rafmacalaba/data-use-mentions-extended huggingface dataset:
where _Yija_ is the outcome variable _j_ in commune _i_ after the shock, and _Yijb_ for the period before the shock. _DT_ is a dummy variable that takes on the value of 1 if a location is in the integrated segment of the economy and thus is affected by the trade cost shock and zero otherwise, and _DNA_ is a dummy variable that takes on the value of 1 when a location is in the non-Abidjan route and zero otherwise. This definition of _DNA_ allows us to focus better on the reallocation of population and economic activities away from the adversely affected Abidjan route to the non-Abidjan routes. The coefficient _β_ 3 is the focus of our analysis. Denoting an estimated parameter with a hat, _β_<sup>ˆ</sup> 3 provides the effect of the trade shock on the non-Abidjan routes relative to the Abidjan route, taking into account both the long-term changes unrelated to the CIV shock and the reallocation across routes driven by mobile factors such as labor. Since the data on skilled labor also come from the censuses, the analysis of spatial concentration of skilled labor also relies on the triple-difference empirical model above.

# **Built-up and Night-Lights Density: A Quadruple-Difference Strategy with Location Fixed Effect**

We have richer time series data on built-up density and night-lights density which allows us to compare the first-differences, thus adding another difference to the triple-difference empirical model set-up for the employment analysis above. The empirical model becomes one of a quadruple difference with location fixed effect. The estimating equation can be written as below: where _△Yijt_ = _Yijt − Yij_ ( _t−_ 1), and _Dyr_ is a time dummy defined appropriately for the post-war observation(s) given an outcome variable. For example, for built-up density as the outcome variable, we use a yearly panel consisting of the following years: 1990, 2000, 2014. In this case, we define a dummy _D_ 2014 that takes on the value of 1 for _△Yij_ 2014 = 19
formal firms operating in sectors that are more prone to informality competition should be disproportionately affected by IDP inflows relative to the other firms. To test this hypothesis we use the manufacturing survey firm data and split the sample according to industry codes in two groups of firms according to whether they are part of a sector that tends to have high levels of informality (e.g., retail sales and construction) or low levels of informality (e.g., financial services, machinery production, production of chemical or pharmaceutical products). The results are presented in Tables VIII through XI, and strongly support our hypothesis. Particularly, we only find negative effects of IDP inflows on the intensive and extensive margins of production for the group of firms that operate in sectors that are more 21
Future studies using mobile data collection might mitigate non-response by sending simple reminders (possibly using SMS) to reduce the number of intermittent compliers. Targeting older, female household members could reduce the number of non-compliers, but such targeting would not be desirable for surveys seeking a representative sample of individuals, e.g. labor force surveys.

> 9 It is not possible to determine, however, whether responses from the 17 percent who _never_ responded would have differed from those of those who did response.

18
inter-bank and bearer deposits are not covered. The Bank of Italy can make low-interest rate loans to facilitate a large pay-out. The coverage has been ITL 200 millions per depositor since establishment, which corresponded to EUR 103,291 as of 2003.

_Sources_ : Own survey of deposit insurers, Garcia (1999), IADI Survey: Italy (2003), Kyei (1995).

**Jamaica.** ( _Deposit Insurance Corporation, Deposit Insurance Act 1998_ ) The deposit insurance system of Jamaica was established in 1998. It is government legislated and administered. Membership to the scheme is mandatory. Insurance coverage limit was initially J$ 200,000 and was raised to J$ 300,000 after July 2001. Coverage is calculated per depositor per institution and it extends to foreign currency deposits as well.

_Sources_ : Own survey of deposit insurers, IADI Survey: Jamaica (2003).

**Japan.** ( _Deposit Insurance Corporation-DIC, Deposit Insurance Law_ ) There are two separate deposit insurance schemes in Japan; one for commercial and Shinkin banks, credit cooperatives and labor and credit associations, and another for agricultural and fishery cooperatives. The first scheme covers demand and time deposits in domestic currency. The coverage was 1 million yens in 1971, 3 millions in 1974, and 10 millions in 1986 covering the principal only; and, it became 10 millions for principal plus interest in 2001. Due to a law amendment in 2002, special deposits for settlement and payment uses have been fully covered. The blanket guarantees were offered for current, ordinary and special deposits in 1996 as well again as a temporary measure. The coverage is otherwise per depositor per institution. The system is government legislated and administered. The government and the central bank provided the initial capital. The fund can borrow from the central bank, and the government can guarantee the DIC’s debt. Membership to the Corporation is mandatory. Between 1996 and 2000, banks were required to pay a special premium of 0.0036% in addition to
- **Calibration issues** . To what extent the Pillar 1 is properly calibrated is an issue for supervisors of non-G10 countries, whose loss experience has not been systematically taken into consideration in the architecture of the capital framework.

In the case of the simpler, standardized approach, regulators and supervisors will most likely need to revise aspects such as the definition of the regulatory retail portfolio. This is particularly true for small and medium enterprises (SMEs), in which they will be treated as retail borrowers and therefore will receive a lower risk weight than corporate obligors. Also the suitability of the supervisory risk weights will need to be reviewed. Risk weights of 35% and 75% for residential mortgages and retail credits (including eligible SMEs) may be unrealistically low in some jurisdictions. Loan default and recovery data stored in PCRs would allow the regulators to assess if the loss experience in these credits would warrant that those risk weights should be increased.

Recalibrating the IRB model is a fairly more complex and controversial issue because it implies deviating from the new standard in regulatory capital. Policymakers must nevertheless bear in mind that its parameters were adjusted to ensure that, when applied to G10 banks, IRB results (i.e. unexpected losses) are similar to those obtained from conventional credit portfolio models. However, that may not be the case when applied to emerging markets banks. Therefore, the impact of the systematic factor on borrowers’ creditworthiness (asset correlation), the suitability of the SME definition, the adjustment in capital requirements for credits with long maturities and the convenience of using a different confidence level (compared to the standard 99.9%) are issues that may need be reconsidered by local authorities. This could complicate and lengthen the adoption of the Revised International Capital Framework. For example, using bootstrapping<sup>30</sup> techniques Majnoni _et al_ (2004b) and Gutierrez Girault (2007) find evidence indicating that the IRB formula for corporate obligors may not be properly calibrated to unexpected losses in
household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented.

Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN‐OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection.

# Some data challenges common to refugees and IDPs

Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities.

**Sampling** . As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay
administrative units found in cities may be more difficult to model, particularly when wealthy and poor neighborhoods are in close proximity to each other. Differences across such neighborhoods are conceivably harder to capture using the satellite images that are currently available in the public domain. On a positive note, the technology underlying satellite imagery data is rapidly evolving, and with it the artificial intelligence algorithms that derive usable data from the images (see e.g. Jean et al., 2016; Burke et al., 2021). In conclusion, validating the approach in different locations will be important. Now that we established that the approach works well in a country like Malawi, it remains to be verified whether it is equally successful in countries with different levels of development, different levels of urbanization and different degrees of population density.

34
Brain Drain Be Good for Growth in the Source Economy?” _Journal of Development Economics_ 53 (2): 287–303.

- MPC. 2013. “Migration Policy Centre: Migration Profile Moldova.” Report.

- Osipian, A.L. 2009. “Corruption Hierarchies in Higher Education in the Former Soviet Bloc.” _International Journal of Educational Development_ 29 (3): 321–30.

- Spilimbergo, A. 2009. “Democracy and Foreign Education.” _American Economic Review_ 99 (1): 528–43.

- Transparency International. 2013. Global Corruption Barometer: Moldova.

- https://www.transparency.org/gcb2013/country/?country=moldova, [accessed 10.06.2015].

21
**Source:** White Paper on Taxes, Polish Ministry of Finance, and World Bank staff estimates. IV. **Cost estimates of personal income tax expenditure programs**

There is no widely accepted operational methodology for estimating tax expenditure. Most OECD countries involved in administration of tax expenditures define the cost of tax expenditures as deviations from a benchmark tax structure. While this conceptual definition is well established, difficulties arise in making the definition operational. The main problem is that the definition of the benchmark tax structure, and therefore the identification of tax expenditures, are inherently subjective. Reasonable differences of opinions always arise in the interpretation and categorization of tax measures, especially regarding the treatment of inflation and possible double taxation.

Leaving these differences aside, the following are some methodological issues in estimating tax expenditures:

- **Historical estimates of tax expenditures.** Upon the establishment of the benchmark tax structure, tax expenditures can be identified and historical estimates obtained either from taxpayer returns or from using income tax models that simulates changes to the income tax system using the statistical sample of the collected returns.

- **Projections of tax expenditures.** These must rely on estimated relationship between tax expenditures and explanatory economic variables. Using these relationships, the values of the explanatory variables are projected into the future, permitting estimations of the future expected values of tax expenditures. Key explanatory

10
**|0.29***|0.29***|
||(0.03)|(0.04)|(0.05)|(0.05)|
|SPI Overall Score||0.02***|0.01*||
|||(0.01)|(0.01)||
|Log Qualitative Papers|||0.64***|0.65***|
||||(0.08)|(0.08)|
|SPI Data Sources Score||||0.01*<br>(0.00)|
|Observations|168|168|168|168|
|R2|0.751|0.774|0.852|0.852|
|R2 Adj.|0.748|0.770|0.849|0.849|
|AIC|414.8|400.1|331.1|330.7|
|BIC|424.2|412.6|346.8|346.3|
|RMSE<br>_Note:_GDP, population and S|0.82<br>PI data are retr|0.78<br>ieved from th|0.63<br>e World Ba|0.63<br>nk's World|

_Note:_ GDP, population and SPI data are retrieved from the World Bank's World Development Indicators. Regressions include all papers using data from 20172019. Standard errors are robust to heteroskedasticity. ***=0.001 level, **=0.01 level, *=0.05 level, +=0.1 level.

Using more detailed data from the SPI makes it possible to assess how the availability of specific data sources relates to academic output. Figure 6 shows the linear regression coefficient for the availability of
.139)|(0.169)|(0.134)|(0.132)|(0.148)|(0.137)|(0.136)|
|N|667981|532121|589933|667981|667981|667981|640152|584494|
|R-sq|0.305|0.319|0.207|0.368|0.342|0.269|0.306|0.274|

Notes: This table presents robustness checks for our main result presented in Table 1, column 6. Robustness checks are: Column 2: as in (1) but excluding the 10 major oil producing countries for which economic production is expected to be significantly less affected by weather. Column (3): as in (1) but excluding China and USA. Column (4): as in (1) but including a WB region Year FE instead of Year FE. Column (5): as in (4) but without country year trends. Column (6): as in (1) but without Year FE. Columns 7 and 8: as in (1) but including one lag and three lags of growth as in a Arellano Bond approach. Clustered standard errors at the Administrative 1 level in parentheses. *** p<0.001, ** p<0.01, * p<0.05, + p<0.1

**Table 5 Robustness checks, alternative specifications using GDP growth rate**

|Dep. Var. Δlog(GDP)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||base|No oil<
through the increase in the probability of having a program placed in the village as well as through the included income, education, and community variables. We include tests to ascertain the validity of the proposed instruments.

The ancillary regression used to instrument the (endogenous) feeding post / Partage variable is reported in Table A1 in Appendix 1.<sup>6</sup> The coefficient estimates show that the intervention targeted villages with worse conditions such as villages with a low ratio of vaccinated children. This suggests that, unless instrumented, the OLS coefficient associated with the presence of a program will be downward biased, a finding in line with recent empirical findings of Behrman, Cheng, and Todd (2004) and Attanasio and Vera-Hernandez (2004) who also evaluated the impact of a nutritional program participation on the health outcomes of children. Their evidence suggest that, without taking into account endogeneity, the impact of the program would be underestimated.

We also instrument per capita consumption to take into account measurement errors in per capita household consumption that could lead to attenuation bias towards zero. We use per capita household income and the quality of the roof of the house where the child resides as instruments. The use of income as an instrument does not, however, address the possibility that a child’s health may affect income, possibly more through adult time allocation than the direct loss of child labor given the age of the children we are considering. The approach used here presumes that this is a minor issue relative to the problem of measurement error.

F-tests indicate that the proposed instruments have predictive power to explain the presence of programs and that they have no predictive power for malnutrition beyond their influence through the nutrition program variable. The latter test is based on regressing the height of the child on all regressors and instruments. This test allows to detect whether instruments have additional power on the current height of the child, beyond through the prediction of the program probability. The value associated to this F test is 1.40,
control, and fabrication (only available for firms in manufacturing). The list of technologies for each business function includes: (i) specialized software and ERP for Business Administration; (ii) specialized software and ERP for Production Planning; (iii) non-integrated and integrated Supplier Relation Management (SRM) for Supply Chain Management; (iv) Customer Relationship Management software (CRM) and Big data Analytics or Machine learning algorithms for Marketing; (v) computer numerical controlled machine, robots, and advanced manufacturing for Fabrication; (vi) online sales and electronic orders integrated to specialized supply chain management systems for Sales Methods; (vii) online or electronic payment through a bank wire and online payment through platform for Payment Methods; and, (viii) statistical process control with software monitoring and data management and automated systems for inspection for Quality Control. For instance, in the case of business administration, we have information on whether firms adopted specialized software or ERP and, more importantly, the date on which the firm adopted it. Using the years of adoption, we create an indicator for each business function equal to 1 from the year the firm adopted a given advanced technology and 0 in the previous years.

We then combine firms’ information on the year of adoption of sophisticated technologies with firms’ export status from the data available in Brazil’s Ministry of Trade. Thus, for each firm in our dataset, we have information on the year it started to export and the year it adopted a sophisticated technology in each business function. We also merge this data with the employer-employee census, including firm-level information on size and average wage. The resulting longitudinal dataset from 1994 to 2020 allows us to use a quasi-experimental design (difference-in-differences estimator) to explore the effect of entering export markets on adopting advanced technologies. In essence, we aim to compare the adoption rates of treated firms over the short and medium run with the adoption that would have occurred if they had not started to export.

In a typical difference-in-differences setting,
- <u>All businesses: Revenues in the last full month for the main business added to those in any</u> other businesses run by the entrepreneur. This variable was winsorized at the 99th and 1st percentiles by survey wave, and coded to 0 for individuals who are not running a business. Note revenue in other businesses was only asked in the long-term follow-up round, and not in the short-term rounds.

- <u>Main business conditional on survival: Revenue in the main business as defined above, but</u> coded as missing for individuals not running a business.

- <u>Main profits and business index: This index averages the standardized z-scores of profits and sales</u> in the main businesses.

- <u>Total labor income: this adds the total profits in all businesses in the last month, as defined above,</u> to total earnings from paid work, farming, retirement and investment income (winsorized at the 99<sup>th</sup> and 1<sup>st</sup> percentiles) to get the total monetary income earned by the entrepreneur.

The mechanism outcomes in Table 2 are defined as follows using the long-term survey:

- <u>Main firm employees: the number of employees in the main firm of the business, coded as 0 for</u> closed firms, and winsorized at the 99<sup>th</sup> percentile. We use the 2016 number of employees as the short-run outcome to represent the size of the firm after 2 years.

- <u>Capital stock: Total value of machinery and equipment, other work tools, vehicles, furniture,</u> other business assets, and inventories and stocks, winsorized at the 1<sup>st</sup> and 99<sup>th</sup> percentiles. Excludes the value of land and buildings given the highly skewed distribution (only 16 percent of firms report a
life (the years by which the capital good provides benefits in the future). Thus, capital depreciation on current and past investment expenditures is charged as a current expense to the operating budget.

Naturally, a measure of fiscal balance that includes net investment in capital may be calculated whether the government has adopted accrual accounting principles or not; however, accrual accounting is most suited to the operating balance approach. Several governments, including Canada and Italy, have adopted accrual on a modified basis, in that accrual methods are not used for the capital budget. Table 1 provides a survey of public sector accounting practices in a number of countries.

When governments are constrained by fiscal rules, the operating balance may be preferred over the cash balance approach to limit “creative accounting” (Milesi-Feretti, 2003). Under cash accounting, governments may, for example, substitute unfunded liabilities (such as those related to contingent liabilities) for market debt as a means of satisfying constraints on borrowing. The operating balance approach, taking a broader perspective of fiscal sustainability, restricts the use of this and many other such 12
|Does this statement describe you?|Y|es||No|_N_|
|---|---|---|---|---|---|
||_L_|_H_|_L_|_H_||
|I have no trouble making and keeping|0.97|0.97|0.00|0.03|62|
|friends.||||||
|If I need help in carrying of a plan of mine,|0.06|0.13|0.84|0.77|62|
|it’s usually difcult to get others to help.||||||
|I often fnd it hard to get my point of view<br>across to others.|0.03|0.17|0.78|0.77|62|
|In attempting to smooth over a disagree-<br>ment, I usually make it worse.|0.09|0.07|0.84|0.83|62|

Table 5: Responses to the survey on self-efficacy.

# **7 Conclusion**

There is increasing awareness that one should not draw inferences about universals in human behavior from the results of experiments with US college students. This paper provides a new example in behavioral economics in which results do not generalize and in which cultural traits have important economic consequences. It presents results from an experiment in coordination with high-caste and low-caste men in ten villages in northern India. In general, the low-caste men coordinated on the efficient convention, just as experimental results with US college students led us to expect, but the high-caste men did not—either when they were interacting among themselves or with members of low castes, though convergence to efficiency was more likely with members of low castes. Under the efficient convention, the endowment in each period grew by 67 percent (from 6 rupees to 10 rupees)