rafmacalaba/data-use-mentions-extended huggingface dataset:
largest database, the introduction of CBs elicits a job growth response among MSMEs that is over six times larger than that among large firms. Finally, MSMEs in industries with high dependence on external finance respond to the introduction of CBs with job growth rates that are almost two times larger than those of large firms in similar industries.

Our paper contributes to the growing literature on finance and labor. Several papers have used the 2007-09 Great Recession in the US as a shock to external finance to identify the causal link between finance and employment. Duygan-Bump, Levkov, and Montoriol-Gariga (2015) find that workers in small firms in the US were more likely to become unemployed during the 2007-09 financial crisis if they worked in industries with high external financing needs. Benmelech, Bergman, and Seru (2011) show that local unemployment rates in the U.S. are affected by the provision of bank credit. Chodorow-Reich (2014) finds that borrowers of banks affected by disruptions in the syndicated loan market following the collapse of Lehman Brothers in 2008 faced restrictions in credit supply, which translated into greater cuts in employment at these firms. Greenstone, Mas, and Nguyen (2012) also show that the 2007-09 lending shocks accounted for significant but modest declines in overall county-level employment.

However, it is not clear how these finance and labor market linkages in the US<sup>8</sup> translate to firms in poorer economies with completely different financial infrastructures and institutional with employment growth rates that are over 0.89 percentage points higher. Similarly, an upward movement by 1 standard deviation in the debt ratio of MSME firms is associated with an almost 0.4 percentage point higher employment growth. These are large movements in response to changes in financing when compared to the median annual employment growth rate of just under 1.5 percent.

> 8 A few studies have used data from other developed countries. Bentolila, Jansen, Jimenez, and Ruano (2013) use matched bank-firm data from the Spanish Credit Register and show that during
Policy Research Working Paper 5526

# **Abstract**

Using Life in Transition Survey data for 27 transition countries, the findings of this paper suggest that higher life satisfaction is correlated with lesser experience of unpleasant events such as labor market shock or economic distress, mostly in the recent past. Social capital such as trust, participation in civic groups, and financial stability lead to higher satisfaction, whereas lower relative position to a reference group leaves one with lower life satisfaction. The paper also finds substantial regional variation in life satisfaction between European, Balkan, and lower and middle-income Commonwealth of Independent States. Finally, after controlling for various events that took place during the interview and the nature of refusal of the respondents across countries, the authors show that reported life satisfaction is lower if the emotional state is negative during the interview.

This paper is a product of the Poverty Reduction & Economic Management Sector Unit, Europe and Central Asia Region. 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://econ.worldbank. org. The authors may be contacted at adabalen@worldbank.org or spaul@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
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
||With i|ndividual co|ntrols|With individ|ual + househol|d controls|With individ|ual + househol<br>controls|d + regional|
||Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|Market<br>work<sup>a</sup>|Unpaid<br>work<sup>a</sup>|Other<br>activities<sup>a</sup>|
|Log of HH annual expenditure per capita||||-0.649*|-0.168|0.817***|-0.602|-0.222|0.824***|
|||||(0.390)|(0.248)|(0.317)|(0.396)|(0.251)|(0.319)|
|HH living standard index||||0.177|-0.043|-0.134|0.182|-0.002|-0.180|
|||||(0.131)|(0.083)|
This research uses _A Survey of State Enterprises: 1980--1989,_ a retrospective survey conducted by the Chinese Academy of Social Science in 1991. The data set is a balanced panel design: no firms were dropped out during the 10 years.<sup>3</sup> Survey questionnaires were sent out by the provincial System Reform Commissions to 800 state enterprises. Valid responses from 769 firms, located in 21 cities in four provinces of China (Sichuan, Jiangsu, Jilin, and Shanxi), were received. The data set was not designed to be a random sample of the state enterprises: making up more than 70% of the sample, large firms were over-represented. The median firm of the sample had 931 employees.

The data set consists of two parts. Part one, intended to be completed by the accountant of the firm, contains quantitative tables with details of the firm's production inputs, outputs, internal incentives, wages, labor composition, and profit distributions between the govenmment and the firn. Part two, to be completed by the manager, contains information on the firm's industry affiliation, age, and size; the contract terms of the manager; the relationship of the firm to the government; when the firm was granted the discretion to plan output levels and what to produce, whether the manager had the discretion to determine wages; the share of the firm's input from subsidized state sources, from the market, and from bartering with other firms, etc. It also has information about how value added was distributed, and how the decentralized reforms evolved over time.

During the 1980s, the Chinese government experimented with decentralizing SOEs to boost productivity. By the end of the decade, those state enterprises had a much different legal structure, and became much more, though not completely, marketoriented.<sup>4</sup> Both the productivity level and input uses of firms changed over this period. The average labor productivity of the firms in the
Table 1.

# **4 Empirical Strategy**

## **4.1 Measuring GDP manipulation**

To infer true GDP, we rely on a proxy for economic activity that is objectively measured and highly correlated with true output. Since Henderson, Storeygard, and Weil (2012) developed an original statistical framework to use satellite data on night lights to improve official economic growth measures, nighttime lights have often been used as a proxy for economic activity (Hodler and Raschky 2014; Michalopoulos and Papaioannou 2014; Henderson et al. 2018). To predict the true GDP growth using nighttime lights data, Henderson, Storeygard, and Weil (2012) run the following regression: ln( _GDPi,t_ ) = _α_ 0 + _α_ 1 ln( _Night lightsi,t_ ) + _μi_ + _δt_ + _εi,t_ (1) where ln(GDPi,t) is the logarithm of real GDP, and ln(Night lightsi,t) the logarithm of the night light luminosity of the region, measured as the area-weighted average of DN values across pixels within the region. _μi_ and _δt_ are the region and the year fixed effects (FEs), respectively. To improve the precision of estimated economic growth, they combine the fitted values from equation (1) with the official GDP growth to arrive at a composite estimate that would have a lower error than that based on either measure separately. That is _Predicted true growthi,t_ = _λ Official growthi,t_ + (1 − _λ_ ) _Light-predicted growthi,t_ (2) where _λ_ is the optimal weight that minimizes the variance of measurement errors in the above estimate relative to the true value of economic growth. Under this framework, GDP manipulation is the difference between the officially reported growth and the predicted true growth in Eq. (2): (1 − _λ_ ) ( _Official growthi,t_ − _Light-predicted growthi,t_ ).<sup>28</sup> However, the relationship between nighttime lights and real GDP may differ
public service provision constructed using the 2021 Podes data, as in column 1 of Table 7. The sample includes: in column 1, all villages in which the village head reports having no relative in the village government; in column 2, all villages in which the village head reports having at least one relative in the village government; in column 3, all villages in which no bureaucrat who was appointed before the most recent election reports a parent previously served as a village official; and in column 4, all villages in which at least one bureaucrat who was appointed before the most recent election reports a parent previously served as a village official. See Section 5 for details.

* p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01. Robust bias-corrected standard errors in parentheses.

**Table A.16:** Effects on Old-Serving Nepotistic Appointees

||Proportion|Binary(=1 if any)|
|---|---|---|
||(1)|(2)|
|New village head|-0.036<br>(0.068)|-0.017<br>(0.130)|
|Observations|510|510|
|Control mean|0.15|0.25|
|Robust p-value|0.825|0.933|
|MSE-opt. bandwidth|22.9|21.3|
|Effective obs.|263|252|

_Notes_ : This table reports RD estimates of _γ_ in equation (1). In column 1, the dependent variable is the share of bureaucrats who were appointed before the most recent election and report that a family member previously served as a village official. In column 2, the dependent variable is a dummy equal to one if at least one such bureaucrat is present in a village. See Section 5 for details.

* p _<_ 0
an issue that arises in almost all lines of insurance. Adequate training of loss adjusters is a critical component needed in the development of a sound insurance program. It is also important that any conflicts of interests between loss adjusters and the insured agents be identified and remedied.

Actuarial considerations must, of course, make a distinction between measuring all peril risk and measuring the risks associated with a specific peril. It is often argued that it is easier to measure the risks associated with a specific peril than to attempt to measure all risks, seen and unseen, that might be expected to cause yield losses. This may be true in cases where exogenous information about risks is available. For example, in the case of hail, historical weather records may allow actuaries to accurately measure the risks associated with hail damages in any particular area. Specific peril lines of insurance may also be provided against events that are very low probability and non-systemic. For example, fire may be a rare event and may not affect large numbers of producers. In such cases, adequate measures of risk may be obtained from considering a large, crosssectional pool of producers.

Specific peril insurance may also rely on historical yield data to measure risks. In such a case, actuaries must be able to identify the exact causes of loss associated with historical yield shortfalls. The methods discussed below for detrending yields can be extended in a straightforward manner to “condition” on non-insured causes of loss. For example, consider a case where factors associated with yield shortfalls can be segmented into a single hazard that is covered (e.g., hail), denoted by **_X,_** and hazards that are not covered, denoted as **_Z_** . One would run a regression of yields on **_Z_** , thereby conditioning out the effects of those factors affecting yields that are not covered by the insurance. The residuals from such a regression represent yield shocks that are associated with the covered factor **_X_** ,
**Figure 6: Regional financial integration instruments that could promote green** **_sukuk_ in Africa**<sup>**18**</sup>

<!-- Start of picture text --> RFI Initiatives in Africa<br><!-- End of picture text --> <!-- Start of picture text --> Currency unions<br><!-- End of picture text --> <!-- Start of picture text -->  West African CFA<br>franc<br> Central African CFA<br>franc<br><!-- End of picture text --> <!-- Start of picture text -->  CFA green sukuk<br> Rand green sukuk<br><!-- End of picture text --> <!-- Start of picture text --> Regional exchanges<br><!-- End of picture text --> <!-- Start of picture text -->  BRVM (UEMOA)<br> BVMAC (Central<br>Africa)<br><!-- End of picture text --> <!-- Start of picture text -->  Listing: green<br>segment<br> Clearing and custody<br> Sustainability index<br><!-- End of picture text --> <!-- Start of picture text --> Regional/sub-regional<br>MDBs<br><!-- End of picture text --> <!-- Start of picture text -->  AfDB/Africa 50<br> BOAD<br> EADB<br> BDEAC<br><!-- End of picture text --> <!-- Start of picture text -->  Green sukuk<br>programs<br>
2

# **1 Introduction**

Virtually every one of today’s high income economies enjoyed sustained agricultural productivity growth coupled with transformation of the rural non-farm economy that jointly sparked rapid industrialization and inclusive economic growth. Indeed, the Nobel Laureate W. Arthur Lewis famously wrote: “industrial and agrarian revolutions always go together, and ... economies in which agriculture is stagnant do not show industrial development” (Lewis 1954, p. 433). Until most recently, the economies of Sub-Saharan Africa (SSA) have also been enjoying robust economic growth (4.5 percent growth in gross domestic product (GDP) per capita per year during 1995-2013), according to World Bank data,<sup>1</sup> and the region shows early signs of agricultural productivity growth and structural transformation.

But much remains to be done. Despite notable progress—the share of people in SSA living on less than $1.90 a day (in 2011 international purchasing power parity PPP) declined from 54 percent in 1990 to 41 percent in 2013)—the number of extreme poor still increased, by more than 100 million (from 276 million to 389 million).<sup>2</sup> Extreme poverty remains high, with the $1.90 a day headcount poverty rates in SSA today twice as high as in South Asia, and more than ten times higher than in East Asia.<sup>3</sup> By 2030, most of the world’s extreme poor (four out of five) are predicted to be concentrated in Africa (World Bank, 2013).

This is partly driven by continuing high population growth (2.7 percent per year during 1990-2013). But the challenge of persistent extreme poverty that has plagued Africa for a generation is also closely bound up with continuing low agricultural productivity and the nature of the region’s structural transformation. Most of the region’s poor still find themselves in rural areas—an estimated 82 percent
(see Bauer, 2014, for recent examples) and in the developing world is a random‐walk. The Afrobarometer survey, which has been conducted in multiple rounds in 35 African countries since 1999, and the Gallup World Poll, which conducted surveys in 29 Sub‐Saharan African countries in 2012, use random walk methodologies. Although the random walk methods do not necessarily produce equal probability samples, they do not collect any information with which to calculate probabilities of selection. For this reason, weights are not calculable for random walk samples; instead, the samples are analyzed as if they were equal probability. Bauer (2014) shows that this assumption is not correct by simulating all possible random routes using standard procedures within a German city and finds substantial deviation from equal probability. These results apply even when interviewers perfectly implement the routing instructions, which is unlikely given the limited ability to conduct in‐field supervision of random walk selection and strong (though understandable) incentives for interviewers to select respondents who are willing to participate (Alt et al 1991). Several other studies have also shown that data collected via random walk do not match the population on basic demographics such as age, sex, education, household size, and marital status (Bien 1997, Hoffmeyer‐Zlotnick 2003, Blohm 2006, Eckman & Koch 2016).

In the context of Mogadishu, a household listing was too dangerous and costly, a random walk too biased, and no household or person register existed. Therefore the researchers explored several alternative methods using a combination of satellite maps and area‐based sampling. As satellite technology has improved in quality and become more readily available, it has been increasingly used for research in the developing world. Barry and Rüther (2001) and Turkstra and Raithelhuber (2004) use satellite imagery to study informal urban settlements in South Africa and Kenya, respectively. Aminipouri et al (2009) use 3
coefficients of the successive size-groups give the relative increase as one moves to a higher size category.

It is seen that the structure of earnings related to size groups in Ghana is very similar to that in the Cameroon. In the Eastern African countries of Kenya and Zambia, the jump in earnings as one moves from micro to small firms are smaller. But then the latter two have a sharper increase in earnings than Ghana between the medium and large categories, leaving the overall differential between large and micro enterprises almost the same in all four countries. Zimbabwe stands alone in having a sector with very large firms, and the earnings differentials increase much more for the larger enterprises.

The era of decline of wages could have been expected to narrow the differential in wage levels between the large and small firms since the wage structure is compressed against a threshold defining either the subsistence or the efficiency level of wages. The finding that average earnings, after controlling for the measurable human capital factors, increased continuously with enterprise size in Ghana, as in other African countries, is an important one. Although the size-related wage differential is much more for Zimbabwe, the extent of the premium enjoyed by workers in the large firms compared to the microenterprises is quite large in the other countries, including Ghana-of the order of nearly 90 per cent.

# **4.3 Causes of the Increase in Earnings with Firm Size**

The first hypothesis which comes to mind is that the increase in earnings with firm size is due to institutional factors affecting the wage level . Ghana prescribes minimum wage levels, and it might be argued that these levels are enforced in larger firms and not in micro or small firms. The average monthly earnings of workers in the manufacturing sector at the date of the 1992 RPED survey have been given in Table 3.4 above together with, the minimum wage level prevailing at this date.

It is seen that the minimum wage was more or less at par with the average earnings in micro enterprises, and as such could be
.<sup>5</sup> There are several possible explanations for why people save in old age. First, elderly may desire to leave bequests for their working aged children or their grandchildren. The literature on this subject is well explored theoretically by, for example, Barro (1971) and Stokey (1979, 1980). In the case of Brazil, however, there is lacking analytical work on this issue, but there is evidence suggesting that intra-household transfers are from retired elderly to their adult children with whom they cohabitate. Therefore, public pension benefits indirectly end up being a transfer that covers the entire household. The economic implications of such two-sided intra-household transfers are also explored theoretically by, for example, Nerlove and Raut (1997) but, again, not in the case of Brazil. This remains as an issue for future research.

# **_Implications of Income Inequality_**

Is there more to the story about age-specific saving rates? On average, they remain unchanged after the age of 40, but this aggregate view on age-specific saving rates may hide some important heterogeneity among households in Brazil. In particular, the high income inequality in Brazil may lead to differential saving behavior by income group.

The difference between age-specific saving rates among the non-poor above the poverty line and the poor below the poverty line are illustrated by Figures 2a and 2b. Clearly, there is a crucial difference between the saving behavior of the poor and the non-poor: Despite the fact that the overall pattern is

> 5 After age 63 the saving rates for both the rich and the poor become extremely low, varying between -500 to - 23,000 percent. These saving are represented by very few households and are therefore not shown here, but are included in subsequent simulations with the partial equilibrium model.

9
financial openness and the reserves-to-GDP ratio have different interpretations because of the way the data have been normalized: reserves-to-GDP ratio is in integer percentage points (1 unit increase in the variable represents a 1 percentage point rise) as in the WDI database, whereas other variables are in decimal percentage points (1 unit increase in the variable represents a 100 percentage point rise). The coefficient of 0.028 on interaction with the reserves-to-GDP ratio indicates that a 1 percentage point higher reserves relative to GDP during contractions is associated with 2.8 percent higher growth rate of construction sector real value added. In contrast, the coefficient of -0.233 on the interaction term with financial openness indicates a 1 percentage point greater de-facto openness during contractions is associated with  0.2 percent lower growth rate of construction sector real value added. The economic significance of these offsetting effects depends not only on the elasticities, but on the average values of each variable as discussed later in the text.

13
reserve amount aggregated into this assemblage and divided by the number of shares each landowner holds. Shapiro and Pincus’ model offers a view of the interaction between reserve setting and bidding on assembled plots. The resulting efficiency and fairness benefits demonstrate welfare maximization and provide a case for public assemblage of land as a form of public goods creation.

Actual land auction data is not widely available, but a number of countries use land parcels sales through auction as a standard component of their land management practice. However, recent large transactions provide examples of the revenue potential of land auctions. However, it is important to keep in mind as one looks at these numbers, that the land sales revenue is a onetime collection. Even if the numbers at first seem high, they do not provide a continue revenue stream as property tax receipts would. Three notable land auctions are documented by Peterson (2009) and Peterson and Kaganova (2010).

- 1) Cairo, Egypt (May 2007): auction of 3,100 hectares of desert land for a new town, generating US$3.12 billion.

- Land auctions are a key tool of Egypt’s Public Asset Management Initiative and New Town development; they reflect part of a long-term policy agenda

- Land auctions seen as a way to enlarge private sector involvement in financing infrastructure and new development

- Private buyers of land are also responsible for installing necessary infrastructure on those sites (Peterson 2009)

- 2) Mumbai, India (January 2006 and November 2007): auction of 13 hectares of land in the centrally located, new financial center—Bandra-Kurla Complex, generating US$1.2

23