rafmacalaba/datause-encoder-data huggingface dataset:
## _12_ **_How FM Ha Cbae ldutiy ww?_**

# D. Do **Local Authoritles Falsify Industril Output Data?**

Well-informed statistical personnel report the existence of incentives for exaggeration of industrial output growth by local govenments in some regions of China. In Jiangsu, for example, municipalities that surpass threshold levels of industrial output (e.g. 1 billion yuan) are granted special privileges in the form of exemption from certain types of regulation (e.g. direct access to provincial funds vs. application through county offices). Falsification, if extant, presumably **taces** the form of inflating gross rather than net output [note that the former can be easily inflated by anranging exchange of materials or semi-fabricates anong producers of similar commodities]. If so, falling ratios of net to gross output will be typical of entities with inflated gross output totals. Unfortunately, there are many other factors influencing this ratio, so that falsification might easily escape detection, especially if the amounts are small relative to local and national totals.

At the same time, other enterprises or localities may conceal some portion of their actual production in the hope of avoiding taxes.

# E. Industrial Output Data Accurately Reflect Recent Inflation Experience?

After decades of considerable price stability, China has experienced growing inflationary pressures during the 1980s. Since Chinese price index compilation has previously focused almost exclusively on consumer prices, the impact of recent inflation on industrial prices is not easily discerned. The only systematic effort to monitor trends in industrial prices appears to come from the State Price Bureau, which now surveys price conditions in several thousand industrial enterprises and uses the resulting data to compile indexes of ex-factory prices for industrial products as well as purchase prices for major raw materials, fuels, and power [personal communication, May 1989]. Summary figures for 1984/85 and more comprehensive results, including price indexes for 15 industrial branches and purchase price indexes for eight classes of materials appear in the Bureau's journal rChia Pnricc 2 (1989): pp. 59-60].

These figures confirm that industry has experienced the inflationary trend reported for urban consumer goods, Annual increases for ex-factory prices averaged 8.7 percent in 1984/85, 3.8 percent in 1985/86, 7.9 percent in 1986/87 and well over 10 percent during the first eight months of 1988 [China Price 2 (1989): 59]. Price increases for materials, fuel, and power were substantially larger in each year [ibid., 60]. The influence of commercial intermediaries (including government agencies), in the inflationary process is visible if we compare trends in ex-factory prices of mining products and raw materals" (these categories appear not to overlap) with trends in purchase prices for wall raw materials" (percent increases over the previous year):
view that to stimulate growth and improve human indicators, a large increase in public investment is needed.

We begin by describing an illustrative PIP for Niger. We then determined the implied aid requirements. We conclude with an examination of the impact of alternative assumptions of tax effort on these requirements.

# **1.  An Illustrative Public Investment Program, 2006-15**

Table 1 shows an illustrative PIP for Niger, with all flows measured at constant 2004 prices. In the table, public investment includes not only the three categories identified earlier (education, health, and infrastructure) but also a category “other”, which represents a relatively small share of total capital outlays. In a typical SPAHD application, this component is generally treated as a flow only, with no impact on the stock of public assets.

The 2005 values of the capital expenditure series are taken from the baseline table estimated by Pinto Moreira and Bayraktar (2005). The growth rate of public investment in infrastructure is assumed to increase gradually from 3 percent in 2006 to 6 percent in 2009, and to remain at that level up to 2015. Regarding maintenance expenditures in infrastructure, user charges, and wages and salaries, we assume that they represent 5 percent of investment in infrastructure. Other spending on goods and services is assumed to account for one percent of capital outlays in infrastructure.

The growth rate of public investment in education is assumed to grow at the lower rate of 2 percent in 2006, 3 percent in 2007, and 4 percent from 2008 to 2015. While maintenance expenditures in education, user fees, and other spending on goods and services are equal to 1 percent of capital expenditures in education, wages and salaries are taken as 5 percent of this component of investment.

13
conditioned on school attendance but which, in addition to increased enrollment and attendance also led to a reduction in HIV and HSV-2 incidence (Baird and others 2012). At follow-up, HIV prevalence among program beneficiaries was 60% lower than the control group (1.2% vs. 3.0%). Similarly, the prevalence of HSV-2 (herpes simplex virus – type 2, which is the common cause of genital herpes) was more than 75% lower in the combined treatment group (0.7% vs. 3.0%). No significant differences were observed between those offered conditional and unconditional payments. Moreover, cash payments offered to the girls who had already dropped out of school at the beginning of the trial made no difference on their risk of HIV or HSV-2 infection. The same program also led to a modification of self-reported sexual behaviors with adolescent girls reporting younger partners (Baird and others 2010).

In Kenya, a national unconditional cash transfer program for orphans and vulnerable children reduced the age of sexual debut among young people ages 15–25 years and reduced the likelihood of pregnancy among women ages 12–24 years by 5 percentage points (Handa and others 2014; Handa and others 2015). Also in Kenya, Duflo, Dupas, and Kremer (2015) found in a randomized control experiment that an education subsidy program had no impact on the HSV-2 infection rate. However, an education subsidy combined with HIV/AIDS prevention education focusing on abstinence until marriage resulted in a significant reduction in the HSV-2 infection rate.

A propensity-score-matched case-control study of child-focused state cash transfers in South Africa found a reduction in transactional sex and age-disparate sex (Cluver and others 2013). Results from two South African randomized controlled trials of cash transfers conditional on schooling obtained mixed results. An individually randomized study of young women conditioned on school attendance observed no impact on HIV incidence, even though the young women who 9
Figure 4: Size Distribution of POEs and SOEs <!-- Start of picture text --> (a) By Employment (b) By Operating Revenue<br>SOEs vs POEs SOEs vs POEs<br>2011-19 2020 2011-19 2020<br>Note: Tests of differences in means and medians between SOEs and POEs, p-valules are, respectively, 0.000 and 0.000 Note: Tests of differences in means and medians between SOEs and POEs, p-valules are, respectively, 0.000 and 0.000<br>POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more POE 10-24.9% 25-49.9% 50% or more<br>10 25<br>8<br>20<br>6<br>15<br>4<br>Log employment<br>Log operating revenue10<br>2<br>0 5<br><!-- End of picture text --> Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. SOEs are defined as firms where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national or subnational governments own 0-9.9%.

# 5. Main Findings

The first part of this section presents the main results regarding whether ownership stakes (i.e., majority and minority) in SOEs and control levels (i.e., directly vs. indirectly owned and centrally vs. locally owned) of SOEs matter for firm performance. It also presents results of whether performance differences between POEs and SOEs depend on the type of sectors they operate, and whether ownership also matters for subsidy allocation. The second part presents the results of whether SOEs could act as buffers in attenuating some of the effects of the COVID-19 crisis. The final part presents how SOEs’ presence in sectors affects sector or market outcomes.

## (a) Does the Ownership Matter for Firm Performance and Subsidy Allocation?

**On average, SOEs in Romania employed more people and paid higher average wages during 20112019 than their POE peers** . They also had higher assets per worker than the POE. However, on average, they were less productive than their POEs counterparts. These effects are irrespective of the various ownership types of SOEs ( <mark>Figure 5</mark> (a)). These findings align with other studies, which find that SOEs in Romania generate less revenue per employee but pay higher wages than POEs (EC 2015; Marrez 2015; and IMF 2019).

**The average SOE in Romania experienced higher job growth, investment, and labor productivity but slower wage growth during 2011- 2019** . However, these growth effects are not uniform across the various ownership types (Figure 5(b)). For instance, the job growth, investment, and productivity effects are driven by majority owned SOEs, which invested more and increased their employment and labor productivity faster than POEs. In contrast, s minority SOEs experienced slower job and productivity growths and invested less than POEs. SOE employees, irrespective of the SOE ownership types, saw slower wage growth than their counterparts employed by POEs.

13
without identify the specific purpose of use. Section 3 describes the new measures of technologies developed using the FAT survey, and provides a conceptual framework for general and sector specific business functions. Section 4 analyzes the level of technology adoption and use for general business functions. Section 5 analyzes the level of technology adoption and use for sector specific technologies. Section 6 describes some of the key barriers and drivers to technology adoption and use. Sections 7 analyzes the relationship between technology use and employment. The last section concludes.

# **2 Standard Measures of Technologies: Electricity, ICT, and Industry 4.0**

Standard measures of general purpose technologies (GPTs) can be linked to different stages of industrial revolutions, following the reference period in which these technologies became available. We organize the information on adoption and use of GPTs in three types according to the period when they were originated and production processes changed: Industry 2.0, 3.0, and 4.0. Industry 2.0 encompasses electricity and generators, which are technologies from the 1880s. Industry 3.0 refers to the ICT revolution, including mobile phone, computer, and Internet. These technologies became available over the 1970-1980 period.<sup>5</sup> Industry 4.0 refers to technologies that in most cases have some digital component, but higher level of autonomy and connection of information across different devices and machines to perform tasks. Among the technologies usually associated with Industry 4.0 are the Internet of Things, Big data analytics and artificial intelligence, 3D printing, advanced robotics, and cloud computing.<sup>6</sup>

## **2.1 Industry 2.0: Electricity**

A large share of firms in Senegal has access to electricity; yet power outages are common across the country. The availability and reliability of electricity and telecommunications services can be decisive in a firm’s decision to adopt a new technology. Table 1 shows the descriptive statistics of access to electricity, power outages, and alternative source of electricity. The first two columns show the average and standard deviation. The means are further divided by formality and size groups. Formality was defined based on whether

> 5Comin and Mestieri (2018b) present the reference year of invention for these technologies: electricity (1882), PCs (1973), cellphones (1973), and Internet (1983).

> 6Nayyar and Hallward-Driemeier (2018) provide further discussion on the emergency of Industry 4.0. Although some of these technologies, such as AI, were already available since the 1960s, they have been increasingly available in recent years.

4
**Figure 4. Predicted Poverty Rates in TA 2 Based on Imputation Using Different Price Adjustments, from NPS 2020/21 to TA 2, Tanzania**

<!-- Start of picture text --> Panel 1: IMF Panel 2: WB annually<br>m1 m2 m3 m8 m9 m1 m2 m3 m8 m9<br>Imputation model Imputation model<br>Panel 3: WB quaterly Panel 4: WB monthly<br>m1 m2 m3 m8 m9 m1 m2 m3 m8 m9<br>Imputation model Imputation model<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br>30 30<br>25 25<br>20 20<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>15 15<br><!-- End of picture text -->

**Note:** 1000 simulations are implemented. Model 3 uses standardized variables. The dashed lines represent the true poverty rates in TA 1 using the 2020/21 poverty lines. The light grey areas represent confidence intervals of the true poverty rates in TA 1 using the 2020/21 poverty lines. The dark grey areas represent standard errors of the true poverty rates in TA 1 using the 2020/21 poverty lines.

41
16 as shown by Fischer (1993), Easterly and Rebelo (1993), Bruno and Easterly (1995). Similarly, we include BMP since international price distortions may impede economic growth as suggested by Dollar (1993) and Levine and Zervos (1994). Thus, we include GOVY, PI, and BMP - and expect them to enter negatively - to gauge the strength of the partial correlation between stock market development and long-run growth.

**Estimation:** We use instrumental variables to estimate equation (5) for two reasons. First, instrumental variables will help us examine the relationship between growth and the predetermined component of stock market development. If the predetermined component of stock market development (as identified by the instruments) is positively correlated with economic growth, this will indicate that (a) stock market development does not simply follow economic development, and (b) it is not only contemporaneous shock to both stock market development and economic growth that are driving the results. Thus, we use two-stage least squares to examine whether predetermined stock market development is closely associated with economic growth. The second reason we use two-stage least squares involves measurement error. The LAPM pricing errors are generated regressors, which can lead to inconsistent standard errors. Thus, we use two-stage least squares to derive consistent standard error as suggested by Pagan (1984).7

For the instrumental variables, we use the logarithm of initial real per capita GDP, the logarithm of the initial secondary school enrollment rate, the number of revolutions and coups, the initial black market exchange rate premium, the initial inflation rate, the initial ratio of government spending to GDP, the initial ratio of exports plus imports divided by GDP, the initial ratio of market capitalization to GDP, the initial ratio of total value traded to GDP, and the initial 7 The two-stage least squares estimator is consistent. Furthermore, we use White's (1980) heteroskedasticityconsistent standard errors. While the Generalized Method of Moments (GMM) estimator is sometimes used in pooled cross-section, time-series samples to obtain more efficient estimators in the presence of heteroskedasticity and serial correlation in large samples, GMM does not offer much value added in the current context. As will be shown, the results are already highly significant, so that a potentially more efficient estimator will only make the t- statistics larger. Furthermore, we are working with a small sample of only 79 observations. Also, we do not have a true time-series dimension to the data. There are only two periods because we average the data over long-time periods in order to focus on the relationship between stock market development and _long-run_ growth. Thus, GMM procedures will not improve the efficiency of the estimates by allowing for serial correlation because there is not a true time-series dimension to the data. Finally, we already control for heteroskedasticity.
Based on gendered indicators included in the 2012 survey, we group empowerment into five factors indicating five types of latent abilities:<sup>5</sup>

- _(1) Decision-making_ is the ability to make decisions about various aspects of one’s own life and household affairs. We combine five indicators to construct this ability: whether a woman can make decisions alone about her own work, about her medical treatment, about her children’s medical treatment, about children’s marriage, and about items to cook on a daily basis.

- _(2) Mobility_ is the ability to move about independently. It is based on the ability to visit various places, including health centers and friends’ or relatives’ homes; to travel a short distance by trains or buses; having traveled outside a rural area to a city or town during the 5 years preceding the survey; and having traveled outside the state during the five years preceding the survey.

- _(3) Financial autonomy_ is the ability to make purchase decisions and access assets or finance. It is based on four attributes: the ability to purchase expensive household durables, such as a refrigerator or television set; the ability to purchase land or other real estate; possession of a bank account; and having her name on the ownership or rental document of her domicile.

- _(4) Reproductive freedom_ is the ability to make reproductive choices. We consider three measures for this ability: whether a woman uses any contraceptives; whether she can decide how many children to have; and whether the actual or desired number of children is less than three. Literature on gender equity and reproductive outcomes mostly suggests that there is an inverse relationship between a woman’s empowerment and the number of children she has (Upadhyay et al.).<sup>6</sup> The third indicator is measured by: i) the number of children for those who would not conceive any more children (either by choice, or because of sterilization), or ii) the total of current and additional desired number of children for those who want more children.

> 5 This categorization is not set in stone; alternate combinations of indicators are possible. 6 We use a cut-off point to convert a continuous variable (number of children) to an indicator variable (yes-no). The choice of cut-off point is arbitrary. Use of other cut-off points, such as 4 children, does not change the results.

10
Policy Research Working Paper 10335

# **Abstract**

This paper advances the understanding of the linkages between trust in government and citizen participation in Latin America and the Caribbean, using machine learning techniques and Latinobarómetro 2020 data. Proponents of the concept of stealth democracy argue that an inverse relationship exists between political trust and citizen participation, while deliberative democracy theorists claim the opposite. The paper estimates that trust in national governments or other governmental institutions plays neither a dominant nor consistent role in driving political participation. Instead, interest in politics, personal circumstances such as experience of crime and discrimination, and socioeconomic aspects appear to drive citizen participation much more strongly in the Latin America and the Caribbean region. This is true across models imposing simple linear trends (logit and lasso) and others allowing for nonlinear and complex relations (decision trees). The results vary across the type of participation—signing a petition, participation in demonstrations, or involvement in a community issue—which the paper attributes to increasing net costs associated with participation.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank 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 npecorari@worldbank.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
# **4. OCCUPATIONAL PLACEMENT AND COUNTRY OF ORIGIN ATTRIBUTES**

# **_4.1. EMPIRICAL FRAMEWORK_**

This stage of the analysis is an exploration of the extent to which the predicted Occupational Placement Improvements can be accounted by country-of-origin specific factors. Previous literature focusing on earnings found country attributes to be significant determinants of both the entry wages and their growth rates (Jasso and Rosenzweig, 1986, Borjas, 1987, Borjas 2000). Mattoo et al. (2008) identified two types of country specific influences on the initial levels of occupational placement. First are related to the quality of education in the home country and explain why nominally identical educational qualifications obtained in different countries are valued differently on the US labor market. The second are selection-related and determine which particular segments of the origin country skill distribution are likely to migrate to the US.

As a first step, we check whether the predicted occupational placement indicators for the two sets of hypothetical individuals, migrating in 1975 and 1985 respectively, are influenced by several quality and selection specific attributes. The functional form is presented in equation (3) The regression is estimated separately by hypothetical individual and census year (t=1980, 1990, 2000) for each OP specification (prestige index, education index 1, education index 2). The dependent variables are the levels of performance in 2000 for the 1975 arrivals, and in 1990 for the 1985 arrivals. This means estimating 6 (2 years*3 OP definitions) regressions for the 1975 arrivals, and 3 (1 years*3 OP definitions) for the 1985 arrivals.

The explanatory indicators capture a country’s condition around migration time, i.e. during the 1970s decade or its first half for the 1975 arrival, and during the 1980s, or its first half, for the 1985 arrival. GDPpc stands for log of per capita-GDP in constant 2000 prices averaged over 1970-1975, and 1980-1985 periods, respectively, and is from the Penn World Tables (Version 6.2).<sup>31</sup> GDPpc may affect migrants’ performance through both quality and selectivity channels. The fact that people coming from high income countries are better off at arrival has been associated with the ease of skill transferability between US and equally developed countries.

> 31 Heston, Summers, Aten (2006)

17