main finding from this exercise, however, is that the small area estimation only slightly improves on the direct estimates in this case, except for the coverage rate because uncertainty is estimated more accurately. For example, for female LFP, the small area estimates only reduces absolute deviation by 0.3 pp, from 2.9 to 2.6 pp, and only improves rank correlation from 0.86 to 0.875. For other indicators, the benefit of SAE when the second stage of the sample is fully enumerated is even smaller.

Figure 4 compares the accuracy of the random sample and full enumeration small area estimates, relative to the full census. The blue dots clearly demonstrate that the random sample SAE predictions for unemployment are close to the overall average, due to the poor predictive power of the unemployment models. In contrast, the orange dots representing the full enumeration unemployment results exhibit far more variation and better track the true rate, as do the LFP results for the random sample. This reflects the absence of variation available in the smaller sample to train an accurate model when predicting unemployment rates.

24
for preferring one year over another for setting the structural break. As a rough approximation, we use the midpoint (1998), to give us roughly the same sample size for the two sub-samples. The years through 1998 also correspond roughly to the “early transition period” which includes the years of the output decline across most of transition economies.

An additional dimension of time-varying effects of determinants of growth is the lagged impact of these variables. Using equation (2), but allowing for a lagged impact of structural reforms on economic growth, gives us: The specification of the overall index of reform in Equation (4) follows that of Selowsky and Martin (1997) and Havrylyshyn and van Rooden (2003). This is based on the idea that the impact of policy reform may be spread over time. In addition, previous empirical studies have suggested that the contemporaneous effects may be negative due to adjustment costs, but may be compensated by subsequent positive effects.<sup>7</sup> _Are there nonlinearities due to governance and institutional quality?_ Data on governance measures are still only available for a limited period. They also have limited variance over time, limiting their usefulness for panel data analysis. Nonetheless, we can still use available indicators to examine how governance affects the relationship between economic growth and its key drivers. In particular, we use governance indicators to create sub-samples of countries based on their institutional quality; we then re-run the baseline regressions. We compare the coefficients estimates from the two sets of regressions and test for statistically significant differences.

_Are there threshold effects?_ From the review of the empirical literature on growth and the size of government in the preceding section, we conclude that a new study of the size of government and growth is warranted and such a study can draw from the availability of more recent and richer data. An important advancement over the existing literature is our effort to test the common assumption of a linear relationship between size and growth and allow, instead, threshold effect in the links between expenditure and growth. We add a measure of government size to equation (2) and estimate it using a linear spline regression.<sup>8</sup>

# **4. Data**

Descriptive statistics and the main data sources of the variables used in the analysis are provided in Table 1. As explained below, most of the variables are from the data collected by World Bank (ECSPE) staff, supplemented with data from the IMF and EBRD and other specialized datasets (such as the World Bank Governance Indicator

> 7 This is also consistent with Falcetti, Raiser, and Sanfey (2002) but they employ a simultaneous equation system.

> 8 We can estimate a threshold model that allows either a continuous jump or a discontinuous jump. We found that the results are invariant to the specification of the threshold model.

11
in which BOS operate and their performance, innovation, employment and employment growth relative to privately-owned firms. Section 4 estimates the wage premium associated with workers in BOS relative to the private sector using firm-level and worker-level data. Section 4.3 uses privatizations of public firms as a natural experiment for providing additional evidence on the BOS wage premium, and estimates the effects of these events on worker and firm-level outcomes. Section 5 presents empirical evidence on business dynamism in Brazil over the last decade, and estimates how the presence of BOS affects multiple measures of dynamism at the industry level. Section 6 concludes.

# **2 Data**

Our main data source is the Rela ̧c ̃ao Anual de Informa ̧c ̃oes Sociais (RAIS), an annual census of active working contracts for all formal employees in Brazil. The dataset includes information on over 3 million establishments and 40 million workers annually. Each entry in the RAIS dataset is an employee-employer match, with both establishments and individuals having a unique identifier. Establishments’ identifiers include 14-digits, with the first 8-digits representing the firm. Throughout our analysis, we will concentrate on firm-level data. The primary use of the RAIS is to compute federal wage supplements, social security benefits, and unemployment insurance. Data collection is mandatory, and given its importance, firms and workers provide high-quality information, especially on wages.

We use data from 2010 to 2020 and focus on the productive sector – we exclude workers in public administration, non-profit organizations, extraterritorial organizations, and specific categories for individual entities such as political candidates and individual rural producers. We use worker-level information including age, level of education, tenure, wage, gender, date of admission, and occupation (CBO classification codes). At the firm-level, we measure employment as the number of active workers on December 31st of each reference year, and we include only establishments with positive employment in the sample. We also use the information on the 4-digit CNAE sector classification codes<sup>2</sup> and the legal nature of firms, which is a key variable in this study used to identify state-owned firms.

We also compute firm characteristics from worker-level information, for instance, we measure average firm wage, the share of college-educated workers, and firm-level innovation intensity, which is measured as the share of employment in technical and scientific occupations (PoTec), a proxy for R&D expenditure proposed by Ara ́ujo et al. (2009). We take advantage of the database panel structure to infer firm entry and exit based on the first and

> 2For multi-establishment firms, we consider the sector code associated with the largest establishment in terms of employment.

4
Table 5: Characteristics of Elder and Child

|_A. Gender of Child and Gender of Elder_|||||
|---|---|---|---|---|
||(1)<br>Girl/Grandmother|(2)<br>Girl/Grandfather|(3)<br>Boy/Grandmother|(4)<br>Boy/Grandfather|
|Treatment|0.566|1.484***|0.300|-0.005|
||(0.469)|(0.429)|(0.489)|(0.388)|
|Observations|756|1029|749|1111|
|_R_<sup>2</sup>|0.388|0.368|0.330|0.346|
|_B. Gender of Elder and Age of Child_|(1)|(2)|(3)|(4)|
||Grandmother/6-12|Grandfather/6-12|Grandmother/13-18|Grandfather/13-18|
|Treatment|0.800**|0.486|-0.116|0.734*|
||(0.389)|(0.385)|(0.580)|(0.422)|
|Observations|758|1041|747|1099|
|_R_<sup>2</sup>|0.364|0.345|0.369|0.373|

Standard errors in parentheses, * _p <_ 0 _._ 05, ** _p <_ 0 _._ 01, *** _p <_ 0 _._ 001
**Figure 2.12. The gender wage gap has decreased 10 percentage points since 2009.**

Average employees real wages, 2009-2019 <!-- Start of picture text --> 1,200,000 84%<br>82%<br> 1,000,000<br>80%<br> 800,000<br>78%<br> 600,000 76%<br>74%<br> 400,000<br>72%<br> 200,000<br>70%<br> - 68%<br>2009 2014 2019<br>Male Female Cambodia Gender wage gap<br>Gender gap women/men<br>Real Wages KHR 2019-2020 prices<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data the CSES household survey.

**Figure 2.13. Agricultural and nonagricultural wages almost tripled between 2009-2019.**

Average real wages, 2009-2019 <!-- Start of picture text --> 1,200,000<br> 1,000,000<br> 800,000<br> 600,000<br> 400,000<br> 200,000<br> -<br>Agricultural wage Non-Agricultural<br>wages<br>2009 2014 2019<br>Real wages KHR 2019-2020 prices<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data the CSES household survey.

Gender segmentation across sectors persists. Women remain employed in agricultural activities more than men. In 2019, men were more likely than women to work in construction (14.8% versus 1.3%), trade (10.7% versus 1.2%) and transportation (8.5% versus 1.0%). Men also show greater participation in public administration (5.2% versus 0.5%) and financial, insurance, and real estate services (1.5% versus 0.5%). Interestingly, the most important export-oriented sectors, apparel and mining, are female-intensive. These export-oriented sectors created opportunities for women to work outside the agricultural sector, improve their earnings, and reduce the wage gap (Robertson et al, 2009). Women have a higher probability of being employed in mining (18.3% versus 0.56%), food and beverage (17.7% versus 1.5%), and textile and apparel (12.3% versus 4.0%) relative to men (Figure 2.14).

9 | P a g e
in 2006 by PASEC,<sup>9</sup> a survey aimed at assessing educational attainment in primary school, and variables that correspond to those in our data. Schools in our sample have fewer students and are more likely to have electricity than the average school in Senegal, but are similar on other measures, including the literacy rates, the number of teachers and their education, and whether the school has a library. At least in terms of these variables, our sample does not look drastically dierent from the average Senegalese primary school.

# 4 Empirical Approach and Inference

We use a regression approach to estimate the impacts of the program. Specically, the impacts are the estimated _βt_<sup>_k_coecientsfromthefollowingregression:</sup> where _Yist_<sup>_k_is the proportion of correct answers in test</sup><sup>_k_, for student</sup><sup>_i_in school</sup><sup>_s_at follow up</sup><sup>_t_(1 or</sup> 2), _Gs_ is a treatment indicator, _Xis_ are conditioning variables measured at baseline, and _ε_<sup>_k_</sup> _ist_<sup>isan</sup> error term. Conditioning variables include household size, number of children, whether the head has any education, distance to school, a wealth index<sup>10</sup> , the interview language, and the baseline scores of all tests. Since household interviews were conducted for only a random subsample of students, two-thirds of our sample has missing household characteristics (at random). In order to keep these observations, we assign zeros to conditioning variables if they are missing and include dummies for observations with missing conditioning variables.<sup>11</sup> We report standard errors, clustered at the school level, and symbols ***, **, and * to denote signicance at the 1%, 5%, and 10% level of standard single hypothesis tests, respectively. In addition, since we are testing multiple hypotheses at once we compute levels of signicance for each

> 9Programme for the Analysis of Education Systems of the (Conférence des Ministres de l'Education des Etats et gouvernements de la Francophonie).

> 10The wealth index is standardized to have unit variance and is dened as the rst principal component of the following variables: the home has electricity, the home has plumbing, the home has a radio, the home has a television, the home has a telephone, the home has a computer, the home has a refrigerator, the home has gas, the home has an iron, the home has a bicycle, the home has an automobile, the home has a bed, the home has a modern toilet, the number of chickens, the number of sheep, the number of cows, the number of horses, the number of donkeys, the amount of land, savings, debt, food expenditure, child expenditure, other expenditure, wall material, ground material, and roof material.

> 11Results without conditioning variables are presented in Appendix Table 8 and they are almost identical, but of course less precise.

11
absence of rights of redress for EU citizens with respect to government access to their data (Schrems v. Data Protection Commissioner 2015).

Under the Privacy Shield, U.S. companies through an industry body or individually self-certify to the U.S. Department of Commerce that they will protect personal data consistent with the Privacy Framework, which includes the Privacy Shield Principles (Privacy Shield Framework 2018). These Principles largely reflect the key elements of the EU Data Directive (U.S. Department of Commerce 2016). The main ones are: to give European data subjects notice that a US entity is processing their data; to provide choice including whether to opt out of providing personal information; accountability for any onward transfers to third parties of personal information; to take reasonable and appropriate steps to protect personal data from loss or misuse; to process personal data only for purposes the organization intends to use it; to give European data subjects access to their personal information and the ability to correct, amend or delete inaccurate information; and to enforce the principles and to give European data subjects access to affordable enforcement mechanisms.

U.S. businesses are required to publish their privacy policies, and the Privacy Shield gives the U.S. Federal Trade Commission jurisdiction over such businesses should they breach their own policy. In addition, the United States provides various means of redress for people, whose personal data has been compromised, including a direct complaint to the business or a complaint to the Department of Commerce. Also, under the Privacy Shield, the United States has agreed to establish an ombudsperson to address complaints about government agencies’ access to personal information from the EU on national security grounds.

Despite the upgrading of privacy protection under the Privacy Shield compared with Safe Harbor, the stability of the arrangement is in doubt. The first annual review of the Privacy Shield by the Commission and the Department of Commerce identified a number of concerns on the EU side that the operation of the Privacy Shield is not providing a sufficient level of privacy protection. In particular, the EU expressed concern over inadequate access by Europeans to redress mechanisms, including failure to appoint officials at the State Department Ombudsperson.

Such an agreement with the EU would give participating US firms big advantages over existing options. First, unlike in the case of BCRS and SSCs, the firms would not be required to establish a costly presence in the EU because the assessment of conformity with EU standards would take place at home by domestic regulators. Second, unlike in the case of national adequacy, firms would not be obliged to adopt more stringent and more costly standards for data involving transactions at home or with countries less demanding than the EU.

The Provisions on Data Flows in the Trans-Pacific Partnership What was originally the TPP provision and eventually became the CPTPP provision on data flows, contained in Chapter 14 on Electronic Commerce, has been widely and justifiably 18
5 sector. Anyone interested in making a<sup>crude international</sup> comparison will find that these levels are significantly lower than the corresponding ones for "middle upper-income countries" as defined<sup>by the World Bank, where service shares were</sup> 50 and Ft percent respectively; for five<sup>of the lower income countries in the</sup> Organization for Economic Development<sup>and Cooperation (OECD), where service</sup> shares were 55 and 43 percent respectively;<sup>for OECD as a whole, where both</sup> service shares were 61 percent; and<sup>foL the United States, where service sharks</sup> were 67 and 69 percent respectively.2 As will be shown below, levels of<sup>publicly provided services are comparable</sup> to, or higher than, those in other<sup>countries; so the gap in private services is</sup> even larger than these aggregate figures<sup>suggest. A major objective of this</sup> study is to provide current estimates<sup>of the gap between the actual and the</sup> expected levels of service activity<sup>in Russia and other former Soviet states,</sup> where adjustments are made to Soviet<sup>and internaticnal data on services to</sup> achieve comparability and where data<sup>is disaggregated to arrive at comparisons</sup> 3 for service sub-sectors and for individual<sup>Soviet republics.</sup> Externalities from the Expansion of<sup>Services</sup> A fairly extensive literature has<sup>developed over the last 25 years on the</sup> growth potential of the service<sup>sector.Aseminal article by Baumol 1967,</sup> followed up by Baumol et al.<sup>1985, argues that the inherent technological</sup> atructure of many F;ervices inhibits productivity growth and results in increasing real costs and increasing difficulties<sup>of government to finance expected</sup> 2 All data for comparator countries<sup>are for a year during the late 1980s.</sup> 3 Previous estimates of the sub-sectoral gaps in USSR services are provided by Ofer 1973, and more recent estimates<sup>of service shares for Eastern Europe and</sup> the USSR are provided in OECD 1991. See<sup>also Schroeder 1987.</sup>
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_RAr_ Nominal Income

# **Instruments:**

- _τirs_ Iceberg trade cost factor

- _tirs_ Tariff

# **Parameters:**

- _d_ 0 _r_ Benchmark value of full consumption<sup>1</sup>

- _y_ 0 _ir_ Benchmark value of gross output of sector _i_

- _a_ 0 _ir_ Benchmark value of domestic and imported supply of good _i_

- _αjir_ Benchmark share of intermediate input _j_ in gross output for sector _i_

- _βfir_ Benchmark share of factor _f_ in value added for sector _i_

- _μr_ Benchmark share of leisure in full consumption

- _θir_ Benchmark share of _i_ in total consumption of goods and services<sup>1</sup>

- _σ_ Elasticity of substitution between Armington, Krugman, or Melitz varieties

- _λ_<sup>_A_</sup> Preference weight in Armington aggregation of varieties

- _λ_<sup>_K_</sup> Preference weight in Krugman aggregation of varieties

- _λ_<sup>_M_</sup> Preference weight in Melitz aggregation of varieties

- _σ_<sup>_L_</sup> Elasticity of substitution between leisure and consumption of goods and services

- _σ_<sup>_M_</sup> Elasticity of substitution between intermediate inputs and value added

- _fkr_<sup>_K_FixedcostassociatedwithaKrugmanfirm</sup>

- _fmrs_<sup>_M_FixedcostassociatedwithoperationofaMelitzfirmonthe</sup><sup>_r_to</sup><sup>_s_tradelink</sup>

- _fmr_<sup>_E_SunkcostassociatedwithentryofaMelitzfirm</sup>

- _a_ Pareto shape parameter

- _b_ Pareto lower support

- _δ_ Annual probability of firm death

- _F_  ̄ _fr_ Endowment of mobile factor _f_

- _SF fir_ Endowment of factor _f_ specific to industry _i_

- _BOP r_ Benchmark capital account surplus

2