rafmacalaba/data-use-mentions-extended huggingface dataset:
which would affect labor productivity, such as improvements in human capital, but these variables would also tend to have similar effects on real wages leaving RULC unchanged.

The data are annual observations. For most of the 28 countries in the sample, the data cover the period 2000 to 2017. However, for five of the countries, the time period is shorter because of missing observations, mostly on remittances in some of the years in the first half of the 2000s. The real unit labor cost data are the same as those used in table 2. The capital output data are taken from the Penn World Tables. The ODA data are taken from the WDI. However, for more than a third of the transition economies, there are no entries for ODA in the WDI, presumably because they did not receive any aid as they were not eligible given their income per capita. For these countries we have entered a zero for the relevant observation.

We use two methods of econometric estimation. First, we estimate a country fixed effects regression using OLS, which includes 27 dummy variables for the countries. The fixed effects regression controls for unobserved differences between countries which are constant over time (time invariant country specific characteristics). Second, we run generalized least squares (GLS) regressions which is a more efficient estimator than OLS when the regression errors are heteroscedastic.

A potential problem for these regressions is endogeneity and in particular the potential reverse causality from the dependent variable to the regressors. It is unlikely that there is contemporaneous causality running from RULC to the capital output ratio. The latter changes only slowly over time as a result of investment decisions taken, for the most part, several years in the past and of the depreciation of the existing capital stock. As such it is unlikely that the 18
and any policy encouraging labor force participation can potentially reduce gender employment gaps.<sup>15</sup> Fiscal policy can also support or undermine social reproduction. As noted earlier, social reproduction is the production and maintenance of human capabilities. It involves both daily household maintenance using women’s and men’s unpaid labor time and the commodities that men and women purchase. Social reproduction also includes unpaid activities such as fuel and water collection; cooking and cleaning and caring for children, the elderly, and other dependents; and providing cultural, social, athletic, recreational, and educational instruction that encourages the thriving and increased capability of dependents in many walks of life.

In the short run, social reproduction activities may positively impact labor productivity when workers are nourished, cared for, and replenished at home (Braunstein and others 2020). Social reproduction may also lead to direct increases in purchasing power. For example, consider the exchange of clothing between a mother with an older child and a mother with a younger child: even if no money changes hands, the time, effort, and social

> 14. In some countries, conditional cash transfers have also negatively affected maternal employment. For instance, Molyneux and Thomson (2011) noted that in a survey of women who received transfers from the Opportunidades program (Mexico), between one-third and half reported that the demands of the program conflicted with their income-earning activities, which echoed findings from other cash transfer programs in Latin America.

> 15. However, the gender wage gaps may be unchanged.

7
marize the characteristics of a generic low income country but allowing simultaneously for country-xed eects, country-specic exogenous variables, non-normal residuals, and exible dynamics using a panel vector autoregression with exogenous variables.

The paper proceeds as follows. Section 2 discusses the basic framework for a single country. Section 3 describes how to implement the methodology using a panel of time series - cross section data. This section also provides a detailed analysis of a ctitious country to illustrate the issues raised above. Section 4 considers Senegal as a case study and briey compares the proposed methodology with the approach currently used by the IMF and the World Bank. Section 5 provides robustness checks and Section 6 concludes.

# 2 The basic framework

This section describes the basic framework and identies key variables that aect the dynamics of external debt. To simplify the exposition, this section focuses on a single country. Details of the implementation and how to cope with the data problems of low income countries are discussed in the next section.

The methodology is an extension of the approach proposed by Garcia and Rigobon (2005), originally designed to study the sustainability of public debt, to the study of the sustainability of external debt. The idea is to write a debt accumulation equation, measuring and estimating a stochastic process for the variables that determine the evolution of debt, and performing Monte Carlo simulations to compute a number of statistics on projected debt trajectories. One contribution of this paper is to replace the Monte Carlo simulation step with a bootstrap procedure based on re-sampling from the set of estimated residuals. This modication makes the methodology robust to the tail risks inherent to probability distributions with fat tails and skewness.

By denition, the international investment position of a country at time _t_ , _IIPt_ , equals the position at time _t −_ 1 , _IIPt−_ 1 , plus the current account balance, the amount of debt relief, aid ows, grants, and valuation eects consisting in the change in the value
it actually arrived on June 5, the difference is 8 days.) This residual uncertainty in the monsoon is reflected in a long and rich folk tradition of methods to predict the arrival of the rains.<sup>4</sup> [Figure 6.2 here] Our survey data from Andhra Pradesh provides direct evidence that uncertainty about monsoon onset is costly. In 2006 about a quarter of our sample had replanted in the past, and 73% had abandoned the crop at least once in the past ten years due to insufficient rain after the sowing period. Respondents report that the extra expense borne by those that replant is large, equivalent to 20% of average total production expenses. This suggests that farmers would benefit from having accurate prior information about the onset of the monsoon (Gadgil et al. 2002).

In Giné, Townsend, and Vickery (2010), we find evidence that farmers’ beliefs about the monsoon are related to the benefits of having more accurate prior information. In particular, when farmers with less access to risk-coping mechanisms had more accurate prior information, the increased accuracy led to average income gains of 8% to 9% of agricultural production.

# 3. Do Households Need Formal Rainfall Insurance?

Innovative risk-management tools, like the rainfall insurance products discussed in this chapter, are beneficial for household welfare only if other existing risk-sharing mechanisms are inadequate (Townsend 1994; Morduch 1995; Lim and Townsend 1998). “Risk sharing” encompasses a wide range of different methods, including the following, that households use to protect their consumption and living standards from a poor monsoon or other adverse events.

- Drawing on accumulated savings of liquid assets (cash, bank account balances, etc.).

- Selling other assets (jewelry, land, livestock, etc.).
the largest negative shock. Between 1996 and 2000, all integrated firms experienced a reduction in labor productivity as opposed to the non-integrated firms, that were catching up. This strongly suggests that the post NAFTA/devaluation trade expansion had asymmetric effects on firms based on their integration status, in particular in terms of their productivity performance.

Figure 5: Labor productivity performance by integration status <!-- Start of picture text --> 1992 1994 1996 1998 2000 2002<br>year<br>Non−integrated Fully Integrated<br>Exporters Importers<br>Data source: EIA<br>4.8<br>4.6<br>4.4<br>4.2<br>4<br>Value Added Labour Productivity<br>3.8<br><!-- End of picture text --> This Section shows that there is a great degree of heterogeneity in size, sector of specialization and productivity between firms with a different integration status. Exporting firms are similar in size to non-integrated firms although their level of labor productivity is higher with a level closer to the one exhibit by importing firms. Descriptive statistics also show that importers, as well as fully integrated firms, are concentrated in two capital intensive sectors: “machinery and equipment” and “chemical products”. Finally, the labor productivity trends show that NAFTA marked a turning point in productivity performance between firms with different integration status.

The rest of this paper will try to explore how much of the differential in labor productivity shown in Figure 5 is attributable to the increase in trade integration observed between 1993 and 2002. In our empirical strategy we take 1993 as the base year (period before NAFTA), compare the productivity growth rate between integrated and non-integrated firms (controlling for firm-level characteristics and allowing for heterogeneous effects across integration status) and attribute these difference to the reforms. Since many other factors can influence the productivity growth rate differentials, a formal econometric analysis is needed to control for
|**City or Province**|**Total Budget**<br>**(USD/year) (millions)**|**Population**|
|---|---|---|
|Hubei Province|$5.5|58,500,000|
|Yunan Province|$3|48,300,000|
|SipingCity|$0.2|594,000|
|ChuxiongYi Prefecture|$0.4|2,684,000|
|Shanghai City|$2.7|24,280,000|
|Suzhou City|$1.1|10,720,000|

# IV. Lessons for Data Privacy in Developing Countries

As the above review demonstrates, the cost of data privacy compliance can be quite high, so high that companies avoid certain jurisdictions entirely or simply ignore the laws. The GDPR can be so difficult to comply with that more than half of the EU privacy professionals surveyed in the IAPP/EY study said that their organizations were not “fully” or even “very” compliant.<sup>223</sup> At the same time, the cost of not having data privacy protection can be quite high as well, and results in consumers and other counterparties avoiding beneficial transactions because of the risks that the information they share will be misused. Concerns over costs of compliance or costs of enforcement might be ameliorated if stronger data protection laws make it easier for local businesses to participate in global value chains.<sup>224</sup> The digital economy depends on a proper legal framework that protects privacy. Based on the studies above and our discussions with experts, we offer a few recommendations below, with the particular needs of developing countries in mind.

_Ensure clear rules_ . Rules that make clear what companies need to do both reduce costs and increase compliance. A common complaint about both EU and Chinese data privacy law among experts we spoke with was
macmap.htm. For details of its incorporation into the GTAP</u> Version 6 dataset, see Dimaranan and McDougall (2005) and <u>www.gtap.org.</u>

> 3 High-income countries include all members of the EU25, NAFTA and the OECD (except Turkey) plus Hong Kong, Singapore and Taiwan. All others (including the wealthy Middle East countries) are considered developing countries and are further split into middle- and low-income categories as defined at the bottom of Appendix Table A1.
**Table 7: Explaining social norms**

||(1)<br>SN ind<br>|(2)<br>ex after factor a<br>|(3)<br>nalysis<br>|
|---|---|---|---|
||Standardized<br>SN index|Standardized<br>SN index|Standardized<br>SN index|
||All|Female|Male|
|female|0.113**|||
||[0.0486]|||
|age|‐0.000618|‐0.0326|‐0.00810|
||[0.0105]|[0.0251]|[0.0162]|
|age_squared|0.000144|0.000659*|0.000176|
||[0.000117]|[0.000340]|[0.000166]|
|years of education|0.0612***|0.0766***|0.0500***|
||[0.00690]|[0.00985]|[0.00997]|
|whether married|‐0.171**|‐0.178*|‐0.129|
||[0.0783]|[0.0940]|[0.138]|
|Has young children|0.0524|0.167**|‐0.00546|
||[0.0557]|[0.0762]|[0.0857]|
|Amman|‐0.117***|‐0.231***|0.00499|
||[0.0437]|[0.0563]|[0.0674]|
|mother worked|0.00757|0.0631|‐0.111|
||[0
noted earlier, is whether the reliance on aid has led to a decrease in tax effort. At the same time, however, we observe a fairly close relationship, except for the early 1990s, between nonfood aid and public investment (see Figure 5). As discussed later, this correlation is corroborated by more formal econometric tests.

During the 1990s, public investment increased significantly in proportion of GDP (see Figure 6), much of it going to infrastructure (transportation, energy, and telecommunications) and education (Figure 7). On average, during 1991-2002, public investment accounted for more than half (around 54 percent) of total fixed capital formation. The road network, for instance, expanded from 19,000 km in 1991 to around 34,000 km in 2003, whereas power generation doubled over the same period. Primary school enrollment rose from 20 percent in 1993 to 62 percent in 2002. According to our index of quality of public infrastructure, which is based on the methodology proposed by Hulten (1996), quality, which appears to have been quite low during the 1970s and 1980s, improved significantly during the late 1990s (see Figure 8).<sup>13</sup> Road infrastructure appears to generate substantial externalities (see Dercon (2004)). Yet, connectivity remains underdeveloped: all infrastructure networks (telecommunications, roads, energy, and water) have coverage well below averages for sub-Saharan Africa. Also, the needs for education and health services remain enormous.

This brief review suggests that, despite significant improvements in living standards during the second half of the 1990s, reducing poverty remains a challenge. Ethiopia continues to be a very destitute country adversely affected by acute diseases (malaria and the HIV/AIDS epidemic) and periodic famines. The evolution of life expectancy, a good indicator of whether a country is meeting its basic needs, and which is strongly correlated with per capita growth, illustrates this point quite well.

> 13Hulten (1996) drew attention to the
# **2 Context, data and the urban gradient**

This section introduces our two data sources: the Kagera Health and Development Survey (KHDS) and the 2002 Tanzania Population and Housing Census. Within each we demonstrate rising living standards from villages across towns to cities, which we refer to as the urban gradient. In KHDS the gradient is demonstrated for changes in migrant welfare in a panel data set, while in the census it is shown to hold cross-sectionally for all residents of these areas.

## **2.1 Kagera**

The migrants we study all originate from the Kagera region of Tanzania. Figure 1 provides a map showing where the region is situated within Tanzania. Kagera, being primarily dependent on agriculture and far away from the ocean, is badly connected to the global economy. It is typical of remote parts of Africa where policy prescriptions for economic growth are elusive (De Weerdt, 2010). Given small plot sizes (typically 3 or 4 acres per household) and relatively low-productivity agriculture, internal migration will be an important aspect of growth and poverty reduction for this region.

In the context of this paper it is useful to highlight some of the geographical specificities of the Kagera region, which may be relevant for the study of destination choice and in particular whether urban migrants end up in large cities or smaller towns. The top panel of Figure 1 shows the baseline villages from the KHDS survey where all our respondents originate (the survey is described in the next subsection) and the bottom panel shows the urban areas in the country, split into small towns (10-100k), big towns (100-500k) and cities ( _>_ 500k).

We see that Kagera lies at the periphery of the country and has, within its borders, only one large town (Bukoba) and no cities. The closest city is Mwanza on the southern shores of Lake Victoria, while Tanzania’s prime city, Dar es Salaam, is located at the other end of the country.
_extremely significant), ** stands for a p-value below 0.05 (i.e., very significant), and * stands for a p-value below 0.1 (i.e., significant)._

**Table I.5   Investment Policies and the Broader Governance Environment**

|Variables|Independence of<br>central bank|Corruption<br>Perceptions Index|Governance<br>Pillar Score|Government<br>Effectiveness|
|---|---|---|---|---|
|Estimated risk of the portfolio|0.275**|0.268**|0.253*|0.265**|
|Allocation to nontraditional assets|0.162|0.114|0.177|0.239**|
|Number of eligible currencies|0.06|0.076|0.041|0.146|
|Number of eligible assets|-0.032|0.091|0.204*|0.217**|
|Investment horizon – Total tranched and untranched portfolio (months)|0.347**|0.348***|0.182|0.237*|
|Duration – Total portfolio (months)|0.219|0.346**|0.087|0.308**|
|Duration – Liquidity tranche (months)|0.349**|0.451***|0.225*|0.433***|
|Duration – Investment tranche (months)|0.250*|0.124|0.185|-0.008|

_Note: The significance level of the pairwise correlations is displayed with asterisks, where *** stands for a p-value below 0.01 (i.e., extremely significant), ** stands for a p-value below 0.05
>2</sup> and �<sup>�</sup> = 50,000 people. Hence, overall, we generate a total of nine different sets of “consistent” estimates of national

> 31 Both _WorldPop_ and _GHS-Pop_ data are available at a higher spatial resolution (100 m in the case of _WorldPop_ ).  For this paper, however, the data were aggregated up to a resolution of 1 km to allow for comparability with the _LandScan_ data.

> 32 _GHS-Pop_ data are available for download from <u>http://ghsl.jrc.ec.europa.eu/ghs_pop.php, while</u> _WorldPop_ can be downloaded from http://www.worldpop.org.uk/.

> 33 See Appendix Table A.1 in Uchida and Nelson (2010) for more details.

> 34 http://sedac.ciesin.columbia.edu/data/set/grump-v1-settlement-points.

> 35 Performing a complete update of the AI remains an obvious area for future research.

11
SIERRA LEONE‘S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 5. Sierra Leone road indicators benchmarked against Africa’s low- and middle-income countries**

|Indicator|Unit|Low-income,<br>fragile<br>countries|Resource-<br>rich countries|**Sierra**<br>**Leone**|Middle-<br>income<br>countries|
|---|---|---|---|---|---|
|Classified road network density|km/1000 km2 of land area|96|98|**119**|278|
|Total road network density [a]|km/1000 km2 of land area|145|128|**126**|318|
|GIS Rural accessibility|% of rural pop within 2 km from all-<br>season road|32|20|**21**|31|
|Main road network condition [b]|% in good or fair condition|55|68|**60**|86|
|Rural road network condition [c]|% in good or fair condition|56|61|**52**|65|
|Classified paved road traffic|Average Annual Daily Traffic|843|1,408|**1,150**|2,451|
|Classified unpaved road traffic|Average Annual Daily Traffic|55|54|**33**|107|
|Primary network over-engineering|% of primary network paved with<br>300 AADT or less|47|15|**69**|18|
|Perceived transport quality [d]|% firms identifying transport as<br>major business constraint|32|27|**30**|18|

_Source_ : AICD Road Sector Database on 40 Sub-Saharan African Countries accessed June 2010.

a. Total network includes the
Source: Serbia LFS 2009 Women are more likely to have supervisory responsibilities in public institutions than in private companies: 51.3% of women employed in state-owned organizations, 46.4% in private institutions and 2.3% in organizations of other types of ownership supervise the work of at least one employee excluding apprentices. There are no differences in the share of men in supervisory positions between public and private organizations (LFS 2009). These figures suggest that stereotypical perceptions about men’s and women’s roles are more prevalent in the private sector and that the private sector, particularly small domestic companies, may be lacking clear and objective criteria for staff evaluation and promotion.

Focus group discussions conducted by UNDP among women employed in the private and public sectors revealed that women face two major problems in building their careers. First, employees of private enterprises report that there is a social bias towards women in senior positions, so that even if a company’s board of directors is ready to promote a woman to a managerial position, such decision is often met by a resistance of male colleagues and in case a woman is granted a management role, she may face lack of collaboration in her daily work from the male employees of the company. Second, increased professional responsibilities are not necessarily followed by a redistribution of household roles and women commonly mention the need to take care of the family as an obstacle to professional growth and career advancement (Babovic, Marija.2008).

# **2.3 Women in Politics**

Political life in Serbia is dominated by men that hold the most influential government positions and outnumber women by almost four to one in parliament (Figures 7 and 8). Over 80% of all ministerial positions are occupied by men, including those of the prime-minister and deputy prime-ministers. This is higher than the average for EU-15 countries, where men represent 71% of political executives<sup>9</sup> . While the

> 9 European Commission. 2010. _More women
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- Herrera Dappe, M., A. Musacchio, C. Pan, Y. Semikolenova, B. Turkgulu, and J. Barboza. 2022. “StateOwned Enterprises as Countercyclical Instruments: Experimental Evidence form the Infrastructure Sector.” Policy Research Working Paper 9971, World Bank _,_ Washington, DC.

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- Huenteler, J., D. Hankinson, N. Rosenthal, A. Balabanyan, A. Kochnakyan, T. C. Nguyen, A. Rana, and V. Foster. 2020. “Cost Recovery and Financial Viability of the Power Sector in Developing Countries: Insights from 15 Case Studies.” Policy Research Working Paper 9136. World Bank, Washington, DC.

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- Laeven, L., and F. Valencia. 2013. “Systemic Banking Crises Database.” _IMF Economic Review_ 61(2): 225– 270.

Lewis, C. M., and A. Mody. 1998. “Contingent Liabilities for Infrastructure Projects: Implementing A Risk Management Framework for Governments.” _Viewpoint_ .

- Melecky, M. 2021. _Hidden Debt: Solutions to Avert the Next Financial Crisis in South Asia_ . World Bank, Washington, DC.

- Monteiro, R., I. Rial, and E. Tandberg. 2020. “Fiscal Risks in Infrastructure.” In _Well Spent: How Strong Infrastructure Governance Can End Waste in Public Investment_ , ed. Gerd Schwartz, Manal Fouad, Torben S Hansen, and  Genevieve Verdier. Washington, DC: International Monetary Fund

- Musacchio. A., and E.