rafmacalaba/data-use-mentions-extended huggingface dataset:
be between 20 percent in Serbia and 40–50 percent in Poland and Russia, but none of the studied countries comes anywhere close to that range (Figure 1).

**Figure 1. Building Structure and Rental Tenure in Some Transition Economies**

<!-- Start of picture text --> 80<br>Apartment & hostel buildings Non ow ner-occupied tenure<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Hungary Serbia Romania Armenia Slovakia Czech Rep Lithuania Poland Russia<br><!-- End of picture text --> _Source_ : World Bank LSMS study, Lithuanian Household Survey, national statistical offices. _Note_ : For the Czech Republic and Slovakia all units in housing cooperatives were counted as non-owner-occupied because of the lack of detailed data on renting.

Demand for private rental is rather strong among its typical household groups - the young and mobile - consistent with patterns found in mature market economies. Higher incomes of many younger households, typically the winners in economic transition, drive up private sector formal and informal rents in the most attractive cities. However, the lack of a consistent investment framework for formal private rental housing accommodates elastic expansion of supply, discouraging additional migration from smaller cities and rural areas. Consequently, the restricted rental tenure choice is likely to compromise macroeconomic performance in the studied countries, and by inference in the other ECA transition countries.

Residual social rental housing, decimated by the mass privatization, cannot accommodate the newly created post-privatization households who are poor, let alone the young and mobile, to any meaningful degree. Occupancy of the social housing stock in these countries is dominated by the previously allocated tenants and thus lacks socioeconomic targeting for the current dynamic situation. In the few cases where the sector has not been marginalized, highly subsidized rents effectively lock sitting tenants into their dwellings, preventing any significant adjustment, restructuring and reallocation within this stock. Inadequate public
**2** THE BACKDROP

# **2.1 Orissa and the study districts**

On the basis of its physical features and agro-climatic conditions, Orissa can be divided roughly into four zones (see Map):

1. the northern plateau covering the districts<sup>2</sup> of Mayurbhanj, Keonjhar, Sundargarh and part of Dhenkanal district, constitutes 23 percent of the state's total geographical area,

2. the central river basin encompassing Bolangir, Sambalpur and Dhenkanal districts, also covers 23 percent of its landmass, 3. the eastern Ghat region includes the erstwhile Kalahandi, Phulbani, and Ganjam and Koraput districts and is spread over 36 percent, and 4. the coastal plains of Balasore, Cuttack, Puri and a part of Ganjam make up the remaining 18 percent.

The coastal plain region is the most agriculturally advanced in the state as a result of high soil fertility and more widespread availability of irrigation.

Orissa is the second poorest state in India (after Bihar). Some 87 percent of the total population of 32 million (1991 census) live in rural areas, and 50 percent of the rural population (head count index) live below the poverty line (World Bank 1998). While employment in Orissa's rural non-farm sector grew at a rate of 2.8 percent a year over 1981-91 (Samal, 1997), the great majority of the rural population continue to depend upon agriculture and allied sectors.

Figure 2 shows the distribution of household operational land holdings by size class. It indicates the change in land distribution brought about by post-Independence land reforms (section 2.3), combined with demographic and socio-economic change. Over the period 1953-54 to 1961-62, the number of households not operating any land increased substantially, following widespread evictions of tenant farmers after the abolition of large landlord estates (sections 2.3 and 4.4). Estates abolition and the enforcement of an upper ceiling on land holding size appear to have been reasonably effective in reducing
versities and colleges), public universities and colleges, and FFRDCs administered by state and local governments (primarily public universities and colleges). The BEA prepares all estimates of current-dollar R&D investment by first compiling data available from the various NSF surveys and then adjusting these data to be statistically and conceptually consistent with BEA in the NIPA tables.

Real R&D investment is derived by deflating detailed current-dollar expenditures by appropriate price indexes. Two price indexes are constructed and utilized in the satellite account: an input price index and an aggregate output-based price index. The input price index is based on an aggregation of detailed price indexes for the inputs used to create R&D output. As noted by Lee and Schmidt (2010), this index is a good measure of the impact of inflation on R&D inputs but less appropriate in measuring R&D output because it does not account for productivity growth; it makes the assumption that real output grows at the same rate as real inputs. On the other hand, the aggregate output-based index indirectly reflects the movement of R&D output prices. In particular, it is a weighted average of the output prices of other products produced by 14 R&D-intensive industries with weights corresponding to each industry’s share of annual business R&D investment. There are two issues related to this index. First, it is influenced by factors that are unrelated to R&D which a↵ect prices of other products produced by the same industries. Second, before 1987, it was constructed based on only the top five industry R&D performers because detailed industry investment measures were unavailable.<sup>21</sup> Despite those issues, the output-based price index is the best price measure available capturing productivity growth in R&D-intensive industries and thus,

> 21For more details about the index refer to Okubo et al. (2006) and Lee and Schmidt (2010).

29
Aside from the observed factors that affect migration, individual and household migration decisions are also shaped by forces often unobservable to researchers. For example, it has long been assumed that individuals more willing to take risks are more likely to migrate. Indeed, studies of rural-urban migrants in China show that less risk-averse individuals, as revealed by self-reported risk preferences, are more likely to engage in migration (Akgüç et al., 2016), and the distribution of other household members’ risk attitudes also shapes individual migration decisions (Dustmann et al., 2023). Hao et al. (2016) posit that out-migration involves risk-taking, just like launching a new business, and that migrants are behaviorally like entrepreneurs. The study uses a field experiment to confirm that migrants are less risk-averse and more willing to engage in risky competition. While risk preferences have been considered in studies of migration in China, other unobserved preferences, including time preference, or the extent to which people value current over future consumption, have not been analyzed. From evidence collected in other developing countries, e.g. Ghana and Indonesia, migrants are not only more risk-tolerant, they are also more patient (Goldbach and Schlüter, 2018). Given that both higher levels of risk tolerance and patience are associated with higher cognitive ability (Dohmen et al., 2010), migration is selected on both unobserved cognitive abilities and unobserved characteristics of such personality traits.

Given the presence of unobservable individual characteristics and community-level characteristics, including both features of villages and unobserved shocks affecting both well-being and migration decisions, estimation of the impacts of migration in sending communities present challenges when using observational data. Use of randomization for identification, such as lotteries into a visa program (Gibson et al, 2011) are a gold standard in the international migration literature, but not available in China. Researchers have thus appealed to difference-in-difference and instrumental variable strategies to identify impacts of migration. In the sections below, we will highlight identification strategies employed and point out those studies for which outcomes
Importantly, the error of model increases only slightly as we move to the 8-month forecast horizon, while errors made with binarized outlooks nearly double when moving from the near-term to the medium-term horizon. This means that the model-based predictions are better suited for forecasting further into the future, which may help increase lead time for preventive action.

# **4.3 Model interpretability**

The previous sections showed that the Random Forest model provides good predictive capabilities. While predictive performance is important, the modeling structure of the transformed classifier may be difficult to interpret. Out-of-the-box diagnostics may be used to help determine which variables are important in the forest, but do not inform about the signs and magnitude of (local) relationships. Parametric models are more transparent in that regard, but while it can be argued that these models are more interpretable, they might not be comprehensible given the number of predictors involved. Moreover, standard methods do not account for effects induced by the transformation of probabilities. To overcome these issues, this section develops a prediction decomposition strategy that relies on assessing the change in prediction values when the model is confronted with observed values compared to reference input data. This provides comprehensible interpretation at an observational level and takes the following algorithmic strategy.

1. Partition the spatio-temporally covariates into a fully exhaustive set of non-overlapping groups. We use the groups defined in section 2.2 but split rainfall variables from the other environmental factors.<sup>29</sup>

2. Construct a country data set by leaving all time-invariant district-level data at local values and setting all spatio-temporally varying covariates at a reference value. We use country means.<sup>30</sup> Make reference predictions with this data set.

3. For each group of covariates defined in step 1, set the levels of the data to the observed values while keeping all the other covariates at reference values as defined in the previous step, then generate a new set of predictions.

4. For each set of predictions generated in the previous
# **V. Conclusion**

Using results from the _Future of Business_ survey among small businesses on Facebook in 97 countries, this paper studies sectoral segregation on a global level. Consistent with previous research, we find that women who enter male-dominated sectors earn higher profits than women who remain in traditionally femaleconcentrated sectors. Second, a hierarchy of earnings (“the profitarchy”) emerges whereby men in maledominated sectors earn the most; women in male-dominated sectors and men in female-concentrated sectors in the middle tier; and women in female-concentrated sectors at the bottom. This particular pattern is driven mostly by the developing economies. In developed economies, the “profitarchy” takes the form of maleowned firms, either in male-dominated sectors or female-concentrated sectors, being more profitable than women in any sector.

While we do document some differences across developing and developed countries in factors surrounding sectoral segregation, what is striking is the large area of commonality. Recent work (e.g. Goldin, 2014) has shown a decline in occupational segregation in wage work in the United States. However, sectoral segregation among entrepreneurs, and the gender earnings gap, remains large and persistent across the world.

Women engaged in male-dominated sectors are less likely to have started their business themselves and were more likely to have inherited the business from family when compared to women in femaleconcentrated sectors, but this is only part of the story. Mentorship, particularly from men, is important: we observe that women in male-dominated sectors are more likely to have had a male role model while growing up. These findings are consistent with having better support networks that could assist women to enter nontraditional business sectors and circumvent some of the challenges they may face. Conversely, women who break into male-dominated sectors do not seem to have more education, and capital does not appear to be a particularly binding constraint to the women who have managed to cross over.

In terms of creating programs to support greater rates of crossing over, it is possible that carefully chosen apprenticeship or mentoring programs could help
<!-- Start of picture text --> 28<br>26-61.<br>26 24.91<br>245 23.80 25.23 25.39.<br>23-46<br>22 22:76 22.22<br>20<br>18<br>16<br>14<br>12<br>: ee e ee9.94<br>e 778 8.40 8.56 8.14 814 Bes<br>6<br>4<br>bo PovertyPoverty Rate Gap 20 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text -->
-97-

# e) A Systematic Shortcut Procedure for Matching

Chapter V presents a systematic short-cut procedure, the ABM method, for matching products in complex multiproduct industries. This method confines matching to products which account for more than 1 per cent of the gross value of output of an industry. Smaller items were only included in case they matched with a similar product in the other country where it is important, or in case they were required to complete a match with an important product. The advantage of our short-cut method over the alternative maximalist procedure, is that it improves the quality of the results by eliminating "outlier" PPPs, and that it offers considerable savings in research time.

# f) The Unit Value Approach is not Inferior to Specification Pricing

It is sometimes suggested that unit values such as we derived from census information are inherently inferior to specification pricing as practiced by ICP. In fact we do not believe this to be true and have explained why in chapter VI.

Specification pricing as practiced by ICP involves meticulous characterisation of the items chosen as representative, whereas our "prices" are unit values derived by confrontation of census information on values and quantities of product. In practice the "products" may be a mix of items and qualities and be very far from the ideal of specification pricing. But there are compensatory advantages in the industry of origin approach:

- 1) the unit values are average transaction values for the whole year for all producing locations of the countries compared, whereas ICP prices are quotes, shelf, list or monitored prices for one point in the year in a limited number of locations.

- 2) with the census one can judge the representativity of the "unit values" which are selected from a much wider range of information than ICP had at its disposal. For instance, our 17 industry sample yielded 1,434 Mexican unit values from which 192 were chosen to match with the USA, and 543 Brazilian unit values of which 171 were matched with the USA. ICP,
Table 3 and Annex Table 2 also report the limitations on duration of stay for CSSs, ISSs and ICTs in the year 2017, which impose recurrent visa application and associated costs, especially on frequent service providers. There is considerable heterogeneity in the duration of stay across countries, ranging from only one year for Switzerland, Costa Rica, Finland, Israel and Turkey (across service professionals) to four years in the case of Australia and Denmark (but only 2 years for ISSs); four-five years for the United Kingdom; and five years in the case of China, Japan, Latvia (but only one year for ISSs) and South Africa (though only four years for ICTs). The sample maxima point to the ground that the remaining countries in the OECD database can cover in extending the duration of stay to these services professionals (if not eliminating limitations on these stay durations altogether), thereby greatly reducing costs imposed on Mode 4 trade.<sup>5</sup> Reducing heterogeneity in such regulation would also form part of a possible TFiS agenda, which is discussed in detail next, together with an analysis of predominantly trade facilitation measures as identified in the OECD STRI database.

# **4. Facilitating regulatory compliance for Mode 4 trade**

Distinct from the measures discussed in Section 3, the OECD STRI data also include 16 measures that relate to providing regulatory transparency for MNP which constitute a reasonable Mode 4 TFiS agenda; the details of these measures are provided in Annex D. Even among the 45 countries covered by the OECD STRI data, there is huge heterogeneity in the measures of regulatory transparency as applicable to MNP, which suggests that there is ample scope for minimizing the associated compliance costs involved in meeting these regulations. At the same time, reducing such regulatory heterogeneity would also directly minimize services trade costs, thus constituting a services liberalization agenda.

This is illustrated below by discussing various attributes related to business visa applications, all of which translate into costs for service suppliers, including in terms of
providing detail on the trajectory of an effect. Such data are also incredibly valuable for structural modeling, and for constructing comparable control groups in non-experimental impact evaluations. Moreover, with a large number of time periods on a smaller number of firms, one can move from standard estimators that rely on large-N asymptotics to estimators that rely on large-T. Bloom et al. (2011) provide an example of this, using over 100 observations per firm and the Ibramigov and Muller (2010) method to implement a t-statistic based estimator that is robust to substantial heterogeneity across firms as well as to considerable autocorrelation across observations within a firm.

# **4.3 Recognize What Experiments Can and Cannot Do**

Employing the two recommendations offers the potential to estimate the average treatment effect of a firm policy for the types of firms that one has restricted the analysis to. Thus if we reduce the C.V. to a manageable level by focusing on firms with 10 to 100 workers and baseline sales below $100,000, this is the group for which we are able to estimate the policy impact. If there are only a couple of firms in the whole country with much larger sales and employment, an experiment is not going to be able to reveal the impact of the policy on these two firms, and pooling these firms in with the remainder of the firm population will make it hard to detect the impact for _any_ type of firm. Note that this is not a limitation of experiments alone – if there are really only two firms like this in the entire country with no firms comparable to them, then nonexperimental methods of impact evaluation are not going to be informative about these largest firms either.

Then what can we say about the effects of a program on the largest firms? This is where I believe time series analysis and structural modeling can play their most important role provided that _lots_ of data is collected on such firms. If we have daily production data on the same firm for a decent period of time pre-intervention
International Country Risk Guide; RUS = Russian Federation; SCC = South Caucasus; UKR = Ukraine; WBK = Western Balkans.

A. Unweighted 2019-20 averages.

B. Unweighted averages. Higher values indicate greater political stability risk and/or economic policy uncertainty. Political stability risk includes 15 ECA economies, as measured by ICRG. Economic policy uncertainty for ECA is an average of Russian Federation and Poland, as measured by national sources and Baker, Bloom, and Davis (2016). C. Long-term investment prospects refer to 6- to 10-year-ahead forecasts, as in the latest Consensus Economics survey. Data prior to 2022 reflects actual investment growth. Shaded areas based on January 2023 survey. Sample includes 7 ECA countries. Solid line uses 2019 real U.S. GDP weights. Dashed line shows the minimum and maximum range.

D. EBRD’s 'well-governed transition’ indicator, measuring the quality of institutions and the processes that they support. Scores range from 1 to 10, where 10 represents a synthetic frontier corresponding to the standards of a sustainable market economy. F. Lines show the percent deviation between the latest projections and forecasts released in the January 2020 edition of the Global Economic Prospects report (World Bank 2020c). For 2023, the January 2020 baseline is extended using projected growth for 2022.
. Data and Methodology**

## **_III_ .A. Data**

The data in our paper come from four primary sources. The first is the loan-level data for the universe of commercial lending in Mexico from 2003 to 2012. The second is the monthly bank balance sheet data set provided by the Bank of Mexico. The third is hand-collected data on personal and professional information on all 256 senators elected in 2000 and 2006. This information includes all political occupations as well as the commission memberships. Finally, and to document variation in real economic outcomes, we use yearly information on firms’ balance sheets using the Orbis data set compiled by Bureau van Dijk.

The loan-level data are a supervisory data set on the entire universe of commercial loans in Mexico from September 2003 to March 2012. The data set was compiled by the Mexican Banking and Securities Commission (CNBV) in its role as bank supervisor. The data come from regulatory reports (known as R04) sent monthly by every commercial bank to the CNBV. Reports are mandatory, updated electronically, and include detailed characteristics of all new and continuing 9
on expenditures. In practice, a dummy variable indicating households’ receipt of remittances, rather than the level of remittances, is used. Following this approach and including interactions between the remittance-receipt variable and other variables in Equation 4, Adams found evidence that the spending behavior of rural Guatemalan households with remittances was significantly different than that of households without remittances. Specifically, households with remittance income spent less on consumption goods than otherwise similar households without remittance income, dispelling the notion that remittances are “conspicuously consumed.” This implies that the second term on the right hand side of Equation 5 is nonzero. Similar results are reported in Adams (1998, 1991) and Alderman (1996), using data from other less developed countries.

The finding that the receipt of remittances influences expenditure patterns naturally raises the question, “Why?” Equation 4 suggests two possible explanations. First, the receipt of remittances may correlated with _other_ determinants of demand, i.e., prices (<sup>_P_</sup> _h_<sup>, which may include household shadow prices for nontradables and transaction</sup> costs for tradables) and/or other variables, _Z h_ . Alternatively, both remittances and expenditures may be influenced by variables not included in Equation 4. The most obvious candidate is migration, itself, which is highly selective on household characteristics that also may influence expenditures.

9