rafmacalaba/data-use-mentions-extended huggingface dataset:
Notes: bootstrapped standard errors in parentheses (200 replications); all estimates are statistically significant at 1% significance level.

It was this switch from the widening to the contracting contribution of sectoral (public/private) segregation that contributed to the shrinking of the gender wage gap in Georgia and in some years played the primary role in that process. For example, between 2004 and 2007 the gap within the public sector widened and its contribution to the gap increased from 29 to 43 log points (Figure 5) (intra component). The overall gap nevertheless contracted during this period because, on the one hand, the sectoral segregation turned in women’s favor as public sector wages increased (inter-component dropped from 13 log points to 0) and, importantly, the gap within the private sector also shrank (intra private in Figure 5 decreased from 22 to 20 log points) The latter finding highlights the growing role of the private sector in contributing to the reduction in the gender wage gap in Georgia.

Hence, the gender wage gap in Georgia exists primarily because the gaps are considerable within both public and private sectors rather than due to the proportionately greater presence of women in the public sector (sectoral segregation). To what extent can these findings be explained by the differences in the composition of the female and male workforce? The BMZ decomposition reveals that 42 percent of the gender inequality within industries (or the intrasector component) is due to explanatory factors, such as marital status, educational attainment, and industry of employment (Figure 5). This finding implies that women’s joint characteristics within each sector place them at a disadvantage relative to men. However, the unexplained portion is even greater: 58 percent of the within sector inequality is due to unobserved factors, such as discriminatory practices in wage setting and promotion. In terms of sectoral segregation (or the inter-sector component), the composition effect is small but negative, suggesting that, given gender differences in the characteristics of workers, more women than men should
# **Stress-Testing Croatian Households with Debt—Implications for Financial Stability**<sup>1</sup>

This paper assesses the financial stability risks that arise from the high debt levels accumulated by Croatian households in the pre-crisis period. As opposed to relying on banking sector data, this paper puts forward a methodology that uses household budget surveys (HBS). In particular, the vulnerability of indebted households to changes in economic conditions is explored. This includes shocks that impact only some households (or household members), such as the loss of income that arises as a result of becoming unemployed, as well as shocks that arise from changes to the terms in which loans have been extended. The latter includes changes in exchange rates and their impact on the debt service of foreign currency (or foreign currency linked) loans and changes in interest rates and their impact on the debt service of variable interest rate loans. The effect of these shocks on households’ vulnerability depends not only on the size of the shocks simulated, but also on the risk thresholds that are used to tag a household as vulnerable.

The use of HBS has limitations. To begin, they cover only a small subset of all households―0.2 percent. But the sampling methodology involved limits the concerns that arise from small samples. Another limitation is that the coverage of these surveys has some weak aspects. For example, while incomes, expenditures and household characteristics are covered in detail, information on financial assets is largely absent. But these limitations should be gauged against the alternative; namely, detailed loan-by-loan borrower information is lacking in macroeconomic data and this also hampers a thorough assessment of household vulnerability. Also, the lack of data on the stocks of household financial assets suggests that the risks identified in this paper are likely to be upper bounds to concerns about household over-indebtedness and its potential implications for financial stability. Finally, the use of HBS allows a distinction between households with and without debt and the characteristics of those that do hold debt,
how to undermine them (Sberna, 2014).

In this paper, we explore the mechanisms of cartels in Lebanon’s infrastructure procurement sector. We analyze a data set of all 394 infrastructure procurement contracts awarded between 2008 and 2018 by Lebanon’s Council for Development and Reconstruction (CDR), by far the country’s most important infrastructure development agency and central pillar of the power-sharing arrangement by providing a major source of rents for sectarian elites (Leenders, 2012; Mahmalat, Atallah and Maktabi, 2021).<sup>1</sup> By identifying the political connections of both the contractor as well as the consultancy firms involved in the implementation of a procurement contract, we go beyond previous studies’ focus on contractors and analyze the interplay between the development agency (in this case CDR), the contractor, and consultants. Notably, we are not primarily interested in understanding _whether_ elites extract rents via CDR. As we have shown in previous work, politically connected contractors receive contracts that are inflated by almost 33 percent vis-à-vis the average contract (Mahmalat, Atallah and Maktabi, 2021). Instead, we examine _how_ cartels operate. More specifically, we leverage a series of expert interviews with politicians, officials, contractors, and consultants to generate and test hypotheses to identify the conditions under which the cartel succeeds in generating rents.

In doing so, we introduce two methodological novelties. First, we focus on procurement consultants, an immensely important player in the procurement process with a large degree of influence and discretion in different phases of a project cycle, earning them the label “masters of the game.”<sup>2</sup> Consultants are involved in the design of a project, the evaluation of bids, the supervision of project implementation, as well as the assessment of claims and variation orders of ongoing projects. Despite their importance, however, the role of consultants has, to our knowledge, not received systematic treatment in the

> 1 As a formally independent institution, CDR enjoys
are found in the public sector relative to those with less education. Those with postsecondary education have inexplicably disappeared from the scene, with only small proportions found in both the public and the private sectors. (In fact, the GLSS for 1991-2 reports that fully a quarter of those with post-secondary education were in the "non-working" category). Whether or not this disappearance of post-secondary employees is a real phenomenon or not, the higher proportion of the educated with secondary levels in the public sector in 1991-2 compared to the earlier date is a clear characteristic of the sample. In so far as the recruitment to the public sector is correlated with secondary education this change in characteristics would tend to increase the importance in earnings differentials relative to the public-private divide.

Secondly, and perhaps more importantly, the large increase in public sector wage scales in July 1992 is unlikely to be reflected in the earnings data from the 1991-2 GLSS survey. Unfortunately we do not yet have an earnings survey of the GLSS type for a more recent date.

In the absence of such analysis, we try to draw some tentative conclusions using additional data sources. We have some detailed information on earnings in the manufacturing sector of Ghana from the sample surveys conducted by the World Bank under the auspices of the Research Program on Enterprise Development (RPED). These surveys of enterprises and a sample of workers attached to each enterprise were conducted in three 'waves' in each of the years of 1992, 1993 and 1994. The data of mean earnings by size group of firms are reported in Table 3.5 together with an estimated mean of monthly earnings in the public sector after the 80 per cent wage hike of 1992. The spread in mean earnings over the firms of different size groups is enormous, and will be discussed later. For the moment we can note that compared to the manufacturing sector, average earnings in the public sector were generally at a higher level than even those prevailing in the large firms. In 1992 mean earnings in the public sector were
surveyed every year) make up more than 60% of the firm-year observations in the panel, 45% of firm-year observations in the full sample of firms, and only 20% of firm-year observations in the estimated population. The panel should not be seen as representative of the population, but rather as a selection of relatively large firms. Nonetheless, the bottom rows in Table 3 show that 71% of firm-year observations that appear in the sample, representing 93% of total deflated output over 9The pre-formed 1998-2004 panel dataset does not contain similar permanent serial numbers or district identifiers, but we also have an older, cross-sectional dataset, which does have permanent serial numbers and district codes, covering the same years. We merge permanent serial numbers and district identifiers into the pre-formed panel dataset using a sample of common variables.

12
their overall performance, linkages with the local economy, and supplier assistance which all influence the firms’ potential to generate productivity spillovers. Second, we also study the relationship between foreign investor characteristics and linkages with the local economy as well as assistance extended to local suppliers. In the second part of the paper, we shift the focus to domestic suppliers and examine the role of supplier firm characteristics – the so-called absorptive capacities – for their linkages with multinationals. Fourth, focusing on assistance and demand effects, we assess how factors within the transmission channels between multinationals and local suppliers affect FDI spillovers.

Studies on FDI spillovers that focus on direct supplier-multinational linkages based on foreign investor or supplier survey data are rare. Focusing on foreign affiliates in five transition economies, Giroud, Jindra and Marek (2012) find that foreign firm characteristics have a positive impact on backward FDI linkages and spillovers. Javorcik and Spatareanu (2009) find evidence of “learningby-supplying” for a sample of Czech manufacturing firms, although there is also evidence of selfselection into supplying due to a higher productivity ex ante. Jordaan (2011) also confirms the existence of positive backward spillovers on manufacturing suppliers in Mexico. Specifically, positive spillovers are facilitated through supplier firms’ absorptive capacities and the level of support from the multinational. Studying the Polish automotive sector, Gentile-Lüdecke and Giroud (2012) examine the mechanisms behind knowledge spillovers of suppliers. While the authors don’t find evidence of a supporting role of suppliers’ absorptive capacities on knowledge acquisition, they find evidence of a supportive role on performance improvement and new knowledge creation.

This study is structured as follows. The next section provides a literature review that identifies major foreign investor characteristics and suppliers’ absorptive capacities which have shown to influence FDI spillovers. It also discusses the main transmission channels through which FDI spillovers can be generated. Section 3 compares foreign investors and domestic producers in terms of their potential to generate productivity spillovers and also studies the role of foreign investor characteristics for their FDI spillover potential. Section 4 then evaluates the
9 _Figure 3. Real quarterly farm gate FOB, CE and producer cocoa prices (1990 US$/mt)_ <!-- Start of picture text --> 3000<br>2500 P|oducer CE +FOB<br>2000<br>1500<br>500<br>83 83 84 85 86 86 87 88 89 89 90 91 92 92 93 94 95 95 96 97<br><!-- End of picture text -->

_Source:_ Authors' calculations.

_The income benefit offorward sales_ A second benefit claimed for forward sales is that they allow exporters a forward premium. From the mid 1980s through the mid 1990s, the world market was in relatively abundant supply. With stocks carried forward, forward prices have generally been higher than prices for spot or nearby deliveries 9. This implies that producing countries could indeed receive higher prices by selling forward, i.e., the existence of a positive premium.

The forward premium is not necessarily positive, however. Futures prices may indeed be either upward (downward) biased, in that they might fall (rise) as maturity approaches. Gilbert (1997) argues that, if short hedging (selling futures to hedge) outweighs long hedging (buying futures to hedge), speculators will tend to be net long (bought futures), and the futures price (of the same contract) must rise over time (as it approaches expiration or delivery) to give a hedging profit. Where long hedging dominates, speculators will be net short and futures prices will fall towards maturity.

Gilbert (1997) analyzed London cocoa market data from 1989 through 1996. He found that the average November price of the December futures contract was 5.2 percent lower than its February average, 4.7 percent lower than its May average and 3.3 percent lower than its August average. This suggests an upward biased futures price consistent with long hedging. 10 The comparable figures for March were 4.4 percent
economies may have contributed to greater risk-taking and increased leverage (Bekaert, Hoerova and Lo Duca 2013, Dell’Ariccia, Laeven and Suarez 2017, IMF 2013). This, in turn, contributed to greater cross-border credit intermediation and investment flows to developing countries (Fratzscher, Lo Duca and Straub 2016, Lim and Mohapatra 2016, Park, Ramayandi and Shin 2014).

Changes in US monetary policy and risk aversion are linked to powerful global financial cycles characterized by large common movements in asset prices, capital flows, and leverage (Miranda-Agrippino and Rey 2015, Rey 2015). Correa et al. (2018) find that monetary policy in source countries influences cross-border banking flows. The authors find evidence of a cross-border bank portfolio channel, whereby tighter monetary conditions erode the net worth of domestic borrowers, leading banks to reallocate credit to safer foreign borrowers. Jaramillo and Weber (2013) find that domestic bond yields in emerging economies are heavily influenced by global risk appetite and liquidity conditions. Cerutti, Claessens, and Puy (2019) find evidence of co-movements in bank, portfolio bond, and portfolio equity flows to emerging markets, but not for FDI flows. The authors report that emerging markets' reliance on global mutual funds, recipient market liquidity, and inclusion in global market indices increase the sensitivity of equity and bond inflows to common global factors. Earlier studies also suggest that time-varying global financial variables are likely to be driven by unobserved common factors conceived as a ‘world business cycle’ (for instance, see Albuquerque, Loayza and Serven 2005).

Several studies also find global risk aversion, captured by the VIX index, to be a significant determinant of capital flows (Bruno and Shin, 2014; Forbes and Warnock, 2012, Ghosh et al. 2014). Nier, Sedik and Mondino (2014) find that for low levels of VIX, country fundamentals are significant drivers of capital flows, but when the VIX index is high, country-specific factors become less important. Abrupt adjustment in global monetary and risk conditions
201.19|261.82|
|P. C. Expend.|656.42|934.48|592.81|792.64|
|Source: NCS, 1985/86 and|1992/93.||||

# **6. CONCLUDING REMARKS**

Poverty declined sharply from 43 percent of the population in 1985 to 34 percent of the population in 1992. However we observe that, despite this, some households suffered a loss in incomes. The top 10 percent of the population had more than 47 percent increase while the bottom 20 percent of households experienced absolute decline by more than 20 percent. The latter group form the extreme poor of Nigeria who have been adversely affected in terms of poverty and welfare during this period Although growth reduced poverty, the distribution of income has worsened during the same period. This is particularly true for the rural sector and the extreme poor for whom the depth and severity of poverty increased between 1985-92. If income distribution had remained unchanged, the national incidence of poverty would have declined further by another 4 percent. The Lagos urban area is the only region where income distribution improved. Hence, for all other regions growth without improvements in income distribution have adversely affected the extreme poor in Nigeria.

Poverty declined in Nigeria mainly due to the 34 percent growth in household expenditures which was a result of economic growth. However, the regional pattern of household expenditure growth is varied in different parts of the country. While the south and middle zones had higher growth rates of household expenditures, the northern zone experienced hardly any growth. This particularly evident in the rural areas of the north.

Poverty in Nigeria differs depending on socio-economic groupings. Those with no education comprise the poor and extreme poor, and this is very true in rural areas. If the household head is older, the probability of being poor increases. Wage workers and self-employed are often poor. Households which are large and families with large number of children are more frequently poor.
# **Proximity without Productivity:**

# **Agglomeration Effects with Plant-Level Output and Price Data**

Arti Grover and William F. Maloney<sup>1</sup>

## **JEL Classification: 018, 040, D24, E24**

**Key words: agglomeration, productivity, cities structural transformation.**

> 1 World Bank.  We thank Alvaro Garcia, Marcela Eslava, Kaleb Girma Abreha and Massimiliano Cali for graciously allowing access to micro datasets in Chile, Colombia, Ethiopia and Indonesia by running our Stata codes, with excellent research assistance from Nicolas Urdaneta Andrade (Colombia) and Bayu Agnimaruto (Indonesia). We are also grateful to Angelos Theodorakopoulos for his work in the early phases of this project, including his contributions to the Stata codes and for sharing his insights on productivity estimations. Our research benefited greatly from thoughtful and constructive comments by Gilles Duranton, Somik Lall and Tony Venables.
0.000158** -0.000154** -0.000152** -0.000158**<br>Population density 0.113** 0.111** 0.112** 0.109** 0.0990** 0.106** 0.113** 0.0990** 0.102** 0.103** 0.100** 0.114** 0.110** 0.103**<br>Country fixed effects Y Y Y Y Y Y Y Y Y Y Y Y Y Y<br>Constant 17.54** 17.23** 17.79** 17.65** 18.60** 17.14** 15.68** 17.83** 18.15** 18.01** 18.19** 17.72** 17.60** 18.10**<br>Observations 1,181 1,181 1,184 1,184 1,181 1,181 1,181 1,181 1,181 1,181 1,181 1,184 1,184 1,181<br>Adj R-Squared 0.368 0.364 0.363 0.360 0.359 0.372 0.371 0.359 0.359 0.360 0.359 0.365 0.360 0.359<br>Note: (1) Data source: EU 2011 Poverty Map (DG-REGIO and World Bank), 2010 Farm Structure Survey (Eurostat), and EU National Statistic Institutes (Eurostat); (2) Symbols: ** p<0.01, * p<0.05, + p<0.1; (3)<br>Missing observations in CAP data are treated as zero; (4) Luxembourg and Cyprus Republic are not being analyzed here;(5) CAP
to trees (both cash crop and fruit trees). Female-headed households tend to be less engaged in tree growing, with the effect being largest for tree cash crops (possibly linked to higher land tenure insecurity for female farmers and consistent with the higher nutritional value of fruit trees).

Despite the already non-negligible prevalence and economic contribution of trees on farms that this paper demonstrates, the numbers are likely still underestimates. The household data are unlikely to fully account for non-crop trees (e.g. for shade or different kinds of non-provisioning ecosystem services) and trees with no immediate productive function because they were not queried explicitly in the LSMSsurveys. The indirect effects of trees on farms on crops, livestock, and other productive activities are also very difficult to account for (Wunder et al., 2014) and information on them was not directly collected in 15
of the India Human Development Survey. Both sources are known to miss the top of the income distributions and report electricity consumption based on self-reported figures. These surveys are not designed to accurately measure residential electricity consumption and therefore do not capture the full distribution of electricity consumed in a region. Figure 5 compares the consumption distribution from the administrative billing data from one of Rajasthan’s power utilities from 2015-16 to NSSO’s consumption expenditure survey of 2011-12 for districts served by this utility. The figure highlights the fact that, assuming distribution neutrality between these sets of years, NSSO consumption data is particularly imprecise in capturing the top ends of the consumption distribution and therefore unsuitable to conduct counterfactual pricing analysis.

This paper uses appliance level ownership from a household survey and household’s billing cycle-level electricity consumption from administrative billing data to estimate a demand model for energy services. Consumption-specific charges under the IBT schedule and variation in weather conditions within a household’s billing cycle are accounted for in the estimation of this model. The model also incorporates heterogeneity in residential demand due to observable socio-economic characteristics-–such as, income, family sizes and other observable factors that differ across customers. This allows us to recover the price-responsiveness of demand into four categories of energy services demand as well as the overall responsiveness of electricity demand to the price of electricity.

# **SECTION III: Data and Descriptive Background**

The data used in this paper are from three different sources: (i) administrative data on billing cycle-level electricity consumption and bills issued for every household in two districts of Rajasthan – Jaipur and Alwar and served by a local electricity utility called JVVNL<sup>10</sup> for four years.

(ii) detailed household demographic characteristics and appliance ownership and use from a survey designed and implemented in 2017; and, (iii) spatially disaggregated daily temperature and precipitation data from Indian Meteorological Department.

Each of these data sources is described below with additional details offered in Appendix 2.

# _2.1 Electricity consumption and prices