rafmacalaba/data-use-mentions-extended huggingface dataset:
this can be mainly explained by the small size of financial systems across the region, limited banking sector development (another precondition for capital market development), and limited domestic savings. By 2018, only half of the region’s countries had a stock market, but with very low liquidity.<sup>18</sup> Development of bond markets is even more rudimentary, with few if any nonfinancial companies in most countries being able to issue bonds.

While recent reforms in several countries in Sub-Saharan Africa have created private pension systems that are rapidly accumulating assets under management, the pension fund industry only intermediates a fraction of those assets into productive long-term investments (reverse maturity transformation). Mortgage finance is very limited (Badev et al., 2013) as discussed above., which is again in line with global experience where it is mostly high-income countries where one sees a significant role for mortgage finance.

Addressing the long-term finance gap first requires addressing a data gap. While data availability on access to finance has improved enormously across the globe and especially in Africa, driven partly by global data collection efforts, such as the World Bank’s Global Findex and the IMF’s Financial Access Survey, and partly by country-specific efforts, such as the FinScope and FinAccess surveys, very limited data is available on the depth, efficiency and accessibility of long-term financial markets, though there are currently attempts at increasing the availability of indicators of long-term finance. Specifically, the donor-funded “Africa Long-Term Finance Initiative” provides data on sources and uses of long-term finance across countries in the region, the depth

> 18 This is illistrated by the following statement by a market practitioner, “an entire year’s worth of trading in the frontier African stock markets is done before lunch on the New York Stock Exchange,” quoted in Christy (1998). It should be noted that some countries are served by regional stock exchanges.

38
**Table 2. Examples of expected impact of decarbonization policies on the demand for labor**

|Policy|Expected impact<br>C|ountryor region<br>Method|Reference|
|---|---|---|---|
|State-level green policy|1% more green jobs|US<br>Historical data|Yi (2013)|
|Regional-level energy|0.24% increase in|EU<br>Simulation|Markandya et|
|shift|employment||al. (2016)|
|Energy efficiency<br>measures and renewable<br>energy targets|4 million+ green job-years by<br>2030|US<br>Simulation|Wei et al.<br>(2010)|
|Investment in<br>renewables and energy<br>efficiency|Around 5 more FTE jobs<br>compared to similar<br>investment in fossil fuels|US<br>Simulation|Garrett-<br>Peltier, (2017)|

State-level green policy refers to market-based tools that provide support for renewable energy and energy efficiency. Energy shift refers to a change from carbon intensive sources toward gas and renewables.

The new jobs that come with the transition to low carbon may require new skills, often referred to as _green skills_ . There is, by our knowledge, very little rigorous evaluation in this area using historical data. One US study finds that changes in environmental regulation are positively related to demand for green skills, in particular for technical and engineering tasks (Vona et al., 2018).<sup>lxvi</sup> Cooling technology in the workplace is an important example of _labor-oriented adaptation_ that may improve worker productivity and labor supply, as well as worker well-being. As mentioned previously, firm adaptation may be less likely for low-productivity jobs (Somanathan et al., 2021). Government subsidies can be beneficial in situations where a change in behavior is considered desirable but faces
# **V. Conclusions**

In this paper we examined measurement errors in household consumption data. This is inherently difficult due to lack of a gold standard for comparing with survey estimates so as to reveal the nature of the errors. Nevertheless we provide indirect evidence on the nature of measurement errors using two different regression approaches and an experiment where eight different consumption questionnaires were randomly assigned to households. The results are most consistent with errors in measured consumption that are negatively correlated with true values, especially for food. Such a correlation with true value is likely because for most surveys reporting tasks become harder as the household gets richer and the consumption pattern more varied. A negative correlation with true values implies mean reversion, so even when mismeasured consumption is a dependent variable there may be bias in regression coefficients, and when consumption is an explanatory variable the usual attenuation bias may not apply. Both cases should be of serious concern to economists who rely on accurate household consumption data for their measuring and modeling of living standards.

15
<br>(1990H1-2017H2)|Index is in nominal U.S. dollars.|World Bank, Commodity Price<br>Data (the Pink Sheet)|
|Global liquidity<br>(1990H1-2017H2)|International claims on all sectors (annual change).|Bank for International<br>Settlements, Global Liquidity<br>Indicators database|
|Global output gap<br>(1990H1-2017H2)|Real GDP-weighted average of country specific output gaps estimated<br>using a multivariate filter.|World Bank (2018)|

Note: CPI = consumer price index; EMDEs = emerging market and developing economies; IMF = International Monetary Fund.

37
Figure 4b reports the counting results when these 98 species are withdrawn from consideration for the second round. Although high counts in the east and southeast are again evident, the maximum cell count in this round is in the northeast part of Amazonas state. This identifies the second-priority area, which provides some coverage for 43 unprotected species. The next iteration identifies the third priority area in the southeast, in eastern Paraná state, which offers some coverage for 32 species. The fourth iteration also identifies the highest-count cell in Paraná, in its northern area, offering coverage for 30 species. Together, these iteration steps identify four 37 km x 37 km areas that provide coverage for 203 (14.4%) of Brazil’s 1,412 unprotected endemic species.

# **Cameroon**

Figure 6 displays Cameroon’s protected areas in dark green. Its protection intensity is substantially less than Brazil’s, with 55,600 sq km (12%) of Cameroon’s 464,319 sq km in protected areas. In our database Cameroon has 645 endemic species, of which 189 (29.3%) are effectively unprotected. Figure 5 displays images of some unprotected species and Figure 6 displays the ranges and public-domain images of some vertebrates (Figure 6a), plants (6b) and arthropods (6c) from the unprotected group. Their geography has a pattern that resembles Brazil’s: They lie outside existing protected areas (by construction), and their occurrence areas are generally small because larger occurrence areas will often overlap with protected areas. In Cameroon, the displayed species are predominantly located in the country’s west and southwest.

10
used by practitioners in preparing welfare aggregates for distributional analysis: the log-linear hedonic model and the self-assessment method. According to a World Bank survey on the main characteristics of the welfare aggregates used for measuring poverty in 70 developing countries, only 28 cases include housing. Among these 28, 12 use a hedonic model, 11 use self-assessment, and other four adopted a combination of hedonic model and self-assessment (World Bank 2015).<sup>9</sup> While we refer the reader to Balcázar et al. (2017) for an extensive review of the literature on rent imputation for welfare analysis, we briefly discuss in the following the log-linear hedonic model and the self-assessment method.

As underlined by Malpezzi (2002), the semi-logarithmic model has several advantages. First, the coefficients have a straightforward interpretation: they show approximately the percentage change in the imputed rent for a given unit-change in the covariates.<sup>10</sup> It also mitigates the heteroskedasticity problem (Diewert 2003). Second, it allows for flexible specifications of the covariates that can be either continuous or binary. Third, and most importantly, it allows the marginal rental value to be a nonlinear function of size and quality of the dwelling, which is a theoretical requirement of the hedonic model (Rosen 1974; Freeman 1993).

The second imputation approach used in this analysis is self-assessment. The self-assessment method relies on the assumption that owners can provide a good estimate of the market value of their dwellings, perhaps with the help of interviewers, as suggested in Garner and Kogan (2007). This information is frequently found in household budget surveys, where home-owners and tenants enjoying subsidized arrangements are asked to estimate the rental price they would pay to live in the current dwelling (Frick et al. 2010). The question may be asked in two ways that differ in whether the homeowner is asked to play the role of the landlord or the tenant. In the first case, the question would be some variant of “ _How much
|N|5,998|5,998|5,998|5,998||||

Notes: All values based on observed – not predicted – per capita household expenditure. Poverty calculations made using poverty line of 684,401 Ghanaian cedis per person per year, which is equivalent to poverty headcount index of 39.5 percent that is cited as Ghana poverty line in Ghana Statistical Service (2000: Table 2). All values are weighted.

In 1999, US $1.00 = 2,394 Ghanaian cedis.

Source: Calculated from 1998/99 Ghana Living Standards Survey (GLSS 4).
**Table 4: Decomposition of Math Scores over Time**

|**(asperce**|**ntage of total test score differential)**<br> <br>|
|---|---|
||Endowments<br>Unexplained<br>(Characteristics)<br>(Returns)|
|Constant|0.0<br>-99.4|
|Institutions|0.2<br>19.2|
|Schools|10.9<br>6.6|
|Family|-8.8<br>37.1|
|Student|5.5<br>128.6|
|Total|7.8<br>92.2|
|Overall|100.0|

The bulk of the overall difference resulting from changes in the returns to characteristics was due to student characteristics. That is, for a given set of student characteristics, Indonesian schools were more able to convert those factors into higher levels of learning in 2006 than in 2003. This is a significant finding since more and more children enter the lower secondary school system with every passing year. As we have shown above, most of the new entrants come from poorer backgrounds and from homes with parents who have received less schooling. According to the 2006 math PISA scores, Indonesia was better able to educate students regardless of their age. Math scores for 2003 and 2006 by mean age are presented in Table 5. In 2003 and 2006, the average-aged student achieved the mean score for the country overall. In 2003, there was not much variation in the ages of students who were one standard deviation above the mean or of those who were one standard deviation below the mean age. With a mean age of 15.71 years and a standard deviation of 0.27 years in 2003, students were between 151⁄2 and almost 16 years of age. In 2006, the mean age was 15.78 years
countries<br> 80 Estimated BRI Debt financing - Planned 80 20<br>Estimated BRI Debt financing - Under constr. 18<br> 70 70<br>second 16<br> 60 60<br> 14<br> 50 50 12<br> 10<br> 40 40 LIDCs EMs<br> 8<br> 30 30 6<br> 20 20 4<br> 2<br> 10 10<br> -<br> - 5 10 15 20<br> - - Loan commitments<br>LIDCs EMs<br>Estimated BRI debt financing<br><!-- End of picture text --> Sources: _International Debt Statistics_ , WIND database and authors’ calculations.

14 The World Bank’s DRS was established in 1951. The DRS system captures detailed information at loan level for external borrowing of reporting countries using standardized set of forms. The primary objective of the DRS is to provide the Bank with reliable and timely external debt information to assess a borrowing country's foreign debt situation, creditworthiness, and economic management; and conduct its country economic work and assess regional and global indebtedness and debt servicing problems. Data submitted by countries are entered into the DRS database, from which the aggregates and country tables are produced and published annually in the International Debt Statistics publication (successor to Global Development Finance and World Debt Tables). For additional information see <u>https://datahelpdesk.worldbank.org/knowledgebase/articles/381934-what-is-the-external-debtreporting-system-drs.</u> 15 Including government agencies, Exim Bank of China, financial institutions and suppliers’ credit. 16 The comparison is limited to 9 LIDCs and 15 EMs countries compared to a total number of 43 countries with identified BRI investment either planned or under construction during the period 2013-2017.

8
# _7.1 Correlations from alternative cyclical components_

The Hedrick-Prescott filter employed in section 4.1 to estimate the correlation of the cyclical components of GDP and the different fiscal variables is often criticized for its so-called end-point problems and spurious dynamics. These issues seem to be especially important when real output is far from its potential value. Recently, Hamilton (2018) suggested an alternative procedure to filter variables that avoids many of the shortcomings of the Hedrick-Prescott filter while preserving many of its advantages. He proposes a “linear projection”, in which a variable at date _t+h_ is regressed on the four most recent values as of date _t_ , resulting in stationary residuals that represent a cyclical component. The value of _h_ is suggested to be 2 for annual data, given that the primary reasons for being wrong in predicting macro variables at a horizon of 2 years ahead are cyclical factors.

**Table** **<u>6</u>** <u>Correlation coefficients between differently filtered fiscal variables and GDP</u>

|**Country**||**Government**<br>**expenditure**|Obs.|**Inflation**<br>**tax**|<br>Obs.|**Tax**<br>**revenue**|Obs.|
|---|---|---|---|---|---|---|---|
|_Bangladesh_|HP filter|0.33*|26|-0.26|22|-0.08|26|
||Hamilton (2018)|0.30|26|0.13|22|-0.15|26|
|_Bhutan_|HP filter|0.35*|26|-0.33*|26|0.2|26|
||Hamilton (2018)|0
# Appendix A1 – Supplementary data from NALO 2018

**Table A1.1 Student reports of selected variables, by school gender**

||||**Male(%)**|**Female(%)**|**_φ/φc_**|
|---|---|---|---|---|---|
||Repeated a year at school because of poor results||14.5|6.6|.129***|
|||_0_|22.6|12.0||
||How man books have ou read?|_1-5_|27.8|33.3|148***|
|Gd 4|y y|_6-10_|15.7|14.4|.|
|rae||_>10_|33.8|40.4||
|||_A lot_|81.4|84.1||
||Math is important in life|_Somewhat_|13.3|12.6|.051***|
|||_Never_|5.3|3.3||
||Repeated a year at school because of poor results||6.9|4.5|.051***|
|||_0_|30.6|19.3||
||H bk h d?|_1-5_|42.6|53.1|137***|
|Gd 8|ow many oos ave you rea|_6-10_|10.6|11.6|.|
|rae||_>10_|16.2|16.1||
|||_A lot_|47.6|36.3||
||Math is important in life|_Somewhat_|38.6|48.0|.115***|
|||_Never_|
remained relatively unchanged in the CPI since 2003 (Australia: 8<sup>th</sup> in 2003 and 2010; Singapore: 5<sup>th</sup> in 2003, 1<sup>st</sup> in 2010; India: 83<sup>rd</sup> in 2003, 87<sup>th</sup> in 2010; Indonesia: 122<sup>nd</sup> in 2003, 110<sup>th</sup> in 2010). However, what is more likely is that the lab measures are capturing one particular type of corruption, whereas the perceptions measures capture perceptions more generally. For example, the Bribe Payers Index by transparency international focuses on petty corruption and bribery in particular. For both 2006 and 2008, Australia ranked above Singapore; with India close to the bottom of the rankings (Indonesia was not measured).

Barr and Serra (2010) conduct a lab study to test the robustness of the results found by Fisman and Miguel (2007) in a lab setting. Using undergraduate students at Oxford University, the authors correlate subject behavior in a simple bribery game with their country of origin.

12
benefits of temporary migration for migrants (Ahmed and Bossavie 2021). Finally, domestic labor market conditions in South Asian countries tend to be poorer compared to contexts where return migration has been previously studied. An examination of the labor market outcomes of return migrants in the context of South Asia can thus bring new descriptive insights to the literature on the topic.

The paper examines the labor market outcomes of return migrants relative to nonmigrants by focusing on three major migrant-sending countries in South Asia: Bangladesh, Nepal and Pakistan where labor migration is low-skilled and temporary by design. The choice of these three countries is motivated by data availability and by the similarities of labor migration patterns from these three countries. The paper utilizes nationally-representative household surveys which collect rich information on workers’ current labor market outcomes and on past migration overseas. This allows to compare the labor market outcomes of return migrants to those of nonmigrants, including overall labor supply, sectoral choice, occupational choice, and earnings. For Bangladesh and Nepal, the data sets also collect information on household members who are currently away for work overseas, which allows to compare the characteristics of return migrants with those of migrants who are still overseas.

The paper examines systematic differences in the labor market outcomes of return migrants and nonmigrants along four main outcomes: (i) labor market status and labor supply (ii) sectoral choice (iii) employment type (iv) earnings. We examine these differences both unconditionally and conditionally on workers’ observable characteristics that may be associated with return migrant status. Labor market choices (i) to (iii) are modeled using multinomial choice models where past migration status is one of the regressors, along with workers’ other observable characteristics. Outcome (iv) is modeled using standard Mincerian regressions where past migration status is the main explanatory variable of interest. The estimations carried out in this paper only control for differences in observable charac– – teristics but not in unobservables between nonmigrants and return migrants. Therefore, the evidence reported in this paper on the association between
# **5.3 Robustness Tests**

In Table 1, we demonstrate the robustness of our results to different specifications. In columns 1, 5 and 9, we reproduce the results from our preferred specification from Figure 3 for comparison purposes. In columns 2, 6 and 10, we show that the results are very similar when considering a balanced sample of taxpayers who file regularly during 2011-2015.<sup>22</sup> All point estimates are small and statistically indistinguishable from zero. The same result emerges when we winsorize the outcome variables more conservatively, at the 95th percentile (columns 3, 7 and 11). It thus does not seem to be the case that smaller or larger firms are affected differently. Finally, we still obtain the same results when extending our main specification to include observations for the year 2016 (columns 4, 8 and 12). The 2016 data we have access to is only partial, covering CEDE firms and about 3,500 non-CEDE firms. The results are hence tentative, but they do not provide any indication that a treatment effect emerges over the medium-term horizon. Graphical representations of these results and confirmations of the parallel trends assumption in each estimation are shown in Figure C.1.<sup>23</sup> While we would like to conduct heterogeneity analyses that split the sample based on firm characteristics predictive of responsiveness, e.g. comparing firms with above vs below medium size, share of card sales or firms with and without a POS, such sample splits are not suitable in our setting, as the treatment can lead consumers to switch from smaller firms or from firms without a POS to firms with a POS. Hence, if we observe an effect in a subsample of firms more susceptible to offer card payments, the effect could be a compliance effect or a real effect driven to consumer switching. The sectoral DiD is not affected by this concern, as consumers are unlikely to switch between buying from a retailer vs buying from a wholesaler
# **_3.6 Robustness of Adequacy Dimensions_**

We perform several robustness checks at dimensions level to test the robustness of our coding framework against macro-level data for all 64 emerging economies (Annex 6).

First, we compare the weighted survey average of each of the adequacy dimensions with the macro variable that best matches the adequacy concept. As the definitions of the seven adequacy dimensions for data available at macro-level and the respective HCES variables’ definitions do not exactly match, some smaller deviations between macro-level data and weighted survey average are to be expected.

For WASH-related data, we compare the country averages obtained by coding access to water and access to sanitation obtained from HCES against macro-level data from JMP which is jointly managed by WHO and UNICEF. An alternative data source would be WDI, which classifies access to water by only considering “drinking water from an improved water source which is located on premises, available when needed and free from faecal and priority chemical contamination”; hence WDI does not capture water that is considered improved by JMP but located outside the HH’s premises (e.g., Tubewells). For the robustness check on water, we therefore revert to JMP data and collapse the first three categories of the adequate drinking water ladder: safely managed, basic, and limited access to secure water.<sup>12</sup> Further, we check our coding of the cooking and electricity dimension against macro data from the IEA, which has published databases on electricity access as well as on the reliance on traditional biomass for cooking since 2000 in their World Energy Outlook (WEO).<sup>13</sup> As we combine PRINDEX data with the HCES ownership variable, we already implicitly include a macro-level variable for the tenure security dimension and therefore abstain from an additional macro-check.

To the best of our knowledge, there are no comparable data sets at macro-level for overcrowding and structural quality for a large enough sample. The results [ _available in supplementary material_ ] show negligible differences
**Figure 2. Mobile cellular and fixed broadband price baskets and coverage in Mexico relative to similar income regions, 2012–17**

# **a. Mobile cellular price basket b. Mobile cellular price basket vs. subscriptions**

<!-- Start of picture text --> 40 40 100<br>35 35 90<br>80<br>30 30<br>70<br>25 25 60<br>20 20 50<br>15 15 40<br>30<br>10 10<br>20<br>5 5 10<br>0 0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Mexico LDC Developed<br>Mobile Cellular Subscriptions Mexico<br>Developing World<br>Source: International Telecommunication Union.<br>Note: LDC = least developed countries. PPP = purchasing power parity. Mobile-cellular basket is composed of the price of standard mobile monthly<br>usage for 30 outgoing calls per month (on-net/off-net to a fixed line and for peak and off-peak times) in predetermined ratios, plus 100 SMS messages.<br>For more information, refer to https://www.itu.int/en/ITU-D/Statistics/Pages/definitions/pricemethodology.aspx.<br>In 2014, 99.9 percent of the market was divided among four companies: Telcel (66.9 percent),<br>Telefónica (21 percent), Iusacell-Unefon (9.1 percent), and Nextel (2.9 percent). The rest of the market<br>was divided between a few virtual mobile operators that had just entered the Mexican market by<br>2014. 23 The simulations in this study use data from 2014; hence, they do not consider the full effects<br>of the reforms, but they highlight the potential benefits these reforms could have in the future.<br>