rafmacalaba/data-use-mentions-extended huggingface dataset:
/sup> Knight and Gunatilaka (2012) argue that ―aspirations‖ for higher income also matter negatively to subjective welfare, and that this mitigates appreciably the direct income effect. Using data for rural China they proxy ―aspiration income‖ by answers to a version of the MIQ and find that the latter has a significant negative coefficient in predicting SWL and ―satisfaction with income.‖ Aspiration income is in turn found to be an increasing function of own income (consistently with past research on the SSPL). They interpret their results as evidence of an ―hedonic treadmill‖ whereby higher actual income need not raise happiness given offsetting

> 23 Ravallion and Lokshin (2000) also find that Russians on a positive (negative) income trajectory are also less (more) likely to support redistribution from the rich to the poor.

14
<!-- Start of picture text --> Besides the prevalence of voluntary exit in the informal sector, other important questions<br>a stepping-stone for entrepreneurs with high-growth potential but who might be constrained by,<br>say, credit constraints? From the workers' perspective, especially young and female workers, do<br>transitions into formal employment very unlikely? For informal workers that choose to be<br>reduction?<br>The existing literature examining workers’ mobility within and across informal and formal<br>rotating panel of labor force surveys for about 50 countries, Donovan et al. (2023) find that<br>transitions in and out of employment and across job types are more common in developing<br><!-- End of picture text -->
---|---|---|---|
|Annual volume|Yuan/m<sup>3</sup>|US$/mcf|Annual volume|Yuan/m<sup>3</sup>|US$/mcf|
|Less than 310 m<sup>3</sup>|3.0|13.84|<300 m<sup>3</sup>|2.28|10.00|
|Between 310 m<sup>3</sup>and 520 m<sup>3</sup>|3.3|15.22|300–500 m<sup>3</sup>|2.50|10.97|
|Greater than 520 m<sup>3</sup>|4.2|19.37|>500 m<sup>3</sup>|3.90|17.11|

_Sources:_ Reuters 2014b for Shanghai, Energy Research Institute for Beijing.

In November 2015, the NDRC announced a new policy for gas pricing and also cut gas prices for non‐ residential consumers by about 30 percent to respond to falling gas prices in Asia. According to customs data, the average price of LNG delivered to China fell from $11.15/mmBtu in January to $7.39/MMBtu in September 2015. Non‐residential city‐gate prices were lowered by 700 yuan per thousand cubic meters, or about US$3.35/mmBtu. Under the new policy, buyers and sellers will be able to negotiate final delivered prices starting in November 2016. The pricing policy has changed from setting ceilings to setting benchmarks for non‐residential gas prices in each region. For the first year, prices can be up to 20 percent higher than the new benchmark gate prices ( _Platts LNG Daily_ 2015).

# **Colombia**

The Government of Colombia established
# **3.2 Transport costs**

Trade and transport cost data are also not widely available for Africa.<sup>24</sup> In the international trade literature, trade costs are sometimes estimated from a gravity equation based on trade flows (Anderson and van Wincoop, 2004), or price dispersion (Donaldson, 2010) but trade flow data between cities and city-level price data are also not widely available. Furthermore, city growth may endogenously decrease transport costs. Among other reasons including the allocation of paved roads (discussed below), more transport companies are likely to compete on a route to a growing city than on a route to a stagnant one.

I deal with this by decomposing variable transport costs into two components: 1) the world price of oil, which varies across time but not across cities, and 2) the road distance between a city and its country’s primate, which varies across space but not time.<sup>25</sup> Figure 7 shows the evolution of oil prices during the study period. In general, they were relatively steady until a consistent rise beginning in 2002. However, there was some movement in the previous period, including substantial decreases (as a fraction of the initial price) in 1992–1994, 1996–1998, and 2000–2001.

Oil is a convenient proxy for transport cost per distance because no countries in the sample are individually capable of influencing its price substantially. However, motorists consume refined petroleum products, mostly gasoline and diesel, not oil, and some countries, especially oil producers, subsidize their prices. Country-specific diesel prices, surveyed in November in the main city, are available for most countries roughly every two years (Deutsche Gesellschaft f ̈ur Technische Zusammenarbeit, 2009). As shown in Figure 7, diesel prices averaged over a balanced panel of 12 countries from the main estimation sample generally rise in parallel with oil prices. Nigeria, Gabon, and Angola, the three sample countries for
conducted by the Pakistan Bureau of Statistics (PBS) every alternate year that collects item-wise data on household expenditures. The latest data available is for 2018-19, which provides a baseline of spending levels and patterns.

**<mark>Obtaining the non-housing consumption aggregate.</mark>** <mark>The residual (non-housing) expenditure aggregate is obtained using aggregate consumption expenditures</mark><sup>21</sup> <mark>obtained from the HIES. The dataset categorizes expenditures by type, including housing expenses, food, and other (non-housing) non-food spending. By construction, the REM allows for an estimate of residual (non-housing) expenditures in a direct manner by summing individual summing reported expenditures on non-housing items. This contrasts with the RIM, in which residual income is estimated by excluding housing costs from total income.</mark>

The non-housing consumption aggregate for Pakistan has three main components, including: (i) the <mark>aggregate nominal non-housing consumption expenditure, which consists of all non-housing related expenditures (food and non-food) of the household converted into the same time unit, for instance annual, monthly or weekly expenditures; (ii) a spatial price index, which adjusts for the cost of living differences across space or regions; and (iii) an equivalence scale, which adjusts for differences in household size and age composition across households.</mark> The residual consumption aggregate includes all food and non-food expenditures, that are not related to housing, and are incurred on a recurrent basis. This includes both cash and in-kind expenditures for items directly purchased in the market, received as wages or salaries, or received as gifts or assistance. In the case of Pakistan, the key non-food, non-housing items included in the residual consumption aggregate include expenditures on clothing, footwear, education, health, fuel, transport, recreation, and communication. Housing expenditures, which are not part of the aggregate, include rental payments (actual and imputed), as well as
financial closure between 1990 and 2010 (i.e., PPPs that have survived five or more years). Of the 5,054 PPPs in the PPI Database, 3,552 projects have survived more than 5 years, of which 2,777 are operational or concluded, whereas 173 are canceled (Table 1).

The main reason not to consider relatively young PPPs (less than six years of potential operability) is simple: a project’s outcome (e.g., conclusion, cancellation, survival) can only be reasonably measured when projects have been operational for enough time to allow their outcomes to become observable.<sup>5</sup> For example, PPP

> 4. Merchant projects are those wherein “a private sponsor builds a new facility in a liberalized market in which the government provides no revenue guarantees. The private developer assumes construction, operating, and market risk for the project.”

> 5. According to the PPI Database, canceled projects have an average duration (between the financial closure year and its cancellation) of 5.89 years. Moreover, only one of the PPP projects that reached financial closure after 2012 has been canceled.

4
|(.01)|(.24)|(.00)|(.00)|(.01)|(.02)|

# Note:

- 0.01 significance level, ** 0.05 significance level, *** 0.10 significance level, and insignificant otherwise EDU2-2: Inputs: orthogonalized public spending on education per capita, teachers per pupil Outputs: gross primary and secondary enrollments

- EDU2-2n: same inputs as EDU2-2, outputs: net primary and secondary enrollment EDU3-2: literacy of adult is added to EDU2-2 as input

- EDU3-2n: literacy of adult is added to EDU2-2n as input EDU3-3: literacy of youth is added to EDU3-2 as output EDU3-3bl: same inputs as in EDU3-2,

outputs: average years of school, first level complete, and second level complete (Barro-Lee education indicators)

- HEA2-2: Inputs: orthogonalized public spending on health per capita, literacy of adult

- Outputs: life expectancy at birth, and immunization DPT

- HEA3-2: orthogonalized private spending on health per capita is added to HEA2-2 as input HEA3-2m: Immunization Measles is in place of DPT in HEA3-2 as output HEA3-3: Immunization Measles is added to HEA3-2 as output

34
changes in the labor market outcomes by quarter for rural and urban areas at the province level.

The master sample is based on the latest three population censuses for 2006, 2011 and 2016.

# **3. Country background and COVID-19 3.1. Iranian context**

As in other MENA countries, the employment and labor force participation rate of Iranian women is low, about one-fourth that of men. This occurs despite high education and below replacement fertility, a combination that Assaad _et al._ (2020) call the MENA paradox. The sharp contrast between male and female LFP rates for prime age men and women (25-54) since 2005 is shown in the left panel in Figure 1. This figure draws on data from all rounds of LFS since 2005. During the period 2005-2022, participation rates for women fluctuated between 15% to 25%, while men’s participation rates stayed above 85%.

The impacts of COVID-19 on participation of both men and women are noticeable in this graph after the winter quarter of 2020 (2020_q1). The right panel of the figure brings the divergent effects on men and women into sharper focus and shows a more dramatic decrease in women’s participation. In this graph, participation is measured as change relative to winter 2019 (2019_q1). A few months after the first COVID-19 cases were reported in the Islamic Republic of Iran, women’s participation rate fell by about 15% in spring 2020 and by more than 20% in 2021. In contrast, men’s participation rates fell by 3% only.<sup>3</sup> Personal characteristics like age and education that affect the participation of women in the labor market could also have influenced the severity of COVID-19’s impacts on labor market outcomes. In MENA, marital status is perhaps the most important determinant of employment.

> 3 Plotting the changes in employment produces a figure that mirrors those in labor force participation (not shown). Women’s employment rates are lower than
, stool microscopy, urine pregnancy<br>test and functional microscope in a facility.|
|Standard precautions for infection<br>control index<sup>2</sup>|Proportion of amenities available out of - necessary guidelines, equipment<br>and supplies for infection control in a facility.|
|Diagnostic capacity for antenatal<br>care services index|Proportion of diagnostic services available out of -diagnostic capacity for<br>hemoglobin, urine protein,urine glucose, blood grouping and Rhesus<br>factor and syphilis in a facility.|
|Medicines for routine antenatal care<br>index|Proportion of medicines available out of - iron tablets, folic acid tablets,<br>combined iron and folic acid and iron or folic acid tablets.|
|Essential medicines and commodities<br>for normal delivery index<sup>3</sup>|Proportion of medicines and commodities available out of - essential and<br>priority medicines and commodities for normal deliveryin a facility.|
|Essential Medicines for newborn<br>care index|Proportion of medicines available out of - essential medicines for newborn<br>care in a facility.|
|Essential and priority medicines and<br>commodities for childcare index|Proportion of medicines and commodities available out of - essential and<br>priority medicines and commodities for childcare in a facility.|
|Family planning commodities index|Proportion of commodities available out of - oral pills, injectable, intra<br>uterinecontraceptive device and condoms in a facility.|

2 Standard infection control measures include- sterilization equipment; equipment for high level disinfection and safe final disposal of sharps waste and infectious waste; appropriate storage of sharps waste and infectious waste; disinfectant, syringes, and needles; soap, running water, alcohol-based hand disinfectant, latex gloves, medical masks, gowns, eye protection and guidelines for standard precautions.

3 Essential medicines and commodities for delivery care include - injectable uterotonic (oxytocin), injectable antibiotic,
Nov-18 Jan-19 Feb-19 May-19 Sep-19 Oct-19 Dec-19<br><!-- End of picture text --> Source: CPB Bureau for Economic Policy Analysis, China Ministry of Finance, Davis (2016), Freund et. al. (2018), Haver Analytics, Institute of Shipping Economics and Logistics, International Trade Centre, United States Trade Representative, World Bank.

A. See Davis (2016) for details. Last observation is October 2019.

B. Vector autoregressions are used for estimation on a sample of aggregate EMDE variables for 1998Q1-2016Q2. The model includes the Economic Policy Uncertainty for the euro area, emerging market stock price (euro area) index, emerging market bond index, aggregate real output and investment growth in six ECA countries, with G7 real GDP growth, U.S. 10-year bond yields, and MSCI World Index as exogenous regressors and estimated with two lags.

C. Trade-weighted average tariffs computed from product-level tariff and trade data, weighted by U.S. exports to the world and China's exports to the world in 2017.

D. Figure shows three-month moving averages. New export orders measured by Purchasing Managers’ Index (PMI). PMI readings above 50 indicate expansion in economic activity; readings below 50 indicate contraction. Last observation is June 2019 for goods trade, July 2019 for container shipping, and August 2019 for new export orders.
|
||(0.0035)|(0.0017)|(0.0022)|(0.00056)|
|Public debt service|0.050|-0.063<sup>_∗∗∗_</sup>|-0.040<sup>_∗∗_</sup>|-0.024<sup>_∗∗_</sup>|
||(0.062)|(0.022)|(0.019)|(0.012)|
|_R_<sup>2</sup>|0.178|0.207|0.294|0.171|
|Hausman||0.000||0.000|
|_β_ ⩽0|0.285|0.475|0.005|0.019|
|_β_ ⩾1|0.000|0.000|0.002|0.000|
|Countries|108|108|108|108|
|Observations|1082|1082|1087|1087|

Note: OLS and fixed effects (FE) results, annual data, 1990-2003. All regressions include time dummies, coefficients not reported. Heteroskedasticity-robust standard errors, clustered by country, in brackets. _∗_ , _∗∗_ , and _∗∗∗_ denote significance at 10, 5 and 1%, respectively. In the case of FE estimation, _R_<sup>2</sup> refers to the within _R_<sup>2</sup> . Hausman shows the p-value of a generalized Hausman test of the null hypothesis that _ηi_ is uncorrelated with the regressors. _β_ ⩽ 0 ( _β_ ⩾ 1) is the p-value for the test of full (no) fungibility for total sectoral aid. Source: Author’s analysis based on data described in the text.

15
no removal date indicated and 52 are to be removed in 2021 or later.

> 2 See https://globalgovernanceprogramme.eui.eu/wp-content/uploads/2020/05/Methodologynote050420.pdf.

> 3 For products this is the Harmonized Commodity Description and Coding System (HS); for sectors the UN use is made of the International Standard Industrial Classification (ISIC).

> 4 In the case of the European Union, some member states also took individual actions to restrict or encourage trade in medical supplies. These countries included Belgium, France, Germany, Greece, Hungary, Italy, Latvia, Poland and Romania. In the analysis that follows the scores attributed to individual members of a custom union are added to whatever measures are implemented by the union. That is, the EU and its member states are identified as distinct implementing jurisdictions. This permits identification of both the EU as an entity and individual EU countries in the scatterplots that follow, with data for member states comprising those measures implemented on top of the EU-level ones (which are identified by the label EU).

2
**|-0.124***|
|||||(0.029)|(0.032)|(0.032)|
|Lagged Dependent Variable|0.641***|0.496***|0.480***|0.544***|0.496***|0.480***|
||(0.018)|(0.031)|(0.032)|(0.027)|(0.031)|(0.032)|
|R-squared|0.489|0.530|0.536|0.507|0.531|0.537|
|No. of Observations|2,925|2,925|2,925|2,925|2,925|2,925|
|No-Contract Treatment Efect||||0.037*|0.038*|0.039*|
|Treated+[Treated*No-Contract]||||[0.097]|[0.078]|[0.059]|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: The additional variables included in Model 1 are a vector of city fixed effects; in Model 2 we also add individual level controls (age, gender, education, marital status, number of children, dummies for self-employment, dummy for working and an interaction between self-employment and lagged (2015) dependent variable and dummy for “willingness to remain in the city permanently if policy permits.”), while Model 3 adds firm level controls (pre-treatment firm size and ownership indicator variables ) as well. Robust standard errors clustered at workplace of household head are in parentheses. P-values are in brackets. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05,
# **Table 9 SME data from CGAP financial access database**

||**Agency**|**monitors t**<br>**to S**|**he level of**<br>**MEs**|**lending**|**Promoti**<br>**access to**|**ng SME**<br>**finance**||**SME definitio**|**n**|**SME**<br>**lending**|
|---|---|---|---|---|---|---|---|---|---|---|
||Yes,<br>regularly<br>(Yes/No)<br>|Yes,<br>irregularly<br>(Yes/No)|No<br>(Yes/No)|No, but<br>another<br>agency<br>does<br>(Yes/No)|Agency is<br>responsible<br>(Yes/No)<br>|Dedicated<br>team or<br>unit<br>(Yes/No)<br>|Number of<br>employees<br>(max.)|Sales|Loan size|Loan<br>value (%<br>of GDP)|
|**Malaysia**|√||||√|√|150.00|7,093,199.00|..|17.43|
|**OIC**<br>**countries**<br>**average**|72.97%|8.11%|10.81%|13.51%|62
this raises is that it can be misleading to compare the performance of poverty mapping methods across different datasets, as is done in Yeh et al. (2020), Chi et al. (2022), and Aiken et al. (2022), among others. Most notably, survey design elements can affect measurement error in the direct estimates (e.g., through different sample sizes), which in turn influences the magnitude of bias in the _R_<sup>2</sup> . All else equal, models applied to low-bias settings will appear to perform better than those applied to high-bias settings.

Our simulations built on these analytical results. First, we showed empirically that the direct _R_<sup>2</sup> exhibits considerable downward bias, with the true _R_<sup>2</sup> being roughly 35 to 50 percent higher than the direct _R_<sup>2</sup> . We also showed that this bias has implications for model selection, as the direct _R_<sup>2</sup> commonly identified the incorrect level of estimation in our data. Second, we assessed the performance of machine learning methods and found that they may outperform or achieve similar bias and MSE as the best performing traditional small area estimation approaches, particularly in out-of-sample predictions. Third, we present a comparison on the potential benefits of publicly available GIS covariates. We show how GIS covariates under the data used here do not outperform census aggregate data. This result suggests that there may be considerable gains in linking census 30
# **1. Introduction**

Informality is a prevalent issue across developing countries, especially in Sub-Saharan Africa (SSA), where roughly 9 out of 10 individuals are informal as of 2020 (ILO 2020). The current global crisis driven by the COVID-19 pandemic has put extra pressure on an already fragile labor market lacking the adaptive capacity to absorb exogeneous shocks. This leaves the livelihoods of those at the low end of the income distribution highly exposed. Lockdown measures may have also destroyed formal jobs in Africa as well as significantly impacting about 325 million informal workers (ILO 2020).

The study of informality in Sub-Saharan Africa has focused on a few countries such as South Africa, Kenya, Ghana and Tanzania, or aggregated cross-country analysis, often due to data limitations such as lack of labor force data (Bhorat and Tarp 2016; Golub 2014; Mbaye and Benjamin 2014; Benjamin and Mbaye 2012; Grimm et al. 2012; Falco et al 2011; Haan 2006). Expanding the share of the workforce in formal employment is a critical policy objective for developing countries since these types of jobs are typically associated with higher productivity (La Porta and Shleifer 2016; Busso et al. 2012; Benjamin et al. 2012; Hsieh and Klenow 2009, Steel and Snodgrass 2008; Gelb et al. 2009; La Porta and Shleifer 2008) and better quality of jobs (World Bank 2020c; Brummund et al. 2016). As such, policies promoting formal employment can contribute to improve productivity and livelihoods across the region.

This paper studies the current state of informality in Senegal, defined as those that do not contribute to social security or do not have a formal accounting system in their nonagricultural enterprise, and estimates the relative importance of self-selection versus entry barriers. Senegal is an interesting case study since 97 percent of firms and 93.7 percent workers are classified as informal, in line with 92.4 percent across West Africa (ILO, 2018), and has among the most rigid labor market regulations in
# **TABLE 4: ARE ATTRITORS MORE MOBILE?**

Quarterly Pseudo-Panel Estimates from the ENEU Dependent Variable: Log of Real Household Income

||(1)<br>Full<br>Sample|(2)<br>Non-<br>Attritors|(3)<br>Attritors|(4)<br>Full<br>Sample|(5)<br>Non-<br>Attritors|(6)<br>Attritors|(7)<br>Non-<br>Attritors|(8)<br>Attritors|
|---|---|---|---|---|---|---|---|---|
|Quarterly lag of Log Income|0.991|0.987|0.977|0.857|0.788|0.789|0.821|0.754|
|T-statistic|157.39|132.22|111.44|43.68|46.84|47|36.51|29.66|
|Confidence interval|[.979,1.003]|[.972,1.001]|[.960,.994]|[ .819,.896]|[.755,.821]|[.756, .821]|[.777,.865]|[.704,.804]|
|Cohort Effects|No|No|No|Yes|Yes|Yes|Yes|Yes|
|Cohort effects restricted to<br>be equal for attritors and<br>non-attritors:|---|---|---|---|Yes|Yes|No|No|
|Cohort-quarter observations:|638|638|638|638|638|638|638|638|
|Adjusted R squared: