rafmacalaba/data-use-mentions-extended huggingface dataset:
# Mapping state capture risks across North Macedonia

In this subsection, we examine the public procurement network in North Macedonia spanning from 2011 to 2022. Our primary objective is to conduct an empirical evaluation of state capture patterns present in the complete contracting network. By utilizing this approach, we aim to gain a comprehensive understanding of the extent and degree of state capture risks across various grouping variables, including geographical regions, time periods, and economic sectors.

The first step in the process is to condense the contract dataset into bi-modal graph data, which is achieved by calculating the average CRI score for each unique pair of buyers and suppliers. This aggregation process involves analyzing a total of 267,295 contracts which ultimately results in 92,195 distinct relationships represented as ties between nodes in the graph. The contracting network analyzed in this study has a total value of 512 billion MKD, which is distributed across 45 CPV sectors. The median procurement authority has 393 contracts, with a total value of 304 million MKD and a CRI score of 0.48. The median supplier in the network has 50 contracts, with a total value of 40 million MKD and a CRI score of 0.44. On average, a tie between a procuring authority and a supplier in the network represents 2.9 contracts, with a total value of 5.5 million MKD and a CRI score of 0.45. However, it is worth noting that the distribution is heavily skewed towards larger organizations, as the median value of a tie is only 500,000 MKD.

For the network representations that follow, our focus is solely on organizations with more than one tie. This allows us to capture contracting patterns across organizations, as isolated nodes offer limited insights in this regard. Additionally, we focus on the main component of the network to present a comprehensive analysis. As a result of this filtering process, we reduce the number of ties under investigation by 22%. In Figure 23 we present the complete contracting network in North Macedonia between
. Inclusion in these indices requires minimum investability of a country’s debt market, which closely relates to key building blocks for market development. GEMX, for example, takes into account capital controls, taxes, secondary market liquidity, size of the investor base, quality of regulations and market infrastructure.<sup>2</sup> These criteria were applied to 33 EMEs including Egypt, Morocco, Tunisia and Lebanon. They received low scores, ranking respectively 21<sup>st</sup> , 29<sup>th</sup> , 30<sup>th</sup> and 33<sup>rd</sup> . Tunisia and Lebanon did not reach minimum scores for inclusion in the GEMX.

## **_Overview of bottlenecks in public debt market building blocks_**

**Country differences notwithstanding, the underdevelopment and low representation of MENA markets are due to several common features in the five key building blocks that sustain deep and liquid public debt markets:**

> 1 For more details see World Bank and IMF (2001): ”Developing Government Bond Markets: a Handbook” and Arvai and Heenan (2008): “A Framework for Developing Secondary Markets for Government Securities.” IMF WP 08/174.

> 2 For more details see Crisil (2009) “Gemloc Investability Indicators — Phase 2 extension.”

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Figure 4. Change in gender gaps in time use over time (men minus women). Hours per day Panel A. Market work Panel B. Unpaid domestic work <!-- Start of picture text -->
-7.0 -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0<br>6.0<br>0<br>5.0 -<br>4.0 -<br>3.0 -<br>-<br>2.0<br>-<br>1.0<br>-<br>0.0<br>0.0 1.0 2.0 3.0 4.0 5.0 6.0 -<br>Gender gap - WDR Gender gap - WDR<br>Gender gap - Our study Gender gap - Our study<br><!-- End of picture text -->

Panel C. Personal care <!-- Start of picture text -->
-1.0 0.0 1.0 2.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>Gender gap - Our study<br><!-- End of picture text -->

**Gender gap - WDR**

Source: Authors’ elaboration based on survey data.

Notes: Gender gaps corresponding to our study were calculated for the sample of men and women aged 18-65 in order to match the sample definition used in the 2012 WDR. The time difference between the surveys used in the 2012 WDR and our study in years is of 4 years for Ghana, 5 years for Guatemala, Iraq, Mexico and Timor-Leste, 6 for Uruguay, 8 years for Argentina, 10 years for South Africa and USA, and 12 years for West Bank and Gaza.
# **Predicting the Poverty Impacts of Trade Reform**

# **Abstract**

An important area of research in recent years involves assessing the micro-economic implications of macro-level policies – particularly those related to international trade. While a wide range of research methodologies are available for assessing the micro-economic incidence of micro-policies, as well as for assessing the impact of macro-level policies on markets and broad groups households, there is a gap when it comes to eliciting the disaggregated household and firm level impacts of trade policies. Recent research addresses this knowledge gap and the present survey offers an overview of this literature.

The preponderance of the evidence from the studies encompassed by this survey points to the dominance of earnings-side impacts over consumption side effects of trade reform. This is problematic, since household surveys are notable for their under-reporting of income. From the perspective of the poor, it is the market for unskilled labor that is most important. The poverty impacts of trade policy often hinge crucially on how well the increased demand for labor in one part of the economy is transmitted to the rest of the economy via increased wages, increased employment or both. Further econometric research aimed at discriminating between competing factor mobility hypotheses is urgently needed.

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# TABLE OF CONTENTS

||**PAGE**|
|---|---|
|**INTRODUCTION ...................................................**|**1**|
|**I.BACKGROUND ...................................................**|**1**|
|A. THE NEED FOR PENSION REFORM ...........................................................|2|
|Insufficient Contributions||
|Generous Benefits||
|Variable Coverage||
|B.ESSENTiALDATA FORASSESSING THE HEALTH OF PENSION SYSTEMS ...........|...............<br>3|
|Demographic and Economic Data||
|Indicators of Financial Health of Pension Systems||
|Pension Fund Cash Balance||
|Contribution Rate Needed for Positive Cash Balance||
|Income Replacement Rate||
|Retirement Age||
|Net Pension Liabilities||
|Rate of Return on Pension Funds||
|C. ECONOMIC CONSIDERATIONS<br>IN PENSION REFoRM ...................................
|0.517|0.475|0.353|-0.238|-0.447|-0.391|
||(1.472)|(1.422)|(1.144)|(1.277)|(1.281)|(1.267)|
|F-frst stage|5.4|6.0|10.3|8.3|8.3|8.6|
|Sargan|0.71|0.76|0.98|0.93|0.98|0.78|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|672|672|672|672|672|672|

Robust standard errors in parentheses

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 All specifications include firm’s fixed effects.

Source: Authors’ calculations using data from ENTIC, INEGI 23
development in a country, respectively. GDP growth rates used to measure growth volatility and indicate years of economic booms are also obtained from the WDI Dataset.

Table 2 presents summary statistics for all the variables of interest. In addition, summary statistics of longer-horizon forecasts are also included. It is interesting to note that the longer the horizon of forecasts, the larger forecast error and absolute forecast error. For example, the average GEP’s same-year one-year ahead and two-year ahead forecasts error are 1.3, 1.8 and 2.6 percentage points respectively. Even when we restrict them to have the same sample (in Appendix Table A1), the same results hold. This finding is consistent with those in Ho and Mauro (2016).

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the largest gains in both absolute and relative terms in comparison with poorer households. Specifically, the combined monetary gains of the top consumption decile will be approximately 2.0 percent, compared with 0.4 percent among the lowest consumption decile (figure 4).<sup>28</sup> These welfare gains among current and new users will also translate into an overall reduction in the poverty rate of 0.31 percentage point and an expected increase in inequality of 0.23 Gini point.

**Figure 4. Relative Incidence of Greater Competition, Mobile Services**

<!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>1 2 3 4 5 6 7 8 9 10<br>Consumption decile<br>New users relative welfare monetary incidence (percentage)<br>Current users relative welfare monetary incidence (percentage)<br>(percent)<br>Relative welfare monetary incidence<br><!-- End of picture text --> _Source:_ Elaboration based on data derived from the WELCOM Stata Tool and the 2015/16 HCES; see CSA 2018. _Note:_ Relative welfare is the per capita welfare incidence, divided by per capita expenditure. Expenditure estimates were carried out by running a quantile regression model and random imputation of residuals. Expenditure estimates were also carried out using random imputation and calculating the average expenditure by primary sampling unit. These serve as lower- and upper-bound estimates. These have been omitted for simplicity.

# **5. Conclusions**

This paper proposes a practical microsimulation approach to assessing the distributional impact of enhancing market competition in key sectors of economic activity. The approach combines data from household budget surveys with the parameters of reform scenarios in selected markets to estimate the welfare gains among current and new consumers likely to
<!-- Start of picture text --> ELMPS 2012 in est. ELMPS 2018 in est.<br>vt +<br>Q iQ<br>o o<br>oO oO<br>iQ iQ<br>a8 a<br>a 0.76 overlap a 73 overlap<br>Q Q<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Economic Census Establishment Census<br>: :<br>nto nt)oO<br>o oO<br>tt tt<br>ge ge<br>- 0.67 overlap a 0.70 overlap<br>re 2<br>Oo Oo<br>0 2 4 6 8 1 0 2 4 6 8 1<br>Probability formal Probability formal<br>Informal ©——— Formal<br><!-- End of picture text -->
those expected they will lower their estimates of their costs, invest, and expand to their long run size. Others may find themselves far less profitable than expected and go out of business. This framework has two implications.

First, the observed distribution of mature firm size and the co-existence of larger, more capital intensive, high earnings firms with very small ones may only reflect the underlying distribution of whatever factors contribute to 0. Assertions of the prevalence of "pre-capitalist," "non-optimizing," or "satisficing" modes of production (Tokman 1992 and Elizondo 1992) may be nested if we argue that entrepreneurs are maximizing if not profits, then utility more generally and that the high implicit valuation of additional labor after basic needs are satisfied makes 0 very high. Second, among small firms we should find long lived firms at their long run size, start-up firms that find themselves extremely profitable and desire to expand, and start-up firms that are about to fail. We should also find differing returns to factors that reflect this normal heterogeneity. On average, the youngest firms should show the highest mortality rates since they have the least refined estimates of their viability and are more likely to discover that they are not than more established firms.<sup>4</sup> In sum, small size, lack of growth, or even differential returns to human and physical capital may be due to variance in individual abilities and preferences and the noisy process of discovering them instead of due to distorted labor and credit markets or an inefficient regulatory framework, which themselves may give rise to other divisions in the small firm sector.

Labor market distortions that lead to a rationing of workers from their preferred position in salaried work should lead to segmentation in the micro-enterprise sector as well. First, we

> 4Numerous US and UK surveys support these models showing extremely high rates of firm mortality among young and small firms that decrease with age and size (See Davis et al 1994, Evans and Leighton 1989, Dunne et al 1989).

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# **Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution for Kenyan  Imperfectly Competitive Goods**

**Formatted:** Right:  0.63"

It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. Among the 73 countries, there were four in sub-Saharan Africa: the Central African Republic, Madagascar, Malawi and Mauritius. We judged that Madagascar was the country closest in characteristics to Kenya, so we took the values of the elasticities estimated for Madagascar as a proxy for the elasticities for Kenya.

Broda et al., estimate 3 digit elasticities for 130 goods sectors, but there are 34 goods sectors in our model, It was necessary to map the sectors estimated by Broda et al. into the sectors of our model. In table C1 of this appendix, we show the mapping for the imperfectly competitive sectors. (These elasticiteis are not relevant in our model for perfectly competitive sectors.) Next, since there are often multiple sectors from Broda et al. mapped into a single sector in our model, it was necessary to determine a method of weighting the Broda et al. elasticities. There are reasons to use both export shares as well as import shares. A larger share of a subcategory in imports reflects more imports, and more likely there are more varieties of imports. So weighting by the import share of a subcategory is better than an unweighted measure. Domestic varieties are also important. Since we do not have production data for the subcategories, we use export shares as a proxy for domestic production by subcategory. Analogously, weighting subcategories by export shares is better than unweighted categories. Since both import shares and export shares are useful in the weighting, we take one half the shares of both exports and imports as the weights. The resulting elasticities are reported in table
test whether any differences in the outcome variables change when controlling for stable household and location characteristics, that is, household and location characteristics that would not be expected to change dramatically season to season.<sup>13</sup> Specifically, for household i , in community j , in region r , in country c , we run a regression of the form:<sup>14</sup> where yijrc is the outcome variable of interest, Sijrc is a dummy variable capturing the season in which the household was interviewed, Xijrc is a vector of household controls, including the age, sex, and education of the household head, and the size and number of dependents in the household, Grc is a series of region fixed effects, and εijrc is the error term. The specific set of household and location controls and

> 10 Leaving consumption in nominal terms does not significantly affect the main results.

> 11 The time horizons for questions on non-monetary poverty indicators, such as access to health and education, are too long in the EHVCM questionnaire to allow for seasonal variation to be captured.

> 12 Section 4 also shows consumption differences across the entire distribution.

> 13 If β remains stable as variables in Xijrc and the fixed effects are added incrementally, this may suggest that unobserved confounding variables are less likely to be biasing the results (Altonji, Elder, & Taber, 2005; Oster, 2019). We therefore report the results with and without controls and region fixed effects.

> 14 This approach follows a wide range of descriptive work looking at the impact of shocks and seasons on household welfare, see for example Dercon, Hoddinott, and Woldehanna (2005), Harrower and Hoddinott (2005), and World Bank (2005).

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(which focuses on rainfall-triggered landslides) are rainfall, land cover, distance to roads, lithology, slope, and distance to road. Fig. 3 shows maps of the five factors.

Spatially distributed data of the distance to the nearest road for each grid within the study area was created (Fig. 3a) based on vector data of roads and highways in the CHT and Sylhet divisions. The road and highway vector data were acquired from RHD and LGED authorities of Bangladesh. A land cover map for the year 2015 (Fig. 3b) was obtained from CEGIS. CEGIS also provided gridded geological data for the study area (Fig. 3c). A slope layer (Fig. 3d) was created based on a 30-meter Digital Elevation Model (DEM) obtained from the NASA Shuttle Radar Topography Mission (SRTM; Jarvis et al., 2008). Spatially distributed annual rainfall data with a 30-year return period (Fig. 3e) were generated based on gage observations from Bangladesh Meteorological Department. The gaging stations considered for generating the rainfall layer are Chittagong, Comilla, Cox'sbazar, Feni, Kutubdia, Rangamati, Sandwip, Shrimangal, Sitakunda, Sylhet, and Teknaf.

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Figure 5 – Demeaned Migration for Work and Demeaned Share of Agriculture Employment by Governorate <!-- Start of picture text --> cairo<br>6-Oct<br>alexandria<br>ismailia<br>gizared seasuez port saidqualiobia kafr el sheikh<br>helwannorth sinai<br>matrouh<br>south sinai luxor damiettanew valley<br>bani suef<br>aswan<br>dakahlia fayoum<br>garbeyya qenamenoufiaassiut<br>sharkia menia beheira<br>sohag<br>-20 0 20 40<br>dagriemp<br>denetmig2 Fitted values<br>10<br>5<br>0<br>-5<br>-10<br><!-- End of picture text --> Source: Authors’ calculations using Labor Force Survey Internal migration is low in governorates with a high share of agricultural employment. Agricultural workers generally earn a low wage throughout Egypt and may be unqualified for other jobs, reducing the motivation to migrate. Additionally, unpaid family workers earn nonpecuniary benefits aside from the food they help raise (including proximity to family and the accompanying help-networks, shared rents, often more living space, cleaner air) making them reluctant to incur the additional costs of migrating to other governorates.

Figure 6 shows the negative correlation between demeaned net migration rate (to work) and demeaned share of unpaid family workers by governorate.

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Australia). Using the estimates for the average values of the parameters, this positive effect ranges from zero in Italy to +1.7% in Australia. Second, over the period 1990-2000, emigration had a mild to signi...cant negative long-run e¤ect on the wages of non-migrants. Still focusing on rich OECD countries, the e¤ects range from 0 for the US (due to the near absence of emigration) to -0.8% in the UK and -0.7% in Portugal. Third, over the period 1990-2000, immigration generally improved the income distribution of European countries while emigration worsened it by increasing the wage gap between the high and low skilled natives.

All three results go counter to the popular beliefs about migration, but they are a result of the nature of migrant ‡ows from 1990 to 2000. European countries (along with the US, Canada, Australia) have experienced both immigration and emigration that were usually more skill intensive than (or as skill intensive as) their domestic labor forces. Under these conditions, in the long run, immigration is associated with average wage gains and emigration with average wage losses for non-migrant natives unless there is no externality of human capital and migrants are perfectly substitutable with natives. In this latter case, both immigration and emigration have no e¤ect on the average wages of non-migrant natives.

The skill composition of migrants relative to non-migrants is crucial in determining our average wage results. We attempt to correct the "e¤ective" skills of migrants to account for certain important phenomena which might not be fully re‡ected in our aggregate statistics. First, we use estimates of the extent of illegal immigration into the main Western European countries to correct for the in‡ows of migrants into Europe. Second, we account for the potential lower quality of schooling for migrants who completed their education in their home countries or for the "downgrading" of their skills in the host countries’labor market. Both corrections reduce
And adding one point for each of the following that the business has done in the last 3 months

- Attempted to negotiate with a supplier for a lower price on raw material

- Compared the prices or quality offered by alternate suppliers or sources of raw materials to the business’ current suppliers or sources of raw material

The _records score_ ranges from 0 to 8, and it is calculated by adding one point for each of the following that the business does

- Keeps written business records

- Records every purchase and sale made by the business

- Able to use records to see how much cash the business has on hand at any point in time

- Uses records regularly to know whether sales of a particular product are increasing or decreasing from one month to another

- Works out the cost to the business of each main product it sells

- Knows which goods you make the most profit per item selling

- Has a written budget, which states how much is owed each month for rent, electricity, equipment maintenance, transport, advertising, and other indirect costs to business

- Has records documenting that there exists enough money each month after paying business expenses to repay a loan in the hypothetical situation that this business wants a bank loan

The _financial planning score_ ranges from 0-12, and it is calculated by adding up to three points for each of the following two questions

- How frequently do you review the financial performance of your business and analyze where there are areas for improvement

- How frequently do you compare performance to your target

- Zero points for “Never”

- One point for “Once a year or less”

- Two points for “Two or three times a year”

- Three points for “Monthly or more often”

And adding one point for each of the following that the business has

-

- A target set for sales over the next year

- A budget of the likely costs your business will have to face over the next year

- An annual profit and loss statement

- An annual statement of cash flow

- An annual balance sheet