rafmacalaba/data-use-mentions-extended huggingface dataset:
electricity and central heating companies from the Central System (representing 95% of the total sector costs). Expert estimates indicate that these reported costs of production fail to include three important components: first, the implicit subsidy that companies receive from coal at a price estimated to be at least 50% lower market interest rate in 2016, and an 8 percent for the program) leads to an annual subsidy of MNT 351 billion which represents 1.5 percent of GDP in 2016.

> 9  Preliminary data from 2016 Mongolian Statistical Yearbook indicate subsidies to government organizations and private companies for 224.4 billion MNT.

10 For a recent use of this method in Central American countries see (Hernández Oré, Sánchez, Sousa, & Tornarolli, <u>2018) and for a more technical discussion see (Koplow, 2009).</u> 5 MONGOLIA Distributional impact of taxes and transfers / May 2018
: Global Findex database.

_Note_ : People may borrow from multiple sources, but the categories here are constructed to be mutually exclusive. “Borrowed formally” includes all adults who borrowed any money from a financial institution or by using a credit card. “Borrowed semi‐formally” includes all adults who borrowed any money semi‐formally (from a savings club) but not formally. “Borrowed from family or friends” excludes adults who borrowed formally or semi‐formally.

The borrowing pattern in ECA looks different from that in the rest of the world. In 2017, 55 percent of adults in the euro area reported having borrowed money the previous year. Forty‐six percent reported that they borrowed money from a financial institution or by using a credit card; only 12 percent reported borrowing from friends or family. In contrast, in the developing economies of ECA, 44 percent of adults reported borrowing the previous year, with 24 percent reporting borrowing from friends or 22
intermediate input bundle. From (2), (52), (53), and (55), Rearranging the terms of (56) gives (21) in subsection 3.1 in the text.

# **A.3 Firm Price**

Firm-specific price deflators are calculated using firm level product data. Using data on product level total revenue and quantity of each product, I get the price of each product of a firm. I then construct firm price deflators similar to Eslava et al. (2004) and Eslava et al. (2010). The growth in prices of firm _i_ is given by the weighted average of the growth in prices of all products of the firm where the weights are given by the share of the product revenues in total revenue of the firm.

where and where _shit_ is the share of product _h_ in revenue of firm _i_ in period _t_ . The industry base price was set at 100 for 1989. Industry price indices for each year were calculated by recursively adding the weighted growth in prices of all firms with weights given by firm share in total industry share in that year. The firm’s initial price was set at the industry average at the particular year it was first observed similar to Eslava et al. (2004) and subsequently, Since price growth had large outliers (both at the upper and lower tails), I winsorized price changes at the 3rd and 97th percentile by year.

36
, M. & Wacker, M. K. (2023), Measuring Multinational Production with Foreign Direct Investment Statistics: Recent Trends, Challenges, and Developments, IMF Working Papers 2023/113, International Monetary Fund. **URL:** _https://ideas.repec.org/p/imf/imfwpa/2023-113.html_

- Cirera, X., Vargas Da Cruz, M. J., Grover, A. G., Iacovone, L., Medvedev, D., Pereira Lopez, M. D. L. P. & Reyes, S. (2021), Firm Recovery during COVID-19 : Six Stylized Facts, Policy Research Working Paper Series 9810, The World Bank. **URL:** _https://ideas.repec.org/p/wbk/wbrwps/9810.html_

- Conte, M., Cotterlaz, P. & Mayer, T. (2022), The cepii gravity database, Working Papers 2022-05, CEPII.

**URL:** _http://www.cepii.fr/CEPII/fr/publications/wp/abstract.asp?NoDoc=13432_

- De Loecker, J., Eeckhout, J. & Unger, G. (2020), ‘The rise of market power and the macroeconomic implications’, _The Quarterly Journal of Economics_ **135** (2), 561–644.

- Fajgelbaum, P., Goldberg, P. K., Kennedy, P. J., Khandelwal, A. & Taglioni, D. (2021), The us-china trade war and global reallocations, Working Paper 29562, National Bureau of Economic Research.

**URL:** _http://www.nber.org/papers/w29562_

- Feenstra, R. C., Inklaar, R. & Timmer, M. P. (2015), ‘The next generation of the penn world table’, _American Economic Review_ **
prudent and adequate both in terms of the fiscal and monetary stimulus (see section on the assessment of the crisis). Yet, while these features have sheltered Egypt from the impact of the financial crisis, some of them, namely, limited financial development and low private investments have also been constraints to long-run growth.

The impact of the crisis fell squarely on the activities that experienced rapid growth in recent years. Only two sectors’ output fell: oil refining (-4.2 percent) and Suez Canal services (-7.2 percent). Restaurants and hotels managed to maintain positive but very low growth at 1.3 percent. On the contrary, some sectors such as construction and building outpaced the previous year, up by 20.2 percent in FY09 compared to 15.7 in FY08. Other sectors kept growing at almost the same modest pace, such as wholesale and trade (at 5.8 percent), agriculture (3.2 percent) and real estate ownership (3.8 percent), while others like the financial services have seen their growth rate declining (4.6 percent compared to around 7.6 percent in FY08).

A World Bank private sector survey (World Bank 2009c) showed that small enterprises’ sales (a proxy for output) fell by more than large enterprises' sales. Sales of small enterprises fell by 32 percent, while those of large firms dropped by 19 percent. Given that the shock was mostly to the tradable sectors, and large firms comprise the majority of exporting units, this was possibly due to the lower initial productivity level of smaller firms. In any event, if output contraction in small enterprises continues, the rise in unemployment will be larger than anticipated.<sup>9</sup> Any policy should, however, be directed towards increasing productivity in these units rather than artificially maintaining unproductive ones in operation.

Input utilization, especially of labor, has been slow to react; this implies lower factor productivity or,
|48.97|33.97|*|**|
|SOM|4,850|4,880|8,888|8,670|45.66|35.27|10.24|[8.84]|***|***|
|SSD|286|318|1,840|2,107|[23.52]||[42.50]|[31.72]|[***]|[]|
|SUD|2,911|2,832|5,916|5,986|32.75|33.52|17.02|16.71|||

Source: Authors’ calculations using the Ethiopia SPS (2017), Nigeria IDP Survey (2018), Somalia HFS (2017), South Sudan HFS (2017), and Sudan IDP Profiling Survey (2018). Significance at 1% ***, 5% **, and 10% * levels.

According to Table 13, intrahousehold inequality in unemployment is relatively infrequent among the multidimensionally poor populations, and thus displacement status does not significantly correlate with intrahousehold inequality for unemployment. The exception is Sudan, where displaced persons are significantly more likely to live in MPI poor households where at least one working-age member is employed, and another is unemployed and looking for work, although there are no significant differences by 25
with Bargain et al. (2020) analyzing the interplay between intra-household decision-making and the migration of the husband, and Mu ̃noz-Blanco (2023) and Champeaux et al. (2023) studying the relationship between migration decisions and the marriage market. We draw out the implications of such changes for assessing migration impacts.

The rest of the paper is structured as follows: Section 2 describes how the decisions concerning migration and remittances, and the analyses of their effects, and shaped by the migrant’s family. Section 3 discusses how the occurrence of a migration episode can, in turn, modify the family of those who move, notably via an influence on marital union and dissolution. Section 4 draws on the American Community Survey to present some stylized facts on the initial marital status of migrants from developing countries, and on the evolution over time of their marital status, accounting for the confounding effect of return migration. Section 5 discusses the influence of the frequent changes in marital status on the analysis of the impact of migration on the family members left behind, while Section 6 draws the main conclusions.

# **2 How the family affects the migration and remittances decision and their impacts**

The vast majority of the literature has taken the family as fixed or predetermined, and then postulated different ways in which it can affect migration and remittance decisions. In the classic individual utility maximization model of Sjaastad (1962), reluctance to leave family is one of the “psychic costs” of migration, and then the optimal decision takes this “taste” for family presence as given. Much of the subsequent literature takes the decision-making unit as the family instead of the individual, with the decision to migrate then depending on the net gain in family income (Mincer, 1978), on how migration of one member enables risk diversification and insurance within households (Stark and Lucas, 1988), and on how it affects the family’s informal insurance arrangements with others in their community (Munshi and Rosenzweig, 2016; Morten
7 whenever information is available so as to avoid including people living in households using one or two solar lanterns. In countries where survey questionnaires asked only about solar energy (Botswana, Burkina Faso, Ethiopia, Ghana, Senegal, South Africa, Swaziland, Togo, and Uganda), definition 4 could well over‐estimate the number of people with access, if households with solar lanterns had replied that they were using solar energy for lighting.

All surveys asked about spending on electricity, most commonly in the last 30 days. There are limitations with this approach. Payment arrears may be common, but no household surveys in this paper probed this point. By contrast, the 2012 National Survey of Household Income and Expenditures in Mexico asked a series of questions to understand arrears, including when the last payment for electricity had been made, while the 2005 Integrated Sample Household Budget and Labor Survey in the Kyrgyz Republic asked for kWh of electricity consumed, the amount billed, and the amount paid for three successive months as well as the amount of subsidy received. Most household surveys, however, simply ask how much the household paid last month for electricity. Asking just one question about how much the household paid over a fixed period of time could under‐ or over‐estimate (the latter if past debts are being repaid to utilities) monthly expenditures on electricity. Cross‐checking household survey data against data from the utilities could indicate the magnitude of these problems and ways of adjusting data for a more accurate picture. Lampietti and Junge (2006) combined billing and payment records from the utility and merged them with household survey data to address recall errors, under‐ and over‐reporting, and the presence of arrears, which enabled more accurate estimation of current and historical electricity consumption as a function of household income and other characteristics. However, obtaining utility data for such cross‐checking was outside the scope of the study.

The survey in Senegal asked if the household had spent anything on electricity over the last 12 months,
. **See Table 4.**

### **Maternal nutrition outcomes**

Five (5) SRM assessed risk factors for maternal nutrition outcomes, including anemia (Seid et al. 2023; Geta, Gebremedhin, and Omigbodun 2022; J. Zhang et al. 2022), thinness (Getaneh et al. 2021), and zinc deficiency (Berhe, Gebrearegay, and Gebremariam 2019). Four of the five were based either exclusively or dominantly on studies in Ethiopia, with extensive overlap of studies included. All five SRM included dietary diversity as a risk factor and proxy for diet quality during pregnancy. Low diet diversity was associated with higher risk of maternal anemia, with the effect size estimates ranging from 2.15 to 2.61. However, there was heterogeneity in the number of food groups and dietary diversity score (DDS) cutoffs used to define low or inadequate diet diversity across the studies included in the SRM.

Consumption of specific food groups (e.g. meat, dark green leafy vegetables) relevant to anemia as an outcome was assessed in two studies. Risk of anemia was over two times higher among pregnant women eating meat ≤1 time per week (OR 2.02; 95% CI 1.55, 2.50) and those eating vegetables ≤3 times per week (OR 2.97; 95% CI 1.59, 4.34) (J. Zhang et al. 2022). In a review of four studies from Ethiopia, zinc deficiency was also associated with a low intake of ASF (OR 2.57; 95% CI 1.80, 3.66) and low dietary diversity (OR 2.12; 95% CI 1.28, 3.53) (Berhe et al. 2022).

### **Maternal health outcomes**

Twelve (12) SRM summarized the evidence for the contribution of diet during or before pregnancy to maternal health outcomes, including gestational diabetes,
While the frontier production function does not substantiate the notion of fragmentation negatively affecting overall output fragmented land plots may have a higher probability of being left idle. Table 3 uses survey weights to provide evidence on the approximate magnitudes involved based on the LSMS survey. The total amount of cropland not used for non-productivity related reasons is estimated at 173,885 ha or about 12% of the total. Some two thirds of this land has not been cultivated for at least 5 years. Interestingly, idling of land does not seem to be concentrated in areas that are marginal; in fact 54% is in the coast and 31% in the central part of the region, with only 13% in the mountains. Also, while about 20% of the affected area remains fallow due to poor soil quality, the remainder is about equally split between economic factors, i.e., the lack of resources to access variable inputs or machinery and location of the plot being too far from the homestead.

Reasons for cropland to be abandoned are of interest in two respects. First, with less than half a percent, the share of area where plot sizes are judged to be uneconomically small is negligible, suggesting that publicly supported consolidation efforts that predominantly target this type of plots are unlikely to fully address the issue. Second, transfer of the roughly 80% of idle plots that are neither unproductive nor too small through rental markets could make owners better off and same time improve efficiency of land use.

Table 4 characterizes households who leave land idle by the reason for doing so and the length of time for which land has been idle.<sup>13</sup> Columns 1 and 2 suggest that households leaving land idle own larger amounts of land (1.28 ha vs. 0.76 by „full land users‟)<sup>14</sup> and are less poor (12% vs. 19%). They enjoy better access to infrastructure and extension and -somewhat paradoxically given that
The template should be completed for each public operator (whether an SOE, parastatal, special fund, or PPP/utility), so as to cover each of the institutions for which quantitative data will be collected. Where one enterprise covers more than one subsector (for example, electricity and water supply), the template should be completed for each subsector, if possible.

Sources of information include interviews with officials (ministry of finance, line ministries, regulators) and with providers (state-owned enterprises, parastatals, special funds).

This guide covers only institutional development indicators classified as _governance variables_ collected at the utility or operator level following institutional data template 7. Template 7 has to be filled for each operator for each of the infrastructure sectors covered (template 1).

When filling template 7, the column for comments should not be overlooked. Even when a clear answer such as ―yes‖ or ―no‖ can be provided, any areas of doubt or ambiguity should always be recorded in the comments column. And in the case of the option ―Other,‖ the comments column needs to be filled in with any necessary explanations and the name of the operator/institution.

# **Governance of public operators (template 7)**

There are 33 governance variables that are laid out in the institutional data template 7, which has been adapted from Vagliasindi (2009). The data are organized along five dimensions: autonomy, transparency, accountability, tools, and universal service obligations.

Governance variables refer to the implementation of (i) measures inside a given enterprise (such as strengthening shareholder voice and supervision, board and management autonomy, and mechanisms for accounting and disclosure) and (ii) measures aimed at improving the external environment in which the enterprise operates (including outsourcing to the private sector and introducing discipline in a competitive labor and capital market).

Governance variables should be collected both from sector regulators and from the central government entity (line ministry), against which data from the given SOE can
and 2008 Sri Lankan Labor Force Surveys.

In both countries, working in apparel pays a positive premium compared to the economy average; however, the premium dipped immediately after the MFA phase-out. This result is in line with theoretical predictions that a decrease in price for apparel leads to a decline in the relative wages in apparel in the short term. In Cambodia, the wage premium for working in apparel was negative in 1996, most likely because the industry was just established and had operated for only three years. In 1999, the apparel wage premium increased significantly, coinciding with Cambodia signing trade agreements with the United States and the EU and significantly increasing its exports. The apparel wage premium declined considerably in 2004, compared to 1999. However, the premium went up over 2007–08.<sup>11</sup> Even though the apparel industry premium in Sri Lanka was lower than in Cambodia—in 2002, it was 0.196—the industry premium was still positive as compared to the economy average. The premium went down in 2006 to 0.05, but it increased to 0.08 in 2008. The increase in the apparel wage premium in 2008 might be associated with the industry switch to manufacturing higher-value goods, such as lingerie.

The change in the female relative wages represents the long run effect of the price shock. In both countries, the wage gap behaved in the manner predicted by the theory—it widened in the three to five years following the MFA phase-out, but then exhibited a declining pattern. In Cambodia the female wage gap decreased over time; in 1996 female wages were 26.6 percent lower than those of males, but in 2009 women were paid only 11.5 percent less than men (see table 3).<sup>12</sup> The wage gap went up after the MFA phase-out, from 12.0 to 13.3 percent over 2004– 08; however, it decreased in 2009 to 11.5 percent. The
and JD‐H|V database.|||||||||||

_<mark>Note</mark>_ <mark>: PA = principal applicant. PA is determined by UNHCR at the time of refugee registration. This table contains data on PAs 54,408 (26 percent) of all tracked Syrian refugees.</mark> Table 3. Income sources, income, and expenditure per capita of male and female principal applicant households

||**All**|**Coup**<br>**chil**<br>**Male**<br>**PA**|**le with**<br>**dren**<br>**Female**<br>**PA**|**Single**<br>**Male**<br>**PA**|**person**<br>**Female**<br>**PA**|**Single**<br>**Male**<br>**PA**|**caregiver**<br>**Female**<br>**PA**|**Couple**<br>**chi**<br>**Male**<br>**PA**|**without**<br>**ldren**<br>**Female**<br>**PA**|**Unacco**<br>**chi**<br>**Male**<br>**PA**|**mpanied**<br>**ldren**<br>**Female**<br>**PA**|**Sib**<br>**Male**<br
- <mark>In Malawi, data from the I</mark> ntegrated Household Panel Survey (IHPS) were assembled across four periods: 2009/2010, 2012/2013, 2015/2016 and 2018/2019. All split-off households were tracked in Malawi. A random half of EAs were dropped from the sample in wave 3 due to budgetary constraints (National Statistical Office, 2020). This panel runs alongside the Integrated Household Survey (IHS), a cross-sectional survey program. The original households sampled into the IHPS are a subset of the IHS 2010/2011.

- <mark>In Mali, data from the</mark> _<mark>E</mark> nquête Agricole de Conjoncture Intégrée_ (EACI) was assembled from two periods: 2014 and 2017. The smallest tracking unit in Mali is the EA, and households are therefore not followed through time. The survey covers all regions and urban/rural areas, except Kidal (Ministry of Rural Development, 2019).

- <mark>In Niger, data were drawn from the</mark> _<mark>E</mark> nquête National sur les Conditions de Vie des Ménages et Agriculture_ – ECVM/A) across two periods: 2011 and 2014. Households, including split off households, were tracked across these waves (Ministry of Finance and National Institute of Statistics, 2016).

- <mark>In Nigeria, data were assembled from the General Household Survey (GHS) across four periods: 2010/2011, 2012/2013, 2015/2016 and 2018/2019. A partial refresh of the panel was undertaken in wave 4. Split off households were not tracked in Nigeria. While the survey is representative at the regional and urban/rural levels, some areas could not be visited in wave 4 due to security concerns (2018/2019) and this wave is therefore only representative of areas that were accessible</mark> (National Bureau of Statistics, 2021a).

5
The aggregate output in this economy in any period is equal to the aggregate household consumption . Market clearing requires that the aggregate output ppaa in any period equals the YY tt aggregate household consumption ppaa . As the focus of the analysis is on resource allocation, I ppaa CC YY normalize the labor resource to unity (ppaa ). The two exogenous forces that affect aggregate CC output are sectoral TFP and labor market wedges ppaa . LL = 1 ∀ pp, tt jjppaa jjppaa **5.1 Model calibration** AA ττ The preference function in equation (10) contains four elasticities together with country-specific sector-preferences weights Ω . In addition, the model entails three sets of production parameters– , and . The first two vary over sectors, countries, and time, and like in the accounting jjpp exercise, the labor share of value-added is country-specific but time-invariant. jjppaa jjppaa jjpp AA , ττ ββ **Preference parameters ( )** jj jjpp I borrow the estimates of elasticities from Comin et al. σσ, εε , ΩΩ Comin _et al._ (2021), which uses panel data , which uses panel data Comin _et al._ (2021), which uses panel data , which uses panel data consisting of 39 countries. The substitution elasticity equals 0.50, and the sector-specific incomeelasticities and are set to 0.11 and 1.21, respectively. The industrial sector serves as the reference σσ sector for measuring income effects, and its income elasticity is normalized to unity (aa ss = 1).<sup>9</sup> εε εε mm I estimate the country-specific sector weights by matching the sectoral allocation of value-added in εε the initial year for each country. The first-order conditions from the household optimization problem yield the following relationship

> 9 Consistent with the value-added production functions employed in this paper, the elasticity estimates correspond to an estimation based on the value-added by sectors and not their final output.

16