rafmacalaba/data-use-mentions-extended huggingface dataset:
of Banks in MENA that Consider Enforcement of Secured Assets a Major Constraint**

|**Region**|**Problems in Enforcing Rights on Assets**<br>**Immovable**<br>**Movable**|
|---|---|
|**MENA Average**|58%<br>36%|
|**GCC Countries**|59%<br>29%|
|**Non GCC Countries**|56%<br>43%|

_Source:_ World Bank MENA SME Lending Survey, 2010 _2.7 Enforcement of security interests: sale of assets in cases of default_ Disposition of the seized property by the secured creditor is an important element in efficient enforcement mechanisms. The secured creditor, rather than a court enforcement officer, should

> 35 _Doing Business 2010_

> 36 Observations based on interviews with Egyptian attorneys and judges.

> 37 _Hawkamah Report_

> 38 Delay by debtors, and numerous opportunities to object are common problems reported worldwide by systems attempting to reform their enforcement systems.  These problems are symptomatic of court-centered enforcement/debt collection.

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contains detailed information on individuals and their wages, but includes less than 2000 observations for which wages are reported or can be derived.

Overall, the LFS appears to be the most appropriate data source to use in analyzing wage structure in Latvia. Since 2002, LFS respondents have been asked to report net wages, and the number of wage intervals has been refined. LFS 2002 data, which contains about 7,000 employees, can therefore provide an accurate data source for estimating earnings functions. To evaluate qualitative changes in the wage structure over time, comparable earnings functions based on the 1997-2002 LFS are estimated. Using this methodology we are also able to compare the market value of education accumulated in the Soviet era and post-Soviet education.

To verify that wage interval information contained in the LFS 2002 indeed provides reliable results, Appendix Table 26 compares several key indicators derived from these data with those obtained from NORBALT (1999) project data. The model specification is restricted by information available for respondents in the NORBALT data, so that job tenure, job location and ownership sector are not controlled for in these benchmark models.

**Human capital and earnings.** By the year 2002 total returns to higher vs. basic education in the Latvian labor market amounted to 80 percent on average. This implies that, on average, an individual with higher

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We then turn to the impact of the earthquake on the food consumption score. First, we want to test whether this score depends on the pre-disaster level of assets as well as on asset losses, that is  ’ > 0 and  ’ < 0. Second, we consider the possibility that in absolute value, asset losses have a lower impact on food consumption score than the pre-disaster level of assets  ’ > |  ’|. Indeed, in connection with the earthquake, households should have enforced coping strategies that enable them to buffer the impact of that shock. Finally, we also want to test whether assistance (in particular food assistance) has an impact on the food consumption score that is  > 0.

For identification purposes, the post-earthquake asset index is measured in February. This will avoid endogeneity bias in equation (1) due to the fact that assets losses might be lower for households who benefited from assistance programs. Furthermore, the housing damage due to the earthquake (variable H) might be a valid instrument for EA in equation (3). Indeed, this variable should not be related to FCS otherwise than through EA. In the sequel, we will thus test this assumption and further discuss the impact of EA on FCS.

# **4. Data and Empirical Findings**

# _(a) Data sources and methodology_

A post-earthquake food security-oriented survey was conducted in June 2010 by the CNSA in collaboration with its main partners (ACF, FEWS-Net, Oxfam GB, FAO, UNICEF and WFP). The sampling used for the household survey is a probabilistic cluster method, using two stages: (i) enumeration sections (geographical areas) and camps and (ii) households. 2003 census data is used to select the enumeration sections, with a probability proportional to population size. Eight households are then selected randomly in each section. Camps are selected using the International Organization for Migration (IOM) data; the number of camps selected was proportional to the size of the
self-employment, and the most rapid rate of income growth was in self-employment, for both mean and median incomes. For Ghanaian men, the most rapid growth was in either wage employment or self-employment income, depending on whether one uses the mean or median. For both Ghanaian men and women, wage income increased over time, and farm income had the smallest growth over time. Table 10 shows that although Ghanaian men's incomes were higher than women's in all sectors, the percentage gap is smallest for self-employment incomes, particularly when looking at mean incomes. Comparing mean income changes, women earned only 15% less than men in self-employment, compared to the 33% and 26% less they earned in farming and wage employment in 1987, respectively. By 1991, the percentage gap between men's and women's mean incomes in self-employment had declined to 11%.

The earnings reported for female-headed households in Uganda were significantly lower than those for male-headed households in both years, and if anything, the average earnings grew further apart over time. The difference between average earnings from female-headed households and those from male-headed households went from 33% to 47% in 1996. The difference between average earnings in self-employment in the two types of households was highest in both years and grew larger over time, from 46 to 56%. These figures cast heavy doubt on the possibilities for female-headed households in gaining earnings equity via self-employment as the poverty analysis suggests.

Ugandan women-as measured by headship-would seem to have much lower non-farm incomes relative to agricultural incomes compared to Ghanaian women and even compared to Ugandan men. However, we only have earnings data at the household level for Uganda, which is c learly problematic because it hides the earnings of females in 17
|145.0|22340|28.4|40.0|
|Inflation (%)|920|9.1|9.6|0.2|53.4|22340|9.4|8.5|
|Hotel, Services Industry (dummy)|920|0.13|0.34|0|1|22340|0.14|0.35|
|Construction Industry (dummy)|920|0.13|0.33|0|1|22340|0.09|0.29|
|Trade Industry (dummy)|920|0.37|0.48|0|1|22340|0.26|0.44|
|Transport Industry (dummy)|920|0.06|0.24|0|1|22340|0.06|0.23|
|Manufacturing Industry (dummy)|920|0.30|0.46|0|1|22340|0.40|0.50|
|<br>Pegged Exchanged Rate (dummy)|920|0.36|0.48|0|1|22340|0.22|0.42|
|Crawling Peg Exch. Rate (dummy)|920|0.53|0.50|0|1|22340|0.54|0.50|
|Band, Managed floating Ex. Rate (dummy)|920|0.09|0.29|0|1|22340|0.16|0.37|
|Current Account Deficit as % GDP|920|7.0|6.2|0.0|25.2|22340|6.6|6.5|

Source: Authors‘ calculations based upon data from the World Bank Enterprise Survey, BEEPS data and from other sources

**Table 2: Firm status at time of Financial Crisis Survey**

|
season bound (cassava). Among staple grains, seasonality is highest for maize (33.1 percent on average) and lowest for rice (16.6 percent). These gaps are two and a half to three times higher than on the international reference markets, pointing to substantial excess seasonality. While excess seasonality is observed in virtually all the maize and rice markets studied, there is wide heterogeneity within and across countries. Seasonality is especially high in Malawi, where maize is also the main staple, causing a double seasonality burden for most households.

In what follows, section 2 sets the stage by reviewing general considerations on the data, seasonality metrics and the overall estimation approach. Section 3 looks at the commonly used methods for estimating the seasonal gap and shows that these can result in upwardly biased estimates when data samples are short. The performance of alternative and more parsimonious seasonality models is examined in section 4. Section 5 introduces the price data from the 13 commodities and seven African countries examined here and discusses the findings. Section 6 concludes.

# **2 Material, metrics and method – general considerations**

Many developing country governments publish monthly prices for staple food commodities for major locations in their territories. These prices are obtained by sending observers to markets in these locations, who record the prices at which the different commodities are transacted. It is unclear how much intra‐month averaging is undertaken, but at least for some countries (e.g. Uganda), the monthly prices derive from weekly observations. Much of this price information results from the FEWSNET initiative, supported by USAID, and the FAO’s GIEWSNET initiative.

Three features of these price data stand out. First, the price data collection initiatives are relatively recent, so that the time series available are usually short. Second, in many of the price series, the frequent occurrence of missing observations compounds the short duration of the series. Gaps may arise for example because the observers did not see transactions in the foods in question
countries promise not to exceed except through the use of WTO-permissible exceptions such as temporary trade barrier policies of antidumping, safeguards, and countervailing duties. Nevertheless, the binding nature of these disciplines may vary both across countries and within countries over time during this period, and any examination of the macroeconomic forces driving emerging economy trade policy must control for such variation.

Consider the data on different trade policy instruments in Table 1. The scope of a country’s tariff commitments under the WTO is most easily summarized through three measures – the share of the country’s total imported products at the 6-digit Harmonized System (HS-06) level that have a maximum tariff rate commitment, i.e., that are ``legally bound’’ (column 1), the simple average of the rates at which these tariffs are bound (column 2), and the difference between this legal binding tariff rate and the MFN applied tariff rate that the country implements over imports at the border (column 2 less column 3 or 4). Table 1 indicates that, for these three measures, there is substantial heterogeneity across the thirteen emerging economies in our sample. The differential between the average applied MFN tariff rates in 1995 and 2010 (columns 3 and 4) also indicates variation within some of these countries over time; for some emerging economies, average applied MFN tariffs in 2010 were higher than they were in 1995, while they are significantly lower in other economies.

Variation in applied tariff rates over the period suggests that an emerging economy’s aggregate-level demand for tariffs under the WTO’s TTB policies may change over time. When a country’s tariff commitments bind or almost bind, i.e. for imported products with applied MFN tariff rates that are at or close to the WTO maximum binding rate, then the only WTO-permitted option to implement additional import protection for that product is through a TTB. Columns (5) and (6) report data from Bown (2012a) on the stock of temporary tariff barriers as
corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990–2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960–1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960–1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses.

# **{Table 8 here}**

The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore‘s official 2000 census records 563,430 foreign-born migrants. The United Nations, however, reports 1,351,806 foreign-born migrants for 2000. Second, there are cases where the current study reports data by nationality, but the corresponding figure in the Trends in International Migrant Stock refers to the foreign born. This situation generally arises when a census does not report the number of foreign-born migrants on a bilateral basis. Examples include Austria and Côte d‘Ivoire. Third, differences in the years to which the data refer can generate large disparities. For example, this study uses the 1966 data for Australia, whereas Trends in International Migrant Stock reports data for 1970. Overall, however, the fact that the totals are remarkably close in every decade adds credence to the estimates here.

# _IV. THE EVOLUTION OF GLOBAL BILATERAL MIGRATION_

The greatest strengths of the
using various online sources. For example, Jair Bolsonaro, the winner of the 2018 Brazilian general election is classified as right-wing, based on his thenparty, Partido Social Liberal. In sum, of the 207 elections in our main sample, there are 91 left-wing incumbents and 116 right-wing incumbents. Separately, there are 82 left-wing election winners, and 125 right-wing election winners.

Finally, voter turnout data are collected, as well as an indicator for whether voting is compulsory for a given election. Most of the data are from the IDEA Voter Turnout Database; any gaps are supplemented using online sources.

The main source of shock is based on changes in international oil prices, weighted by the average country-specific oil import values. International oil prices are obtained from the World Bank “Pink Sheet” data. These data contain real and nominal crude oil prices; for our main analysis, we use real oil prices. Data on the value of oil imports and GDP in US dollars are obtained from the IMF’s World Economic Outlook.

5
social interaction are important, in large cities associational forms are sturdier than everyday forms of engagement, especially when confronted with attempts by politicians to polarize people on ethnic lines.

# **Quadrant 4 - Coping: Poorly Functioning State, Abundant Cross-Cutting Ties**

Societies with higher levels of cross-cutting ties rather than deep social cleavages and ineffective or co-opted states are characterized by the substitution of the formal system by informal systems. If the state is not totalitarian or coercive, societies may not disintegrate into widespread crime, violence or war, but large segments of society are left to their own devices to cope in the best ways they can.

Richard Rose (1995) uses the term "hour-glass society" to describe such societies, like Russia during the transition. There is a rich social life at the base, consisting of strong informal networks relying on trust between friends, relatives and other face-to-face groups and which may include friends of friends. At the top of the hour glass, there is rich political and social life, as elites compete for power, wealth and prestige. There is co-operation within and between elites and formal institutions as the elites use the resources of the state for their own betterment. The narrow mid-point of the hour-glass insulates individuals from the influence of the state, and citizens can be described as "negatively integrated" from the demands of potentially oppressive state. The state on the other hand tolerates small-scale "rogue" organizations as long as they do not interfere with the affairs of the state.

Using data from the New Russia Barometer, a nation-wide survey, Rose documents that only one in eight reported that they earned enough from their official jobs to meet their basic needs. The majority get by because in addition to the official economy, they rely on a multiplicity of unofficial economies such as growing food, exchanging help and services with friends, having a second job or depending on tips and bribes. There is a vast disconnect between the rich social mechanisms and exchanges at the bottom and local formal institutions. Three-quarters
within countries if households live in urban or rural areas and based on their income. Based on the reviews, the paper ends on a standard set of guidelines and sample questions to be integrated into future household surveys, with a particular focus on questions related to climate change and road safety. Including more detailed questions on the type of vehicles owned by households can be used to understand the efficiency and redistributive impacts of restrictions on vehicle type or fuel taxes motivated by climate-change concerns. Including questions related to traffic accidents can be used to better quantify the gains from improving road safety.

A few papers have used household surveys to understand transport choices and expenditures. Gandelman et al. (2019) look at transportation affordability in Latin America and the Caribbean (LAC), with a focus on urban public transportation. They provide analysis of transportation expenditures across income groups and build a transportation affordability indicator to rank cities in accordance with their performance. In a companion paper, Gandelman and co-authors (2018) provide detailed transport expenditure patterns for Latin American countries and calculate income elasticities per mode of transportation. This study extends the work to countries outside Latin America. Finally, related work on transport affordability by Estache et al. (2018) was conducted for a sample of 90 countries for developing and emerging economies and for 26 European countries, as a proxy for developed countries practice. In a recent study, Mitra et al. (2021) investigated the nationally representative surveys and censuses in Low and MiddleIncome Countries (LMICs) for tracking the incidence of deaths and injuries from road traffic crashes in LMICs to see if these population-level data sources can be utilized to monitor progress made in SDG 3.6. The authors concluded that relatively small modifications and additions to questions in forthcoming surveys could provide countries with a way to benchmark their existing surveillance systems and result in a substantial increase in data for tracking road traffic injuries globally.

The contributions of the paper are twofold. This paper provides a unique review of transportrelated questions in LSMS surveys and discusses possible improvements
_ut_ is the error term.<sup>13</sup> The inclusion of the post-reform indicator and its interaction with the time trend allows both the trend and the level of the outcome to change with the reform. In our preferred specification, we set _p_ = 1, fitting a linear trend. Figure 1, Panel B, plots the de-seasonalized outcomes _log_ ( _Z_<sup> ̃</sup> _t_ ) = _log_ ( _Zt,m_ ) _− g_ ˆ _m_ .

Our coefficient of interest is _γ_ 0, which measures the VAT-rebate-driven jump in the outcome in August 2014, under the assumption that no other policy or economic change coincides with the reform to provoke a change in the outcome. Put differently, the outcomes are assumed to evolve smoothly around the reform time in the absence of the reform. Our preferred specification uses weekly outcome data and weeks as running variable. Weeks are defined such that the first day of a week coincides with the first post-reform day. In auxiliary analyses, we also estimate a firm-level version of Equation 1 in which we include firm fixed effects, hence estimating the average effect of the reform across firms while weighting all firms equally.

Ideally, we would also like to examine the estimate for _γ_ 1, capturing whether the reform was associated with a change in the growth rate of the outcome. However, a causal identification of _γ_ 1 would require us to make the very strong assumption that the outcome would have evolved according to the same growth trajectory before and after the reform, in the absence of the reform. This is unlikely to be true. Instead, we conduct a non-parametric comparison of the month-on-month growth-rate distributions before and after the reform, to evaluate the presence of suggestive evidence for a trend acceleration.

# **4.2 Results**

Considering first the raw and de-seasonalized data (Figure 1), it is clear that the number of card transactions jumps sharply in August 2014, precisely when the VAT rebates first become available. This immediate and
Aryeetey et al. (2014). We make further refinements to this line of research by underscoring the need for policies that could propel aggregate productivity from growing north-south differences in intersectoral linkages and regional specialization.

The remainder of the paper is organized as follows. Section 2 provides an overview of the mining productivity shock and national and regional trends in structural transformation for Ghana. Section 3 discusses how the mining productivity shock in the south of Ghana leads to growing regional differences in the pattern of intersectoral linkages based on regional I-O tables. Section 4 shows how spatial differences in intersectoral linkages affect sectoral output and productivity based on census data for firms. Section 5 concludes.

# **2. Overview of Mining Shock and Structural Transformation in Ghana**

We begin with a brief account of the recent mining productivity shock in Ghana, followed by an overview of the annual and spatial trends in mining and manufacturing based on statistics from the GGDC-UNU-WIDER economic transformation database (ETD)<sup>3</sup> and district-level data from two censuses on Ghana firms: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).<sup>4</sup> The 2003 NIC and 2014 IBES were administered by the Ghana Statistical Service (GSS). The 2003 NIC covered only industrial sectors (mining, manufacturing, public utilities, and construction), whereas 2014 IBES covered industrial as well as all services sectors (wholesale and retail trade, transport, communications, finance, real estate, government services, and private services). Both censuses were conducted in two phases. Phase II involved a detailed questionnaire, capturing workforce, wages and salaries, stocks, value of fixed assets, quantity and cost of inputs purchased, other operating costs, and sales and other income. We use the data from phase II for both surveys. The reference year for phase II of the 2003 NIC is the calendar year 2003 and the reference year for phase II of the 2014 IBES is the calendar year
Drivers, and Policies, Washington, DC: World Bank.

Ha, J., Kose, M. A., and Ohnsorge, F., (2021), “Inflation during the Pandemic: What Happened? What Is Next?”, CEPR Discussion Paper 16328.

Ha, J., Kose, M. A., and Ohnsorge, F., (2022), “Global Stagflation”, CEPR Discussion Paper 17381.

Ha, J., Kose, M. A., and Ohnsorge, F., (2023), “One-stop Source: A Global Database of Inflation”, Journal of International Money and Finance, Vol. 137.

Ha, J., Kose, M. A., Ohnsorge, F., and Yilmazkuday, H. (2023), “Understanding the Global Drivers of Inflation: How Important Are Oil Prices?”, Energy Economics, Vol. 127.

Ha, J., Kose, M. A., Ohnsorge. F., and Yilmazkuday, H. (2024), “What Explains Global Inflation”, IMF Economic Review (forthcoming).

Ha, J., Kose, M. A., Otrok, C., and Prasad, E. S., (2020), “Global Macro-financial Cycles and Spillovers”, NBER Working Paper 26798, National Bureau of Economic Research, Cambridge, MA.

Harding, D., and Pagan, A., (2002), “Dissecting the Cycle: A Methodological Investigation”, Journal of Monetary Economics, Vol. 49, pp. 365–81.

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