rafmacalaba/datause-encoder-data huggingface dataset:
procurement and private firms. The last two columns give the percent change in the CHB for each country in the sample. These numbers are showed graphically in Figure 6. At the aggregate level like at the sector level, we find that home bias in government procurement – has gone down over time the average change equals a 11 percent decrease (remarkably similar to the changes observed in the goods and in the services sectors), and more so than in private markets. Large variation however emerges across countries. The largest percent – increases in government home bias are observed among developed economies e.g., Japan, Germany and France. These countries have however also low levels of CHBs. Developing countries such as China, Vietnam and India report the largest drops in government home bias. Interestingly given the large trade effect of the EU single market (see Tables 1 and 2), most old EU member states experience an increase in home bias in government procurement during the sample period, whereas home bias went down in countries that entered the EU during the period (Croatia being the exception).

Figure 5: Relative change in CHB indexes at the country level between 2015 and 1995 <!-- Start of picture text --> CHN<br>VNM<br>IND<br>KHM<br>CRI<br>ROU<br>PER<br>SAU<br>EST<br>CHL<br>LTU<br>IDN<br>MLT<br>BGR<br>MEX<br>LVA<br>SVK<br>RUS<br>KOR<br>AUS<br>SGP<br>COL<br>POL<br>TUR<br>LUX<br>TUN<br>CZE<br>MYS<br>PHL<br>NZL<br>HKG<br>ARG<br>IRL<br>ISL<br>ZAF<br>MAR<br>USA<br>NOR<br>CYP<br>GBR<br>CHE<br>ISR<br>CAN<br>HUN<br>ESP<br>SVN<br>NLD<br>FIN<br>BRA<br>TWN<br>BRN<br>SWE<br>BEL<br>HRV<br>PRT<br>GRC<br>ITA<br>DNK<br>AUT<br>FRA<br>DEU<br>JPN<br>-1 0 1 2<br>Government Private<br><!-- End of picture text --> Overall, the analysis using the CHB indexes reveals that, while government procurement is significantly more home biased than private purchases, it has gone down faster. Important 32
> - 31 -

public enterprises with regard to their access to credit, foreign exchange and raw material imports. Clearly, so long as public enterprise companies rely heavily on the resources of NIB to finance their investment and so long as the existing investment procedures are not completely revised to take into account proper project selection and appraisal, interest rate adjustments would do little to improve the marginal efficiency of investment in the public sector. Concerning private investments, it was shown in this paper that under the prevailing conditions concerning tax rules, depreciation, interest rate and inflation, a standard project yielding a low real rate of return of even negative 4 percent per annum would still be acceptable to the investors. An upward adjustment of interest rates, therefore, is both desirable and needed, provided that the magnitude of adjustment is not too high to raise the moral hazard issue.

More generally, chs.nges in the level and structure of interest rates should be planned in several steps and to be carried out in conjunction with the implementation of other aspects of the adjustment program, particularly the reduction in the budget deficit, the reform of public enterprise, and the streamlining of the public investment program. At the same time, the increases in the level of interest rates should be sufficiently high to represent a clear departure from the past policies and to give the proper signal to economic agents. Ideally, to avoid creating more distortions in the economy, the corresponding increases in the lending rates should be the same. However, it was shown very clearly in Section V.B that the impact on the Government budget and public enterprises would be very severe. To keep the cost to public enterprises at a manageable level, their existing debt to NIB should be kept insulated from the interest rate increases. This means, in turn, that NIB's debt to the Social Insurance Fund and the Pension Fund (but not to the Post Office Savings and the Investment Certificates) should be kept insulated from the interest rate increases. This way, a ten percentage point increase in interest rates on public enterprises would have an impact of about LE .1 billion, still about 40 percent of the entire gross profit of public enterprises, but still about one-third lower
# **References** :

Acemoglu D. and F. Zilibotti (1997). “Was Prometheus Unbound by Chance? Risk Diversification and Growth”. Journal of Political Economy, 105, 709-751.

Adams F. Gerard (2008), “Globalization,” _World Economics_ , Vol. 9 (2), pp. 153-74.

Agénor Pierre-Richard (2002). “Does globalization Hurt the Poor?”. World Bank Policy Research Working Paper No. 2922, The World Bank Group, Washington DC.

Agénor Pierre-Richard, Alejandro Izquierdo and Hippolyte Fofack (2003). “The integrated macroeconomic model for poverty analysis: a quantitative macroeconomic framework for the analysis of poverty reduction strategies,” World Bank Policy Research Working Paper; No. 3092.

Agosin, Manuel R. (2005). “Export Diversification and Growth in Emerging Economies.” Mimeo, Department of Economics, Universidad de Chile.

Akyuz, Yilmaz and Charles Gore (2001). “African Economic development in a Comparative Perspective,” _Cambridge Journal of Economics_ , Vol. 25, 265-288.

Nadreosso-O’Callaghan Bernadette (2002). “Human Capital Accumulation and economc Growth in Asia,” Working Paper No. 30.

Artadi V. Elsa and Xavier Sala-i-Martin (2003). “The Economic Tragedy of the 20<sup>th</sup> Century: Growth in Africa”, National Bureau of Economic Research Working Paper No. 9865.

Bayraktar, Nihal and Hippolyte Fofack. (2007). “Specification of Investment Functions in SubSaharan Africa,” World Bank Policy Research Working Paper No. 4171, the World Bank Group.

Berg, Andy and Zia Qureshi. (2005). “The MDG’s: Building a Momentum,” Finance and Development, the International Monetary Fund. Vol. 42. Number 3.

Ben-David, Dan. (1998). “Free trade, growth, and convergence.” _Journal of Economic Growth_ . Vol. 3. pages. 143-70 (June).

Berthélemy Jean-Claude (2001). “The Role of Capital Accumulation, Adjustment and Structural Change for Economic Take-Off: Empirical evidence from African Growth Episodes”. World Development, Vol. 29, No. 2, pp. 323-343.

Bu Yisheng (2006). “Fixed Capital Stock Depreciation in Developing Countries: Some Evidence from Firm Level Data”, Journal of Development Studies, Vol. 42, No. 5, 881-901.

Coulibaly Brahima and Jonathan Millar (2008). “The Asian Financial Crisis, Uphill Flow of Capital and Global Imbalances: Evidence from A Micro Study”. Board of Governors of the Federal Reserve System, Washington DC.

Dixit A. and Pindyck R. (1994). Investment under Uncertainty. Princeton University Press.

Easterly William and Ross Levine (1997). “Africa’s Growth Tragedy: Policies and Ethnic Divisions”, Quarterly Journal of Economics.

Easterly William (2002). “How did Heavily Indebted Poor Countries Become Heavily Indebted? Reviewing Two Decades of Debt Relief”, World Development Vol. 30, No. 10, pp. 1677-1696.

Easterly William (2009). “Can the West Save Africa?”, Journal of Economic Literature, 47:2, 373— 447.

Elbadawi Ibrahim, Benno Ndulu, Njuguna Ndung’u (1997). “Debt Overhang and Economic Growth in Sub-Saharan Africa”, in Iqbal and Kanbur eds. External Finance for Low-Income Countries, International Monetary Fund, Washington DC.

25
and does not capture acute deprivations in health or education, as the OPHI MPI does. For example, a third of those experiencing multi-dimensional poverty are not captured by the monetary headcount ratio (World Bank, 2020). Hence, it is not surprising that efforts are underway to combine the advantage of both measures like the World Bank’s Multi-Dimensional Poverty Measure (MPM; Nguyen et al., 2021).

In the case of FDPs, monetary and multi-dimensional measures of poverty are both indicated and, as it is often the case in other settings, they complement each other. In this respect, there is not much difference with other types of populations. However, the money metrics used for monetary indicators may be more challenging to estimate for FDPs than regular populations, and the composition of multi-dimensional indexes should be adapted to FDPs’ characteristics.

Moreover, especially in a context of very high deprivation, a poverty headcount might not be able to reveal the actual gravity of the situation and qualitative information should be sought to complement standard questionnaires and possibly inform the development of future questionnaires. Collection of socio-economic data is a passive process where respondents are asked pre-formulated questions. This constrains the respondents in sharing their own narratives and emphasizing what they feel is important. The implementation of household surveys can be seen as an opportunity to collect qualitative information and transform a one-sided narrative into one that provides voice to the poor. Pape (2020), for example, describes how short, voluntary video testimonials with informed consent from people living in South Sudan and Somalia<sup>9</sup> can be used to empower the poor in voicing their own needs. While this is a practice that can be developed in any context, it is particularly promising in contexts where measuring poverty is still relatively new and the specific challenges associated with this measurement are still little known, as in FDP contexts.

# Poverty metrics

Income, consumption or expenditure can be used as monetary metrics to estimate poverty. The correct measurement and classification of income can be a real problem in low-income countries with large informal economies (Deaton and Zaidi, 2002; Mancini and Vecchi, 2022) and, especially, for FDPs. FDPs rarely have regular labor income and tend to rely on occasional, informal income, or have no labor income at all. They have various forms of in-kind and cash assistance that vary from household items such as blankets and kitchen items to food vouchers and cash assistance. The combination of these income sources is not always well captured in surveys because many of these items are provided occasionally and by multitudes of donors. Food vouchers are often traded and can function more as cash than food. FDPs may also produce products that they exchange as they try to use their crafts to supplement incomes, and some

> 9 <u>www.thepulseofsouthsudan.com</u> and www.thesomalipulse.com.

14
# Table I.15. Robustness Analysis for the Self-Employed Outcome

|_Panel A. Polynomial of order zer_|_o_||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|1[PEP_i_ = 1]|-0.359*|-0.423**|-0.416**|-0.317*|-0.246|-0.357**|-0.359*|-0.423**|-0.416**|-0.317*|-0.246|-0.357**|
||(0.187)|(0.212)|(0.211)|(0.169)|(0.174)|(0.181)|(0.187)|(0.212)|(0.211)|(0.169)|(0.174)|(0.181)|
|Obs. left|207|180|180|223|180|183|207|180|180|223|180|183|
|Obs. right|446|342|346|732|957|767|446|342|346|732|957|767|
|Mean values (Non-RAMV)|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|
|_Panel B: Polynomial of order one_|||||||||||||
||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(22)|(23)|(24)|
|1[PEP_i_ = 1]|-0.471*|-0.487*|-0.680**|-0.378|-0.278|-0.421*|-0.679**|-0.871**|-0.822**|-0.558**|-0.568**|-0.558**|
||(0.265)|(0.276)|(0.310)|(0.232)|(0.239)|(0.242)|(0.305)|(0.407)|(0.372)|(0.265)|(0.287)|(0.274)|
|Obs. left|239|228|199|233|228|199|239|228|199|233|228|199|
|Obs. right|495|471|410|852|804|837|495|471|410|852|804|837|
|Mean values (Non-RAMV)|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|
|_Panel C: Polynomial of order two_|||||||||||||
||(25)|(26)|(27)|(28)|(29)|(30)|(31)|(32)|(33)|(34)|(35)|(36)|
|1[PEP_i_ = 1]|-0.867**|-0.982**|-0.868**|-0.525*|-0.506|-0.474|-0.728**|-0.621|-0.822**|-0.537*|-0.402|-0.537|
||(0.396)|(0.455)|(0.401)|(0.295)|(0.317)|(0.295)|(0.369)|(0.399)|(0.372)|(0.309)|(0.310)|(0.330)|
|Obs. left|238|208|238|238|208|238|238|208|199|238|208|238|
|Obs. right|493|447|493|948|981|973|493|447|410|948|981|973|
|Mean values (Non-RAMV)|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|0.430|
|Bandwidth type|mserd|mserd|mserd|msetwo|msetwo|msetwo|cerrd|cerrd|cerrd|certwo|certwo|certwo|
|Kernel|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|triangular|uniform|epanechnikov|

_Notes:_ All columns include department (Antioquia, Atl ́antico, Bogot ́a, and Norte de Santander) and sampling-city fixed effects. Individual controls include: age, gender, and years of education before migration. Labor history in Venezuela controls include: ever worked [=1], type of job, had a written contract [=1], and gap between last job and migration. Household controls in Venezuela include: number of children; household size; if had energy, water, and sewage [=1]; owner of dwelling [=1]; and had smartphone [=1]. Migration decisions controls include: had family/friends in Colombia before migrating [=1], knew of job opportunities before migrating [=1], if the head migrated for health reasons [=1], and time of settlement in Colombia. Standard errors are reported in parentheses and False Discovery Rate (FDR) q-values are reported in brackets.<sup>_∗∗∗_</sup> significant at the 1%,<sup>_∗∗_</sup> significant at the 5%,<sup>_∗_</sup> significant at the 10%.
BDFs for short, include information on the total wage bill and employment, organized according to various break-downs. Although all firms in the two countries should in principle submit this information to the corresponding BDFs, in practice only formal sector firms do so, and not for all years. The result is an unbalanced panel of some two thousand firms in CMte d'Ivoire, and of roughly one thousand firms in Senegal. This section focuses on the determinants of average labor costs by industry in these panels. There are 53 such industries in the BDF of CMte d'Ivoire, and 35 in that of Senegal.

A simple specification is used to assess the link wages and other nominal variables. The chosen dependent variable is the annual change in the average labor cost by sector, while the explanatory variables are the annual inflation rate, the annual change in economic activity and the annual change in the terms-oftrade level. The explanatory variables are defined at the economy-wide level; the latter two are lagged one year.3 To better identify the sources of wage rigidity, the inflation rate is measured in three different ways. In addition to the change in aggregate consumer prices, the change in the minimum wage (SMIG) and the variation in the average government wage are also included among the explanatory variables.

The results, reported in Tables 4 and 5, show that average labor costs follow closely the fluctuations of average government wages and also, to some extent, those of consumer prices. They do not appear to be affected, however, by changes in the minimum wage. The coefficient on the latter variable is not statistically significant in any of the regressions for C6te d'Ivoire. In the case of Senegal, it is significant only when the change in the average government wage is omitted from the regression, which means that the 3 These variables can be considered exogenous, because each of the sectors is too small to affect macroeconomic aggregates on its own.

20
# **7. References**

Asociación Gremial de Administradoras de Fondos de Pensiones, 2007. Memoria Corporativa.

Andrade, L., D. Farrell, and S. Lund, 2007. Fulfilling the Potential of Latin America’s Financial Systems, _McKinsey Quarterly_ , Electronic edition, available at www.mckinseyquarterly.com.

- Arrau, P. and R. Chumacero, 1998. Tamaño de los Fondos de Pensiones en Chile y su Desempeño Financiero, _Cuadernos de Economía_ 35(105), 205-235.

- Berstein, S.M., and R.A. Chumacero, 2006. Quantifying the Costs of Investment Limits for Chilean Pension Funds, _Fiscal Studies_ 27(1), 99-123.

- Blommestein, H., 2001. Ageing, Pension Reform, and Financial Market Implications in the OECD Area, CeRP Working paper 9/01.

- Braun, M. and I. Briones, 2006. The Development of the Chilean Bond Market, Working Paper, IADB Research Department.

- Broner, F., A. Martin, and J. Ventura, 2006. Sovereign Risk and Secondary Markets, NBER Working Paper 12783.

- Broner, F., A. Martin, and J. Ventura, 2007. Enforcement Problems and Secondary Markets, NBER Working Paper 13559.

- Candia, F., 1998. International Diversification for LAC Funds: Why or Why Not? paper presented at the IMD/EDI World Bank Conference LAC Pension Systems: Investing for the 21<sup>st</sup> Century, Washington, D.C., July 27-29.

- Catalán, M., 2004. Pension Funds and Corporate Governance in Developing Countries: What Do We Know and What Do We Need To Know? _Journal of Pension Economics and Finance_ 3(2), 197-232.

- Catalán, M., G. Impavido, and A.R. Musalem, 2000. Contractual Savings or Stock Market Development: Which Leads? _Journal of Applied Social Science Studies_ 120(3), 445-487.

- Corbo, V. and K. Schmidt-Hebbel, 2003. Macroeconomic Effects of the Pension Reform in Chile, in <u>Pension Reforms: Results and Challenges, ed. International</u> Federation of Pension Fund Administrators, 241–329, Santiago, Chile.

- Dahlquist, M., L. Pinkowitz, R. Stulz, and R. Williamson, 2003. Corporate Governance, and the Home Bias, _Journal of Financial and Quantitative Analysis_ 38(1), 87110.

- Davis, E.P., 1995. Pension Funds: Retirement Income Security and Capital Markets-An <u>International Perspective, Oxford University Press, Oxford.</u>

- Davis, E.P. and B. Steil, 2001. Institutional Investors, MIT Press, Cambridge, MA.

- Dayoub, M. and E. Lasagabaster, 2007. General Trends in Competition Policy and Investment Regulation in Mandatory Defined Contribution Markets in Latin America, World Bank Policy Research Working Paper 4720.

- de la Torre, A., J.C. Gozzi, and S.L. Schmukler, 2007. Financial Development: Maturing and Emerging Policy Issues, _World Bank Research Observer_ 22(1), 67-102.

- de la Torre, A. and S.L. Schmukler, 2006. Emerging Capital Markets and Globalization: <u>The Latin American Experience, Stanford University Press and World Bank.</u>

- De Ferranti, D., D. Leipziger, and P.S. Srinivas, 2002. The Future of Pension Reform in Latin America, _Finance and Development_ 39 (3), 39-43.

42
2000s. In the case of Peru, a rapidly rising share of Peru‘s exports goes to China (Figures 11(b)); the share has more than doubled since the 1990s.

**Figures 11(a) and (b):  Chile and Peru in the ―Emerging Pacific Rim‖**

<!-- Start of picture text --> (b) Destination of Peru's Merchandise<br>(a) Destination of Chile's Merchandise<br>Exports (% shares)<br>Exports (% share) 18%<br>25%<br>16%<br>To China<br>To China<br>20% 14%<br>To Japan<br>To Japan 12%<br>15%<br>10%<br>8%<br>10%<br>6%<br>5% 4%<br>2%<br>0%<br>0%<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> Source: Author‘s calculations based on WITS database Another dimension of the increasing degree of trade integration is increasing specialization across tasks.<sup>12</sup> Understanding trade patterns based on the origin of final products can be misleading, given that the final product generally incorporates value-added that has been imported from a variety of destinations. Measuring the value added or domestic content of exports is fraught with difficulties; however, a variety of new techniques have been developed and new efforts have been made to improve data collection that could deepen our understanding of patterns of trade specialization based on tasks.<sup>13</sup> Much of this literature has focused on China and a few particular high income or emerging market economies. With increasing trade integration among developing countries, this will be an important area of research in the future. It will also be an important area for policy discussions as policy makers attempt not only to understand but also influence their country‘s comparative advantage for tasks in complex supply chains. Clearly, skill levels have a lot to do with where countries position themselves in these supply chains, and policies can contribute by strengthening public education systems, and also through incentives for private sector training and skills development.

> 12 See Mattoo, Wang and Wei (2011) for an overview of the topic, and Grossman (2011) for an overview of the theoretical and empirical implications.

> 13 A recent World Bank workshop provided a state of the art in this area:  papers available at <u>http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/TRADE/0,,contentMDK:22894003~menuPK:2644066~p</u> ~ ~ <u>agePK:64020865 piPK:51164185 theSitePK:239071,00.html.  The workshop was entitled ―The Fragmentation of</u> Global Production and Trade in Value-Added—Developing New Measures of Cross Border Trade.‖

19
# **Notes**

1 Franklin 2012.

2 ONS 2009.

3 Belich 2009.

4 See Dyos (1968) and Porter (1995).

5 This secured a supply of local public goods, but citywide public goods, such as a complete sewerage system, were supplied only after the health scares of the mid-19th century and the development of effective city government—first with special purpose authorities and starting in 1889 with the London City Council.

6 Regulations were formalized in the London Building Act of 1667, which specified building materials and the width of streets. Surveyors were appointed to enforce the regulations (www.buildinghistory.org/regulations.shtml). 7 Personal communication from Maya Hoek-Smit.

8 Bertaud and Malpezzi (2003) present comparative work on urban density in 38 cities. Urban density gradients are prevalent in world cities but not in the two African cities in the sample, Cape Town and Johannesburg. We know of no systematic cross-country work on rent gradients.

> 9 _The Nigerian Voice_ 2011.

10 British building societies, which came to provide mortgage finance, started as societies of builders who pooled working capital.

11 Von Pischke 1975.

12 Personal communication from Steve Malpezzi.

13 Further, the enhanced infrastructure benefits landlords who are largely absentee. For example, connection to electricity raises the rent per room in informal settlements 27 percent in Nairobi and 44 percent in Dakar (Gulyani Bassett, and Talukdar 2012).
**Figure 3: Export diversity index by county**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0 2007<br>0.5 2012<br>0.0<br><!-- End of picture text --> _Source: Staff calculations based on data from the CBS. No units._

**Figure 4: Destination diversity index by county**

<!-- Start of picture text -->
1.8<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br><!-- End of picture text -->

<!-- Start of picture text --> 2007<br>2012<br><!-- End of picture text --> _Source: Staff calculations based on data from the CBS. No units._ The county specific variables that are used in the analysis are summarized in Table 9. Average wages are the average monthly salaries in thousand HRKs for 2010. We observed the highest average wage in Zagreb with 5799 HRKs. GDP data for 2010 is expressed in million 11