rafmacalaba/data-use-mentions-extended huggingface dataset:
of DNWR and the extent of wage indexation.

# 3 Methodology

There are different approaches in the literature to estimating rigidities from individual worker data.<sup>4</sup> Focusing on job stayers between two consecutive years, a number of studies draw inferences about rigidities based on asymmetries in the wage change distribution (see, e.g., Card and Hyslop (1997) and Dickens et al. (2007)). A second group of estimates

> 4 Using industry data, Holden and Wulfsberg (2009) and Holden and Wulfsberg (2008) study the incidence of DRWR and DNWR, respectively, in developed countries.

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Figure 6 **Trust in the Technology** <!-- Start of picture text --> Leave BDT 1,000 in account Leave BDT 5,000 in account<br>p=0.012<br>p=0.032<br>p=0.045 p=0.005<br>55% 47% 52% 42%<br>N=558 N=285 N=679 N=333<br>Mobile payroll Mobile account Mobile payroll Mobile account<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Notes: Panel (a) displays the share of participants that feel “comfortable” or “very comfortable” leaving BDT 1,000 and BDT 5,000 in their account for the mobile payroll group, based on data from the endline survey. Panel (b) shows the share of participants who feel “very comfortable” or “absolutely comfortable” leaving BDT 1,000 and BDT 5,000 in their account for the mobile accounts group, based on data from the endline survey. For reference, the mean of the control group is 38% for both the BDT 1,000 BDT 5,000 questions. Error bars show 95% confidence intervals of the mean. Horizontal bars show p-values for _t_ -tests of equality of means between the treatment conditions.

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/sup> On average, in the 14 countries for which data are available, growth in hours worked fell by a striking 7 percentage points but the drop was highly concentrated in only half of these countries (Figure 3). The decline in hours worked not only accounted for the entire decline in earnings but also allowed for a slight increase in real hourly wages (0.7 percentage points) (Figure 4). However, the extent to which the reduction in hours was an active policy response rather than simply an adjustment mechanism is unclear. For example, an ILO survey of 54 countries (10 LICs, 10 lower middle income, 17 upper middle income and 17 HICs) shows that reductions in hours worked was a common response to the downturn in countries as different as China, Vietnam, the Philippines, Indonesia, Mexico, Argentina, Colombia and Jordan (ILO, 2009).

> 11 Table A1 in the appendix shows that these conclusions hold when examining pre and post-crisis average growth in a slightly larger sample of 28 countries that report quarterly, semi-annual, or annual data.

> 12Figure 2, however, underestimates the extent of the fall in hours, because it only reflects the 8 countries that report hours quarterly

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potentially under-sampled in Iraq’s Integrated Household Socioeconomic Survey (IHSES) on which the _AHI_ relies. To provide more frequent estimates, a Continuous Household Survey (CHS) was implemented in 2014 on a sub-sample of IHSES clusters. Given the large number of people displaced within the country since 2014, the survey was designed to capture a representative sample of internally displaced persons. However, the fieldwork was disrupted in the summer of 2014 in some parts of the country due to the deterioration in the security situation.

**Table 2:** **_Adequate Housing Index_ across Income Deciles**

|**Country**|**D1**|**D2**|**D3**|**D4**|**D5**|**D6**|**D7**|**D8**|**D9**|**D10**|**AHI**|**Housing**<br>**Gini**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Albania|0.77|0.84|0.89|0.90|0.92|0.93|0.94|0.94|0.95|0.96|**0.90**|**0.05**|
|Angola|0.36|0.40|0.46|0.52|0.60|0.66|0.73|0.79|0.85|0.90|**0.63**|**0.25**|
|Argentina|0.94|0.95|0.
program exit to improvements in household income or living conditions. However, little has been researched on the income and labor market dynamics of CCT beneficiaries in developing countries.

Being among the few CCTs in developing countries that frequently reassess eligibility via household income tests, the BF program is ideal for exploring this question.<sup>6</sup> A rich set of administrative data on formal employment at the municipal level allows us to capture the relationship between “rising tides” in the local labor market and program exits, while individual data on job matches allows us to identify one of the likely channels for exits, the increase in formal employment of its beneficiaries.

In addition, the program itself has several optimal features to explore our research question. First, while much of the literature implicitly assumes that duration in CCTs programs is either exogenously determined by time limitation rules, or, absent these, largely indefinite, with families lasting in CCTs until children reach adulthood<sup>7</sup> , the basic benefit of BF allows families and individuals to stay in the program as long as they meet the eligibility criteria of a per capita income below the eligibility threshold. In other words, not all families have to exit when children become adults. Second, the eligibility determination in BF depends on a centralized means test, instead of the more common proxy means test targeting. Families at the time of application self-report their income from all sources: this information then undergoes cross-verification with several other registries. The same verification process is repeated at each re-certification episode. As such, the program is sensitive to income fluctuations at entry and exit (Gazola Hellmann, 2015). These design features make BF an ideal case study on the factors that affect exit from CCTs, a question that, to the authors’ knowledge, has not been studied before for CCTs or for programs in developing countries.

Our paper contributes to different strands of the literature. First, we add to the literature by examining the reasons that explain exits from
and exit those companies successfully, new funds will be attracted to the market. Also, more companies and entrepreneurs will be willing to consider private equity and venture capital as a source of financing. However, in Kenya, exits have been challenging for various reasons.

While the public markets can provide an important path to liquidity, private equity investors in Kenya and East Africa have rarely accessed the public markets to generate exits. Given SMEs comprise a large number of investments, many of the companies in which they invest lack sufficient scale to consider filing for an IPO. Instead, the most viable exit strategies for PE and VC investors in Kenya and the East Africa region are buybacks, trade sales and secondary sales. While data for Kenya alone are not available, between 2007 and 2014, there were a total of 21 exits by PE funds in East Africa (totaling US$260 million). Of this total, 52 percent of exits were achieved via share buybacks.<sup>20</sup> Of the remainder, six exits came through sales to strategic investors, one was a sale to a financial investor and one resulted from a listing. The sole listing, executed by Actis Capital portfolio company Umeme, took place in Uganda.<sup>21</sup> The paucity of IPOs in Kenya has not been lost upon investment managers. In general, private equity investors in Kenya expect to exit their investments via strategic investors, financial sponsors, or refinancing. A survey of private equity investors in East Africa over two consecutive years found that no respondents anticipated that they would exit an investment via an IPO in the next 12 months. Instead, they expected to exit their investments via a sale to a strategic investor, a secondary exit to another private equity player, or a refinancing. _A detailed discussion of the Kenyan capital markets and the environment for IPOs is provided in Section IV.B: Capital Markets Overview._

**Table 10: Exit Expectations of Private Equity Investors During the Next
1. True poverty indicators _τa_ , using the “census”.

2. Direct estimators _τ_ ˆ _a_<sup>_DIR_</sup> using the “survey”, defined as the sample versions of _τc_ .

3. Census EB estimators _τ_ ˆ _a_<sup>_CEBac_</sup> presented in Marhuenda et al. (2017) based on a twofold nested error regression model, and obtained using a Monte Carlo approximation with _M_ = 50 replicates. Note that, for this estimator, the fitted model agrees with the true data generating process (3).

4. Census EB estimators _τ_ ˆ _a_<sup>_CEBa_</sup> presented in CMN (2020) based on a nested error model with only **area** random effects obtained using a Monte Carlo approximation with _M_ = 50 replicates.

5. Census EB estimators _τ_ ˆ _a_<sup>_CEBc_</sup> presented in CMN (2020) based on a nested error model with only **cluster** random effects and including the aggregate cluster and area means of the considered auxiliary variables, where _M_ = 50.

6. Traditional ELL estimators _τ_ ˆ _a_<sup>_ELLc_</sup> , based on a nested error model with only **cluster** random effects and including the aggregate cluster and area means of the considered auxiliary variables, where _M_ = 50.

7. Unit-context Census EB estimators _τ_ ˆ _a_<sup>_UC−CEBa_</sup> based on a nested error model with random effects at the **area level** . This estimator follows the approach from Masaaki et al. (2020) that is a modified version of that of Nguyen (2012), which uses only area means for some of the right hand side variables.<sup>13</sup> Nguyen (2012) proposes this solution for the case when only a dated census and a recent survey are available.

8. Unit-context **two-fold nested error** Census
a major factor.<sup>27</sup>

# **5 Data**

## **5.1 Survey Data**

In order to establish a sampling frame, the implementing partner conducted a community mapping exercise and identified project sites covering urban, peri-urban, rural and internally displaced persons (IDP) communities. For the evaluation, 33 community clusters were selected across the six project districts based on accessibility and population size.<sup>28</sup> The research team completed a census to list all the children aged 10-14 years in at least 400 households located within each of the targeted communities. The census also collected information on the adolescent’s willingness to participate in the _Choices_ training to predict program take-up. The listing also asked for parental consent to participate in the program

> 26Strata for mixed-gender or all enrolled in school were considered for the multiple adolescent households. 27The few attitudes that change over time may be driven by cohort effects as adolescents are 3 years older.

> 28The program team had originally identified 37 communities. However, following the household listing exercise, 5 communities which had fewer than 80 young adolescents listed were excluded. In addition, 5 of the larger communities were divided into 2 separate clusters each: Daami B, October IDP, Bacadweyn, Waaberi and Statehouse. Approximately 100 households were dropped from the census listing for having incorrect GPS coordinates and another 50 households did not provide study consent.

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begin by calculating the effects on poverty of changes in the characteristics of the population, beginning with age and gender, followed by changes in geographical, educational, occupational and sectoral structure of the population. With these results we then calculate changes in farm and non-farm earnings due to changes in the returns to these characteristics, followed by changes in non-labor incomes (first private and then public) and finally changes in the consumption-to-income ratio.

# **Results**

Focusing on the contributions to the reduction of moderate poverty based on national poverty lines, Table 6 presents the results of each counterfactual distribution when simulating only one element at a time, and keeping everything else constant. We refer to these as the marginal contributions to poverty reduction. The main result that emerges is that the largest contributions to moderate poverty reduction in all three countries came from labor market-related factors: 61 percent in Bangladesh, 75 percent in Peru and 65 percent in Thailand. Within this, it was the increase in the returns to endowments or characteristics, rather than changes in these endowments or characteristics, that explain poverty reduction, pointing to an increase in real

> 19 Net revenue was calculated using available information on total revenue stemming from agricultural production and the cost of inputs from detailed household enterprise modules included in the surveys.

> 20 The Thai household survey does not contain information on land holding and access to irrigation.

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unemployment insurance benefits, social safety net payments). These effects are long-lasting and are present even in the fourth year after the takeover. We show that these effects are not driven by lower wages. Instead, we estimate a 1% reduction in the likelihood that workers are employed anywhere and no impact on overall hours worked, suggesting that workers who are not retained are not able to find employment quickly.

We next turn to heterogeneity analyses to better understand which workers are affected by takeovers the most. We find that a dominant mechanism driving job loss in this setting is labor restructuring, changes in the consolidated firm’s production process. We highlight two types of workers affected by restructuring activities: “over-placed” workers who are compensated at higher rates than what would be expected based on their human capital and “duplicative” workers who are likely to be made redundant by the consolidation.

We present two pieces of evidence consistent with the importance of labor restructuring. First, we show that labor restructuring leads to more job loss among “over-placed” workers. As in Schmieder, von Wachter and Heining (2022) and Lagaras (2023), we characterize over-placed workers as workers who have secured employment at a firm with a higher wage premium than is expected for workers’ skills that are valued equally across employers, estimated using a standard Abowd, Kramarz and Margolis (1999) decomposition. We find that over-placed workers are 3.3 times more likely to be separated from the consolidated firm post-takeover compared to workers who are not over-placed, consistent with firms targeting restructuring activities at more generously compensated workers.

Second, we use worker-level skill data to create a measure of human capital relatedness (HCR) between acquirer and target firms participating in a takeover (Lee, Mauer and Xu, 2018). We find that high HCR takeovers, that is, takeovers where the target and acquirer workers have a more similar skill distribution, generate larger employment reductions at the consolidated firm in terms of both retention and hours worked. We then exploit
restrictions were removed. The conditional exogeneity assumption is plausible, given that the effect of import restrictions on goods that _are key for innovation_ affects productivity levels precisely through their effects on the firm’s decision to conduct or increase its innovation activities, once these barriers are alleviated. A graphical explanation of the logic of this instrument is presented in figure B.1 in appendix B.

It is important to highlight that the constructed IVs are cross-sectional and are used to instrument the _difference_ in innovation investment between 2014 and 2016 (we are left with only one observation per firm). As a result, the database loses its panel data structure. First differences, however, eliminate the effect of time-invariant omitted covariates.

# **4.3 Measurement Issues**

# **4.3.1. Measuring the Capital Stock**

To estimate equation [1], we needed to produce estimations for the firms’ capital stock because data on this variable are not collected in innovation surveys. Due to serious measurement errors, ENDEI 2 does not provide information about two variables that could have been used as proxy of physical capital stock (which were provided in ENDEI 1):

1. Consumption of energy, gas, and fuel. This variable could have been used as a proxy under the argument that industrial capital stock should increase at a similar rate as the consumption of energy used to operate such equipment (Frank 1959).

> 21 We assume that restrictions present during the ENDEI1 period (2010–12) can represent restrictions present in 2014, given that both the political administration and the trade regulations were the same during both periods.

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penetration is very low, then in those cases the Operational Risk charge, on top of essentially an unchanged 8% requirement, may well imply a <u>significant increase in capital requirements</u><sup>34</sup> <u>.</u>

> 33 CreditMetrics is a model developed by JP Morgan offshoot, Riskmetrics and Creditrisk+ a model developed by Credit Suisse Financial Products of CSFB.

> 34 This is supported by the results of the Basel Committee’s Quantitative Impact Study Number 3 (QIS 3 – see BCBS (2003c) and BCBS (2003d).  The first publication finds a 12 % increase in capital requirements for “other” (non G13, non EU) countries for the standardized approach.  Indeed credit risk capital requirements <u>rise</u> by 2% and operational risk capital requirements are about 11% of the current capital charge.  Curiously, QIS 3 also finds an 11% inccrease for large banks from G13 (and yet only a 6% increase for Eu large banks implying that this comes from non EU G13 countries), but then these banks will most likely not apply the standardized approach and will most likely apply IRB.  In BCBS (2003d), which apparently takes into account certain modfications of the new proposals embodied in Consultative Paper 3, no information is given on banks from “other countries’.

22
The foregoing discussion has argued that the vocational school syscem in Israel is far broader in scope and aim than is typically the case in many other countries. It is the country's main vehicle for technologically oriented education and for the achievement of national policies of integration of diverse ethnic groups and equality of opportunity through universal secondary education. The training of skilled workers for the manual trades, historically its major role, while still significant today, does not now constitute its ma<sup>1</sup> ..r focus.

2. Vocational and Academic Schools: Compar.son of Labor Market Jutcomes The major concern of this section is to compare the labor market (earnings) outcomes of the two major alternative forms of secondary schooling in Israel. Have those who attended vocational secondary schools achieved higher earnings than those attending academic secondary schools? Have any such earnings differences continued to hold over the longer term? We look at these issues using relevant data drawn from the 20 percent sub sample of the 1983 Census of Population and Housing.<sup>8</sup> / Data: The Census of Population 20 percent sub-sample questionnaire collected information on level and type of terminal schooling. This made it possible to identify two broad groups of individuals that are the focus of 8 A fuller account of sources and method is given in Neuman and Ziderman, 1988A.

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- Simeon Djankov, Caroline Freund, and Cong S Pham. Trading on time. _The Review of Economics and Statistics_ , 92(1):166–173, 2010.

- César Ducruet and Theo Notteboom. The worldwide maritime network of container shipping: spatial structure and regional dynamics. _Global networks_ , 12(3):395–423, 2012.

- Jonathan Eaton and Samuel Kortum. Technology, Geography, and Trade. _Econometrica_ , 70(5):1741–1779, September 2002.

- Pablo Fajgelbaum and Stephen J. Redding. Trade, Structural Transformation and Development: Evidence from Argentina 1869-1914. Mimeo, Princeton University, 2021.

- Lixian Fan, Xinlu Li, Sijie Zhang, and Zimeng Zhang. Vessel size, investments and trade. In _Handbook of International Trade and Transportation_ , pages 518–541. Edward Elgar Publishing Limited, 2018.

- Robert C Feenstra and Hong Ma. Trade facilitation and the extensive margin of exports. _The Japanese Economic Review_ , 65(2):158–177, 2014.

- Robert C. Feenstra and John Romalis. International Prices and Endogenous Quality. _The Quarterly Journal of Economics_ , 129(4):477–527, 2014.

- Carsten Fink, Aaditya Mattoo, and Ileana Cristina Neagu. Trade in international maritime services: How much does policy matter? _The World Bank Economic Review_ , 16(1):81–108, 2002.

- FMC. The Impact of the Ocean Shipping Reform Act of 1998. Tech. report, Federal Maritime Commission, 2001.

- Sharat Ganapati, Woan Foong Wong, and Oren Ziv. Entrepôt: Hubs, Scale, and Trade Costs. Technical report, University of Oregon, 2020.

- Tamara Gurevich, Peter Herman, Nabil Abbyad, Meryem Demirkaya, Austin Drenski, Jeffrey Horowitz, and Grace Kenneally. The dynamic gravity dataset: Technical documentation, 2018.

- Tilo F. Halaszovich and Aseem Kinra. The impact of distance, national transportation systems and logistics performance on fdi and international
There is a close association between per capita income and attraction of FDI across the income spectrum. Richer regions attract the most FDI per capita, and many of the poorest regions attract none (Figure 24). All seven regions that attracted no FDI at all are among the 10 lowest-income regions in Russia. Five are in the North Caucasus (Ingushetia, Karbardino-Balkar, Karachevo-Charles, North Ossetia, and Chechnya); one in the Southern (Kalmyk, bordering North Caucasus); and one in Siberia (Altai Republic). Most have difficult investment climates, as reflected in ratings from the advisory service Expert RA.

Firms in the Central and Northwest regions are more likely to have foreign majority ownership than those in other regions (Figure 25). The Far East region has absorbed the most FDI relative to regional GDP and on this measure has been the leading destination for FDI since 2002 (Figure 26 and Figure 28). Nationally, about 2.3 percent of all active firms in the Ruslana database had majority foreign ownership in the year most recently observed (usually 2012). This suggests that in the Far East foreign-invested firms may be significantly larger on average than in the Central and Northwest, probably due to a different sectoral composition of FDI.

Russian mining and quarrying firms are most likely to have foreign investors, followed by financial and insurance businesses (Figure 27). The attraction of mining and quarrying (including oil and natural gas) is consistent with Russia’s natural resource endowments and its pattern of exports. The relatively high frequency of majority foreign ownership in financial services has occurred even though restrictions on FDI in financial services are high (Figure 27), which suggests that there is room for policy moves to make these sectors even more attractive. From the fact that about half of all FDI in financial services is obvious round-tripping it can be inferred that many of the investments are driven by insufficient law enforcement and insecure property rights. The Central region, including Moscow, is relatively attractive for wholesaling, retailing,
, in each year. For example, the global effective tax rate on capital equals worldwide capital tax

> 15In some cases, this split is not available in the headline, official income tax revenue aggregates, so we look to expert studies elsewhere.

> 16In the most recent years we do not cover 100% of world GDP, as we did not try to collect data from countries with fewer than 1 million inhabitants when these were not available through online sources. 17See Appendix B.3 for a case study of China.

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# **Figure 2. Outcome variables over time**

- a. Consumption across the three waves and three regions

# b. Food insecurity over time

_Note:_ “No or low” means CSI ≤3 (no food insecurity); “medium” means 3 < CSI ≤9 (medium food insecurity); and “high” denotes CSI > 9 (high food insecurity). Figures are plotted using weights _Sources:_ Based on GHS panel survey data collected by the World Bank and the National Bureau of Statistics (NBS).

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