rafmacalaba/data-use-mentions-extended huggingface dataset:
sessions versus just business bank accounts. The decision to evaluate the combined effects of these interventions was based on the idea that firms may face information barriers in transforming business registration into increased access to formal credit markets. NBS Bank was interested in increasing its reach and saw this combined intervention as a potentially inexpensive mechanism for targeting potential clients (as they would have the assistance in registering the business as well).

Firms were invited to NBS Bank’s information sessions in the businesses' area of operation. Each session included a maximum of 30 participants and was led by both NBS Bank representatives experienced in dealing with small business clients and a professional trainer in financial literacy. The information sessions comprised 20 hours of activities (two days of eight hours each and a follow-up session one week later, lasting four hours), with information provided on the following modules: (i) formal and informal financial institutions, and the role of banks; (ii) the benefits of bank accounts; (iii) identifying the specific problems that businesses face, namely the intertwining of business and household responsibilities; (iv) the benefits of separate business and household responsibilities; (v) how business bank accounts allow for the mental and physical separation of household and business funds and (vi) practical examples of using bank accounts for business purposes. At the end of the second day, NBS Bank offered a recently launched business bank account, which had a lower minimum balance (MWK 500) than previous products offered by the bank. This business bank account was available to all firms in Malawi with a BRC.

# **3.7 Sources of Data for Measuring Impacts**

We use two sources of data for measuring the impacts of these interventions. The first are data from our administrative records of program take-up. This includes information on which firms we assisted to get business registration certificates and a TPIN, as well as information on attendance at the bank information sessions and on which firms signed up for business bank accounts at the conclusion of
It is seen that while Ghana is more or less on par with the other African countries as far as the increase in relative productivity of labor with increase in capital intensity is concerned, the strength of increasing returns to scale is the lowest in the sample. This may be because of the low productivity of State owned enterprises in Ghana which tend to be of large size. A second relevant factor is the relative inefficiency with which capital stock is used in Ghana's manufacturing sector. The liberalization of the economy under the ERP was expected to ease the foreign exchange constraint impeding adequate supply of essential inputs and thus improve the utilization of capacity. This did happen to some extent as it pulled up from a low of 18 percent in 1984. But the level reached in 1992 of 45 percent is still pretty low by international standards.

The difference in wage levels by size of enterprise in manufacturing is of relevance to another basic aspect of the labor market in Ghana: the difference in the levels of earnings between the "informal" and the "formal" sectors. The wage levels in micro enterprises can be expected to be near the mean earnings of workers - wage earners as well as the self-employed - in the informal sector, particularly in the urban economy. But more detailed analysis of the formal-informal earnings differential must await further work on the GLSS data sets. In the next section we give an overview of the informal sector activity and some of its salient features from a cursory look at the data available.

# **4.7 Informal Sector Activities in Ghana**

The complexity in the definition and measurement of informal sector activity is a perennial one. Although we do not want to get into the semantics of this issue it is important to recognize that informal sector activities play an important part in the lives of people in developing countries and Ghana is no exception.<sup>2</sup> Hart (1973) who coined the term "informal sector" ironically used a study of Accra and Nima in
enrollment rate 14 percentage points lower than children whose parents do not have disabilities.

Among children in school, those with disabled parents also have worse educational performance. However, this difference is not very large. Table 2 shows that the fraction of pupils achieving excellent academic performance in the last school year is 14 percent for children without disabled parents, while this figure is around 11 percent for

> 1 In the 2006 VHLSS, people are asked about their difficulties in different functions including seeing, hearing, walking, cognition, communication, and self-care, and their responses are classified into 4 categories:

> a. No - no difficulty

> b. Yes – some difficulty

> c. Yes – a lot of difficulty

> d. Cannot do at all.

6
# **Smoking prevalence in Russia**

Despite the gradual decrease in recent years, smoking is still common in Russia (figure 1, panel a). According to RLMS-HSE data, 29 percent of the population ages 16–75 were smoking in 2016. Smoking is three times more popular among men (45 percent) than among women (15 percent). Smoking prevalence among men has been falling over the last decade and has declined by 10 percentage points since 2010, while, among women, prevalence has remained stable.

Smoking is common across the entire expenditure distribution (figure 1, panel b). While there is no specific pattern in smoking among men across deciles, smoking among women is clearly more popular among the more well-off population. Smoking in Russia involves mostly filter cigarettes; the share among all cigarettes smoked is between 90 percent among the lower deciles to 100 percent among the richer deciles. Men smoke an average of five or six packs a week, while women smoke an average of four packs a week; these numbers are rather stable across the distribution.

<!-- Start of picture text --> Figure 1 – Smoking Prevalence, Russian Federation<br>(ages 16–75)<br>a. Over time b. By decile<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 1 2 3 4 5 6 7 8 9 10<br>both men women both men women<br>c. By age-group d. By educational attainment<br>60 60<br>50 50<br>40<br>40<br>30<br>30<br>20<br>20<br>10<br>10 0<br>0 below general above higher<br>16-24 25-34 35-44 45-54 55-64 65+ secondary secondary general
118|
|Observations|2,382|2,382|2,382|2,382|2,382|2,382|2,382|2,382|

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

_Note:_ Average marginal effects (AME) of probit models. Additional control variables identical to those listed in notes of Table 2. Bonferroni-adjusted p-values taking into account correlations between outcomes. One-sided equality tests of T1 and T2 are Wald tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

21
example being their rights to land ownership. Mugyenyi writes that only 5 percent of land is owned by women as a result of cultural practices that restrict women's inheritance and property ownership. Quoting from a UNICEF study on this subject, she writes: "There are no statutes that prevent a woman from acquiring property but according to custom, property acquired during marriage belongs to the husband. If a woman leaves her husband, she may have to leave most of her property." (p.137). Mugyenyi describes other women's work in Uganda as being comprised of domestic service and informal sector trade in petty commodities, both of which she says provide very low returns. The overall patterns of division of labor are similar to the ones found in Ghana, but possibly more restricted for women in Uganda.

# **3 Data and Methodology**

The analysis in this paper is based on two comprehensive sets of household level data over periods spanning four to five years for each country. The period of analysis for Ghana is from 1987/88 to 1991/92, and though this period does not correspond with the major agricultural reforms, we would expect the rural economy to have benefited from the liberalization of trade and exchange rates that occurred just prior to the period. The period of analysis for Uganda is from 1992 to 1996, corresponding to the implementation of agricultural as well as macroeconomic and trade reforms.

For the Ghana analysis, we use data from the Ghana Living Standards Surveys from 1987/88 and 1991/92 (GLSS 1 and GLSS3). These surveys were modeled after the Living Standards Measurement Surveys (LSMS) designed by The World Bank for in-depth poverty measurement. We use the poverty lines and mean per capita expenditure 6
Smith, A. (1776). _An inquiry into the nature and causes of the wealth of nations_ (republished, eds. R.H. Campbell and A.S. Skinner. Oxford: Clarendon Press, 1976).

STATSSA (2022). Available at: https://www.statssa.gov.za/?page_id=739&id=1 United Nations (2016). “Leaving no one behind: The imperative of inclusive development.” _Report on the world social situation 2016_ . New York: United Nations.

World Bank (2005). “Empowering people by transforming institutions: Social development in World Bank Operations.” _Social development strategy paper_ . Washington, DC: World Bank.

World Bank (2013). “Inclusion matters: The foundation for shared prosperity.” Washington, D.C.: The World Bank.

<mark>World Bank (2018).</mark> _<mark>South Africa - Systematic country diagnostic: An incomplete transition - Overcoming the legacy of exclusion in South Africa</mark>_ <mark>(English). Washington, D.C.: The World Bank.</mark> <mark>World Bank (2022). World Bank Poverty and shared prosperity Report 2022: Correcting course. Washington DC: The World Bank</mark> **<mark>.</mark>** <mark>World Bank (2022a).</mark> _<mark>Peru Systematic Country Diagnostic Update (English)</mark>_ <mark>. Washington, D.C: The World Bank.</mark> Yonzan, N., A. Cojocaru, C. Lakner, D. Gerszon Mahler, and A. Narayan. A. (2022). “The Impact of COVID-19 on poverty and inequality: Evidence from phone surveys.” _World Bank Data_ (blog), January 18, 2022. https://blogs.
the post-war market price (of $1,369) for agricultural land (Deininger & Ali 2023), pointing towards broad awareness of market conditions after the 2021 opening of sales markets for agricultural land.

Compared to the near-universal desire to buy more land, only 3% are willing to sell land, a share highest in the <50 ha farm size group (6%) and zero for farms in the > 500 ha category. The mean proposed sales price, $3,142 (ranging from $4,787 in the > 50 ha group to $1,206 in the 120-500 ha group), is mostly above what potential buyers are willing to pay, consistent with a strong endowment effect (Zhu _et al._ 2021) and highlighting that, with strong fundamentals, agricultural land continues to be a promising investment.

As a proxy for farms’ capital stock, information was obtained about ownership and current value of tractors and mechanical equipment. With 84% (82%, 88%, and 72% in the < 50 ha; 50-500 ha, and > 500 ha groups, respectively) of sampled farms owning tractors, a significant share of large and small farmers rely on machine rental services. The per ha value of equipment ranges between US$610 and US$680 for all size groups, except the < 50 ha one ($1,213), possibly due to indivisibilities and frictions in markets for rental services.

Data on credit market participation suggests that access to credit is limited and biased in favor of larger farms: 55% of farmers overall—34% in the < 50 ha group vs. 84% in the above 500 ha group ever accessed credit. While 2.3 years passed since the average credit recipient obtained her last loan, the average farm in the > 500 ha class received its last loan 9 months ago (0.78*12), i.e., in the 2022 production season after the invasion. This compares to
rises above the floor. The final benefit level of a household is given by the maximum benefit level minus 30% of household “net countable income,” which is the household’s gross income minus expenses considered indispensable. Examples of indispensable expenses include the standard utility allowance (SUA) determined by each state to cover utility expenses, rental costs, and a homeless deduction for living expenses conditional on being homeless (e.g., transportation). Some components of the formula, such as the federal maximum benefit level and the state-determined SUA, are determined by government policy, whereas others, such as household gross income and rental costs, are determined by household characteristics.

We first introduce the instrument using a few examples before laying out the details. To construct the instrument, we use the SNAP benefits formula to simulate what a fixed sample of participants would have received throughout the sample period, by allowing only policy parameters to vary over time while holding household economic variables such as household gross income and rental costs fixed, using a sample of about 50,000 SNAP participating households surveyed in the pre-period of 2006 from the SNAP QC data. We then sum over these simulated benefits for each recipient to construct our IV: simulated (used interchangeably with “synthetic”) benefits per population at the state-level.

Variation in the instrument is almost entirely driven by two policy changes. First, federal maximum benefits were raised and state-specific adjustments to the SUA were made during the Farm Bill. We illustrate these changes in Figure 5 by again focusing on the comparison between New Hampshire and Wyoming, two states that had the largest difference in percentage changes of the instrument during the Farm Bill. In Figure 5a, we show that New Hampshire raised the heating and cooling SUA (HCSUA) by about 29% in October 2008 while Wyoming lowered it by about 23%. These changes led to a much larger percentage increase in synthetic benefits per recipient for New Hampshire as shown in Figure 5b. The two states had almost identical synthetic benefits
<u>products/international‐macroeconomic‐data‐set.aspx (Accessed on September 27, 2015). There</u> was a relatively small (9 percent) difference in the total estimated shortages in 2030 based on the two methods (15.6 million with the main method we used, and 17.0 million using USDA numbers).

Second, we examine the possible upper and lower bounds of the predictions older than 65 resulting from high and low future population estimates. Because population is the largest driver of demand in our model, using the high and low variant estimates, we can obtain predicted total health worker deficits that may result from population growth among people older than 65 that is higher and lower than expected, compared to the median estimate that is presented in the main results. These alternative low and high estimates (shown in Annex Figure A1) indicate a tight band for the resulting predicted values.

# _Projecting supply_

The supply of health workers was projected to 2030 using historical data to predict the changes in health worker densities (per 1,000 population) for each country. We assume that the historical growth rate of health workers density for each country will continue into 2030 at the same rate. This assumes that supply growth is exogenous and only trends with time. This assumption may be plausible if the health labor market is relatively rigid, for example, due to the strong influence of professional associations and trade unions in the sector. Given that data are only available beginning from 1990, and that many countries only have a few data points, the growth rate method enables us to obtain projections using minimal empirical data inputs.

We separately estimate physician and nurses/midwives density for each country from time _t_ = {1990, ... 2013} using the following equations:

* (Eq 2) _Physicians per 1000 populationt_ = _α_ 0 + _α_ 1 _yeart_ + _εt_

* (Eq 3) _Nurses/midwives per 1000 populationt_ = _β_ 0 + _β_ 1 _yeart_ + _εt_
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data We also observe underreporting in the survey albeit at a lower level. Underreporting in the survey is especially high for firms in Darkhan and Edernet (23.2% and 24.8%), in the tourism sector (37.6%), and for large firms (32.7%). The high level of underreporting for

**TABLE 4**

**Mean % of Total Sales Underreported by MIMIC Approach**

|**Und**|**erreporting in**|**Sales**|**To tax**|**office**|**In the s**|**urvey**|
|---|---|---|---|---|---|---|
|||Obs.|Mean|Std. Dev.|Mean|Std. Dev.|
|**_City_**|_Ulaanbaatar_|126|36.9|22.4|9.4|38.0|
||_Darkhan_|50|46.6**|24.7|23.2**|29.9|
||_Edernet_|38|46.2**|22.4|24.8**|29.2|
||_Hovd_|17|38.0|20.5|9.5|38.6|
|**_Industry_**|_Manufacture_|125|42.4|24.0|13.3|32.0|
||_Construction_|62|33.9**|18.4|5.3*|44.4|
||_Service_|30|36.7|21.7|14.1|26.3|
||_Tourism_|14|43.7|24.2|37
# **Table A2. Sampling frame and survey structure, by country**

|**Country**|**Sampling**<br>**Procedure**<br>(1)|**SCPs**<br>**universe**<br>(2)|**Replace-**<br>**ment rate**<br>(3)|**Assumptions for**<br>**power calc.**<br>(4)|**Stratification levels**<br>(5)|**Sampling Frame**<br>**(Universe) Source**<br>(6)|
|---|---|---|---|---|---|---|
|**Brazil**|Represen-<br>tative<br>sample|2,388|50%|Effect size: 0.08<br>Power: 80%<br>Alpha: 0.05<br>Formal<br>employment at<br>baseline: 9.23%|_State_: Ceara, Sao Paulo<br>_HEI Administration_: Public, private<br>_HEI academic organization_:<br>Universidade, Centro Universitário,<br>Faculdade, Instituto Federal de<br>Educação, Ciência e Tecnologia|Higher Education Census (_Censo_<br>_da Educação Superior_), 2017.|
|**Colombia**|Represen-<br>tative<br>sample|2,130|50%|Effect size: 0.08<br>Power: 80%<br>Alpha: 0.05<br>Formal<br>employment at<br>baseline: 10%|_Region:_Caribe, Centro-Oriente,<br>Centro-Sur, Cafetero-Antioquia,<br>Pacifico<br
transitions in Mexico with Monte Carlo simulations of repeated discrete-time Markov chains. Next, this paper provides regression analysis of the potential determinants of labor market transitions in Egypt, with a focus on transitions to and from the dominant absorbing labor market states in Egypt for both women and men—public sector employment for both genders, non-participation for women, and informal sector employment for men.

The remainder of this paper is structured as follows. Section 2 describes the data we use for Egypt and Mexico, followed by Section 3 discussing the background and context of the Egyptian labor market. Section 4 describes the methodology employed to compute the transition matrices in Egypt and that to simulate their Mexican counterparts. Section 5 presents the results from Egypt’s transition matrices, puts the Egyptian matrices in context by way of comparing with those of Mexico, and regression analysis output on the determinants of transitions in Egypt. Section 6 presents some concluding remarks.

# **2. Data**

## **2.1 The Egypt Labor Market Panel Survey (ELMPS)**

We use data from the ELMPS (Egypt) and ENOE (Mexico). The ELMPS is a nationally representative survey conducted by the Economic Research Forum (ERF) in collaboration with Egypt’s Central Agency for Mobilization and Statistics (CAPMAS). In this paper, we rely on the 3
for key correlates of household welfare. It then aims at obtaining causal estimates using the propensity score matching technique by exploiting the timing of the 2020 survey. Finally, we use detailed information on household and individual welfare indicators pre and post the Covid19 lockdown to ascertain the changes in expenditure, poverty and the distributional impact on household expenditure. We also examine the channels through which Covid-19 impacts household welfare, such as asset ownership and labor market outcomes. Furthermore, we extend our analysis to assess the evolution of our indicators as the period after the lockdown elapses. This analysis informs on the immediate impact and the dynamism in the recovering trend of household welfare indicators post-Covid-19 lockdown.

The paper finds a negative impact of Covid-19 induced national lockdown on household expenditure, thereby leading to an increase in poverty. The negative effect is prominent within the first three months after the lockdown, with a somewhat sluggish recovery. The result appears to be driven by a loss of employment as evidenced by a decline in the share of working household members. Nevertheless, there was no significant impact on monthly salary for those that remain employed. Exploring the effect of the Covid-19 lockdown on coping mechanisms, we find a negligible impact on asset ownership. Our evaluation suggests that the sale of assets as a welfare mitigating strategy for Comorian households during the lockdown was limited.

The remainder of this paper is structured as follows: the following sections outline the relevant literature, context and data description. Section 4 discusses the empirical methodology and section 5 presents the key results while the final section highlights the policy implications and concludes.

5
In order to test the validity of the CDR analysis, its findings were compared to World Bank poverty estimates, which are based on Guatemala’s National Living Conditions Survey (ENCOVI) for 2006 and 2011 and the 2002 Population and Housing Census. The ENCOVI is a traditional household survey, and its sample size does not provide reliable estimates at the municipal level. However, small-area estimation techniques<sup>7</sup> which combine the ENCOVI data with the census data allow for poverty to be estimated at the municipal level. For the purposes of the study these estimated rates were treated as “ground truth data.” In machine-learning analysis, ground truth data are obtained by direct observation, rather than by modeling or inference. In this context, however, the term refers to the poverty rates determined by standard statistical estimation methods, which provide the only existing measure of ground truth. Nonetheless, it should be borne in mind that all poverty-rate estimation methodologies are predicated on assumptions, and in this area no ground truth data can offer a perfect representation of reality.

Supervised machine-learning models, as described here, require a “training dataset,” which comprises benchmark data that represent the ground truth. Since CDRs relate directly to people, they can be considered unit-record data. Then it is the detail of the ground-truth data that largely determines the resolution of the model. In the case of Guatemala, ground-truth data are aggregated This is often the only available option when the ground truth data are based on estimations using household survey data, in which no mobile phone numbers are collected during the survey. In this case, individual-level features are extracted from the CDRs, then combined to form statistical aggregates at the chosen geographic level (e.g. mean, median, maximum or quantiles by region). These aggregates are used to build an area-level predictive model calculated for each area (e.g. poverty headcount). A relatively small sample size (for example, Guatemala’s 338 _municipios_ ) means that internal