rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5 Effect on Wages**

## **5.1 Main Results**

Motivated by the illustrative model predictions presented in Section 2, we study the impact of the tax cut on wages in this section. First, we study the impact on the wages of new entrants by estimating the following regression equation: where _wit_ is the net wage of individual _i_ in May at year _t_ . Note that for wages, we use annual data throughout this section as this is the level of observation available.<sup>31</sup> In our case, _treform_ is 2013.

A key limitation of the regression equation above is that it considers the same proportional wage changes across the entire wage distribution. Nevertheless, given the lump-sum nature of the tax cut, we expect that the proportional increase in wages will be quite small for high wage earners and could be much larger for low wage earners. To take this into account, we assess the impact of the policy by the tax cut rate – the size of the payroll tax cut relative to the wage in the previous year, formally _TCRit−_ 1 = 14 _,_ 500 _/wit−_ 1, where HUF 14,500 is the tax cut amount. This variable goes from 14.5% for low wage earners to zero for very high wage earners, and reflects the percent change in wages that would occur if all of the tax cut were passed through to the worker. Notice that the tax cut rate is calculated for both treated and control workers. For the latter, the tax cut rate reflects the size of the tax cut (relative to their income) that would have been received if the workers were treated.

Then we estimate the following regression: where we interact each term in regression equation (11) with the tax cut rate, _TCRit−_ 1. To calculate _TCRit−_ 1, we need to rely on the previous year’s wage and so we can only estimate this
value from alternative job options within the current labor market and the option value from having alternative job options in other markets.

Our model shows that the change in relative welfare of workers across labor markets is a function of only the probability of moving and the labor supply elasticity. This is a powerful result that greatly simplifies the analysis of the welfare impacts of trade shocks. The relative welfare result does not depend on the underlying moving cost structure, observed changes in future wages, or moving probabilities across jobs within a labor market.

We then structurally estimate the model using the employer-employee panel data from Brazil. In the first stage of the estimation, we pin down the common value attached to each labor market and the moving cost between labor markets using a gravity-like equation. The implied probability of moving between labor markets is then calculated with the estimated value of each market and the estimated moving cost. In the second stage, we pin down the labor supply elasticity of our model. We derive an estimable equation describing the relationship between a change in the transformed value of the labor market and a change in wages, with the labor supply elasticity governing the responsiveness of the former to the latter. We exploit variation in residual wages induced by the same instrument we used earlier for motivating evidence: the exogenous change in import demand directed to the labor market. By plugging in these estimates into the relative welfare result previously mentioned, we find that, during the sample period, a 10% rise in exports increases the lifetime welfare of a median formal sector worker by 2.99% of her annual wage.

Finally, we turn to the full general equilibrium simulation of our model to quantify the effect of a trade shock on workers’ welfare and various labor market outcomes. Our benchmark trade shock is a 20% permanent decline of trade costs in the manufacturing sector from Brazil to each of its trading partners. This positive export shock to the Brazilian manufacturing sector reallocates labor from the agriculture sector toward the manufacturing and service sectors. On average
EMDEs as moderately negative.

# **9. Concluding Remarks**

This paper studied the effect of Basel III implementation on SME financing in EMDEs. The literature on the consequences of implementing (global) financial regulations, including Basel III, is burgeoning (Boissay et al. 2019). But it typically centers around the regulatory effect on overall bank lending or economic activity. This paper contributes to the scant and recently growing literature studying the effect of Basel III on SME financing—an effect in which policy makers are highly interested.

We used firm-level data from the World Bank’s Enterprise Surveys as the core data set and merged it with several other data sets containing bank as well as country characteristics. We analyzed 37
and to investigate and act against any participant or player in the electricity industry for violations of any law, rule or regulation governing the same, including the rules on cross‐ ownership, anti‐competitive behavior, abuse of market positions, must‐offer rule of the pool, and other similar or related acts. In compliance with its responsibility under the EPIRA, the ERC promulgated in August 2006 competition rules and complaint procedures (ADB, 2016). The Philippine Competition Commission, a new anti‐trust agency, was created by law in 2017. The Commission oversees mergers and acquisitions and has primary responsibility to investigate and rule on market competition, including uncompetitive behavior.

Enforcement is targeted towards two types of breach that are believed to have the most impact on efficiency of the market and its ability to achieve its objectives, namely compliance with offer rule and with dispatch schedules, and dispatch tolerance by scheduled generating units. Deterrence of breach is anticipated to be achieved through regular monitoring of compliance and immediate initiation of investigations for probable breach; and imposition of stiff financial penalties (ADB, 2016). ERC has

> 11 See for example market monitoring reports for PJM (http://www.monitoringanalytics.com/reports/PJM_State_of_the_Market/2018/2018q1-som-pjm.pdf) and CAISO (http://www.caiso.com/Documents/2017FourthQuarterReport-MarketIssues-PerformanceFebruary2018.pdf), which focus on the performance and competitiveness of the markets (energy, capacity, etc.).

20
governance and service delivery in LITS2 and presents the key findings on the prevalence of unofficial payments, and utilization of and satisfaction with public services. Section 3 analyzes differences across individuals and across countries (over time) in utilization and satisfaction with public services. We conclude by discussing a few measures that governments can implement to further increase citizens‘ satisfaction with public service delivery.

# **2. UTILIZATION AND SATISFACTION WITH PUBLIC SERVICE DELIVERY**

# **2.1. The data:  Government performance and service delivery in the 2010 LITS**

The Life in Transition Survey was conducted jointly by the European Bank for Reconstruction and Development and the World Bank in 2006 and 2010. Between 1,000 and 1,500 households were interviewed in every country. In total, in 2010, almost 39,000 households were surveyed in 34 countries of Europe and Central Asia to assess public attitudes, well-being and the impacts of economic and political change.

The governance and service delivery module focuses on eight different public services or interactions: traffic police, official documents (such as passport or birth certificate), civil courts, primary or secondary education, vocational education, the public health system, unemployment benefits and other social security benefits. For each service, respondents were asked their 3
lower bound since, firstly, moving it up (down) the income distribution includes (excludes) a small percentage of the population; secondly, an income threshold above $50 in 2005 PPP would limit the representativeness of the upper class in some countries; and, thirdly, household surveys do not collect realistic information on the richest population. Using the proposed methodology in this study with the 2017 PPP, the upper bound for the middle class reaches $81 per person per day, compared with $70 in the 2011 PPP. It is shown that this rounded value is robust to different methodologies.

Third, this study analyzes the impact on the Latin American and Caribbean regional poor, vulnerable, and middle-class groups estimated using the international poverty lines (IPL), the regional vulnerability and middle-class lines, and the 2017 PPPs. The 2017 PPPs would slightly increase historical estimates of poverty at the $2.15 and $3.65 lines, but significantly at $6.85, compared with the 2011 PPPs. Extreme poverty (measured at the $2.15 line) and poverty at $3.65 would increase marginally by 0.3 and 0.9 percentage points (pp), respectively in 2017, with the 2017 PPPs. The regional count of the extreme poor increased by 1.9 million, which is largely driven by 1.3 million and 300,000 more poor people in Brazil and the Andean region, while poverty increased in all other subregions except the Southern Cone.<sup>4</sup> Considering the upper-middle-income country line, $6.85 per person per day, the incidence of poverty in the region rose around five percentage points in 2017 with the 2017 PPP. The headcount shifted almost five percentage points since 2000, compared with the 2011 PPP series. This represents 27 million more poor people in LAC, driven by 12 million and 5.5 million in Brazil and the Andean region, respectively.

A significant increase in the poor population is mirrored
(18% to 30%). Re-employment was quite high in each country, with 75% to 93% entering work again at some point between July and October 2020. The average number of months without work for people that lost their job between April and July 2020 in Nigeria and Ethiopia was 1.2 months.<sup>8</sup> But a large share of re-employment involved a change in industry, especially in Ethiopia. These rates are particularly high given the fact that we distinguish only six industries. For the four countries, 38 percent of respondents were reemployed in an industry that is different from the one in which they were working prior to the onset of the pandemic.

**Figure 5: Early-phase labor market indicators**

<!-- Start of picture text --> 5A: Job loss (up to July 5B: Re-employed (July-Oct 5C: Changed industry (July-<br>2020), conditional on 2020), conditional on job Oct 2020), conditional on re-<br>working pre-COVID-19 loss employment<br>100%<br>100% 93% 93% 100%<br>86%<br>83%<br>80% 80% 75% 80%<br>61%<br>60% 60% 60% 55% 58%<br>46%<br>40% 30% 28% 40% 40%<br>18%<br>20% 20% 20%<br>0% 0% 0%<br><!-- End of picture text --> _Source_ : Figure 5A is based on HFPS R1-3 for Ethiopia and Nigeria, R1-2 for Malawi, and R1 for Uganda. Figures 5B and 5C are based on HFPS R1-6 for Ethiopia and Nigeria, R1-4 for Malawi, R1-3 for Uganda. Figure 5C also uses pre-COVID LSMS-ISA surveys for industry of work.

To analyze the medium-run impacts on the labor market, we construct
**Table A14: Impacts of mental health on labor outcomes – Past-employment experience**

|Dependent variable:|Employm|ent status|Weeklylabor inco|me ($1,000AUD)|
|---|---|---|---|---|
||No work<br>experience|Have work<br>experience|No work<br>experience|Have work<br>experience|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.027*|-0.078***|-0.131**|-0.293***|
||(0.014)|(0.011)|(0.062)|(0.083)|
|_First stage of 2SLS_|||||
|Trauma experience*Year<br>after arrival|-0.044**<br>(0.017)|-0.040***<br>(0.011)|-0.050**<br>(0.022)|-0.046***<br>(0.013)|
|Kleibergen-Paap F stat.|15.150|17.151|15.670|16.416|
|AR 95-CIs|[-0.117, -0.008]|[-0.195, -0.010]|[-0.237, -0.074]|[-0.663, -0.169]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|
coverage was initially full and in 2001 it was changed to four times the per capita GDP, which was still in existence as of 2003.

_Sources_ : Garcia (1999), IADI Survey: Ecuador (2003).

**El Salvador.** ( _Deposit Guarantee Institution, Bank Law 1999_ ) The Guarantee Institution of El Salvador was established in November 1999 and the system is government legislated and administered. Initial funding was provided through the central bank which is later augmented by premiums collected at a 0.1% annual rate on total deposit liabilities from the members. The membership is mandatory and the coverage limit was an equivalent of $6,700 (approximately Colon 58,424) as of 2003. The previous limits were $4000 (Colon 35,000) in 1999 and $6286 (Colon 55,000) in 2000. Only savings and checking accounts are eligible for coverage.

_Sources_ : Own survey of deposit insurers, IADI Survey: El Salvador (2003).

**Estonia.** ( _Deposit Guarantee Fund_ ) The deposit insurance system of Estonia was established in October 1998. The system initially guaranteed 20,000 Estonian kroons (EEK), which was raised to EEK 40,000 as of January 1, 2000. The Guarantee Fund Act entered into force on July 1<sup>st</sup> , 2002 and set coverage levels at EEK 40,000 initially to be raised to EEK 100,000 on December 31<sup>st</sup> , 2003, then to EEK 200,000 on December 31<sup>st</sup> , 2005 and EUR 20,000 starting on December 31<sup>st</sup> , 2007 the latest. The initial funding was granted by the government and banks paid EEK 50,000 at the start-up. The fund can borrow without a government guarantee or ask the government to borrow a limited amount on its behalf. The types of deposits not covered are deposits in foreign currencies
a set of political variables as an index variable. Hence, BHS constructed two weighted average composite variables which were used in a regression on a sample of 86-87 countries. However, for a sub-sample of 38 countries, BHS had data on judicial tenure. They claim that this had the same explanatory power and magnitude as the other variables.

The overall BHS result was that increasing the degree of political commitment (as measured by their indices) of a middling score DTE by one standard deviation would be expected to raise the level of generating capacity by 1.2 MW per 1000 population – a 49% elasticity at the mean.<sup>42</sup>

> 41 Bergara, Henisz and Spiller op cit, p 20

> 42 See BHS op cit, pp 23-25.

33
controls, which absorb little of the variation, and Column (3) shows the fully-specified regression including student controls. These controls account for a further point in the student performance. While we refrain from ranking management indices across countries, Figure 5 plots the coefficients of country-level regressions of management on PISA math test scores using the specification of Column (1). The estimation loses precision once we restrict to individual country samples but still broadly supports the positive relationship found in Table 1.

For the Prova Brasil student-level dataset, we run standard OLS regressions, also clustering the standard errors at the school level. Results are reported in standard deviations. In these Prova Brasil specifications, we include state fixed effects, and successively introduce school controls (student-teacher ratio, log of the number of students, dummy variables indicating the presence of an IT lab, science lab, and library, a dummy for male principals, dummies for educational attainment, and dummies for experience as principal), and then student controls (gender, race, socio-economic status, and mothers’ educational attainment). Column (4) shows the raw relationship between the Prova Brasil-based school management index and student performance, only controlling for state fixed effects. One standard deviation higher score in the management index is strongly correlated with 0.068 standard deviations higher Portuguese scores and 0.078 standard deviations higher math scores. Column (5) shows that including school characteristics absorbs very little of the variation, while Column (6) shows that including student characteristics absorbs only slightly more. The fully specified regression supports the general positive relationship between the school management index and student performance.

# **3 Why does management matter in schools?**

There are myriad uses of our new indices. In this paper, we push the frontier of understanding the mechanisms behind the management and performance relationship by focusing on teachers. Our aim is not to provide a theoretical contribution _per se_ , but rather to formalize a policy discussion around teacher incentive and selection mechanisms and their relationship to management
13.7|27.2|15.3|
|HE non-agri|47.6|39.6|46.2|34.4|40.9|35.2|
|wage agri|0.7|3.0|1.1|0.7|1.9|0.8|
|Family farming|19.9|17.2|19.4|45.8|20.2|42.7|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Note: Job type refers to primary employment only .Migrant indicates a migration in the last 5 years only. Source: Calculations based on the ILFS 2000/01 and 2006 data_ 26. **Sectoral and Occupational Distribution of HEs:** Although HEs operate in different sectors, there is a high concentration in certain sectors or activities. In 2006, over 70 percent of HEs in the urban areas and more than 60 percent in the rural areas were in small businesses related to trade, hotels, and restaurants. These activities have no or low barriers to entry, and are highly competitive. However, HEs are increasingly diversifying out of the trade sector. Between 2001 and 2006, the share of HEs engaged in trade declined (Table 3.2). Following a transformation in the economy, more changes in the sectoral distribution of HEs can be expected as HEs develop new expertise and operate in other areas.

14
basic characteristics of borrowers. This dataset covers all KUR loans issued between December 2015 and March 2020 and includes information on over 8 million debtors. Second, we use a unique, quantitative survey dataset that is the largest and only nationally representative data on KUR borrowers to date. Third, qualitative interviews with KUR borrowers enable a deeper exploration of KUR borrowers’ perspectives and experiences. Finally, qualitative interviews with individuals who did not receive KUR reveal some challenges related to implementation and access to KUR.

The unique quantitative survey was collected for this study and includes a nationally representative sample of 1,402 KUR borrowers. To ensure a representative sample and enable subgroup analysis, we used weighted stratified sampling to select firms to interview from the national program database.<sup>16</sup> Strata including less than 1% of KUR beneficiaries were oversampled to ensure that each subgroup of interest would have sufficient representation in the sample to allow precise estimates at the subgroup level, and all analysis in this report incorporates design weights to account for sampling design. Due to the COVID-19 pandemic, all interviews were carried out over the phone in January and February 2021. All firms were asked modules on basic business information, business practices, workers, revenue, financial history prior to receiving KUR for the first time, and financial history after receiving KUR for the first time. In addition, firms were asked one of two of the following modules: experiences with the KUR program or impact of COVID-19 on the business.<sup>17</sup> To better understand how KUR borrowers with different characteristics perceived and experienced the KUR program, we conducted qualitative interviews with a separate sample of 100 KUR borrowers, drawn from the program database. The qualitative sample was not drawn to be representative and includes KUR borrowers with a variety of different characteristics. These qualitative interviews covered the following topics: perception and information received about KUR, KUR loan application process, use and benefits of KUR loans, business productivity and access to financial services, alternative financing
Policy Research Working Paper 10932

# **Abstract**

The spatial configuration of domestic supply chains plays a crucial role in the transmission of shocks. This paper investigates the representativeness of formal sector firm-to-firm trade data in capturing domestic trade patterns in Kenya, a context with high informality. It documents stylized facts showing that formal sector trade exhibits a distinct spatial concentration relative to overall economic activity. Linking transaction-level data with data on informal economic activity, the paper estimates a structural model and predict a revised network incorporating informal firms. Findings show that formal sector trade flows underaccount for trade within regions and across regions with stronger social ties. The higher the incidence of informality, the more one underestimates vulnerability to domestic shocks and overestimates exposure to import shocks.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at vwiedemann@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Cost of Inaction**

Improving human capital development in Pakistan by improving ECE attendance and the quality of ECE provided has immediate costs and long-term benefits. In 2022, Pakistan’s Human Capital Review calculated the cost of inaction of failing to invest in ECE (Tomlinson et al., 2023). The cost society pays for failing to implement such an intervention was estimated for three scenarios in Pakistan.<sup>3</sup> Low coverage was estimated by equalizing enrollment rates between households in the highest income quintile and those in the other quintiles to achieve 27.2 percent enrollment across income levels (based on the Pakistan Social and Living Standards Measurement Survey 2019-20 enrollment rates for children in the highest income quintile). Medium coverage was estimated using the enrollment rate of a regional peer, Nepal, at 61.9 percent, and full coverage was estimated as achieving one year of universal ECE enrollment, meeting Pakistan’s commitment to SDG 4.2 and 2009 National Education Policy objectives.

Continuing coverage at present levels and not raising coverage levels would be expensive. The cost of inaction would be 0.34 percent of GDP for the low-coverage scenario (failing to boost enrollment to 27.2 percent of all 3- to 5-year-olds in preprimary school), 1.79 percent of GDP for the medium-coverage scenario (failing to enroll 61.9 percent of 3- to 5-year-olds), and 3.39 percent of GDP for the full-coverage scenario. The cost of inaction on medium coverage is conservatively estimated at US$ 4.7 billion for each cohort that fails to receive medium coverage. The cost of failing to invest in ECE is high. As such, the next step is understanding the upfront costs of investment in ECE.

# **Evidence from Other Countries**

Costing and simulation models are fairly common in education sector planning. However, because of data availability and consolidation challenges, ECE is often not included as its own education level. In most cases, ECE is grouped