rafmacalaba/datause-encoder-data huggingface dataset:
communities if they only affect other household level outcomes through their effect on migrant labor supply decisions. While the prospective migrant observes expected wages at a set of potential destinations, and then uses that information to choose where to migrate, the full set of wage offers and potential destinations faced by any individual migrant are unobserved by the analyst. Therefore, implementing this approach involves assumptions regarding potential destinations and use of a proxy for labor demand.

First, both the RCRE surveys and the 1995 and 2005 agricultural censuses suggest that nearly 70 percent of rural migrants find work within their home provinces, we matched villages to the nearest city outside their home counties. In using the nearest city, we pick up the nearest urban labor market signal for potential migrants, and implicitly assume that it would be the best potential proxy for news about labor demand for young, first-time migrants. We use measures of city level GDP from the National Bureau of Statistics (CNBS), and construct year-toyear city-level growth rates.<sup>6</sup> We test several potential specifications of instruments based on city GDP growth rates. Signals of labor market conditions may be picked up over the year and could show up in either lagged or current growth in city GDP. Thus, it is unclear whether year on year growth measured in year t , or t−1 , is exerts stronger influence on the migration decision, and so we tried both. The strength of the growth signal might also depend upon the distance from the village to the nearest city, but as it is not clear that the relationship between distance and signal strength would be linear, we tested four different relationships between GDP growth and distance. First, we used the GDP growth rate in the nearest city itself, without controlling for the distance to the nearest city. Second, we divide the GDP growth rate by the distance, and in the third specification, we divide by the distance squared.

None of the above distance weightings above account for the fact that the effects of signal may vary non-linearly with distance. Individuals who live in relatively close proximity to a growing urban area may be able to commute for work, as opposed to migrating, even if the city is located in another county. Alternatively, after some distance X from the village, the effect of signals related to economic growth may decline more rapidly with distance. We thus posit that city GDP growth might have its strongest pull on village residents at that distance X , and that

> 6 While we would prefer to use information on employment by industry sector, these data series were incomplete for many cities and in many years, and further, only include formal sector employment, and migrants are frequently employed in the small scale informal sector. For these reasons, we make use of the more complete citylevel GDP from the series, from which we construct year to year growth rates.

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# **3.2 Data**

For our empirical analysis we use a data set with balance sheet and profit and loss accounts of 3000 Bulgarian industrial firms for the period 1993 - 1996. We have included only those firms that reported for the entire period 1993 - 1996, which gives us a balanced panel of 1003 fims. The data set is taken from the AMADEUS data base and the data are presented in millions of US $.7 Because of the short time period, we used cross-section data for our analysis by averaging the variables in our data set. We did compute the average value of each variable for the full sample of 1003 companies over the period 1993 -1995. Due to the limited time period for which data are available we apply a cross section analysis on the averages of each variable. Estimating a dynamic investment model (hence taking the time series information into account) by using only three observations for each variable is misleading, since the adjustment process cannot be identified. Consequently, the coefficients we present refer to long-run average effects (Pesaran and Smith, 1994). This procedure of averaging along the time dimension is also used in similar studies in which the dataset covers only a few years (Lensink and Sterken, 1998 and Chirinko and Elston, 1997). We have excluded 1996 because of the severe financial crisis in this year, which distorted the data considerably.

All variables are thus expressed in US dollars. During the period 1993-1995, the dollar/leva rate fluctuated considerably (the leva depreciated by about 70%). Such changes in the nominal exchange rate tend to dominate the results. We, therefore, corrected the nominal variables for changes in the real US dollar/leva rate (see Annex 2). Annex I provides a list of all the variables. Net investment, I, is the dependent variable in

> 7 To be included in AMADEUS, a company must comply with one of the following size criteria: i., a turnover greater than EurolO million; ii., a number of employees greater than 150; iii., total assets greater than Euro 10 million. Note that the AMADEUS data set presents the data in USD million. For more information on the data and the adjustments of data for fluctuations in USD/Leva exchange rate, see Annex 1 and Annex 2, respectively.

11
weaker competition in the grocery sector, often known as “food deserts,” may improve the targeting properties of SNAP by shifting more surplus to SNAP consumers. Third, interpreting our findings using a partial-equilibrium model of incidence, we find that while the intended beneficiaries, SNAP consumers, obtain most of the consumer surplus, the increased SNAP benefits also benefit producers at the expense of non-SNAP consumers. Fourth, increased SNAP benefits could raise total welfare and reduce deadweight loss due to the expansion of output by producers with market power. Fifth, given that SNAP benefits per population rose by about 30% due to the Farm Bill and the ARRA, our findings suggest expansions in SNAP countered deflationary pressures by contributing to price hikes of 2.4% in grocery stores. Sixth, the fungibility of SNAP benefits would lower the magnitude of the effects on the market for food, and fully capturing incidence would require a study of the effect on other product markets. A recent experimental study by Egger et al. (2022) examining price effects of a cash-transfer program in rural Kenya, as referenced earlier, shows that indeed marginal consumption propensities in response to undesignated cash transfers are far more dispersed across goods and the aggregate price response far lower. Policy changes in non-recessionary periods and transfer-payment changes in sectors beyond groceries could be useful for further elucidating the significant roles that in-kind designation of electronic benefits, product-specific MPCs, and product-specific market power can play in influencing consumer demand, welfare, and market outcomes in future research.

# **References**

- Aaronson, D., E. French, and J. MacDonald (2008, July). The Minimum Wage, Restaurant Prices, and Labor Market Structure. _The Journal of Human Resources 43_ (3), 688–720.

- Agarwal, S. and W. Qian (2014, December). Consumption and Debt Response to Unanticipated Income Shocks: Evidence from a Natural Experiment in Singapore. _American Economic Review 104_ (12), 4205–4230.

- Almond, D., H. W. Hoynes, and D. W. Schanzenbach (2010, August). Inside the War on Poverty: The Impact of Food Stamps on Birth Outcomes. _Review of Economics and Statistics 93_ (2), 387–403.

- Andreyeva, T., M. W. Long, and K. D. Brownell (2010, February). The Impact of Food Prices on Consumption: A Systematic Review of Research on the Price Elasticity of Demand for Food. _American Journal of Public Health 100_ (2), 216–222.

- Autor, D. H. and D. Dorn (2013, August). The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market. _American Economic Review 103_ (5), 1553–1597.

- Baker, S. R., S. Johnson, and L. Kueng (2017, August). Shopping for Lower Sales Tax Rates. Working Paper 23665, National Bureau of Economic Research.

- Banerjee, A., R. Hanna, B. A. Olken, E. Satriawan, and S. Sumarto (2021, April). Food vs. Food Stamps: Evidence from an At-Scale Experiment in Indonesia. Technical Report w28641, National Bureau of Economic Research.

- Beatty, T. K. M. and C. J. Tuttle (2015, March). Expenditure Response to Increases in In-Kind

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Gollin, D. (2002) “Getting Income Shares Right”, _Journal of Political Economy_ , vol. 110, pp. 458-474.

Gómez-Plana, A. G. and Latorre, M. C. (2014) “When multinationals leave: A CGE analysis of divestments”, _Economics-The Open Access Open-Assessment E-Journal_ , vol. 8, pp. 1-41, available at: http://www.economics-ejournal.org/economics/journalarticles/2014-6 Guerriero, M. (2012) “The Labour Share of Income around the World. Evidence from a Panel Dataset”, University of Manchester, Institute for Development Policy and Management (IDPM). Development Economics and Public Policy. Working Paper Series. WP No.

Hosoe, N. (2014) “Japanese Manufacturing Facing Post-Fukushima Power Crisis: a Dynamic Computable General Equilibrium Analysis with Foreign Direct Investment”, _Applied Economics_ , vol. 46, pp. 2010–2020.

ILO (2009) “Global employment trends for women”, Geneva.

ILO (2010) “Global Wage Report 2010/11: Wage policies in times of crisis”, International Labour Office, Geneva.

Jafari, Y. (2013) “Ad Valorem Equivalents of Barriers to Services Providers in Tanzania”, mimeo.

Jensen, J. and Tarr, D.G. (2010) “Regional trade policy options for Tanzania: the importance of services commitments”, World Bank Policy Research Working Paper No. 5481.

Jensen, J. and Tarr, D. G. (2012) “Deep Trade Policy Options for Armenia: The Importance of Trade Facilitation, Services and Standards Liberalization”, _Economics: The Open-Access, Open-Assessment E-Journal_ , vol. 6, p. 1-55, available at: http://dx.doi.org/10.5018/economicsejournal.ja.2012-1 Jensen, J., Rutherford, T. and Tarr, D. G. (2007) “The impact of liberalizing barriers to foreign direct investment in services: The case of Russian accession to the World Trade Organization", _Review of Development Economics_ , vol. 11, pp. 482-506.

Jensen, J., Rutherford, T. and Tarr D.G. (2010) “Modeling Services Liberalization: the Case of Tanzania”, _Journal of Economic Integration_ , vol. 25, pp. 644-675. Previous version available as World Bank Policy Research Working Paper No. 4801.

Kingdon, G., Sandefur, J. and Teal, F. (2004) “Africa Region Employment Issues: Regional Stocktaking Review”, Centre for the Study of African Economies, Department of Economics, Oxford University, UK.

Lakatos, C. and Fukui, T. (2014) “The Liberalization of Retail Services in India”, _World Development,_ vol. 59, pp. 327–340.

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We establish the extent of industrial and occupational segregation by using the Duncan index (Duncan and Duncan, 1955), defined as: where m� is the percentage of males in total males in category _i_ and w� is the percentage of females in total females in category _i_ . The value of the index varies from 0 to 1. The value of 0 implies that there is no segregation by category _i_ , and the value of 1 indicates complete segregation. The value of the index increases with the level of disaggregation. For example, Anker (1998) reports that the value of the Duncan index in France in 1990 was 0.393 when using single-digit standard industrial classification (SIC) categories for occupation and 0.607 when using three-digit SIC categories. In this analysis, we present the values for industries and occupations based on five sets of categories: public and private sector; 13 industry categories; 4 skill categories, 9 single-digit ISCO-88 categories; and 299 ISCO-88 four-digit occupation categories.

Table 2. Duncan index in Georgia.

||||Skill<br>(4|Occupation<br>(9<br>single-<br>digit ISCO-<br>88<br>categories)|Occupation<br>(299<br>categories,<br>four-digit<br>ISCO-88|
|---|---|---|---|---|---|
|year|public|industry|categories)||categories)|
|2004|0.2137|0.4489|0.3229|0.3763|0.7007|
|2005|0.1859|0.4458|0.3329|0.3737|0.6697|
|2006|0.1964|0.4987|0.3581|0.3897|0.7044|
|2007|0.2162|0.5176|0.3637|0.3903|0.6996|
|2008|0.1807|0.5068|0.3784|0.3995|0.7012|
|2009|0.1536|0.5159|0.3608|0.3873|0.6859|
|2010|0.1561|0.4735|0.3508|0.3784|0.7048|
|2011|0.1796|0.4666|0.3493|0.3579|0.7180|
|2012|0.1748|0.4671|0.3514|0.3716|0.7316|
|2013|0.1695|0.4613|0.3298|0.3570|0.7058|
|2014|0.1575|0.4479|0.3196|0.3388|0.6884|
|2015|0.1444|0.4588|0.3378|0.3546|0.7290|
|average|0.1774|0.4757|0.3463|0.3729|0.7033|

# Source: GHBS data

The value of the index is 0.1774 when using the public and private sector categories, indicating that 17.74 percent of workers have to be moved in order to obtain an equal gender distribution of employment (Table 2). There is a strong correlation between public ownership and 10
# Table 4a. Multinomial logistic regression of ECED sequence

**Sequence** (base: Playgroup then kindergarten then primary)

||No ECED|Playgroup<br>then primary|Kindergarten<br>then primary|Other<br>combination|
|---|---|---|---|---|
|_Child characteristics_|||||
|Age|1.644***|1.254***|1.566***|1.293***|
||(0.0711)|(0.0506)|(0.0583)|(0.0582)|
|Girl|0.749***|1.007|0.965|0.815***|
||(0.0588)|(0.0690)|(0.0581)|(0.0614)|
|Stunted|1.237*|1.303**|1.087|0.00229***|
||(0.136)|(0.139)|(0.0997)|(0.00230)|
|_Family characteristics_|||||
|Wealth Z-score|0.405***|0.588***|0.863**|0.617***|
||(0.0368)|(0.0511)|(0.0578)|(0.0496)|
|Mother's education (years)|0.786***|0.871***|0.948***|0.796***|
||(0.0177)|(0.0182)|(0.0148)|(0.0133)|
|Parenting practices|0.903**|0.996|1.016|0.935*|
||(0.0424)|(0.0460)|(0.0358)|(0.0350)|
|_ECED quality_(base: Low quality)|||||
|Medium quality|0.973|0.959|0.860|1.006|
||(0.287)|(0.269)|(0.177)|(0.257)|
|High quality|0.358***|0.432***|0.464***|0.441***|
||(0.101)|(0.117)|(0.0871)|(0.101)|
|_ECED fees (base: No fees)_|||||
|≤ 10,000 IDR|0.232***|0.277***|0.252***|0.255***|
||(0.0830)|(0.104)|(0.0647)|(0.0968)|
|> 10,000 IDR|0.193***|0.188***|0.270***|0.278***|
||(0.0716)|(0.0727)|(0.0679)|(0.106)|
|Constant|1.300|3.857***|0.709|6.264***|
||(0.680)|(1.974)|(0.294)|(3.535)|
|Observations||12,|710||

Note: Exponentiated coefficients. Standard errors clustered at the village level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 27
The studies in our sample exhibit considerable variations in the outcomes examined as well as the definitions of outcomes, shocks, and the level of analysis. To compare studies that examine different outcomes, we constructed two binary indicators from each estimate: first, “are the poor more exposed to climate shocks,” and second, “are the poor more adversely affected by climate shocks.” The first takes the value 1 when the poor are found to be more exposed to climate shocks and 0 otherwise. Similarly, the second indicator takes the value 1 when the poor are found to be more adversely affected by climate shocks and 0 otherwise. We included estimates with dependent or independent variables or sample restrictions related to poverty, poor and the level of wealth. Studies that document that the poor are adversely affected by climate shocks but do not compare the effect of such shocks on the poor and non-poor were excluded from the analyzed sample. This approach allowed us to identify studies that separate the effect of climate shocks on the poor from the effect on the rest of the population. Our sample included observational studies, simulations, and studies using quasi-experimental methods—experimental evidence on this topic is not available. By including a diverse range of methodological approaches, we aim to provide a comprehensive synthesis of the existing literature on the distributional impacts of climate shocks.

The events include climate change, natural disasters, temperature changes, heatwaves, typhoons, rainfall, floods, droughts, earthquakes, and landslides. We then created indicator variables for each of these climate events. In the analysis, we focused on flood, drought, extreme heat, cyclones, or typhoons—because these events have been widely studied in the literature. We also included earthquakes because their occurrence is uncorrelated to climate change and global warming, serving as an exogenous variable that could be used to compare results, a practice widely adopted in the literature (Kahn 2005). We also included climate change as an all-encompassing event for all climate shocks because many studies only analyze the broad impact of climate change without references to specific climate shocks.

Our sample has wide geographic coverage. It includes 116 countries and regions out of the 195 countries and regions in the world. This wide coverage ensures that our analysis captures the diversity in the distributional impacts of climate shocks in different geographical settings. Studies in our sample use data at various levels of aggregation—country, sub-national, household, and individual.

5
(2006) who find the effect of state urban and private rural ownership to be positive, while that of state rural and private urban ownership to be negative. Several studies of China examine ownership concentration, with Ligang Song and Yang Yao (2004) finding with that state and private majority ownership has a positive effect relative to non-majority state and private ownership, with the latter not being significantly different from one another. Tian and Estrin (2008) in turn find that state having small shareholding has the largest positive value on corporate value, followed by high state shareholding, while intermediate state shareholding has the lowest effect. Finally, Qian Sun and Wilson Tong (2003) find that majority state or foreign ownership does not have a significant effect on the operating income/sales ratio.

In CEE, CIS and China, the effect of private foreign and domestic ownership on profitability is hence found to be positive or statistically insignificant, with the significance depending on the particular type of ownership. Concentrated domestic private ownership, managerial ownership, and to a lesser extent foreign ownership generally tend to have a positive effect on profitability, while evidence from CEE also suggests that profitability is unaffected by whether or not the state keeps a golden share or workers wield a more concentrated ownership.

# _4.3 Revenues_

In Figure 3 we report the privatization effects on revenues from 14 studies. Since these studies do not control for input use, they effectively measure the effect of privatization on the scale of operation of the firm. In most studies carried out in CEE there is a strong and positive effect of private ownership on the level of revenues (Figure 3A). The effect is detected in studies that cover either the more recent period or both the earlier and more recent periods. Studies that derive their estimates only from the early period generate small (less than 5%). The positive effect is found with respect to both domestic and foreign private ownership, with foreign ownership appearing to have greater positive effects. A similarly strong positive effect is found in a study covering privatization in the early period in CIS. However, two studies that cover the later transition period in the CIS find small positive and negative effects, respectively. The CIS studies do not distinguish between domestic and foreign ownership.

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The regulatory framework covering life insurance requires considerable strengthening. There is a need to provide for cooling off periods and for clear disclosure of meaningful information to policyholders, including surrender values, benefit participation rates, expected and realized rates of return, product risks, commission levels, and other aspects of life insurance contracts. Some of the leading companies already follow best practice in their selling of life policies, but a strong regulatory framework offering adequate consumer protection will contribute to building public trust in insurance companies and their intermediaries.

As regards motor insurance, development of an insurance information exchange with data on loss experience, driving records, and fraudulent claims is required in order to replace the Unified Bureau mechanism. This will also allow the elimination of administered tariffs. The information bureau could also cover all other types of general insurance.

# **I. Off-Site Surveillance and On-Site Inspections**

Current rules stipulate the financial reporting requirements of insurance companies. Audited consolidated annual statements must be provided 2 months after the end of the fiscal year (which the insurance act defines as the calendar year for all companies). Various statistical returns on specified forms, reviewed by external auditors, must be submitted one month after the end of each calendar quarter. Insurance companies are required to appoint licensed external auditors and to comply with international accounting standards. The responsibilities of auditors, including their obligation to report to the IC any material shortcomings and deviations from acceptable norms, are set out in the act.

The IC undertakes off-site financial analysis of insurance companies. Considerable progress has been made in developing an Insurance Regulatory Information System that is able to identify companies with weak operating and financial structures. This early warning system should be completed and formally introduced. It could play an important part in improving the effectiveness of off-site surveillance and guiding the program and timing of on-site inspections.

The IC has broadly adequate powers of inspection and investigation. It can assign staff to verify or audit, at suitable times, any of the transactions, records or documents of insurance companies. The IC is empowered to take copies or extracts of relevant documents and to conduct regular as well as unannounced inspections. Based on the findings of the inspections, the DG may appoint experts, consultants, auditors or actuaries to undertake special audits of insurance companies.

In practice, the IC has been able to conduct inspections of all operating companies, jointly with international experts. It is in the process of expanding its capacity in this area by hiring more qualified staff and upgrading their skills through training. Increasing the frequency of inspections and focusing on internal control and risk 30