rafmacalaba/data-use-mentions-extended huggingface dataset:
and older. Data weighting is used to ensure a nationally representative sample for each economy. Final weights consist of the base sampling weight, which corrects for unequal probability of selection based on household size, and the poststratification weight, which corrects for sampling and nonresponse error. Poststratification weights use country-level population statistics on gender and age and, where reliable data are available, education or socioeconomic status.

The core GWP (excluding our new data on financial inclusion) has been used in previous academic studies. For example, Deaton (2008) uses GWP questions on life and health satisfaction and looks at the relationships with national income, age, and life expectancy. Stevenson and Wolfers (2008) and Sacks, Stevenson, and Wolfers (2010) use the GWP as part of their research to analyze relationships between measures of subjective well-being and income. Clausen, Kraay, and Nyiri (2011) analyze the relationship between corruption and confidence in public institutions. Stevenson and Wolfers (2011) examine trust in institutions over the business cycle.

We use these data to calculate our measures of account use. Below we provide precise definitions (Table 2) and summary statistics for each of these indicators (Table 3). Appendix 1 shows summary statistics, by country, for all variables used in our analysis.<sup>8</sup>

# **_2.2 Account Ownership_**

To calculate account ownership, we use the question: “ _Do you, either by yourself or together with someone else, currently have an account at a bank, credit union, cooperative, post office, or microfinance institution? An account can be used to save money, to make or receive payments, or to receive wages and remittances._ ” On average, 45 percent of adults in our sample of countries report having an account. Not surprisingly, there is enormous variation in the use of

> 8 Individual-level data are available at www.worldbank.org/globalfindex.

7
Figure 3 splits the sample by rural workers who have completed secondary education and shows that the sharp increase in non-farm casual workers and employment in the construction sector is driven by workers lacking secondary education. Rural workers who have completed secondary or higher education are more likely to be employed as salaried workers in the service sector.

## **2.4.2 Rural Consumption**

The top left panel of Figure 4 shows the evolution of rural households’ monthly per capita consumption between 1999 and 2011. In real terms (1993 values), rural monthly per capita consumption displayed an average annual growth of 2.7 percent – from INR 324 to INR 445. The latter two figures in the top panel splits households by whether they have any a) secondary educated individual and b) non-farm worker. The middle panel splits household consumption by whether there is at least one household member participating in non-farm self-employed, casual or salaried work. The bottom panel splits rural households by whether they have some worker employed in either the manufacturing or service sector.

Figure 4, we see that rural households with some non-farm worker had 8-24 percent higher average monthly per capita consumption. This is driven by almost entirely by the – presence of salaried workers household with at least one salaried worker had per capita expenditures between 33 and 50 percent higher than households without any salaried worker. A large divergence in household per capita consumption is also observed for households with at least one secondary educated worker: monthly per capita consumption for these households is twice the per capita monthly consumption for households without any secondary worker. Finally, households with at least 1 worker employed in the service sector has 25 percent higher monthly consumption levels across this entire period.

# **3 Results**

We begin by discussing the predictors of rural non-farm employment using the historical NSS data. We subsequently study whether rural non-farm employment affects household wellbeing. Next, we explore barriers to informal micro-enterprise performance, which comprises a main source of rural non-farm employment. We conclude the empirical analysis
Since rays from the origin in figure 1 represent constant food shares, one can see that there is a an expenditure level (e*) such that at that expenditure level the food share chosen equals the food share in the poverty basket. Since o(e*)>co(e=FPL)>co(e=PL) this implies e*<FPL<PL. In 1996 we can calculate that the expenditures of those whose food share was that of the poverty basket (e*) was Rp. 17,395. This is a level well below the poverty line of Rp. 28,516 and also well below the mean expenditures of those below the poverty line of Rp. 23,920. This implies that the price index using the food share of the poverty basket represents the welfare change of a very, very poor group. This price index will _overstate_ the real expenditure loss of those above that level and hence will moderately overstate the increase in poverty rate.

So the second, not entirely intuitive but not completely unnatural, fixed weights price deflator to use is: Using this deflator, table 4 shows that the poverty rate increases by around 6.52 percentage points (from 9.75 to 16.27 percent) using the CPI series, or around 8.14 percentage points (from 9.75 to 17.89 percent) using the Susenas unit prices. The reason for this difference, as shown in table 5, is that the Susenas unit prices total inflation rate is 7 percentage points higher than the CPI inflation rate.

The third possible procedure (method III) is to inflate the food poverty line (FPL) from 1996 to 1999 by a food price index and then compute the non-food allowance again in 1999 using the same Engel curve methodology as for 1996. This 18
Jamasb, T., Nepal, R., & Timilsina, G. R. (2015). _A Quarter Century Effort Yet to Come of_ _Age: A Survey of Power Sector Reforms in Developing Countries._ World Bank Policy Research Working Paper No. 7330.

Lamb, W. F., & Minx, J. C. (2020). The political economy of national climate policy: Architectures of constraint and a typology of countries. _Energy Research and Social Science_ , _64_ .

Macquarie, R., Naran, B., Rosane, P., Solomon, M., Wetherbee, C., Buchner, B., Choi, J., Davila, E., Dreyer, C., & Falconer, A. (2019). _Updated view on the Global Landscape of Climate Finance 2019_ .

Mallawaarachchi, T., Nepal, R., & Ross, H. (2021). Meeting environmental objectives through energy sector reforms in Asia and the Pacific.

_https://doi.org/10.1080/14486563.2021.1989009_ , _28_ (4), 305–313.

Moner-Girona, M., Bender, A., Becker, W., Bódis, K., Szabó, S., Kararach, A. G., & Anadón, L. D. (2021). A multidimensional high-resolution assessment approach to boost decentralised energy investments in Sub-Saharan Africa. _Renewable and Sustainable Energy Reviews_ , _148_ , 111282.

Murray, M. P. (2006). Avoiding Invalid Instruments and Coping with Weak Instruments. _The Journal of Economic Perspectives_ , _20_ (4), 111–132.

Nagayama, H. (2009). Electric power sector reform liberalization models and electric power prices in developing countries: An empirical analysis using international panel data. _Energy Economics_ , _31_ (3), 463–472.

Peñasco, C., Anadón, L. D., & Verdolini, E. (2021). Systematic
extra moving cost paid by the unobserved type II workers, _C_<sup>3</sup> .

First, we run the regression without types, hence imposing that _C_<sup>3</sup> = 0. The results are reported in the first row of Table 4. We find that _C_<sup>1</sup><sup>_,s_</sup> _/ν_ is equal to 2.68 for young workers who are less than 30 years old, similar to what we find with CPS data. We find that _C_<sup>1</sup><sup>_,s_</sup> _/ν_ is equal to 3.09 for the workers between 30 and 36, and with less than seven years of sectoral experience. We find that ( _C_<sup>1</sup><sup>_,s_</sup> + _C_<sup>2</sup> ) _/ν_ is equal to 4.38 for the workers who are older than 30 years and with more than 7 years experience. All estimates are significant at the 99% level.

The workers are divided into two unobserved types. In reality, if there is a continuum of types with different moving costs, then allowing two types of workers is equivalent to discretizing a continuous distribution into two pieces. Ideally, we would like to discretize into finer grids but it is not possible given the available data. Therefore, we consider two types, and impose the restriction that _C_<sup>3</sup> _/ν_ = 1, then estimate _C_<sup>1</sup><sup>_,s_</sup> _/ν_ and _C_<sup>2</sup> _/ν_ , while we calculate the mass of each type with an EM loop. Although it is theoretically possible with very detailed data, we can not estimate _C_<sup>
ownership is associated with a less adverse (or more beneficial) impact of higher volatility in occupancy rates on the efficiency of hotels. To the best of our knowledge, this is the first paper to find such a gendered effect in any industry.

The survey of hotels that we use is nationally representative of registered private hotels in Malaysia. The survey provides information on various hotel characteristics and hotels’ experiences with different aspects of the business environment. We exploit this rich information to raise our confidence against the omitted variable bias problem. We also use the methodology of Oster (2019) to formally test for omitted variable bias. Nevertheless, causality-wise, our results should be treated with due caution as they are based on pure cross-sectional data.

# **2. Literature review and conceptual framework**

# 2.1 _Volatility and firm performance_

Fluctuations in market demand can affect the performance of hotels significantly. Low demand during off-peak seasons results in excessive capacity (Cuccia and Rizzo 2011, Park et al. 2016) if there are fixed costs in production or a high cost of adjusting inputs such as the number of rooms (see Butters 2020) and labor (see Alemayehu and Tveteraas 2020, Park et al. 2016). Too high demand during peak seasons can also put pressure on available resources, leading to poorer quality of service and lower performance (see Parrilla et al. 2007). In an early theoretical contribution, Sheshinski and Dreze (1976) showed that, compared to stationary demand, fluctuating demand leads to a higher expected cost per unit of output. At least to some extent, the problem of fluctuating demand can be solved by using a more flexible production method or technology. However, flexible technologies are limited, and so is their efficacy in improving efficiency (see, for example, Bryson 2007, Kleinknecht et al. 2006). More importantly, flexible technologies may impose 4
size, and population density, as well as a variety of other characteristics (Appendix A). Municipality-level data therefore offer a relatively fine level of geographic aggregation. Data on population and population density are taken from official sources. Since DRC lacks most common socio-economic and demographic statistics, data on income, commercial development, and financial development at the municipal level had to be created by a local consulting firm.

Specifically, in order to measure these municipality characteristics, we consulted Experts SARL, a market research firm with over 15 years of experience working in the DRC. Given their focus on the city of Kinshasa and their extensive experience with fieldwork across all municipalities of the capital city, we asked them to indicate the level of urban, commercial and financial development of each municipality, as well as their average income levels on a scale of low-medium-high. We also asked them to identify the predominant type of financial services provider in each municipality (banks, microfinance institutions, or providers of mobile banking services).

While Experts SARL has an extensive network of enumerators, the core team in Kinshasa is composed of 9-10 enumerators. Each person on the core team is assigned a subset of municipalities, on which they have in depth knowledge and therefore have become the go-to experts in the team. The input of the enumerators was taken into account when determining the municipality characteristics used in our analysis. Their assessment was based on prior knowledge accumulated during a variety of market research projects in Kinshasa, as well as direct observation of area characteristics completed under the first phase of a scoping mission to identify potential locations for future rollout of FINCA agents.

The ultimate questions that motivate our research are whether and why some clients would prefer to do their banking transactions with an agent rather than at a formal branch, and what types 10
selection of priority areas. The value added of satellite-based methane observations can only be judged after a round of comparative empirical work. In this paper, we contribute with a pilot study of CH4 emissions from irrigated rice production, which accounts for about 10% of agricultural CH4 emissions. Rice production in flooded fields produces methane because oxygen does not penetrate the soil when it is blocked by water. This promotes the growth of methaneproducing bacteria. A variety of techniques can shorten the period of water blockage, including a single field drainage in the mid-season; alternate wetting and drying (AWD), dry seeding instead of transplanting rice into flooded fields, and “aerobic” systems that grow rice in well-drained soil. Our study aims to use the Sentinel-5P (S5P) database to learn more about local variations in methane emissions created by differences in water use and the configuration of rice fields.

We draw monthly CH4 anomaly observations from the database for areas and seasons identified by spatially-referenced data on irrigated rice production from IFPRI and seasonal production timing from RiceAtlas. We compute mean values by grid cell for the period 2020-2022 and explore their relationship with three spatially-referenced data sets that are resampled to match the 25-km global grid: (1) IFPRI estimates of irrigated rice field extents; (2) estimates of agricultural methane emissions from EDGAR, a “bottom-up” inventory that combines detailed 23
**Table 3.6c: Transition Matrices for the Recovery Period**

||||Secondqua<br>|rter 2010<br>||
|---|---|---|---|---|---|
||Inactive or|Inactive<br>|Employed<br>|Unemployed<br>|Total<br>|
||out|26,533,723|6,482,882|944,029|**33,960,634**|
|Second quarter 2009|Employed|5,423,962|36,760,882|1,159,437|**43,344,281**|
||Unemployed|574,547|1,408,068|382,459|**2,365,074**|
||Total|**32,532,232**|**44,651,832**|**2,485,925**|**79,669,989**|

**Source: Own estimates using rotating panels from ENOE.**

Therefore, even though the recovery seems to be creating more jobs, there is also more persistence in the recovery period in the three categories: that is, more people remain as inactive, employed, or unemployed.

_An econometric analysis of transition probabilities_ The previous section described probabilities of transition from one employment status to another. However, these probabilities do not indicate the effect that a particular characteristic, such as age or education, has upon the likelihood of moving from one status to another. The effect of a single characteristic upon employment transitions is known as conditional probability. It is called so because it defines the effect of having one characteristic instead of another (for example, being a woman) upon a particular employment transition (for example, finding a job), under the condition that all the
**Ethiopia** The datasets we use for Ethiopia are the _census_ of Large and Medium Scale Manufacturing Industries Survey (LMSMI) and Small Scale Manufacturing Industries Survey (SSMI), both conducted by the Ethiopian Central Statistical Agency (CSA). The LMSMI covers all formal manufacturing firms in the country that use _power-driven_ machines in production process and employ _at least ten_ persons. The CSA conducted this census on annual basis since 1976.<sup>8</sup> In 2011, the raw dataset contains 1,936 establishments.

The SSMI survey covers establishments which use _power-driven_ machinery and engage _less than ten workers_ . The CSA conducted five waves of SSMI surveys: 1994–1995, 2001–2002, 2005–2006, 2007– 2008, and 2010–2011 - each wave collected on a _sample_ basis. The CSA sampling frame consists of all registered establishments employing less than 10 workers and using power driven machines. The SSMI survey was conducted using stratified sampling procedure to ensure representativeness of all establishments in the country. The CSA also provide a sampling weight for each firm. By merging the two datasets, we obtain complete distribution of establishments sizes for the formal manufacturing sector in the country. After merging, the share of small firms (included in the SSMI survey), in terms of number of establishments, accounts for 96 % of all manufacturing firms.<sup>9</sup>

**Ghana** The data for Ghana are based on the 2003 National Industrial Census (NIC) dataset, conducted by the Ghana Statistical Service (GSS). Three industrial censuses have been conducted: 1962, 1987 and 2003. The study is based on the 2003 census data , which includes establishments employing less than 10 workers. The census is similar in sampling design with the Ethiopian data; it covers the universe of establishments employing more than 10 workers and takes a representative sample of firms employing less than 10 workers. The census was undertaken in two phases. In the first phase, the registry covers
tax administrative data from the TOT regime in Kenya. Kenya provides an insightful case study of the challenges governments face when taxing small businesses, as despite there being more than 1 million potential TOTpayers, the regime contributes less than 1 percent of total tax revenue. In recent

> 1For example, in February 2024 in Kenya, small businesses with an annual turnover between USD7,500 and USD187,500 were only required to calculate their monthly turnover and to pay 3 percent of this amount in tax. The minimum threshold is set to align with the personal income tax exemption threshold.

1
# **3.2 Non-migrants versus Return Migrants**

We focus on working-age men, aged 15 to 59 years old at the time of survey, as Egyptian migration is mostly male dominated. In Table 1, we report descriptive statistics on the sample of non-migrants (individuals who never migrated) and return migrants (individuals who migrated for work purposes and returned to Egypt). The two groups of individuals are significantly different along a set of individual, geographical, and job characteristics. Returnees are found to be significantly older compared to nonmigrants, and also significantly more likely to be married. Returnees are also found to be more likely to have secondary education but less likely to have above secondary education compared to non-migrants. The incidence of rural residence is also significantly higher among return migrants compared to non-migrants.

In terms of geographical locations, returnees are found to be less likely to live in Cairo, Alexandria, Canal cities, and urban Upper Egypt, and significantly more likely to live in rural Lower Egypt.<sup>7</sup> Returnees also have greater job tenure compared to non-migrants and we find that the incidence of having a work contract is also higher among return migrants. In terms of sectors of employment, returnees are significantly more likely to be employed in the public sector for their job in 2012 compared to nonmigrants. In terms of economic activities, we find that non-migrants are significantly more likely to be employed in manufacturing, mining, and quarrying, and in wholesale and retail trade compared to return migrants.

# **3.3 Defining Undocumented Migration**

In the ELMPS 2012, return migrants were asked whether they had a visa or official document to enter the destination country during the first migration episode, as well as the type of document they had.<sup>8</sup> Relying on this information, we are able to identify documented and undocumented migrants among the pool of return migrants. For most of the destination countries, Egyptian migrants are required to have a visa
# **Figure 5. WFH amenability and GDP per capita**

<!-- Start of picture text --> (a) PIAAC surveys (b) STEP surveys<br>0.6 0.8<br>SRB<br>0.6<br>0.4 NLD KOS MKD<br>BEL 0.4 GEO<br>FIN DNK PHL<br>0.2 ESTHUN CZEISRJPNFRACAN SWE NOR 0.2 VNM ARM UKR<br>AUT SGP<br>LTU KOR DEU<br>0 SVK SVN NZL GBR 0<br>9 9.5 10KAZ POL ESP10.5 ITA IRL USA11 11.5 -0.2 7.5 8 KEN 8.5 9 9.5COL 10<br>GRC<br>-0.2<br>RUS -0.4 BOL<br>CHL GHA<br>SLV<br>-0.4 -0.6<br>TUR LAO<br>-0.8<br>-0.6<br>ECU LKA<br>PER -1<br>MEX<br>-0.8 GDP per capita PPP (log) -1.2 GDP per capita PPP (log)<br>(c) LMPS<br>0.4<br>JOR<br>0.3<br>0.2<br>0.1<br>0<br>9 9.05 9.1 9.15 9.2 9.25 9.3 9.35<br>EGY<br>-0.1<br>-0.2<br>TUN<br>-0.3 GDP per capita PPP (log)<br>WFH index - adjusted by home internet access WFH index - adjusted by home internet access<br>WFH index - adjusted by home internet access<br><!--
Survey data also reveal that taxpayers’ perceptions of corruption vary, as a result of the reforms.<sup>36</sup> Indeed, the differences are striking. In Peru 73% of respondents classified the agency’s corruption level as _Fairly_ or _Very Low_ , as opposed to 20% for Mexico and 6% for Venezuela. In contrast, 29% in Venezuela classify corruption as _Fairly_ or _Very High_ , as compared with 17% for Mexico and 2% for Peru.

In terms of the impact of the reform on corruption, Peru again surpassed the others, with 85% responding that there was _Substantially_ or _Much Less_ corruption than before the reform, as compared with only 26% in Venezuela and 21% in Mexico. The relatively smaller impact in Venezuela reflects the perception of backsliding since the reform, whereas the value for Mexico reflected the perception of the of reforms’ minor impacts.

Of the African cases, Kenya and South Africa have gone farther than Uganda. Uganda, however, was not without some progress in this area. It is thought that the URA reform has reduced the amount of corruption in the tax agency, as compared with the prereform agency, and has reduced the impunity of the corrupt officials that remain. The combined effect of higher salaries and stiffer penalties for malfeasance vis-à-vis public service employment conditions seems to have made an impact. The URA reform is reported to have led to less moonlighting. The URA recently established an on-line recruitment system, in which specific posts are advertised against the establishment register and application processing is handled electronically.

Very encouraging is the perception in Kenya, held by both KRA officials and private sector tax experts, that corruption has decreased since the reform. According to one professional tax accountant, “I’m less likely to get asked for a bribe than in the old days.”<sup>37</sup> However, as would be expected, significant corruption still exists, especially in customs. According to a previous Commissioner General (CG