rafmacalaba/data-use-mentions-extended huggingface dataset:
the United Nations' Statistical Yearbook for Asia and the Pacific 1995 and refer to 1992.

Fiji Unemployment data are taken from the United Nation's Statistical Yearbook for Asia and the Pacific 1995 and refer to 1994.

Central and Non Central Government and State Owned enterprise employment comes from IMF Report No SM/94/280 and refers to 1994 and includes both wage and salary earners.

India Data on paid employment in non-agricultural activities are taken from the ILO's Yearbook of Labor Statistics 1995 and are for 1989. Unemployment data are taken from the ILO's Yearbook of Labor Statistics 1995, refer to 1992 and source 1II, Code 2: _employment office statistics_ and include persons in employment who are seeking a change of job or extra work and are therefore also registered at employment offices.

Data on Central Govemment employment is from IMF Report No. SM/96/260 of October 1996 and relates to Budget 1996/97. Data on non-central govemment employment is a staff estimate based on data provided by Cristina Almero-Siochi (SA2CI) after consultation of RBI Rerort on Currency and Finance. 1994. (and relating to 1990) Health employment comes from WHO, 1993 and refers to 1990. The composition is 330,630 doctors, 9,796 dentists, 245,405 nurses and 132, 923 midwives. Education employment is taken from the UNESCO Statistical Yearbook i and relates to 1990. Military employment do not include people currently assigned to paramilitary units (over 1,000,000 men), i.e. National Security Guards (counter-terrorism/insurgency unit - 7,500 men), Central Reserve Police Force (120,000), State Armed Police (400,000), Border Security Force (185,000), Assam Rifles (under the Ministry of Home Affairs - 52,000), Indo-Tibetan Border Police (35,000), Special Frontier Force (10,000), National - Rifles (under Ministry of Defense - 30,000), Central Industrial Security Force (
# **1 Introduction**

Kenya has undergone a remarkable information and communications technology (ICT) revolution. At the close of the 1990s, less than 3 percent of Kenyan households owned a telephone, and fewer than 1 in 1,000 Kenyan adults had mobile phone service. By the end of 2011, 93 percent of Kenyan households owned a mobile phone.<sup>1</sup> A unique facet of the ICT phenomenon in Kenya has been the widespread proliferation of mobile money. Starting with the M-PESA system launched by Safaricom in 2007 and later joined by other systems, mobile money has become a fixture in the lives of Kenyans, extending a basic form of financial access to a wide population.

Mobile money platforms have evolved since inception and have entered a new phase with the advent of bank-integrated mobile savings products. The first such product, M-KESHO, was launched in March 2010 as a partnership between Safaricom and Equity Bank.

In this paper we examine the mobile savings phenomenon, using data collected in a survey during October and November of 2010. The concept of ―savings‖ on mobile platforms is not well defined, and we begin by putting forward a classification of the existing innovations. We differentiate between ―basic mobile savings‖ and ―bank-integrated mobile savings.‖ Basic mobile savings refers to the simple storage of credit using a mobile system such as M-PESA. Bank-integrated mobile savings refers to systems which include a fuller set of banking services such as interest payments on deposits or overdraft facilities. This is the first study that examines patterns of use of bank-integrated mobile savings in Kenya.

The paper is organized as follows. Section 2 presents findings on the overall prevalence of mobile phone and mobile money usage in Kenya based on the Afrobarometer survey conducted at the end of 2011. Section 3 reviews the existing literature on the broader mobile money phenomenon. Section 4 describes how mobile money works in Kenya and shows the growth of mobile money usage over time. Section 5 describes the data on mobile savings analyzed in this
sick leave and maternity leave, among other things. We proceed to test whether the main characteristics, recognized by the literature to be conducive to the adoption of these practices, are also significant in the adoption of familyfriendly policies in the private sector in Myanmar.

# Data and Summary Statistics

## • **Research methodology**

The data used for this section come from the MGES 2019. The survey was conducted to collect data to understand how policy changes, and formal and informal support programs, can achieve greater impact for female entrepreneurs (description of variables used in the analysis in Appendix A). The focus is on how gender can impact specific entrepreneurs in the non-farming agriculture, light-manufacturing, hospitality and tourism, and trade and logistic sectors. Data was collected with the aim to inform the WBG/IFC operations on private sector development and dialogue with government counterparts on promoting more inclusive enterprise development in Myanmar.<sup>18</sup> Particularly the survey aims to shed light on the following:

- How cultural norms and decision making in the household interact with decision making within an enterprise;

> 18 Myanmar Gender Entrepreneurship Survey Report, Myanmar Survey Research, 2019.

10
market shocks that may hit specific educational groups. We cluster the standard errors by group, and weight the regressions by the number of observations used in calculating group-level exposure.

The results of the difference-in-difference estimations using the grouping estimator are displayed in Table 10. The estimate reported in Column 1 shows a negative effect of the minimum wage on total employment (including both formal and informal employment).This indicates that the minimum-wage increase had negative effects on employment overall – although the estimated effect is only statistically significant at the 10 percent level. As with the estimates using the EIS data, estimates from the HLFS data show a highly significant negative effect of the minimum wage on formal employment – which includes both private and public sector. The estimates are of similar magnitude with using the HLFS data as with the EIS data (Column 2). As shown in Column 3, we do not find that that the minimum-wage hike increased informal employment. This finding contrasts with the findings of some studies in other contexts.<sup>28</sup> Our findings seem incompatible with the notion that formal workers who were employed by firms that wound up exiting the market transitioned instead into informal employment – at least in the short run. One possible explanation for our finding is the higher degree of segmentation between formal and informal employment in Turkey, documented by some studies (Tansel and Acar, 2017; Duman, 2020). This higher level of segmentation contrasts with the situation that characterizes other contexts, such as in some Latin American countries, where frequent transitions between formal and informal employment have been evidenced.<sup>29</sup> The difference-in-difference coefficient in Column 4 indicates that raising the minimum wage led to a sizeable increase in unemployment – although the coefficient is insignificant due to large standard errors at the group level. The estimate reported in Column 5 also shows a significant effect of the minimum-wage increase in raising levels of inactivity. This suggests that the destruction of formal firms and formal jobs was accompanied by an increase in the
for Russia among the government‘s trade reform team and parts of the research community. Prior to this project, editorials were rampant in the Russian press expressing fears of WTO accession and focusing on the fact that the government has not explained the consequences of WTO accession or the source of the benefits. Now polls of regional politicians reveal that they anticipate exactly the impacts suggested by the analytic studies. Moreover, polls of the public have shown progressively increasing endorsement, and now significant majority supporting WTO accession. Finally, as the analytic studies strongly emphasized the crucial role of liberalization against barriers to foreign direct investment in business services, Russian has agreed to major openings in the business services markets as part of its bilateral market access agreements in the WTO negotiations (see Tarr, 2007).

> 57 Research papers included the following: Jensen, Rutherford and Tarr (2004; 2006, 2007); Rutherford and Tarr (2008; 2008a; 2010); Rutherford, Tarr and Shepotylo (2005); Shepotylo and Tarr (2008); Tarr (2006; 2007; 2010; 2010a); Tarr and Thomson (2004); Tarr and Volchkova (2010; forthcoming).

78
_Table 6. Non-response, by gender of self-respondent_

||Sel<br>Parc|f-Respond<br>el Level (A|ent,<br>rm 1)|Self<br>Aggreg|-Respond<br>ate Level (|ent,<br>Arm 2)|
|---|---|---|---|---|---|---|
|Variables|Male|Female|T-test|Male|Female|T-test|
|Non-response (1=yes)|0.21|0.06|0.00|0.14|0.09|0.03|
|Non-response as out of the marz (1=yes)|0.13|0.03|0.00|0.07|0.03|0.00|
|Non-response as ill/not well (1=yes)|0.03|0.01|0.08|0.02|0.02|0.80|
|Non-response as refused (1=yes)|0.01|0.00|0.49|0.01|0.01|0.34|
|Other reason for non-response (1=yes)|0.04|0.01|0.04|0.04|0.03|0.45|

# **VI. Conclusion**

Individual-level data on land rights is essential for understanding how land rights differ by gender, as well as how those differences affect development outcomes. SDG indicators 1.4.2 and 5.a.1 both require data on individual land rights, encouraging countries to relinquish previous practices of collecting household-level land data that implied an assumption of a unitary household model. However, approaches to collecting individual-level land data are varied and different methodological choices may result in biased understandings of the importance and level of land rights by gender.
: The above table reports the simulated changes to employment following a 5 percent increase in the international price. Never exporters are firms that only sold to the domestic market in periods 1 and 2. New exporters are firms that sold to the domestic market in period 1 and switched to exporting in period 2 following the international price increase. Always exporters are firms that exported in period 1 and 2. Source: Authors’ simulation.

**Table 5. Changes in log Employment relative to Changes in Sales and Export Share between 2016 and 2020**

|Export Share|Sales|Increase<br>Decrease or unchanged|
|---|---|---|
|Increase||(1)<br>(2)|
|||N= 50<br>N= 21|
|||Δln emp. mean =0.86<br>st. dev.=0.74<br>Δln emp. mean = 0.17<br>st. dev.=0.77|
|Decrease or||(3)<br>(4)|
|unchanged||N= 243<br>Δln emp. mean = 0.16<br>st. dev.=0.69<br>N= 179<br>Δln emp. mean = -0.26<br>st. dev.=0.66|

Note: This table reports the mean change in log employment, for four scenarios: (1) sales and share exported increased, (2) sales did not increase and export sales did, (3) sales increased while the share going to exports did not, (4) neither sales nor export shares increased. Excludes upper and lower one percentiles of change in log employment. The sample is limited to exporting firms. Calculated using 2016-2020 panel weights. Source: Author calculation using Enterprise Survey 29
al, 2013). These techniques are particularly critical for agricultural phone survey samples where issues of coverage and nonresponse are likely to be magnified given a lower share of mobile phone ownership and likely less reliable mobile networks and electricity in rural areas. The availability and success of these different adjustment methods will also vary depending on the sampling method for the survey and the amount of reference information available on the general population.

One commonly used reweighting approach is weighting class adjustments in which the sample is divided into cells based on the characteristics of sampling units (households, farms, individuals, etc.) such as location, size, or sex that are demonstrated or assumed to be associated with coverage or nonresponse bias. Then, the sampling units in each cell are weighted proportional to the general population total of each cell (Little, 1986).

A second reweighting technique is response propensity adjustment (Valliant et al, 2013). This technique models the probability of response (i.e., selection and successful interview) for each sampling unit given a range of its observable characteristics. Then the inverse predicted probability of selection derived from this response probability model is used to adjust sampling weights and counteract the bias (see Himelein et al. (2020a) for an in-depth discussion). Response propensity adjustment techniques can accommodate many variables, allowing reweighting to be more fine-grained, while weighting class adjustment can only accommodate a handful of characteristics as otherwise the number of different cells becomes too large.

In addition, it is good practice to calibrate the adjusted sampling weights to match known population totals (e.g., number of households by urban, rural) which can help to reduce overall standard errors and also help to reduce bias due to coverage or nonresponse. There are several calibration methods that can be implemented, depending on the level of detail of information available for calibration (Valliant et al, 2013; <mark>Lundström and Särndal, 1999; Andersson and Särndal, 2016).</mark> With a previous
defined. For instance, in the case of Iraq, the questionnaire allows to separate out private/public/governmental sector ownership from occupancy status. Moreover, household surveys may allow for country-specific ownership statuses as in the case of Peru for the category “ownership by invasion.” Starting from the occupancy status, as a preliminary step in the analysis, households have been regrouped in two mutually exclusive ownership sets: owners and non-market tenants in one group and market tenants in the second group. The first group includes outright owners, owners paying mortgage, and individuals living for free or paying a subsidized rent; market tenants are defined residually as households paying a rent. The rent paid by market tenants is reported either in monthly or annual values. Table 2 summarizes categories and regrouping specifications for each country and year.

Table 3 summarizes the main differences between tenants and owners with respect to dwellings’ and households’ characteristics, focusing on the most recent year of analysis. The share of market tenants is typically rather small in all four countries, ranging from 3.6 percent in Albania (2008) to 11.1 in Bangladesh (2010). The large majority of tenants live in urban areas: 82 percent in Albania, about 90 percent in Bangladesh, and virtually all tenants in Iraq and in Peru. In all 4 countries, individuals living in rented dwellings are concentrated in the region of the capital. For instance, one out of three in the region of Tirana (Albania) and two out of three in the region of Dhaka (Bangladesh).

The high prevalence of tenants in largely urbanized areas, and within these areas mainly in the capital region, poses a problem to the rent-imputation exercise as the characteristics of dwellings as well as rental markets in such areas might be very different compared to those in the rest of the country. Hedonic models assume that urban and rural rental markets price houses similarly, although this is well-known to be very unlikely, whereas self-assessment might be less accurate in areas of the country
is unclear whether and how the concept of shared prosperity defined in this way would resonate within a country.<sup>8</sup> The shared prosperity measure offered by Dang and Lanjouw (2016) provides a complementary angle by explicitly focusing on the poorer income groups. In particular, the 14 countries with lower poverty rate are considered as having positive pro-poor growth (i.e., marked with stars, column 8); similarly, the remaining countries with more poverty are regarded as not having pro-poor growth (i.e., marked with minuses, column 8). Furthermore, countries with less vulnerability but in the same group are judged as having better performance. For example, among the 14 countries with positive pro-poor growth, five with less vulnerability (including Mauritania, Ethiopia, Togo, Eswatini, and Malawi) are ranked as having even more positive growth. Of the seven countries with more poverty, five (including Burkina Faso, Zambia, Madagascar, Côte d’Ivoire, and Cameroon) managed to reduce vulnerability. If we combine this second measure with the first measure of shared prosperity, certain countries come out as consistently performing very well, like Botswana or Mauritania, or near the bottom, like Senegal and Nigeria.

We turn next to discussing the trade-off between economic growth and the quality of the environment. We plot in Figure 1 the global GDP per capita level against air quality, as measured by the number of micrograms per cubic meter (μg/m3) PM 2.5 using the WDI database for 1990 to 2016. Figure 1 indicates that all countries in the world as a whole almost tripled their GDP per capita during this period. While this good news should clearly be celebrated, it did not come

> 8 Other issues exist as well. For example, why should the income threshold be set at the 40th percentile, rather than say, other percentiles such as the 30<sup>th</sup> percentile or 50<sup>th</
br>incentives if they succeed in<br>bringing 20 women for ANC.|The one area where the team<br>is confident that they have had<br>an impact so far is with MNCH<br>weeks.<br>With<br>the<br>other<br>interventions there does not<br>appear to be any certainty/<br>evidence as yet of a positive<br>impact.|Rivers<br>had<br>a<br>21.5%<br>improvement<br>on<br>its<br>baseline according to the<br>2016 MICS survey. We<br>are not privy to the<br>breakdown.<br>Given<br>that<br>no<br>implementation<br>had<br>begun by the time these<br>results came in, they<br>seem to be testament to<br>the impact of the broader<br>context, specifically the<br>Governor’s<br>efforts<br>to<br>address health worker<br>performance<br>and<br>improve<br>health<br>infrastructure.|

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6.07 The data presented in this study show that it is incorrect to imagine that a water authority without central government subventions is without resources. At least in the case of Onitsha, the ASWC's greatest resource is its right to supply piped water services to a ready market which would make any businessman envious. In industrialized countries water authorities are typically regulated to prevent them from abusing this monopoly privilege and charging prices which are more than their costs (including a "fair" rate of return on capital). Similarly, the results of this study show that the revenue potential of the ASWC is tremendous, and that the population of Onitsha would be much better served if the ASWC viewed itself as a regulated utility, not as an agency providing a social service.

6.08 In summary, the scale and magnitude of water vending activities in metropolitan areas of developing countries has not been widely realized, nor has the value of information on such water vending systems been adequately appreciated by water resources engineers or policy analysts. This case study has shown that a rapid reconnaissance survey of water vending activities and the willingness of households to pay can yield valuable information for water supply planning and, in this case, unexpected policy insights. Studies such as this are very inexpensive relative to the capital costs of urban water projects, and can provide valuable information for use in the planning and design of water supply projects in developing countries.
# **4. Methodology**

# **4.1. Data Sources**

# _Micro Data_

The main database for our analysis is the second Employment and Innovation Dynamics National Survey (ENDEI 2). The use of this data set is completely novel. Access was granted to us by the National Directorate of Scientific Information (Dirección Nacional de Información Científica, DNIC) and the Under-secretariat of Studies and Prospective (Subsecretaría de Estudios y Prospectiva), specifically for the needs of this project.<sup>14</sup> This survey covers the 2014–16 period and was carried out jointly by the Labor and Employment Secretariat (Secretaría de Trabajo y Empleo) and the Science, Technology and Productive Innovation Secretariat<sup>15</sup> (Secretaría de Ciencia, Tecnología e Innovación Productiva). The sample was drawn to be representative of manufacturing firms with at least 10 employees, in terms of size (small, medium, and large firms), region (five geographical areas), and sector (mostly at the 2- digit ISIC level).<sup>16</sup> The sample includes 3,945 firms. For some exercises, we used the previous ENDEI (hereafter ENDEI 1), comprising 3,691 firms, which covers the period 2010–12 and which is representative at the size and sectoral level. We could not use panel data because both waves were not matched at the firm level.<sup>17</sup> The ENDEI has two structured questionnaires, one self-administered and one that requires a face-to-face interview. The former contains questions that require inputs from different areas of the firm: income; expenses (such as wages and salaries, intermediate consumption, and investment in fixed assets);<sup>18</sup> employment (according to hierarchies and qualification); and remuneration and spending in innovation activities (such as R&D, consultancy, and acquisition of machinery and equipment). The latter contains mainly
76%|
|**Middle 40% income share**|46.55%|45.38%|43.94%|
|**Top 10% income share**|20.66%|32.75%|30.30%|
|**Top 1% income share**|2.91%|8.28%|7.19%.|

_Source: own estimation based on the EU-SILC and tax data._

> 14 To get a sense of magnitudes, Atkinson et al. (2011) and Burkhauser et al. (2012) have analyzed income data for the United States and found that the survey-based estimates of the top 1% income share are lower by several percentage points compared to the estimates based on tax return data.

22
Table A4: Generic and energy-centric management score statistics for high, upper middle, and lower middle income groups. (G) indicates generic management; (E) indicates energy-centric management. Uses the winsorized sample and computes management scores using straight averages of non-missing questions scores.

|**Income Group**|Shr Obs = 0|Shr Obs _<_ 0.25|Shr Obs _>_ 0.75|Mean|SD|p10|Median|p90|
|---|---|---|---|---|---|---|---|---|
|(G) High|0.017|0.072|0.045|0.491|0.209|0.167|0.517|0.738|
|(G) Upper Middle|0.022|0.077|0.043|0.481|0.211|0.167|0.504|0.737|
|(G) Lower Middle|0.020|0.065|0.031|0.477|0.200|0.2|0.5|0.725|
|(E) High|0.345|0.209|0.122|0.291|0.304|0|0.167|0.778|
|(E) Upper Middle|0.387|0.283|0.070|0.237|0.273|0|0.167|0.719|
|(E) Lower Middle|0.213|0.162|0.102|0.365|0.296|0|0.359|0.806|

**40**