rafmacalaba/data-use-mentions-extended huggingface dataset:
characteristics include literacy of the father and the mother.<sup>16</sup> Due to the depth of the LSMS survey, there are numerous household variables that capture the income status of the household which include floor type, roof type, and access to improved toilet facilities.<sup>17</sup> Because child undernutrition is highly influenced by sanitation, we control for improved sanitation facilities.<sup>18</sup> For community-level characteristics, we use distance to main road and distance to market as control variables. Since there is a non-linear relationship between the community variables, we control for distance to main road, distance to main road squared, distance to market and distance to market squared.

We apply a panel data fixed effects approach to equation (1). We measure changes for children who were included both in wave 2 and wave 3 of the LSMS survey data set – those aged 6-41 months old in wave 2 and 24-59 months old in wave 3. A panel data fixed effects model controls for unobservable time-invariant factors, thereby focusing on what drives changes between periods and eliminating the effects of time invariant factors such as gender. We note that other characteristics, for example, the literacy rates of mothers and fathers, are very unlikely to change in a relatively short panel.<sup>19</sup>

# **5. Results and Analysis**

# **5.1. Descriptive Statistics: Child Undernutrition in Ethiopia**

Table 3 summarizes the information on nutritional outcomes for children between 6 and 59 months from the LSMS data set. Stunting increased from 40 percent in wave 2 (2013/14) to 41.7 percent in wave 3 (2015/16). The LSMS data also show similar small increases in the proportion of children who were wasted. It is noteworthy that in Ethiopia there is little difference in the prevalence of stunting between boys and girls. For wasting the prevalence is

> 16 While some studies use mother’s
(p-value)|0.145|0.010|0.711|0.778|0.144|0.016|0.711|0.742|0.176|0.042|0.745|0.830|
|Endline control mean|2.4|63|2.8|68|2.4|63|2.8|68|0|.163|0.|251|
|Additional controls|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|
|(Pseudo)R-squared|0.029|0.495|0.196|0.520|||||0.008|0.289|0.049|0.282|
|Observations|1,958|1,958|1,352|1,352|1,958|1,958|1,352|1,352|1,958|1,958|1,352|1,352|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ Poisson and probit models (columns (5) to (12)) show average marginal effects (AME). Number of ISFM technologies adopted ranges from 0 to 5. Integrated adoption of full ISFM package is a dummy variable. FREG stands for ‘Farmer research and extension group’. FREG- and non-FREG samples consist of actual FREG respectively non-FREG farmers in treatment mws, and control observations weighed with Kernel matching weights according to their similarity with FREGs and non-FREGs, respectively. Additional control variables identical to those listed in notes of Table 2. One-sided equality tests of T1 and T2 are F-tests or Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0
to avoid biased estimates of the model parameters. Thus, this approach is only of limited usability in the case of fragile countries as it might not be feasible to administer the full consumption module in particular insecure areas, creating a downward bias in poverty estimates for those areas.

The proposed methodology uses statistical imputations to obtain estimates for deliberately absent consumption values. Statistical imputation techniques are widely used to replace missing values in surveys (Little and Rubin 2019; Ambler, Omar, and Royston 2007; Van Buuren 2007). Straight-forward methods simply replace the missing values with aggregate statistics like a mean. However, this makes the strong and often violated assumption that data are missing at random (Carpenter, Kenward, and White 2007). Model-based approaches can take into account covariates and often use a regression framework to estimate missing values but distort the variance if based on point-estimates. Multiple imputations help to mitigate this by drawing multiple estimates from the posterior distribution using a Bayesian approach (Rubin 2004).

This paper is organized as follows. We first present the proposed methodology with its statistical properties as well as the data in Section 2. In Section 3, we apply the methodology to different scenarios showing the trade-offs between the performance and parameters of the approach, and then compare it to a reduced consumption approach as well as a more sophisticated reduced consumption approach adjusting the poverty line. The section ends with a real-world example based on a pilot survey in Kenya, assessing the performance of the new methodology and comparing it to a cross-survey imputation approach. The paper finishes with Section 4 concluding the findings and discussing some of the limitations of the new approach.

3
four rows of Table 3 (between $2.5 and $3.2 per person per day), the extreme poverty rate would have ranged from 7.7 percent to 11.9 percent in 2013. Similarly, using the moderate poverty lines (between $5 and $6.8 per person per day), the moderate poverty rate would have varied from 24.2 percent to 35.9 percent in the same year.

The figure also shows an alternative approach that could be used for updating the regional poverty line: the “continuous headcount approach” (Jolliffe and Prydz 2015). This method consists on finding the value of the regional poverty line in 2011 PPPs that results in the same regional poverty rate as the one estimated using the 2005 PPPs. According to this alternative approach, in order to maintain the same poverty rate with the new extreme poverty line as the one obtained with the previous extreme poverty line of $2.5 per person per day (i.e., 11.5 percent), the new extreme 14
# **Tables**

Table 1: Descriptive statistics of our study samples: riders through the app and platform survey respondents

||Riders<br>Pl<br>Mean/SE<br>(1)|atform survey: women<br>Mean/SE<br>(2)|Platform survey: men<br>Mean/SE<br>(3)|Diference<br>(2) - (1)<br>(4)|Diference<br>(2) - (3)<br>(5)|
|---|---|---|---|---|---|
||_Panel A: Demo_|_graphic variables_||||
|Employed|0.678|0.913|0.927|0.235***|-0.0150|
||(0.025)|(0.018)|(0.017)|||
|Age|32.51<br>|37.22<br>|36.41<br>|4.712***|0.808|
||(0.617)|(0.665)|(0.763)|||
|Years of schooling|12.95|11.31|11.01|-1.643***|0.304|
||(0.186)|(0.277)|(0.241)|||
|Number of rides in a typical week|6.055|8.467|8.934|2.412***|-0.467*|
||(0.260)|(0.169)|(0.183)|||
|Crime rate at rider home station|1367|1233|1178|-134.073*
definition of trade enshrined in the WTO’s General Agreement on Trade in Services. Under the GATS, four ”modes of supply” are defined through which trade can occur. These include foreign direct investment (FDI) and temporary cross-border movement of suppliers. As a result there is a tension between how trade in services has come to be defined in policy circles and how trade is defined in the national accounts and balance of payments, which does not consider movement of factors of production as international trade per se.<sup>4</sup> In this paper, we examine how the recent multi-country CGE literature has moved from goods trade into quantification of market access in services. This includes both a discussion of measuring barriers to trade and investment (generally with a mix of firm surveys, price comparisons, and econometrics), and a discus-

> 2The revealed comparative index is a measure of relative specialization, defined as the share in total exports of a country of a given product, divided by the same ratio for the world as a whole. If this measure is greater than one a country is said to have a revealed comparative advantage in the product. The concept was first defined and used in Balassa (1965).

> 3See WTO (1998) for a survey of the early literature in this area.

> 4See Stern and Hoekman (1987), Francois (1990c) and Francois and Hoekman (2010) for further discussion. In practice market access negotiations in services involve a mix of rules on FDI, movement of persons, and cross-border commerce, and thus analysis and models must consider these various modes of supply.

2
that plot-level information and agricultural practices are collected for all plots in the survey, providing a common basis for the covariates used in statistical modeling. In this context, an important question arises: What proportion of crop-cuts is necessary to obtain valid crop yield statistics? This paper addresses this question by varying the proportion of the sample used to train the model.

# **2.2 Completely missing: Across surveys imputation**

In general, the scenarios we consider for completely missing crop-cut data involve living standard surveys with integrated agricultural modules where crop-cut data were not collected, yet there exists a farm survey within the statistical system that does collect plot-level agricultural practice data. To illustrate, consider Mali or Ethiopia, which conduct repeated cross-sectional crop cutting annually. One might question whether it is feasible to conduct crop cutting less frequently—perhaps every other year or every three years—and rely on data imputation to bridge the gaps while reducing costs. This approach is particularly pertinent for these countries, given

> 2Among countries in West Africa conducting annual farm surveys, Burkina Faso with the Enquˆete Permanente Agricole (EPA) conducts crop-cuts on all plots of the households sampled.

> 3Mali, Ethiopia, Uganda, and Malawi are among the countries with a survey design that includes crop-cut collection on a subsample.

> 4Details of the crop-cut sampling protocol can be found in the survey documentation manual on The World Bank Microdata Catalog: https://microdata.worldbank.org/

4
business owners doing household chores.** In a month, women owners will spend 24 hours more than men doing household chores, again indicating the burden of cultural norms imposed on female entrepreneurs. This is consistent with studies on the gender barriers in the country. One of the most consistently raised issues by women trying to enter the business ecosystem in Myanmar is their excessive burden of domestic and household responsibilities.<sup>30</sup>

**Despite women business owners facing discrimination, gender is not perceived as a barrier to growth by women entrepreneurs themselves.** This can probably be imputed to the fact that gendered roles are so engrained in Myanmar society that the different opportunities one has depending on their gender is accepted as the inviolable norm. Despite women entrepreneurs carrying out twice as much household related work than men, they do not report their gender as a barrier to the growth of their business (Table 3b Appendix A). In our sample, only 18% of women reported their gender as some sort of barrier, with the vast majority reported gender as not being a barrier at all.

> 29 Myanmar MSME Survey, 2017.

> 30Ibid.

16
>2010Q1 2010Q3 2011Q1 2011Q3 2012Q1 2012Q3 2013Q1 2013Q3 2014Q1 2014Q3 2015Q1 2015Q3 2016Q1 2016Q3 2011 2013 2015 2011 2013 2015 2011 2013 2015 2011 2013 2015<br>Machinery Overall Machinery Overall Machinery Overall<br>Construction Construction Construction Private Public Private Public Private Public Private Public Private Public Private Public<br><!-- End of picture text --> <!-- Start of picture text --> D. Share of countries with contracting<br>investment<br>Percent<br>75<br><!-- End of picture text --> A. GDP-weighted averages. Includes quarterly data for Bolivia, Brazil, Chile, Colombia, Costa Rica, Guatemala, Mexico, Nicaragua, Paraguay, Peru, and Uruguay. Central America includes Costa Rica, Guatemala, Mexico, and Nicaragua.

- B. Averages weighted by investment levels. “SA” stands for South America. “MCC” stands for Mexico, Central America, and the Caribbean.

E. For Chile, 2003-08 data begins in 2004.

- F. Figure shows growth rates of gross fixed capital formation in constant 2010 U.S. dollars.

29
Given that it is relatively harder to tax the agricultural sector, it is expected that as the share of _agriculture value added_ in percentage of GDP increases, collected taxes in percentage of GDP drop due to a smaller tax base (Leuthold, 1991; Tanzi, 1992; Piancastelli, 2001). Thus, the expected sign of the agriculture value added ratio is negative.

Institutional and governance quality is considered as one of the most essential factors determining the adequacy of tax collection (Tanzi and Davoodi, 1997; Ghura, 1998; Bird, et al., 2004; Gupta, 2007). Countries can collect higher taxes only if the tax collection process is efficient. In this regard, _bureaucracy quality_ index and _corruption_ index, which are two possible measures of institutional and governance quality, are expected to have a significant impact on tax collection. The ICRG reports several indicators of institutional and governance quality. In the original database, bureaucracy quality index and corruption index are reported as index numbers from 1 to 6. While “1” indicates the lowest bureaucracy quality or highest corruption, “6” corresponds to the highest bureaucracy quality or lowest corruption. Similar to the case in Le, Moreno-Dodson, and Rojchaichaninthorn (2008), we re-index each of these measures in this paper such that lower numbers indicate a higher bureaucracy quality or lower corruption. Rescaling consists of defining a new range where -10 (least corrupt or best bureaucratic quality) and -1 (most corrupt or worst bureaucratic quality). With this new definition, we expect tax revenues to drop with increasing index values, meaning negative estimated coefficients of these variables.

A simple correlation matrix among these variables indicates the expected signs (see Table A5 in the Annex). Tax revenue is positively correlated with GDP per capita, and trade openness; and negatively correlated with age dependency ratio, population growth, agriculture value added, as well as bureaucracy quality index and corruption index as defined above. When we compare these results with the correlation values reported in Le,
**-35.**

followed by South **Asi** with 17%, LAC with 15%, East Asia with 13%, and EMENA with 10.5%.

Other Indicators of service quality are either not available or require further analysis before any conclusions can be drawn. The need for load shedding because of Insufficient generating capacity cannot be Inferred directly frT the available data, for example, on the basis of system load factor or the ratio of peak demand to Installed capacity. Load factors could Increase either due to load shedding or changes In demand characteristics. The ratio of peak cemand to Installed capacity Is an Indicator of the level of reserve margin available and therefore the ability to meet peak demand with allowance for both scheduled and forced outages. The peak demand to total Installed capacity ratio was analyzed using the PPAR basic data sheets and was found to lie In the range of 40-100% at the time of project appraisal. For utilities at the high end of the range, generation additlons were clearly required. For utilities with an Implied reserve margin of around 50%, i cannot be concluded Immediately that there is excess generating capacity because of the varying technical characterlstics of the systems. In countries where maintenance Is Inadequate due to lack of skilled staff and available spares, a higher reserve margin would be required to achieve a reasonable level of service as measured by Loss of Load Probability (LOLP).

The Qptimum level of LOLP will depend on the economic cost of outages such that more Industrialized countries In EMENA, LAC, and increasingly In East Asia Regions would require a lower LOLP and, hence, higher reserve margin. The optimal reserve margin requirement 13 also a function of the nature of the sys!em, Its mix of plant, the seasonal firm power available In hydro based and mixed systems, and the extent of derating of older thermal plants. In short, the optimal reserve margin requirement varies depending on circumstance, so that the maxlmum peak demand to Installed capacity figure would show significant
Estimate|Supply-Use<br>Table|Authors'<br>balanced table|
|---|---|---|
|Income<br>3149283|2860584|3044640|
|Expenditure<br>3149283|3137365|3044640|
|Production<br>3149283|2906200|3044640|
|Source: Authors' calculations|based on data fr|om National|
|Statistical Office of the Repu|blic of Armenia||

Table 3. Composition of GDP by expenditure category, 2007

|Total<br>Household|Government|Investments|Net Exports|
|---|---|---|---|
|(Millon Dram)<br>(%)|(%)|(%)|(%)|
|Supply-Use Table<br>3137365<br>72|10|38|-20|
|Authors' balanced table<br>3044640<br>67<br>Source: Authors' calculations based on data from National St|10<br>atistical Office of t|42<br>he Republic of Ar|-19<br>menia|

109
**Figure 2.3. Annual Infrastructure Investment Needs in the MENA Region**

<!-- Start of picture text -->
1. Total Amount 2. Breakdown by Sector<br>(Billions of U.S. dollar) (Billions of 2005 U.S. dollar)<br>140 25<br>120 OEC GCC OIC<br>20<br>100<br>15<br>80<br>60 10<br>40<br>5<br>20<br>0 0<br>Market IFC (2016) WEF (2015) IMF (2014) Transport ICT Electricity Water and<br>Intelligence Sanitation<br>(2015)<br>Sources: World Bank and International Monetary Fund. Source: Ianchovichina, et al. (2012)<br>Note: ICT = Information and Communication<br>Technology.<br><!-- End of picture text -->

**Figure 2.4 Public - Private Partnerships in the Global Context**

<!-- Start of picture text -->
1. Total value of PPPs in millions of U.S. 2. Total number of PPPs projects per<br>dollar per million capita million capita<br>70 0.25<br>60<br>0.2<br>50<br>0.15<br>40<br>30<br>0.1<br>20<br>0.05<br>10<br>0 0<br>AFR EAP ECA LAC MNA SAR AFR EAP ECA LAC MNA SAR<br>Sources: World Bank, Private Participation in Infrastructure Database; and World Bank staff calculations.<br>Note: Regional grouping follow s the World Bank classification. High income countries are not included. Total value of<br>investment and number of projects are in accordance to year 2016, or
**Table 1: Tourist arrivals and contribution to GDP in selected countries** , latest year available

|**Country**|**Tourist Arrivals**|**Tourist Arrivals**<br>**(% ofpopulation)**|**Direct Contribution to GDP**<br>**(% of GDP)**|
|---|---|---|---|
|**Maldives**|1,286,000|300.6%|39.6|
|**Seychelles**|303,000|320.0%|26.4|
|**The Bahamas**|1,482,000|378.8%|19.0|
|**Vanuatu**|95,100|35.2%|18.2|
|**Cabo Verde**|598,000|110.8%|17.8|
|**St. Lucia**|348,000|195.5%|15.0|
|**Belize**|386,000|105.2%|15.0|
|**Fiji**|792,000|88.1%|14.4|
|**Antigua & Barbuda**|265,000|262.5%|13.0|
|**Barbados**|632,000|221.8%|13.0|
|**Dominica**|78,000|106.1%|12.4|
|**São Tomé and Príncipe**|**29,000**|**14.5%**|**10.8**|
|**Jamaica**|2,182,000|75.7%|10.3|
|**Iceland**|
inconsistent with the approach described here. For instance, both Rauch and Loayza require an exogenous change in government policy to induce transitions between the formal and informal sectors. But this arises purely because they were not concerned witi modeling firn dynamics. Within the context of our approach, such a policy change is equivalent to altering pa and/or p, which leads to comparable comparative static results as those described in both Rauch and Loayza.

# Conclusion

Using a unique data set from Mexico, we have provided a more detailed characterization of the nature of informal production than previously possible. Beyond the empirical regularities, we also have offered a new approach to analyzing the informal sector. This approach assumes that informal firms in developing countries behave similarly to those in the industrialized countries, and is based on a model of firm dynamics frequently used in the industrial organization literature. It offers an alternative motivation for informality which, unlike much of the literature on tax evasion and regulatory compliance, asserts that participation in societal institutions may be essential to growth, and therefore at least partially voluntary. It also can nest many existing models that base existence of the informal sector solely on institutional distortions, market failures, or excessive government regulation. Though the data do not permit a definitive test of competing models, they are consistent with the predictions of our approach. This suggests that our framework is an important benchmark to be considered when analyzing the informal sector and regulatory compliance.

# References

Ashenfelter, Orley and Robert S. Smith (1979), "Compliance with the Minimum Wage Law," _Journal of Political Economy,_ 87:2, 333-350.

# Banerji, Arup and Sanjay Jain (1996), "Quality Dualism and the Informal Sector," mimeo.

Besley, Timothy (1995), "Savings, Credit and Insurance," in J. Behrman and T.N. Srinivasan, eds., _Handbook of Development Economics,_ Volume III, Amsterdam: Elsevier.

20
While there are differences in design across the Gujarat and Andhra Pradesh surveys, to the extent possible, we harmonize variable definitions. Full definitions of the construction of each variable are presented in the Data Appendix.

Overall, the state of Gujarat has richer soil and is substantially wealthier than Andhra Pradesh. However, in Gujarat, insurance is sold to poor households (SEWA members), while in

> 6 Subsequently, two of the 100 villages were deemed to be so close that it would not be possible to treat one and not the other, so they were grouped together and assigned the same treatment status.

> 7 For the Gujarat household survey 15 households were selected per village: five randomly selected from the SEWA member list; five randomly selected from the remaining SEWA members with a positive savings account balance; and five households selected (non-randomly) based on suggestions from a local SEWA employee that they would be likely to purchase rainfall insurance. However, the entire sample of 1,500 households has similar summary statistics to the 500 selected randomly from the SEWA list, implying that the overall sample is close to representative of SEWA’s overall membership in these 100 villages.

8
<sup>***</sup><br>(0.000)|||
|Year FE|||yes|yes|
|Grid Cell FE|||yes|yes|
|Observations|1,560|1,560|7,020|7,020|
|R-squared (within)|0.150|0.089|0.073|0.072|
|R-squared(overall)|0.313|0.103|0.030|0.007|

_Notes_ : Difference-in-difference regressions at the grid-cell level with robust standard errors. The dependent variable is average masked nighttime radiance in each cell (where the size of each grid cell is 100 square kilometers). * p < 0.10, ** p < 0.05, *** p < 0.01. p-values in parentheses. The administrative level 1 corresponds to districts; the administrative level 2 corresponds to subdistricts or cities.

_Source:_ Authors’ analysis using data from Global VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights, Annual VNL 2 Composite.

# **VI. Conclusion**

This paper studies the impacts of a large-scale development intervention aimed at improving energy efficiency in Malawi, using a DiD approach based on a combination of remote-sensing (satellite) data and data from national household surveys. The estimation strategy takes advantage of the geographical variation in the implementation of different project components, which allows us to identify treatment and control groups to construct a counterfactual.

We find that a combination of demand-side and supply-side interventions was associated with a statistically significant increase in electricity access, a decrease in the reported frequency of blackouts, and a switch from traditional fuels to electricity as the main source of energy for lighting (albeit not for cooking). These results are based both on evidence obtained from national household survey data and from the fact that nighttime radiance (as measured by satellite images) increased relatively more in grid cells belonging to areas with project