rafmacalaba/data-use-mentions-extended huggingface dataset:
14 futnctioning capital markets) current expenditures are a better measure of permanent income than is current income. Second, extensive experience with household surveys has shown that it is tremendously difficult (if not impossible) to measure the incomes of the agrarian self-employed who constitute the bulk of our sample (Deaton, 1997).

In order to estimate the impact of social capital on incomes we first adopt a specification of the determinants of per person household expenditures that includes both individual (variables Z1j) and village (Xj) level variables'<sup>2</sup> : _Hj=P *Social Capital + a * Z, +_ y _* X +e_ A number of household characteristics are included: the average years of schooling of male and female adults (over 20) in the household'<sup>3</sup> , a dummy variable if the head of the household was 'self-employed in agriculture," a dunmmy variable for female headed households and an index of non-land, non-agricultural, physical assets".

12 This specification was previously developed and used in an examination of rural poverty in Tanzania (World Bank, 1995).

**_'3_** The average of both males and females adults is used for simplicity, although it is worth noting that when the genders are included separately the average education of adult females in the household had a much larger estimated impact on incomes than male schooling.

" The index was created by assigning the following weights to ownership of the following assets if they are in working condition (in the SCPS); sewing machine, bicycle, car, motorcycle, van or truck, 16, radio 8, table 6, clock, watch or bed 4, chair 3, lamps 2. The HRDS has a less complete list of assets; bicycles 16, radios or cameras 8, watches 4. Originally a weight of 400 was assigned to bicycle, car, motorcycle, van or truck, but the very few observations with ownership of those assets
32|
|6.5|Information requirements under diferent target functions and alternative test<br>functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|33|
|**7**<br>**Sim**<br>|**ulating alternative guarantee mechanisms in a real life highway concession**<br>|**34**|
|7.1|Guaranteed return<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|36|
|7.2|Guaranteed equal returns among partners<br>. . . . . . . . . . . . . . . . . . . . . .|38|
|**8**<br>**Con**|**clusion**|**39**|
|**A Pro**|**ofs**|**40**|
|A.1|Proof of of Theorem 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|40|
|A.2|Other proofs<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|42|
|**B Sim**|**ulations’ data**|**46**|

4
Gini index) and the log of population size (which we include with a quadratic polynomial). We also include a set of cohort-level variables ( **_H_** _ct_ ), such as the average years of education of the workforce and its standard deviation. These two variables aim to control for different levels and allocations of human capital, respectively. We also include

> 6 Including any control variable that relates to individual or household characteristics would then result in estimates of IP conditional on those characteristics. This could be desirable depending on the objective of the study. In our current setting, where we are interested in comparing the correlation between differences in mobility across countries and differences in country-level economic outcomes, we explicitly do not want to condition the estimates of mobility to individual or household level characteristics.

> 7 Several studies note that mothers’ years of schooling have a stronger effect than father’s years of schooling on the intergenerational educational mobility of both daughters and sons (see Ranasinghe (2015) for Australia; Bjorklund <u>Lindahl, and Plug  (2006)</u> for Sweden; <u>Tansel (2015)</u> for Türkiye; and <u>Azomahou and Yitbarek (2016)</u> for eight countries in Sub-Saharan Africa). However, Lam (1999) and Girdwood and Leibbrandt (2009) demonstrate that, in South Africa, the link between children and the father’s education is stronger than that of the mother. Also, while maternal education is a stronger determinant of children’s achievements in urban China, paternal education plays a more important role in rural China (Wang et al. 2024). The Life in Transition Survey collects information on the education of both parents. Our study is based on mothers’ years of schooling; we use specifications with the education of the father and the education of both parents for sensitivity analysis.

9
Directorate of Migration Management (PDMM), where they need to provide the required identification information and submit any available documents brought from Syria. In the absence of any official ID or documentation, authorities conduct the registration based on testimony. Upon registration with the PDMM, Syrian refugees are issued a Temporary Protection ID card with an ID number. Households then need to register their address at the Directorate of Civil Registry (“Nüfus”) where the address information is recorded in the Central Registration Administration System (MERNIS). Refugees are required to reside in the province of registration to be able to benefit from public services or any available assistance. Households wishing to change their province of registration at a later date can do so on an exceptional basis after submitting an official petition to their provincial PDMM office and receiving approval.<sup>13</sup> Once a household is registered, it can prove difficult to add new household members that may join the household later on – mainly caused by absences of documentation, spelling differences in existing documents, or a backlog of cases to be processed.

The ESSN application can then be made by an adult from a registered refugee household at the Social Assistance and Solidarity Foundations (SASF) or TRC Service Centers, where applications are digitized and processed into the same consolidated “Integrated Social Assistance System” (ISAS) used for Turkish citizens. ISAS is an e-government system that facilitates all steps related to the management of social assistance electronically, including the application, verification of eligibility, approval to disburse transfers, and auditing. It integrates data from more than 20 public institutions, which enable verification of application information in a matter of minutes. ISAS’s linked databases include the registration records of PDMM, the address records of MERNIS, social security information from the Ministry of Family, Labor and Social Services, and vehicle ownership information from Ministry of Finance, among others. ISAS has been touted as international best practice, significantly reducing delays and errors in processing applications and social assistance.<sup
search, relaxing these barriers will enrich a pool of potential matches for the firms, enabling them to match faster (Pissarides, 2000). Under certain conditions, this faster (and better) match may lead to increasing aggregate productivity. For example, Beerly et al. (2021) show that the increase in the available pool of workers due to easing of migration rules in Switzerland enabled firms to find workers who are better matches for available jobs and increased employment and wages.

We examine the link between ECED expansion and firm productivity in the context of Indonesia. This is a suitable laboratory for our analysis for several reasons. First, the female labor force participation (FLFP) rate in Indonesia is relatively low: 49% in 2002, the beginning of the period we study, compared to 70.6% in the rest of developing East Asia and Pacific (World Development Indicators, 2021). The FLFP rate remained low over our study period: by 2014, the FLFP rate in Indonesia only increased to 52.8% while the number declined to 66.47% in the rest of developing East Asia and Pacific. However, average rates conceal heterogeneous changes across the educational distribution. More educated women significantly increased their labor force participation, while less educated women at the lower end of income distribution withdrew from the labor market, as incomes grew (Schaner and Das, 2016). Second, starting in 2003, the Government of Indonesia adopted several pieces of legislation and increased public spending to facilitate public and private provision of ECED programs including preschools, which simultaneously provide early learning for children and serve as childcare. As a result, the gross pre-primary (age 3-6) enrollment rate more than doubled, jumping from less than 25 percent in 2004 to 60.3 percent by 2016 (World Development Indicators, 2021). Importantly, this growth has been geographically heterogeneous due to differential implementation across districts.

Such a dramatic increase in access to preschools enabled Indonesian women, who have relatively high levels of human capital
# **1. Introduction**

The phenomenon of shadow employment in Poland is monitored by the Polish Central Statistical Office (CEO). The estimates of the number of the shadow employees in Poland are published annually in the Statistical Annuals. Apart from that, more detailed assessments were performed three times between the years 1995 and 2004 by the dedicated irregular module of the Labor Force Survey (LFS) performed in Poland on a quarterly basis. According to CEO estimates, the number of shadow employees in Poland in years 1995-2006 fluctuated between 663,000 in 1998 and 1,079,000 in 2006 - from 4.3% to 7.3% of total employment in Poland.

Unfortunately, the micro-data from the dedicated survey tend not be made available for researchers for further analysis and therefore anyone willing to analyze this issue more deeply has to either conduct an additional dedicated survey or try to estimate the number of shadow employees using the existing information.

As a result, a number of dedicated, mostly one-off surveys of the shadow employment in Poland have been performed by other institutions. In 1994 and 1997, such surveys with samples of 1,000 people each were performed by The Gdansk Institute for Market Economics and published in Grabowski (2002). Another survey was performed by the CEBOS company in 2004 (CEBOS, 2005). The former indicated that the share of shadow employees in Poland decreased from 29.6% in 1994 to 14.1% in 1997. According to the latter, the estimated share of shadow employees amounted to about 13% in 2004.

The two biggest dedicated surveys of shadow employment in Poland were performed in 2007. They were run independently by two institutions, both commissioned by the Polish Ministry of Labor and Social Affairs.

One of surveys was performed by the Institute of Labor and Social Affairs in cooperation with CEBOS institute. According to its results out of the total sample of 9,038 persons aged 15+ interviewed 4.8% answered that they performed any unregistered work within
_line z such that Xz_ = _XQ_ ( _{u}_ ) _and a poverty score function p such that for all_ ( _y,_ _<u>y</u>_ <u>)</u> _,_ ( _y_<sup>_′_</sup> _,_ _<u>y</u>_<sup>_~~′~~_</sup> ) _∈ Xz,_ _Proof._ See online Appendix S7.

■ We emphasize two implications of Proposition 2 for the properties of the global poverty line. First, the global line corresponds to the indifference curve passing through the reference bundle (see Figure 4). That is, the global line yields, for each individual (separately), the same utility in all countries.<sup>28</sup> Second, the global line yields a _perfect identification_ of the welfare poor; that is, all individuals below the global line are welfare poor and all individuals above the global line are not welfare poor. This is because all individuals hold the common utility function.

The trade-offs that the measure makes between own income and relative income can be graphically illustrated by means of its iso-poverty-score map (see Figure 4). An iso-poverty-score map is a collection of iso-poverty-score curves, which are sets of bundles that yield a constant poverty score. The global line is one iso-poverty-score curve, or, more precisely, it is the frontier of a ‘thick’ iso-poverty-score curve. Below the global line, iso-poverty-score curves exactly correspond to indifference curves. The two properties (fairness and welfare-consistency) completely characterize the trade-offs made by the measure; that is, the comparison of any two bundles is determined.

# **4.3 General Impossibility and Weak Pareto**

In many settings with heterogeneous preferences, the Pareto principle conflicts with the Domination principle (Fleurbaey and Trannoy, 2003; Brun and Tungodden, 2004). This conflict also arises in our
In addition, we explore whether the characteristics of the environment in which banks operate affect the impact of competition on access to finance.<sup>9</sup> To do that, we interact our measures of competition with country-level measures of financial development, the availability of credit information, and government bank ownership. We find that countries with higher levels of financial development and better information availability experience a less pronounced decline in access to finance as a result of low levels of competition (high values of the Lerner index). The flip side of this finding is that low competition is more detrimental for firms operating in countries with low levels of financial development or lacking credit information. In addition, we find that significant government bank ownership exacerbates the damaging impact of low bank competition.

The rest of the paper is organized as follows. Section 2 introduces our multiple datasets and presents summary statistics. Section 3 outlines our regression model. Section 4 presents our baseline results. Section 5 discusses the results interacting the competition measures with different aspects of the environment in which banks operate. Section 6 concludes. Appendices A1 and A2 contain detailed descriptions of the construction of the firm-level measure of access to finance and the estimation method for the Lerner index, respectively.

# **2. Data**

We combine firm-, bank- and country-level data from various sources. Table 1, Panel A gives a list of all the variables used in the paper and details their sources. The firm-level data come from World Bank Enterprise Surveys.<sup>10</sup> The data are collected in several waves and contain repeated cross-sections for the countries in our sample. Because our goal is to isolate within country variation in competition across time, we only focus on countries that have survey data for at least two years.

We use firm survey data to construct our measure of access to finance and several control variables. A _ccess to finance_ is an indicator variable that equals one when a firm has a loan, overdraft, or
# **How Do Special Economic Zones and Industrial Clusters Drive China’s Rapid Development?**

## **Douglas Zhihua Zeng**

China’s meteoric economic rise over the past three decades is an unprecedented “growth miracle” in human history. Since the Open Door policy and reforms that began in 1978, China’s gross domestic product (GDP) has been growing at an average annual rate of more than 9 percent, with its global share increasing from 1 percent in 1980 to almost 6.5 percent in 2008 (see figure 1.1) and its per capita GDP increasing from US$193 to US$ 3,263 (see figure 1.2). Total exports have been growing at an average annual rate of 13 percent (21.5 percent from 1998 to 2007), with China’s share of total exports increasing from 1.7 percent in 1980 to 9.5 percent in 2008 (see figure 1.3).

**Figure 1.1 China’s GDP Growth, 1980–2008**

<!-- Start of picture text --> GDP (constant 2000<br>US$)<br>GDP (% of Global total) 16%<br>2,500<br>14%<br>GDP growth (annual %)<br>2,000 12%<br>10%<br>1,500<br>8%<br>1,000 6%<br>4%<br>500<br>2%<br>0 0%<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>Source: World Bank GDF and WDI central database Sep 2009<br>Billions (constant 2000 USD)<br><!-- End of picture text --> In 2007, China’s incremental growth in real GDP actually exceeded its entire real GDP
outsourced. The study reduces maintenance staff assumed in Table A1.6 by 70 percent.

- **Small plants:** There are 5 staff members for O&M of any plants smaller than 2 MW.

- **Limit on operational staff** : The number of operational staff members is limited to 30 per plant for any plant with more than four units, except coal-fired generation which has no limit.

- **Technology specific assumptions:**

- **Gas turbines versus motors:** If no turbine data are available, assume gas plants are using motorized units unless there is a turbine listed by a known manufacturer of motors, such as Wärtsilä, MAN, Hyundai, Sulzer, Catepillar, Jenbach, and Cummins.

- **Wind and solar** : Assume a flat rate of five O&M staff members for plants smaller than 2 MW and 20 O&M staff members for plants larger than 2MW.

- **Nuclear** : Assume a flat rate of 600 staff members for South Africa’s nuclear plant.

78
impacts on their economies. The results raise questions regarding the competitiveness of African zones programs and the potential of zones which aim to compete for labor intensive assembly employment under a traditional export platform (EPZ) model. In analyzing investment climate performance, we find that, across the criteria that matter most to investors, economic zones in Africa offer an improved business environment relative to what would be available outside the zones. However, in most cases the scale of improvements is limited. In contrast, the zones in the non‐African countries in the survey appear to be able to make much more dramatic improvements on the national investment climate. The survey results highlight the critical importance of addressing the basic day‐to‐day challenges that investors face inside the zones.

The remainder of the paper is structured in seven sections. Section 2 provides a very brief introduction to economic zones in Africa. In section 3 we outline a basic framework for assessing performance of economic zones programs. This is followed in section 4 by a discussion the relationship between the investment climate and FDI, and its specific implications in the economic zone context. Section 5 gives a brief description of the data and methodology used in the analysis. In section 6 we present a detailed assessment of zone performance, covering investment, exports, employment, and structural economic change. Section 7 presents the findings from the survey on the investment climate. Finally, in section 8 we summarize the findings and discuss briefly the implications for African economic zones programs.

3
# A On-line Appendix: Estimation Details

This appendix provides more details on the estimation procedures introduced in section 3 of the paper. We estimate the parameters related to the rm and worker decision problems using Argentine data. We combine a panel of rms with a panel of workers.

## A.1 Data

### Firm data

The estimation of the rm problem requires panel data with detailed information on the investment decision of rms. In particular, to t the capital adjustment cost model, we need data on purchases of new capital as well as on sales of installed capital. We use the Encuesta Industrial Anual (EIA, or Annual Industrial Survey), which meets these requirements. In the EIA panel, we have data for the period 1994-2001. The EIA dataset provides information on revenue, value of production, employment, wages, costs, intermediate inputs, inventory stock, and both gross expenditures and gross sales of capital.

We express all monetary variables in real terms using dierent deators. For wages we use the consumer price index; for investment, capital and intermediate inputs we use the general level wholesale price index; and for revenue, sales and prots a wholesale price index at a four digit level of disaggregation according to the ISIC classication.

To construct the real investment series, we generate an initial measure of the real capital stock at the plant-level and then complete the series using the perpetual inventory method according to the rule _Kijt_ +1 = (1 _− δ_ ) _Kijt_ + _Iijt_ . Real investment is dened as _Iijt_ = _Eijt − Sijt_ , where _Eijt_ is real gross expenditures on capital equipment, and _Sijt_ is real gross retirements of capital equipment.

Since our dataset does not contain information on the book value of capital, we approximate the initial capital stock of the rm as the average across years of the ratio between the amount of capital depreciation declared by the rm and the estimated depreciation rate. We deate our measure of initial capital stock by the general level of
. Section IV discusses the poverty trend based on the international poverty line of $1.90 per day per capita. Section V discusses the importance of the various price indices used to arrive at comparable poverty estimates, and section VI revisits the debate on the poverty trend. Section VII concludes.

# **II. Official poverty estimation methodology in Rwanda**

Poverty measurement essentially hinges on two main decisions; (i) choosing a _welfare indicator_ , and (ii) setting a _poverty line_ , which is the minimum value of the welfare indicator that households (or individuals) must have to fulfill their basic food and non-food needs. In most developing countries monetary poverty is measured using absolute poverty lines, which are based on a fixed welfare standard. To make welfare comparisons over time, these poverty lines are updated using price data such that they represent the _same_ purchasing power year after year (Ravallion 1998).

<mark>II.1. The Welfare Indicator</mark> <mark>Welfare indicators are either based on income or consumption.</mark><sup>2</sup> <mark>In Rwanda, as in most developing countries, the welfare indicator is based on aggregate consumption expenditure obtained from EICV surveys. The</mark>

> 2 In most developing countries, consumption expenditure is used as a proxy for income, since income is difficult to measure due to the size of the informal economy, large agricultural sectors and seasonality in the measurement of income (see http://siteresources.worldbank.org/INTPRS1/Resources/383606-1205334112622/5467_chap1.pdf).

2