rafmacalaba/datause-encoder-data huggingface dataset:
Table A1: Country Abbreviations

|Name|Short name|Name|Short name|
|---|---|---|---|
|Argentina|arg|Norway|nor|
|Australia|austlia|New Zealand|nzld|
|Brazil|bra|Peru|per|
|Canada|can|Philippines|phlp|
|China|china|South Africa|safrc|
|Chile|chl|Saudi Arabia|sarbia|
|Eurozone|euro|Singapore|sing|
|India|india|Sweden|swe|
|Indonesia|indns|Switzerland|switz|
|Japan|japan|Thailand|thai|
|Korea|kor|Turkey|turk|
|Malaysia|mal|United Kingdom|uk|
|Mexico|mex|USA|usa|

Note) “Eurozone” includes Austria, Belgium, Finland, France, Germany, Italy, Netherlands, and Spain.

economies. Since the measurement of closeness of countries is key for the GVAR model, we should also consider using foreign direct investment data. If we can capture the financial linkages between countries, we can, for example, analyze the impact of monetary policy changes in advanced economies on developing countries.

# **Appendix**

## **Data**

We obtained the data files that cover the period between 1979Q1 and 2011Q2 from the “The GVAR toolbox” website at Centre for Financial Analysis & Policy, Judge Business School, University of Cambridge.<sup>17</sup> We have added the recent data for the period between 2011Q3 and 2014Q3 mainly based on Appendix B in Smith and Gales (2011). We used the World Bank’s commodity price data downloaded from the following website.<sup>18</sup> We have replaced Smith’s oil price data with the World Bank’s index, and added the metal index and agriculture index. The more detailed information about the added data is available from the authors upon request.

> 17 URL is http://www.cfap.jbs.cam.ac.uk/research/gvartoolbox/download.html.

> 18URL is http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTDECPROSPECTS/0,,contentMDK: 21574907 menuPK:7859231 pagePK:64165401 piPK:64165026 theSitePK:476883,00.html

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# **III.B.3 Cox’s Bazar Panel Survey**

The Cox’s Bazar Panel Survey (CBPS) tracks social, economic, and health outcomes among a representative cohort of the displaced Rohingya population after August 2017, alongside Bangladeshi households residing close to the refugee camps in Cox’s Bazar and Bandarban. The survey covers 5,016 Bangladeshi households from six _upazilas_ (subdistricts) in Cox’s Bazar and one _upazila_ in Bandarban that also hosts Rohingya.

The CBPS is representative of two primary demographic segments in Cox’s Bazar: Rohingya living in camps (comprising newly arrived as well as previously settled individuals) and the host population, encompassing both native households (with heads born in Bangladesh) and non-natives, including Rohingya.

The survey protocol includes two main components. The first is a household interview administered to one adult member that encompasses household demographics, food security, consumption patterns, assistance utilization, asset ownership, household income, and anthropometric measurements of a randomly selected child under five. The second is an adult interview conducted with three randomly chosen adults (18 years and older) that covers topics such as labor market participation, migration history, health care utilization, experiences of crime and conflict, and overall well-being. Across both rounds of data collection (2019 and 2023), CBPS conducted household interviews with 5,020 households (constituting 25,421 individuals) and adult interviews with 9,386 adults.

In the 2023 round, the same set of adults was reinterviewed, with a response rate of 97 percent of households and 80 percent of adults, corresponding to 4,996 households and 7,486 adults. The second round retained the core components of the household questionnaire and added a credit and coping module. The adult questionnaire was modified; the sections on labor market history, migration history, and experiences of crime and conflict were omitted, and three new sections were added to assess individuals’ intentions to return to Myanmar, host population cohesion with the refugees, and host population perceptions of the Rohingya.

For the purposes of this analysis, we restricted the dataset to the host population. Follow15
24 estimates of a production function (see section 2.2 above).'<sup>7</sup> Like Anderson's equation, this Cobb-Douglas type equatior implies that cost is zero if either output is zero.

# 6.1 Are Nigerian health care institutions over-capitalized?

The positive estimates of a6 and a7 suggests that Wouters' equation, notwithstanding her claims to the contrary, is best interpreted as a short-run total cost function rather than as a variable cost equation. As in the Ethiopia study, these positive coefficients imply over-capitalization. But once again one ought to be wary about taking this conclusion at face value, since the estimating equation does not satisfy the restrictions required of a short-run total cost function.

# 6.2 Inefficiency in Nigerian health care institutions

Of the four studies included in this survey, Wouters' is the only one which analyses efficiency explicitly. She explores both technical and allocative inefficiency. Her method differs slightly from that outlined in section 2.2 above. Rather than estimating a SFM, she first divides her sample into efficient and inefficient facilities on the Table 5: Parameter Estimates of Wouters's basis of the number of visits per EquatiOn health worker per year and then Variable Parameter Value estimates a conventional Cobb-Douglas production function Constant a0 1.63 on the 'efficient' subsample. The InA _al_ 0.01 shortcomings of this approach are obvious: it is arbitrary and fails InOUTP _a2_ 0.60 to provide any evidence on In(%DRUGS) a3 -1.36 variation in technical inefficiency within each group. InwHw _4_ 0.59 Nonetheless, it does have an _InwNHw_ a5 0.39 advantage over the frontier production function approach in DBEDS _ae_ 0.22 that whereas the frontier approach D, 05 .inB _a7_ 0.09 models technical efficiency simply as a shift factor, Wouters' InAl _a8_ -0.16 approach allows the shape of the production function to differ between 'efficient' and Adjusted R2 0.91 'inefficient' facilities. Note: Dependent variable is InC. Moreover, it is interesting to note that almost all of the private facilities in Wcouters' sample fall below the relatively generous cutoff point for inclusion in the 'efficient' subsample.

". Because of zero values admissions and beds are, in fact, not entered in logarithms but are instead transformed using the Box-Cox metric (L=0.10). The interpretation of the coefficients is much the same as<sup>for</sup> variables expressed in logarithms.
human as well as physical capital in a community. To do so empirically, we define a vector of mean household level characteristics Xi as well as community level variables **Zi** that correspond to the parameters discussed above. Adding an error term allows to estimate a cross-sectional equation for the incidence of civil strife as follows.

Pooling the observations from 1992 and 1999/2000 allows us to test for structural changes in the contribution of some of the right hand side variables by interacting them with a year dummy. In a similar manner, we explore whether, after controlling for other factors, there has been a net increase in civil strife either in the aggregate or for specific regions of the country.

Although estimation of this relationship within a given country will avoid many of the issues associated with unobservable policy factors in cross-country regressions, cross sectional estimates are still likely to omit unobservable factors (e.g. households' party affiliations or historical incidents) which might affect the level of civil strife. To deal with this and at the same time use the availability of two observations per community in part of our data we complement this with an estimation of detenninants of changes in the incidence of civil strife according to _(5) AS, = 1'_ **_(Xit - X,,d_** + **_6 Zit_** - Za-l) _+YSi-l+ E_ where AS, is the change in the incidence of civil strife in community ib etween the two periods, SE, is the level of such conflict in the same community during the initial period, and the expressions in brackets are changes in the independent variables. Under the condition that one can find variables the change of which is exogenous to the community, this can be estimated using standard techniques.'<sup>5</sup> Specifically, we will estimate a probit equation with an indicator for increases in the level of civil strife as the dependent variable and changes of the variables discussed earlier on the right hand side.

> _s_ Note that changes in education will be endogenous because households decide whether or not children stay in school. Also, with high levels of decentralization, even supply of schooling infrastructure can no longer be considered completely exogenous, conhtary to, for example, roads.. Similarly, changes in movable asset endowments are likely to be endogenous and can thus not be used as a right-hand side variable.

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**_<u>________________________________________________________</u>_**

Though the audit is tailored mainly for World Bank operations, it can be applied in several projects by other agencies. For example, it can be implemented within the context of a transport or export competitiveness project, or as a stand alone exercise. Additional information is available at: www.worldbank.org/trade.

# _<u>Transit Corridors Europe Caucuses Asia (TRACECA)</u>_

This program was initiated by the European Union in 1993 to develop a transport corridor on west-east axis from Europe, across the Black Sea, through the Caucasus and the Caspian Sea to Central Asia. <mark>As part of TRACECA’s Harmonization of Border Crossing Procedures Project, a ‘BordAudit’ Database from 70 border crossing points in several countries</mark><sup>5</sup> <mark>has been established providing detailed information on the procedures observed (including a breakdown of the time</mark> spent on each procedure) and main documents required when crossing the borders by road, rail, and sea. Subsequent recommendations concerning harmonized border procedures have been based on this database.

The online country overview includes the list of the Technical Assistance Studies and the Investment Projects for the Rehabilitation of Infrastructure in each country and general information on the country.

# **_2.3.1  Tariff and Customs_**

The International Chamber of Commerce (ICC) produced _<u>Customs Guidelines</u>_ with the WCO, a comprehensive set of practices that the ICC consider should characterize all modern Customs administrations. The World Bank produced _<u>Customs Modernization Handbook,</u>_ which provides practical guidelines that draw lessons from successful customs modernization examples and reform initiatives. These reports are useful guidance for the recurring issues of current customs modernization initiatives.

_<u>Asia Pacific Economic Cooperation Tariff Database</u>_ Tariff rates for all 21 members of APEC .

The APEC Tariff Database is the Internet site with tariff information for the APEC member economies. After registration, you can access the following features:

- Tariff Chapter Listing Search

- Harmonized System Code or Tariff Heading Search

- Alphabetized Tariff Heading Listing Search

- Description Search

- Customs Guides and other related information (e.g. prohibitions, concessions, contacts, etc.)

- Global Search

The APEC Tariff Database is updated twice each year. Changes to tariff rates may occur between these update periods. These updates are dependent on the submission of data by the participating economies. Users are tariff related information such as Contacts, Prohibitions, Customs Guides, etc., provided by the member economies. Choose the one below that suits your

> 5 Armenia, Azerbaijan, Bulgaria, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Mongolia, Romania, Tajikistan, Turkmenistan, Turkey, Uzbekistan and Ukraine.

21 ____________________________________________________________________________________________________________
depressed agricultural productivity and reduced incentives for improving productive techniques. The political crisis in Afghanistan starting August 15, 2021, caused a large economic contraction, with GDP per capita dropping 23 percent between 2021 and 2022. Since then, the economy has seen slow recovery in agriculture and stabilization in the service and industry sectors. Using the macroeconomic data on a nonneutral distribution, projections of poverty in 2023 are between 63 and 59 percent (See Annex D for details).

**Figure 6: Evolution of sectoral growth**

<!-- Start of picture text --> 50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-10%<br>-20%<br>-30%<br>-40%<br>Agriculture Industry Services GDP per capita<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022p 2023<br><!-- End of picture text --> _Source: NSIA and World Bank projections_ _Note: Macroeconomic data up to 2021 come from the NSIA, while 2022 and 2023 data are World Bank projections. After 2021, the NSIA stopped producing official national accounts data. GDP data for 2022 and 2023 are projected information produced by World Bank staff using information from different monitoring sources._ Poverty projections using economic growth do not account for household adaptation strategies, economic stabilization, or conflict reduction. These conditions make it feasible for rural areas to experience lower poverty rates in Q3 2023 than in the corresponding periods in 2019/20 and 2021. In the case of Afghanistan, welfare monitoring shows that to cope with the economic instability after August 2021, households mobilized extra women and youth labor. This has resulted in structurally higher labor force participation and unemployment compared to 2020, especially among women and youth. Increasing wages and recent deflationary dynamics contribute to the observed improvement in Afghan households' capacity to satisfy basic needs. After an initial decrease in real wages following the regime change, nominal wages have started to recover to pre-crisis levels. Since April 2023, a sustained negative inflation rate has driven the value of real wages for both skilled and unskilled workers above pre-crisis levels, supporting improvement in household welfare (Figure 7, panel b). The mobilization of additional labor and rising wages has allowed households to maintain consumption levels, even with a lower GDP. Moreover, rural areas' reliance on agriculture, where 70 percent of the population lives, has acted as a shield against the large contraction experienced in the industry and service sectors, in particular in a context where conflict, 18
To its critics, BCA is a flawed technique that excessively emphasizes the quantification and monetization of health and safety risks and environmental amenities, trivializes the future through discounting, fails to meaningfully assess the value of avoiding catastrophe, and ignores distributional concerns. For a more detailed discussion of the advantages and disadvantages of BCA, we direct the reader to Harrington, et al. 2009.

# **III** . **The U.S. Regulatory System**

Requirements for economic analysis have been a part of the U.S. regulatory system for at least three decades. Although the roots of U.S. efforts to evaluate the benefits and costs of new regulations can be traced to Executive Orders (E.O.s) issued during the 1970s governing “Quality of Life Reviews” and “Inflation Alerts”, formal procedures for the conduct of benefit-cost analysis of environmental, health and safety regulations were first established in 1981, in E.O.12291. That Order required that major regulations, defined as those with an effect on the economy of $100 million or more, undergo a Regulatory Impact Analysis (RIAs) prior to issuance in the _Federal Register_ . Interestingly, the $100 million cutoff was set in nominal as opposed to real, inflation-adjusted terms. Thus, given the virtual doubling of price levels over the past thirty years, the real cutoff is currently about $50 million in 1981$.

E.O. 12866, issued in 1993, and E.O. 13563, issued in 2011, reaffirmed the key components of regulatory benefit and cost estimation, as well as the review process by the Office of Management and Budget (OMB), and placed emphasis on the transparency of the review. E.O.s 12866 and 13563 also put a heightened focus on the non-quantified consequences of major federal rules.

Regulations aim to establish specific requirements about which types of behaviors are legal and which are not. For example, a regulation issued by EPA to implement the Clean Air Act might determine what levels of a pollutant - such as sulfur dioxide - adequately protect human health and the environment. It also tells firms how much sulfur dioxide they can legally emit into the air, and what the penalty will be if they emit too much. BCAs and RIAs are systematic processes for calculating and comparing benefits and costs of a project or regulation. The results of such analyses can help identify whether a regulatory option is expected to have net benefits and also whether it may be net beneficial compared to other regulatory options. More generally, RIAs serve to inform decision-makers in regulatory agencies, Congress, and the public about the expected outcomes of regulatory decisions.

BCA compares the expected costs and benefits (expressed in money terms) of each option in a "present value" framework. Individual well-being is assumed to depend on the satisfaction of individual preferences, and monetary measures of welfare change are derived by observing how much individuals are willing to pay or give up in terms of 6
# **_5.  The Variable Selection and Description of Data_**

## **_i. Variable Selection_**

The analysis employs annual data covering the period from1959 until 2013. Data were obtained from the IMF’s International Financial Statistics (IFS) database, and were last accessed in January 2015. The data series that was obtained included the following:

- **Monetary Aggregates:** this is defined as nominal local currency in circulation in addition to demand deposits and time deposits (M2) deflated by the CPI with base year 2010 in logarithms.

- **Scale Variable:** This is a measure of activity of the real sector that reflects transaction demand for money. The volume of transactions is greater as the output grows. The scale variable will be represented as real GDP deflated by the CPI with a base year of 2010 in logarithms. The real consumption will also be employed to assess the feasibility of both variables and identify the most appropriate scale variable for modeling the demand for money in Egypt. This paper will assume absence of money illusion following Tobin (1972).<sup>15</sup> This means that people will not behave differently when they receive payoff information in real or nominal terms. Dropping this assumption is beyond the scope of this paper.

- **Price Variable:** The price variable is included to represent yields on real assets as alternatives to holding money. This will reflect on individuals' preferences of portfolio holdings. The choice of the price variable has been constrained by data availability and the degree of the openness of the economy (Sriram 1999). The literature has shown flexibility using a GDP deflator, a GNP deflator, and a wholesale price index in addition to the consumer price index. The Egyptian economy has been suffering from chronic inflation problems. Accordingly, inflation should be included as an opportunity cost for real assets in Egypt. This paper will use CPI to compute inflation as a price variable since it is the best available consistent series that can serve as a price deflator. This is defined as the year on year percentage growth in the CPI with base year 2010 (not in logarithms). It is worth noting that many earlier studies employed inflation as one of the price variables including Ericsson and Sharma (1996), Ericson, Campos and Tran (1990), and Johansen (1992).

> 15 Tobin argues that “an economic theorist can, of course, commit no greater crime than to assume money illusion”.

19
one regulatory-grade monitor functioning at any given time. Therefore, this data implies a uniform level of pollution across the city (Figure 8 Panel A). The PM 2.5 level from the regulatory-grade monitor aligns closely with that from the satellite data (Figure 8 Panel B), but both sources miss variation across space. A network of low-cost monitors captures significant spatial variation in pollution across the city, but the levels are substantially lower than those from the regulatory or satellite data due to the limitations in capturing dust (Figure 8 Panel C). The presence of at least one regulatory-grade monitor makes it possible to conduct a local calibration, which can then be used to adjust the PM2.5 data from the low-cost monitors to better align with the levels from the regulatory monitor, while preserving spatial variation across the city (Figure 8 Panel D). We can also calibrate the low-cost data using the satellite data, since there may not be high-quality regulatory data available for calibration in LMIC contexts. Calibrating the low-cost monitors using the satellite data shows potentially an overestimate of air pollution across the city, with the average value for the city higher than the regulatory one.

For the two calibrations, we use the values generated based on the hourly calibration and average the calibrated hours across the day. Alternatively, we can average the hours across the day and apply the daily calibration. The daily calibration, both with the regulatory and the satellite data, has a much higher out-of-sample R2, which may suggest that it could lead to better estimates (Tables A2). Applying the daily calibration for the same day, the daily local calibration looks quite similar to the one using the hourly calibration, but importantly the satellite calibration is now much more closely aligned with the local calibration and closer to the average regulatory value. These results suggest that investing in a combination of regulatory and low-cost monitors allows for a much richer dataset that can capture both magnitudes and variation in space. In the case where regulatory data is not available, though, it may be possible to calibrate low-cost monitors using satellite data at a lower temporal frequency (daily or lower).

21
function in the process of structural transformation by offering opportunities to those with better non-farm opportunities to move out while allowing land-poor but labor-rich and more productive households to increase the amount of land they cultivate. Although data on land sales market participation is too scant to allow generalization, this seems also true for land sales markets in Uganda, in line with earlier evidence.

Our analysis also points towards a number of areas where improvements in household questionnaire design and associated data collection protocols on land could make the data even more useful. Key areas in this respect are (i) a more consistent recording of the history of land acquisition to proxy for purchases; (ii) identifying rights held by individuals, including rights to identify plot manager and owner, possibly linking to land acquisition or inheritance history; (iii) matching renting in and renting out; (iii) training enumerators to distinguish different types of formal and informal documents (including maps) that may have been obtained to document ownership; (iv) including proxy variables for tenure insecurity; and (v) cross-checking actual or hypothetical land prices to ensure their realism.

Improving the capacity of African land institutions implies considerable scope for complementing household surveys with administrative data to exploit complementarities and cross-check, (e.g. to explore the extent of informal transactions and potential reasons for them), to cross-check information (e.g. on sales prices), and to follow up in more detail on rare events such as disputes. Exploring these opportunities, together with links to remotely sensed imagery, is likely to further expand the usefulness of household data to understand structural transformation in Africa and beyond.

13