rafmacalaba/data-use-mentions-extended huggingface dataset:
# VI. References

- Betcherman, G., 2014. Labor Market Regulations: What do we know about their impacts in Developing Countries?. _The World Bank Research Observer,_ pp. 124-153.

- Betcherman, G. & Pages, C., 2009. _Estimating the Impacts of Labor taxes on Employment and the Balances of the Social Insurance Funds in Turkey,_ Washington DC: The World Bank.

- Bourguignon, F. & Spadaro, A., 2006. Microsimulation as a tool for evaluating redistribution policies.. _The Journal of Economic Inequality 4, no. 1 ,_ pp. 77-106.

- Creedy, J. & Duncan, A., 2002. Behavioural Microsimulation with labour Supply Responses. _Journal of Economic Surveys,_ 16(1).

- De Agostini, P. P. A., Sutherland, H. & Tasseva, I., 2014. The effect of tax-benefit changes on the income distribution in EU countries since the beginning of the economic crisis.. _EUROMOD Working Paper,_ Issue No. EM9/14.

- Figari, F., Levy, H. & Sutherland, H., 2007. Using the EU-SILC for policy simulation: prospects, some limitations and suggestions.. _EUROMOD Working Paper Series,_ Issue No. EM1/07.

- Galiani, S. & Weinschelbaum, F., 2012. Modelling Informality Formally: households and firms. _Economic Inquiry,_ Volume 50, pp. 821-838.

- Gimpelson, V. & Kapeliushnikov, R., 2011. Labor market adjustment: Is Russia different?. _Series WPR labour Markets in Transition_ , Issue WP3/2011/04.

- Gimpelson, V. & Kapeliushnikov, R., 2014. Between Light and Shadow: INformality in the Russian Labour Market. _IZA Discussion Paper_ , Issue 8279.

- Immervoll, H. & O‘Donoghue, C., 2009. Towards a multi-purpose framework for tax-benefit microsimulation: lessons from EUROMOD. _International Journal of Micro Simulation,
political views (see Williams, 1964; Schuman and Converse, 1971; Hatchett and Schuman, 1975; Reese et al. 1986; Davis, 1997; Davis and Silver, 2003).

Studies of the enumerator effect in developing countries are few and all present a quasi-experimental design due to problems with randomization in the field (Bischoping and Schuman, 1992; Flores-Macias and Lawson, 2008)<sup>3</sup> . The number of studies looking at enumerator effects in shaping answers to political questions in developing countries is extremely limited. Bischoping and Schuman (1992) document the impact of perceived enumerator political opinions on voting intensions in Nicaragua in 1990 while Adida et al. (2016) show that co-ethnicity between respondent and enumerator influences answers to voting behavior questions in the Afrobarometer survey. To the best of the authors’ knowledge, this is the first paper which provides evidence of the enumerator effect affecting the stated political preferences in a developing country using an experimental design.

# **2. Experimental design**

The enumerator experiment described in this paper was conducted during the data collection of a separate research project, described in detail in Fiala and Premand (2018). That project was an experimental evaluation of a large-scale local accountability program conducted in 2016 that included 1064 villages and 8403 respondents. The authors of the present paper utilize the sample of participants in that study for the experiment that is described here.

> 3 The only two exceptions are Himelin (2016) and Blayedes and Gyldum (2013). The former explores the presence of an enumerator effect in responses to a survey on corruption, women rights, and community values in Timor Lest. The second looks at the effect of having an interviewer who wears a _hijab_ on responses to questions related to religiosity and Islamic cultural norms in a survey experiment in Cairo (Egypt).

6
Thus there is empirical evidence that:

- Institutions do indeed matter;<sup>49</sup> and

- Some performance areas are more susceptible to the institutional environment than others ."

**Figure** 7. **Performance and Institutional Environment in sampled Bolivian public sector organizations**

<!-- Start of picture text --> PERFORMANCE: INSTITUTIONAL ENVIRONMENT<br><!-- End of picture text --> <!-- Start of picture text --> 8 ----- - ---- __ __ __ __ __<br>y 0.7429x + 0.4709<br>R= 0.7026<br>* audit banking-regulati n<br>7<br>/ + central bank<br>06<br>A tax 4 + ine t evteleom-regulation<br>agrarian refotennstiution<br>Santa Cru2 *<br>components5ofperformance-whic agrepresentednAppendiculture<br>Te e t customs<br>labor road<br>4 .<br>5 ~~~~~6 7 8 9<br>institutional environment<br><!-- End of picture text --> 49 This assertion was based on differences in the estimated coefficients, i.e., slope, and in goodnessof-fit measures, i. e. R2, for the relationships between institutional enviromment and three different components of performance-which are presented in Appendix 5. _50_ This same relationship is being tested using data sets from the public sectors of Armenia, Bolivia, East Caribbean states, and Guyana. Results of these tests will provide more empirical evidence.

23
<!-- Start of picture text --> 100<br>88.0 86.6<br>90<br>80 71.5<br>67.5<br>70 64.758.3 59.9 64.3<br>55.5<br>60 52.0<br>48.0<br>50 40.1 41.744.5<br>35.3 35.7<br>40 32.5<br>28.5<br>30<br>20 13.3 12.0<br>10<br>0<br>Children Adult IDP/Refugee Non-IDP/Host<br>Age Displacement Status<br>Ethiopia N.E. Nigeria Somalia South Sudan Sudan<br><!-- End of picture text --> Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018).

_Table 3: Sex ratios_

||**Ethiopi**|**a**|**N.E. Ni**|**geria**|**Somalia**||**South S**|**udan**|**Sudan**||
|---|---|---|---|---|---|---|---|---|---|---|
|Sex Ratio (%)|Male|Female|Male|Female|Male|Female|Male|Female|Male|Female|
|Population|48.9|51.1|48.9|51.1|50.4|49.7|46.8|53.2|50
26.6|
|**Services**|51.0|67.2|49.3|27.8|9.4|10.6|8.5|
|**Government services**|7.4|12.5|16.1|||||
|**Private services**|43.6|54.7|33.2|27.8|11.0|10.6|10.0|
|Construction services|||||1.1||1.4|
|Hotels and restaurants|||||32.1||38.2|
|Transport|||||33.2||26.3|
|Post and telecommunications|||||19.7||14.5|
|Financial intermediation|||||1.7||4.6|
|Real estate services|||||||1.4|
|Renting, IT-services, res. and dev.|||||10.7||21.1|
|Other business services|||||2.1||8.7|
|Recreational,cultural and sportingact.|||||||2.1|
||100.0|100.0|100.0|100.0|16.7|100.0|33.0|

Source: Own calculations based on Moldova MAMS SAM 2008.

7
goods and services that households use. One drawback, however, is the difficulty in pinning down the exact minimum level of energy required for basic needs, owing to the significant country and regional differences in cooking practices and heating requirements. Energy consumption is often location-specific due to the vast differences in climatic conditions worldwide. Furthermore, the minimum needs for physical quantities of energy are chosen somewhat arbitrarily.

Among the methods based on physical energy requirements, an important one was proposed by Bravo et al. (1979). The Bravo measure quantifies a household’s direct energy needs in considerable detail including those for cooking and lighting. The average household in a tropical country such as Bangladesh requires about 27.4 kilograms of oil equivalent (KgOE) per capita per month to meet its essential direct energy needs. A second interpretation of physical energy requirements, proposed by Goldemberg (1990), includes a wider range of energy-using activities, placing that estimate at 32.1 KgOE per capita per month. A third, more universal interpretation, which examines the physical needs of daily cooking and lighting based on various surveys from around the world, results in a much lower minimum household energy requirement (50 KgOE per year) (Modi et al., 2005). Thus, varying assumptions yield vastly different results.

A second approach defines energy poverty as the level of energy used by households below the known expenditure or income poverty line (Foster et al., 2000).<sup>4</sup> Because the expenditure-based poverty line is well-defined in most countries, this approach is fairly attractive since it is unnecessary to measure how much energy people are actually using. One can simply assess the average fuel used by the households at the expenditure poverty line based on a household energy survey. But the drawback of defining energy poverty based on such general criteria, as opposed to an energy basket

> 4 The terms _expenditure poverty_ and _income poverty_ are used interchangeably throughout this paper, although the measurement of such poverty involves expenditure only.
Athey, S., & Wager, S. (2021). Policy learning with observational data. _Econometrica_ , _89_ (1), 133– 161. https://doi.org/https://doi.org/10.3982/ECTA15732 Becker, G. S. (1968). Crime and punishment: An economic approach. _Journal of Political Economy_ , _76_ (2), 169–217. Retrieved January 22, 2024, from http: //www. jstor. org /stable/ 1830482 Belloni, A., Chernozhukov, V., Fernández-Val, I., & Hansen, C. (2017). Program evaluation and causal inference with high-dimensional data. _Econometrica_ , _85_ (1), 233–298. https://doi. org/https://doi.org/10.3982/ECTA12723 Belloni, A., Chernozhukov, V., & Hansen, C. (2013). Inference on Treatment Effects after Selection among High-Dimensional Controls†. _The Review of Economic Studies_ , _81_ (2), 608–650. https://doi.org/10.1093/restud/rdt044 Besley, T., Jensen, A., & Persson, T. (2023). Norms, enforcement, and tax evasion. _Review of Economics and Statistics_ , _105_ (4), 998–1007.

Bhargava, S., & Manoli, D. (2015). Psychological frictions and the incomplete take-up of so- cial benefits: Evidence from an irs field experiment. _American Economic Review_ , _105_ (11), 3489–3529.

Bishop, C. M. (2006). _Pattern Recognition and Machine Learning_ (M. Jordan, J. Kleinberg, & B. Schölkopf, Eds.; Vol. 4). Springer. https://doi.org/10.1117/1.2819119 Boning, W. C., Hendren, N., Sprung-Keyser
# **Table A1: Variables for sign-restricted VAR (monthly data)**

|**Variable**|**Transformation**|**Source**|
|---|---|---|
|**2-Year treasury note yield at constant**<br>**maturity**|First difference|Haver Analytics|
|**10-Year treasury bond yield at constant**<br>**maturity**|First difference|Haver Analytics|
|**Standard & Poor's 500 Composite Index**|Log first difference|Haver Analytics|
|**5-Year inflation expectations (Jan 1982-Dec**<br>**2002)**|First difference|Haubrich, Pennacchi,<br>and Ritchken (2012)|
|**5-Year breakeven inflation rate (5-year**<br>**nominal Treasury yield less the 5-Year**<br>**TIPS—inflation-protected—yield)**|First difference|Federal Reserve Bank<br>of St. Louis|

43
<!-- Start of picture text --> Figure 3 Linkage to manufacturing GVCs (%): Non-resource rich country<br>cases<br>100<br>90<br>80<br>70<br>60<br>50<br>44<br>40<br>34<br>30 29<br>24<br>20<br>10<br>0<br>-10 Malawi Rwanda Senegal Uganda<br>-20<br>Upstream 2015 Downstream 2015 Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

<!-- Start of picture text --> Figure 4 Linkage to manufacturing GVCs (%): Non-oil resource rich<br>country cases<br>100<br>90<br>80<br>70<br>65<br>60<br>50<br>40 39<br>30 31<br>27<br>20<br>10<br>0<br>DRC Ghana South Africa Zambia<br>-10<br>-20<br>Upstream 2015 Downstream 2015 Δ GVC 2015/1995<br>Δ Upstream 2015/1995 Δ Downstream 2015/1995 GVC 2015<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

26
For the purposes of exploring drought-driven displacement, we kept the analysis constrained to Badghis Province in Northwestern Afghanistan, but the same approach is easily replicated in other regions or even country wide. The input data used in this analysis were the UN IOM DTM dataset from 2020, and the Nighttime Lights data were monthly composites from January 2018 to December 2020.<sup>2</sup> To replicate the methodology, one may open the Google Earth Engine script at the universal link<sup>3</sup> or download the script directly from GitHub<sup>4</sup> . In the GitHub repository there is a folder titled “NTL,” which contains the monthly NTL script that can be extracted to the buffers, as well as a script for extracting a monthly average of NTL data for the entire area of interest. The user then may update the boundaries of the analysis by simply uploading a new dataset and preserving the object name ’table’ so that the script will apply the same processes on the new area of interest. The user would also update the export location to ensure that the file saves to a location on their personal Google Drive account (or alter the code to save it as an Earth Engine ’asset’, or dataset. The outputs of the methodology are a panel dataset in the form of a CSV (unless otherwise specified in the code) and can be exported from Drive onto one’s local machine for further statistical analysis.

# Appendix C: Quadratic model

## C.1. Benchmark NTL growth (quadratic)

Table 5 displays the linear and quadratic regression of NTL levels on its past values, at the settlement level, controlling for district fixed effects. As seen previously, there are 294 settlements, spread across 7 districts. Let’s define N the average NTL in the 1km buffer around the settlement _i_ in district _j,_ at date _t (_ each month from Jan 2018 to Jan 2020) _._ The following regressions
as well as the level of education and the age dependency ratio, all of which are likely to differ across countries. Indeed, Beenstock, Dickinson and Khajuria (1986) develop a theoretical model for life insurance supply and demand, which is tested with 1970-1981 insurance premium data of 10 industrialized countries from Swiss Re’s Sigma publication. They argue that life insurance demand varies with income because higher income allows to buy more of it, but also because higher income increases the insurable interest, that is the financial loss of the dependents of a deceased income earner. Estimating the parameters of their mathematical model on the data of the 10 countries, they find income elasticity of insurance demand to be positive for all countries but to vary considerably from country to country.

In a longer term perspective, and in line with theorical insights from Kuznets (1955) and Greenwood and Jovanovic (1990), Enz (2000) postulates that “Researchers usually assume a constant income elasticity which, together with an income elasticity greater than one, implies that there are no limits to insurance penetration” (without naming or quoting any particular researcher), and argues that this assumption leads to unreasonable levels of insurance penetration if GDP grows constantly (even at moderate rates). This criticism motivates him to explain the relation between insurance premium and GPD with a logistic function that has additional parameters - minimum and maximum insurance penetration – resulting in variable income elasticity and an inflection point at which the increase in insurance penetration slows. He then introduces the term “S-curve” for this function. His theory proposes to explain non-life insurance as well as life insurance, and he estimates the parameters of the S-curve with 90 countries’ life and non-life premium data from the years 1970 to 1998 (sourced again from Swiss Re’s Sigma publication). As a result, he can quantify the inflection points quoted above and points of maximum income elasticity for life (15,000 USD) and non-life (10,000 USD) insurance and estimate a maximum life insurance
rate, an indicator for exchange rate misalignment, and the occurrence of banking crises. Note that the analysis in Section 3.2 uses only the inflation rate and real effective exchange rate given that none of the WAEMU countries experienced a banking crisis over the period of analysis.<sup>7</sup> External conditions constitute another potential growth driver. Akin to most SSA economies, WAEMU countries are vulnerable to fluctuations in global commodity prices. The analysis controls for externally induced economic shocks using terms of trade and commodity export prices. The role played by external factors is particularly important in light of the recent downward spiral in commodity prices. Commodity price fluctuations might entail only transitory growth effects; however, resource windfalls can also affect long-term aggregate supply by increasing capital accumulation and influencing institution building. To ensure consistency with the baseline model in Brueckner (2014), this paper constructed a commodity export price index for the WAEMU countries based on the procedure in Arezki and Brueckner (2012).

# **3.1 Empirical Model**

The baseline cross-country regression model specifies real GDP as a function of several growth determinants and takes the following form<sup>8</sup> : where _yi,t_ is the log of real GDP per capita of country _i_ at time _t_ ; _yi,t-_ 1 stands for one-period lagged GDP per capita, with _β_ � measuring the speed of convergence in income per capita; _Xi,t_ for variables capturing structural, stabilization and external effects; _β_ � is the key parameter of interest and captures the effect of the variables on output per capita; _ηi_ is a set of country fixed effects accounting for unobserved country-specific and time-invariant factors (such as geographical location, historical legacies, and legal origins) that may have significant impact on both income and the explanatory variables; _vi_ is a vector of time dummies capturing universal time trends; and finally, _εit_ represents the error term. The original analysis is based on data for 126 countries spanning the period 19702010. Variable definitions and
# **7. Annexes**

# **I. Calculating comparable poverty trends in Nepal**

Poverty in Nepal has been measured using surveys of living standards conducted periodically by the Central Bureau of Statistics (CBS). Three rounds of the Nepal Living Standards Survey (NLSS) had been conducted in 1995/96, 2003/04 and 2010/11. Poverty headcount rates were 42 percent in 1995, 31 percent in 2003, and 25 percent in 2010. While the estimates from the first two rounds of the NLSS are comparable, the same is not true for the most recent estimate. This is due to two reasons: refinements introduced to the survey instrument in the 2010 NLSS and methodological changes in the way the poverty lines were calculated in 2010.

The most important change introduced in the 2010 NLSS was the way food consumption data were collected.<sup>24</sup> In addition to asking the households about their consumption of food items during a “typical month” (as in the first two rounds of the NLSS), the 2010 NLSS also enquired about food consumption during “the last 7 days”. This focus on the most recent consumption was in accordance with international best practice to minimize the so-called “recall bias” in estimating food consumption (Beegle et al. 2012). This had direct implications on the way poverty is measured. While the official estimates for 1995 and 2003 were based on a “30 day recall” for food consumption, the 2010 estimate relied on a shorter, “7-day recall” horizon.

Second, there was a major revision in the way poverty lines were calculated following the 2010 NLSS. In simplest terms, a poverty line is a threshold expressed in monetary terms below which individuals are deemed poor. This threshold pertains to the minimum amount of Nepalee Ruppe to satisfy minimum caloric requirements and basic needs for nonfood goods and services. In 1995, the official poverty rate of 42 percent was based on the poverty line of Rs. 5,
Figure 4 further looks at specific cases where individuals were identified as owners/managers of a business in one module, but not the other. Again, the nonfarm enterprise module captures a greater share of individuals as owners/managers than the labor module; although the number of cases is not large, we do see much wider gaps across modules in the IHS4 compared to the IHPS, and the labor module in the IHPS also tends to capture a greater share of individuals owning/managing an enterprise. Table A4 in the Appendix also runs regressions of individuals’ work in enterprises using data strictly from the enterprise module and finds very few differences by survey approach. Where there is significant underreporting under the business-as-usual approach, the magnitudes of the coefficients are smaller compared to Table 4. Overall, therefore, underreporting of enterprise/business participation under the business-as-usual approach is linked significantly to the level of detail of questions in the labor module, which the individual-interview approach helps address.

**Figure 4.  Discrepancies across Labor and NFE Modules in Identifying Individuals Who Own/Run an NFE for IHPS versus IHS4**

# **_3. Interaction effects of business-as-usual approach on labor reporting: the role of household wealth quintiles_**

To further understand whether under- and over-reporting in the business-as-usual scenario are stronger for certain subpopulations, Appendix Tables A5 and A6 summarize the interaction effects (on participation in the last 7 days; results were very similar for participation in the last 12 months) of the business-as-usual survey approach with education, relation to household head, age, household wealth and location. The results are presented separately for the samples of men and women and show that along with age (being in the younger 15-24 age group), interaction effects with household wealth are associated with a greater range of labor participation outcomes for men 20
, W. Griffitshs, and D. Yaukey. 1972. _The Dacca Family Planning Experiment._ Special Issue No. 3, Pacific Health Education Report.

Greenwood, J., A. Seshadri, and M. Yorukoglu. 2004. “Engines of Liberation.” _Review of Economic Studies_ 72 (1): 109-33.

Gupta, S. 1999. “The Effects of Marital Status Transitions on Men’s Housework Performance _._ ” _Journal of Marriage and the Family_ 61: 700-11.

Gürsoy, A. 1996. “Abortion in Turkey: A matter of states, family or individual decision _._ ” _Social Science and Medicine_ 42 (4): 531-42.

Han, W.J., C. Ruhm, and J. Waldfogel. 2009. “Parental Leave Policies and Parents' Employment and Leave-Taking _.” Journal of Policy Analysis and Management_ 28 (1): 2954.

Heathcote, J., K. Storesletten and G. Violante. 2010. The Macroeconomic Implications of Rising Inequality in the United States. Journal of Political Economy, 118 (41): 681-722.

Hochschild, A. 1989. _The Second Shift: Working Parents and the Revolution at Home_ . Avon, New York.

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OZIER - REPLICATION REDUX – p. 9 of 41

**Figure 3**

<!-- Start of picture text --> How replicable are studies in economics?<br>100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Dewald, Thursby, and Anderson Chang and Li (2015) Galiani, Gertler, and Romero (2018)<br>(1986)<br>Replication conceivable: Are ‐either‐ data ‐or‐ programs available?<br>Replication viable: Are ‐both‐ data ‐and‐ programs available?<br>Conditionally successful: If ‐both‐ data ‐and‐ programs are present, do results replicate?<br><!-- End of picture text --> When is a replication even conceivable? 30 years ago, Dewald, Thursby, and Anderson (1986) were only able to obtain something resembling replication data for just over half of the economics papers for which they sought data; that figure has risen to between 60 and 80 percent; in other words, the rate of data unavailability has nearly been cut in half.<sup>5</sup> Getting usable data and code in hand to make a viable replication attempt was very hard in those days: of 54 received data sets, only 8 satisfied a basic usability criterion (yielding the 15 percent number from the 1986 study), driving the chances of even being able to try a replication below 10 percent. As Figure 3 shows, that viability number has risen considerably, though how high depends on which sample of journals is examined.<sup>6</sup> Galiani, Gertler, and 5 Dewald, Thursby, and Anderson (1986) found that replication datasets were twice as likely to be