rafmacalaba/data-use-mentions-extended huggingface dataset:
biases the results. Appendix Table A2 shows that time-invariant physical features of countries explain 73 percent of the variation in the 2008 precipitation data, which implies that country effects should do a good job of controlling for differences in mean precipitation between countries.

# **4. Econometric methods**

The number of floods in a country-year in equation (1) and the number of deaths during a flood event in equations (2)-(3) are non-negative count variables. Our preferred estimator is a quasi-maximum likelihood fixed effects Poisson model with robust standard errors (Wooldridge 2002, pp. 674-6), with the year effects included as dummy variables. Aside from requiring the fixed effects to have a multiplicative effect on the conditional mean, this model places no restrictions on the conditional distribution of the dependent variable. It provides consistent estimates of model parameters and their standard errors even if the distribution is overdispersed (the variance exceeds the mean; in a Poisson distribution, they are equal),<sup>6</sup> includes a large number of zeros, or exhibits serial correlation. These are substantial advantages compared to the negative binomial regression model used in previous disaster studies (Anbarci et al. 2005, Kahn 2005, Kellenberg and Mobarak 2008, Keefer et al. 2010), which generalizes the Poisson by assuming that overdispersion is caused by an unobserved effect that follows a Gamma distribution. Moreover, the unconditional version of the fixed effects negative binomial model does not provide consistent estimates when the number of panels is much above 20 (we have 92-108 countries, depending on the specification), while the conditional version does not control

> 6 We formally tested for overdispersion using the tests in Hilbe (2007, p.47) and Cameron and Trivedi (2009, p.575). Neither rejected the null that there is no significant overdispersion ( _p_ = 0.29 and 0.71, respectively).

14
4 on estimators that attempt to control for heterogeneity and simultaneity, two problems that likely plague most previous empirical studies.

# 3.1 The Data

.

Our basic data set draws from the saving database recently constructed at the World Bank, and described in detail in Loayza, Lopez, Schmidt-Hebbel and Servén (1998a). To our knowledge, such database represents the largest macroeconomic data set on saving and related variables presently available. It comprises a maximum of 150 countries and spans the years 1965-1994. The data have been subject to extensive consistency checks, and hence they also represent an important improvement in terms of quality relative to other existing data sets.° The data set excludes the countries for which we found inconsistencies in basic National Account, fiscal and financial data. These data limitations prevented the construction of reliable saving measures, their disaggregation into public and private saving, and/or the calculation of the inflation adjustments for the latter. For some of the key . variables in this paper, the effective data coverage in countries and years is therefore limited. Nevertheless, for the "core" private saving regression, presented below, we initially count with 1,254 complete observations spanning the years 1966-95.

From this initial sample, we decided to exclude the observations corresponding to high inflation episodes. We base this decision on the fact that high inflation distorts severely measured public and private saving (particularly the inflation-adjusted saving measures).° Moreover, in general high inflation renders National Account statistics largely unreliable. For practical purposes, we set a threshold of +/- 50 percent annual inflation. We apply the same threshold to the real interest rate, which in cases of high inflation is mostly driven by inflation. For the "core" specification, these data adjustments lead to the direct loss of 49 observations.’ In order to achieve a minimum time-series dimension, as well as to reserve sufficient observations to implement our instrumental-variable estimators described below, we limit our sample coverage to those countries with at least 5 consecutive annual observations.
human capital are represented in the equation, so that the TFP factor _A_ picks up the contribution to income of all ‘missing’ assets.

> For equation (7) we have included intangible wealth as the third production factor. If _ic_ actually

> is measuring a wide range of assets (human, social, institutional, etc.), then the contribution of _A_ to explaining the variation of output per worker should be small – zero in the limit.

So one way to bring together the wealth accounting and growth accounting literatures would be to estimate the two success indicators and examine the estimated contributions of _yfactors_ , (with _ic_ instead of _h_ ), and _A_ to see if the contribution of _A_ is indeed small. The second column of Table 3 shows that to be case. When _ic_ is the third factor of production instead of _h_ , both measures of success of the factors-only model are 97 percent.<sup>14</sup>

> 13 We take the estimates of total physical capital and natural capital wealth and divide them by the labor force series, (SL.TLF.TOTL.IN), to obtain estimates of _k_ and _s_ .

> 14 We repeated the calculations in Table 3 for each of the years, 1995, 2000, and 2005, independently, and for the subsamples of OECD and non-OECD countries. The results, not reported, are available from the authors. The relative contributions of _A_ and _yfactors_ are constant over time and similar to those in Table 3. The differences between OECD and non-OECD countries are minimal when factors of production are conventionally defined, but if _ic_ is used

10
- Amuedo-Dorantes, Catalina and Cynthia Bansak (2011) “The Impact of Amnesty on Labor Market Outcomes: A Panel Study Using the Legalized Population Survey,” _Industrial Relations: A Journal of Economy and Society_ , 50 (3), 443–471.

- Amuedo-Dorantes, Catalina, Cristina Borra, and Noelia Rivera-Garrido (2022) “Fertility .

- implications of family-based regularizations,” _Journal of Economic Geography_

- Amuedo-Dorantes, Catalina and Sara De La Rica (2007) “Labour Market Assimilation of Recent Immigrants in Spain,” _British Journal of Industrial Relations_ , 45 (2), 257–284.

- Avitabile, Ciro, Irma Clots-Figueras, and Paolo Masella (2014) “Citizenship, Fertility, and Parental Investments,” _American Economic Journal: Applied Economics_ , 6 (4), 35–65.

- Bahar, Dany, Bo Cowgill, and Jorge Guzman (2022) “The Economic Effects of Immigration Pardons: Evidence from Venezuelan Entrepreneurs,”Technical report, National Bureau of Economic Research.

- Bahar, Dany, Ana Mar ́ıa Ib ́a ̃nez, and Sandra V Rozo (2021) “Give me your tired and your poor: Impact of a large-scale amnesty program for undocumented refugees,” _Journal of Development Economics_ , 151, 102652.

- Bailey, Martha J. (2012) “Reexamining the Impact of Family Planning Programs on US Fertility: Evidence from the War on Poverty and the Early Years of Title X,” _American Economic Journal: Applied Economics_ , 4 (2), 62–97.

- Baker, Scott R. (2015) “Effects of Immigrant Legalization on Crime,” _American Economic Review_ , 105 (5), 210–13.

- Becker, Gary (1960) “An Economic Analysis of Fertility,” in _Demographic and Economic Change in Developed Countries_ , 209–240: National Bureau of Economic Research, Inc.

- Becker, Gary and H Gregg Lewis (1973) “On the Interaction between the Quantity and Quality of Children
more transient nature of the HGF phase relative to FDI. This contributes to the generalizability of the FDI spillover methodology to other problems.

The paper is organized as follows. Section 2 describes the data used, the construction of the main variables of interest and the empirical strategy. Section 3 presents our results. Finally, Section 4 summarizes the evidence presented.

# 2 Data and Empirical Strategy

## 2.1 Data

The primary data set is firm level corporate income tax statements during the period 1998‐2014 from the Hungarian National Tax Authority (NAV). The data set has almost universal coverage as it covers all firms that require double‐entry bookkeeping.<sup>2</sup> The sample includes more than 95 percent of employment and value added of the business sector and about 55 percent of the full economy in terms of GDP. The data set includes information on a wide range of matters such as ownership, financial information, employment, and industry at the NACE 4‐digit code level and the location of their headquarters. The Centre for Economic and Regional Studies, Hungarian Academy of Science (CERS‐HAS) extensively cleaned and harmonized the data.

Given the scope of our analysis, we restrict the data in several ways. First, we exclude non‐profit organizations. Second, we drop firms that operate either in agriculture or in the non‐market service sectors of the economy.<sup>3</sup> Third, we drop all firm‐year observations with fewer than 5 employees.<sup>4</sup> Finally, we drop all firm‐year observations with missing, negative or zero employment, turnover, material cost, capital or value added. As we will discuss in detail in section 2.3, our regression will be run for 3‐year intervals, which further reduces the number of observations. We also restrict the sample to surviving firms, given our primary interest in TFP change. Finally, we exclude firms which operate in the
its level in 2001 (figure 9). Wholesale, retail trade and hospitality sectors also pose a relatively larger share of informal output compared to other sectors though the share declined significantly compared to 2001. This is similar to a broad informality trend in other countries where informality tends to be higher in sectors that are labor-intensive, low-skilled, less productive, and with relatively low wages.

<!-- Start of picture text --> Figure 9. Output informality in Indonesia, simple definition (share, weighted)<br>Percent of total output in the corresponding sector<br>80 2019 2020 2021 2001<br>60<br>40<br>20<br>0<br>Agriculture, Wholesale Transportation, Constr- Mining Community, Manuf- Electricity, Finance,<br>forestry, and retail trade, storage and uction and social and acturing gas, and insurance,<br>livestock, restaurants communications quarrying personal srv water supply real estate<br> and fishing and hotels and bus.srv<br>Source: BPS, WB staff estimation.<br>Note: weighted estimates (using the sample weights from the SAKERNAS) are used.<br><!-- End of picture text --> 34. **The regions with the largest output informality are also regions with the largest rural population, weaker urbanization, prevalence of agriculture, and lower income.** For example, West Sulawesi and West Nusa Tenggara are two provinces in the top five largest output and employment informality. The share of the agriculture sector in those two provinces is the largest 14
in magnitude than estimates from international shipping data by Brancaccio et al (elasticity=1.03) and but similar to Wong (2018) (elasticity=2.8-3.7). The implied elasticity of substitution derived from the above estimate of trade elasticity is about 21-29, confirming the a priori expectation that this elasticity should be higher in poorer countries where consumers tend to be more sensitive to prices (Duranton, 2014).

In the absence of data on the universe of trucking transactions, we developed an empirical methodology to define the extensive/spatial margin of trade by using observable route characteristics to predict the 23
0.24||
|Venezuela|||||||||
|Rep. Bol.|1994|2|-0.25|0|6|C|0.28|0.62|

Source: Author’s calculations based on data from the IMF. See text for details.

*Notes: A = The increase in FDI occurred first or there was an increase in FDI but not in Exports: 43.33% B = The increase in FDI and the increase in Exports occurred in the same quarter: 6.67% C = The increase in Exports occurred first or there was an increase in Exports but not in FDI: 43.33% D = There was not an increase in FDI or in Exports: 6.67%

13
the liberalization of the land-lease market remains an obstacle to the rapid enactment of this law. The initiative behind this unified land administration law is most welcome, since the complexity of the existing legislative framework, inherited as it is from diverse pieces of legislation designed to bring together quite different revenue systems, accounts for much of the wide scope for evasion of the law in practice.

# 2.4 Women's **access to land**

The survey of the legislative framework in Orissa confirms that 'land reform policies have been based on the principle of redistributive justice and on arguments regarding efficiency (land to the tiller, fixation of ceilings, prevention of fragmentation, etc.); but on neither count are gender inequalities taken into account' (Agarwal, 1994: 216). No law has dealt specifically with increasing women's access to land. Rights to land for Hindu women are according to the Hindu Succession Act 1956 which provides for daughters, widow and mother of a Hindu man dying intestate to inherit property equally with his sons. In practice, however, significant and persistent gaps exist between women's legal rights and their actual ownership of land, and between the limited ownership rights women do enjoy and their effective control over land (Agarwal 1994).

For example, the Orissa Land Reforms Act 1960 does not mention the order of devolution at all. So whether the devolution of tenancy land will be according to personal law, or would follow a different order of devolution, is open to interpretation. Gender inequalities in OLRA have also arisen from enactments relating to the fixation of ceilings<sup>6</sup> on two counts, namely:

- **_the_** _definition of 'family':_ Article 37 of the Act defines a family as the individual and his/her spouse and their children, whether major or minor. Later the law was amended to include married daughters whereas a childless widow is not considered to be a member of her deceased husband's family
connections are neither significantly less prevalent nor significantly less valuable in the manufacturing sector than in other sectors.

# **6 Conclusion**

Using Indonesian manufacturing plant-level survey data spanning Indonesia’s democratic transition, we have shown that Suharto’s resignation substantially eroded the premium on being connected to him. His fall also had a modest but significant pro-competitive impact on industries in which his family had extensive business interests relative to industries that did not. Democratization thus improved competition.

The contrast between the erosion of the firm-level premium on being connected and weak procompetitive impacts at the industry level is plausibly in part due to the nature of privileges having been targeted to specific firms rather than entire industries. Second, connected firms remained large; the capacity, capital, know-how, and relationships they had accumulated during the Suharto era enabled many previously connected firms to remain competitive even after the privileges that propelled their initial growth had been removed. More fundamentally, the nature of state–business relationships did not alter dramatically.

Notwithstanding the persistence of cronyism, our results point toward the potential of democratic institutions to promote competition and curb state capture. Yet Indonesia’s experience simultaneously serves as a reminder that democratization alone does not suffice to sustain competitive markets.

24
22

**_Question 7.02_** _: In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with getting connected to public utilities? 1= common, 7 = it never occurs._

The average rating by income groups is:

- High-income countries: 6.30

- Upper-middle-income countries: 5.33

- Lower-middle-income countries: 4.59

- Low-income countries: 3.33

**_Question 7.04_** _: In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with public contracts? 1= common, 7 = it never occurs._

The average rating by income groups is:

- High-income countries: 5.42

- Upper-middle-income countries: 3.93

- Lower-middle-income countries: 3.58

- Low-income countries: 2.83

These responses suggest that the poorer a country is, the bigger is the perceived corruption problem in infrastructure. The presentation of the data, however, poses a few problems. One is that for confidentiality reasons the data cannot be presented at the sector level—because in many countries infrastructure service providers are monopolies, and reporting what the sector thinks about corruption would boil down to reporting what the firm thinks. But the main problem with this data is that it is based (as most international databases are) on executive surveys. Though useful, this approach tells us little about what the government thinks about corruption. And, more important in this context, it tells us nothing about how poor people see the problem. Unfortunately, the few surveys polling large numbers of people around the world (such as Gallup International) do not cover infrastructure services.<sup>29</sup>

# **The effects of corruption in infrastructure**

While there are many publications on how corruption affects growth, foreign direct investment, trade, and public sector finances, there is little evidence on how it affects infrastructure performance. Most of the existing evidence is for utilities.
population density, the government encouraged rural to rural and urban to rural migration, and migration flows to urban areas were strictly controlled through a variety of policies. The most important of such policies was the system of household residence certificates ( _ho khau_ ), which were required for access to subsidized food, housing, education and social services (Dang _et al._ , 2003).

The _doi moi_ policy adopted since 1986 brought about a number of changes that facilitated the development of a variety of migration flows (Dang _et al.,_ 1997; Dang _et al._ , 2003, Phan and Coxhead, 2010). First, the household contract system released farmers from collective employment and allocated land-use rights to individual households. This in turn raised agricultural productivity and exacerbated labor surpluses in rural areas. Second, although the _ho khau_ system continued, the subsidy system was abolished, making the _ho khau_ system less effective as a tool to control labor movement. Finally, the development of transport systems and telecommunications across regions has facilitated spatial mobility.

According to the 1999 _Population and Housing Census_ , about 4.5 million individuals or 6.5 percent of the population aged five and older in Vietnam changed their places of residence during the period 1994-1999 (GSO/UNDP, 2001). The 2009 census indicates that internal migration accelerated in the past decade as 6.7 million individuals at or over five years of age (8.6 percent of the population) moved during the period 2004-2009 (GSO, p.21, 2011). In particular, inter-provincial migration rose faster than intra-provincial migration: the number of individuals moving across provinces increased from 2.0 million in the 1999 census period to 3.4 million in the 2009 census period whereas those who moved within provinces rose from 2.5 million to 3.3 million during the same period.
generation activities should be promoted in both farm and nonfarm businesses. In order to do so, it is critical to improve the rural investment climate (RIC) for rural businesses.

This study assesses the RIC and provides directions for key improvement in the RIC to promote the rural private sector in both farm and nonfarm sectors. The study provides unique analyses. First, unlike urban enterprise surveys, which only include enterprise data, it uses community and household survey data provide rural micro-level environment, such as business services, financial services, market, infrastructure, education, and health. Second, the RIC related constraints to farm and nonfarm enterprises, which were not studied in previous RIC literature, can be analyzed. Third, the new approach of the RIC over labor migration at the community level could be studied to see which community RIC components influence the migration.

Using this rich survey data set, the following questions can be addressed. Which RIC components are critical to rural business entry and performance? How do business services and management innovation influence business entry and performance in rural settings? What are the differences in major constraints between farm and nonfarm enterprises? What are women specific business constraints? What are the major different constraints in rural areas from those in urban areas? How do we need to improve those identified RIC constraints for businesses?

# **2. Literature Review**

The RIC is a largely uncharted area though there is rich research related to urban investment climate survey and assessment (Klapper and Inessa 2002; Dollar et al. 2005; Himmelberg et al., 1999; Dinh et al. 2010; Jensen and Meckling 1976; La Porta et al. 1998; and Shleifer and Vishny 1997). Urban investment climate assessment (ICA) has been more successful in improving investment climate compared to the RIC. The first pilot RIC studies of Sri Lanka, Nicaragua, and Tanzania revealed the RIC to be much different and more complicated than the urban investment climate (World Bank 2006a). Informality, small scale in
Table 8 Percentage of Providers Observed during Consultation from Voucher and Nonvoucher Facilities That Performed Various Clinical Examinations in 2010

|Clinical examination|A|NC|P|NC|
|---|---|---|---|---|
||Voucher<br>facilities<br>(_N_= 95)|Comparison<br>facilities<br>(_N_= 51)|Voucher<br>facilities<br>(_N_= 59)|Comparison<br>facilities<br>(_N_= 24)|
|Weight|70|55|n.a.|n.a.|
|Height|21|4**|n.a.|n.a.|
|Temperature|19|6*|10|4|
|Blood pressure|80|51**|34|4**|
|Anemia check|66|59|39|4**|
|Breasts/nipples exam|n.a.|n.a.|20|0*|

_Source:_ Population Council, Health Facility Survey 2010–11.

_Note:_ Differences between voucher and nonvoucher facilities are significant at * _P_ <0.05, ** _P_ <0.01. Abbreviation n.a. = not applicable.

Among women who participated in the 2010–11 household survey, nearly all (98 percent) of those who had ever used the HealthyBaby voucher indicated they would recommend its use to a friend. Their most commonly cited reasons included the fact that (1) it offers free, cheap, or affordable services, (2) it is good for or helps poor pregnant women access relevant services, and (3) it enables women to receive good quality or prompt service. Among the 2 percent who indicated they would not recommend the use of a voucher to a friend, the major reasons were (1) poor quality services, including rude, untrained,