rafmacalaba/data-use-mentions-extended huggingface dataset:
2010 to USD 91 billion, and real GDP per capita rising from USD 2,857 to USD 3,774 by 2018. We discuss the baseline assumptions in sub‐sections 2.2.1 ‐ 2.2.2 below. Sub‐section 2.2.3 considers upper and lower estimates for the calibration.

## _2.2.1 Key parameters and initial conditions_

Key parameters and initial conditions (panels A and B of Table 1) either take their 2010 values or are calibrated. In panel A of Table 1, Syria’s share of labor income of 0.520 is proxied by the 2010 average value for relatively conflict‐free middle‐income MENA economies from PWT 9. This value is close to the average for lower‐middle income (LMI) countries (0.510), as well as “oil‐based” middle‐income economies with fuel exports amounting to less than 60% of their merchandise exports (0.540). The Conference Board (2018) also uses a similar value of 0.500. We calibrate the depreciation rate for aggregate capital to 4.8%, which is the PWT 8.1 data point for 2010 (PWT 9 gives a value of 5.5% for the same year). Generally, for Syria, PWT data suggests a relatively low share of structures (compared to other forms of physical capital) and hence a high depreciation rate, one that is above the MENA and LMI averages. So, we choose the lower of the two PWT depreciation values given the possibility of measurement error. The public capital depreciation rate of 3.1% follows the PWT 9 depreciation rate for non‐residential structures. Calculated as a residual, the private capital depreciation rate of

> 13 TFP is measured using growth accounting based on the standard Solow‐Swan growth model with human capital. Syria data for output, physical capital, human capital, and employed persons are taken from Penn World Tables (PWT
|---|
|Variable|Mean|sd|p10|p90|Mean|sd|p10|p90|
|Computers-per-worker|0.24|0.22|0.06|0.50|0.28|0.23|0.07|0.57|
|Share of labor with Internet|0.20|0.19|0.03|0.40|0.20|0.19|0.03|0.40|
|Share of labor with computer|0.21|0.19|0.05|0.43|0.24|0.22|0.06|0.50|
|Sales-per-worker (thousand pesos)|997.36|1,638.24|123.01|2,192.49|1,005.39|1,407.87|131.20|2,494.11|
|Share of white-collar workers|0.22|0.17|0.05|0.42|0.25|0.23|0.06|0.59|
|Investment fxed assets/worker|46.10|111.03|0.00|130.11|41.34|91.19|0.05|113.05|
|(thousand pesos)|||||||||

*The sample includes 715 firms.

_Source:_ Authors’ calculations with data from ENTIC 2009 and 2013, INEGI

> 3We are unable to construct TFP measures because the survey does not include information on physical non-ICT related capital stocks

> 4Considering that according to data from Mexico’s Ministry of Economy, the U.S. market accounts for 80% of Mexican exports, we also analyzed the share of China in U.S. imports. Results are similar. This analysis is shown in Iacovone et al. (2015).

4
15 case of developing countries (whereas for high-income countries they are based on the much more thorough estimates of producer support estimates provided by OECD 2005a).

Also, the GTAP database includes only a few export taxes (see Table 1(b)), yet there is evidence that others exist even if they are small in most cases (Piermartini 2004). In Argentina’s case some sizeable ones were re-introduced in 2002 (OECD 2005b, Annex A). To see how much difference they could make to the effects of reform, we re-calibrated the model assuming not only that Argentina’s agricultural tariff protection in 2001 was zero but also that the country had export taxes of 20 percent for cereals, oilseeds and livestock products, 10 percent for other (including nonagricultural) primary products, and 5 percent for other processed food products and other manufacturing.<sup>11</sup> From that new base we then re-estimated the effects of full unilateral reform. As shown in Table 6, that database amendment makes a huge difference to the impact of unilateral reform on agricultural value added in Argentina. It also raises the estimated impact of reform on non-agricultural value added, mostly because of the boost it gives to food processing. Instead of having just a minor effect, the presence of these new export taxes mean that own-reform by Argentina would boost net incomes of its farmers by a dramatic one-third; and global reform would boost them by more than one-half, instead of an estimated one-fifth as of 2001 before those export taxes were imposed.

> 11  Based on the Ministry of Economy and Production’s resolutions 11/2002, 35/2002, and 532/2004, kindly provided by Maximiliano Mendes-Parra.
al. 2017). Behavioral measures are often used to build on or leverage existing frameworks, which can therefore ease their adoption from both a financial and political perspective.

# **References**

<mark>Abdou, C. M., Fingerhut, A. W., Jackson, J. S., & Wheaton, F. (2016). Healthcare stereotype threat in older adults in the health and retirement study.</mark> _<mark>American journal of preventive medicine</mark>_ <mark>,</mark> _<mark>50</mark>_ <mark>(2), 191-198.</mark> <mark>Acemoglu, D., & Jackson, M. O. (2017). Social norms and the enforcement of laws.</mark> _<mark>Journal of the European Economic Association</mark>_ <mark>, 15(2), 245-295.</mark> Adams MA, Hurley JC, Todd M, Bhuiyan N, Jarrett CL, Tucker WJ, Hollingshead KE, Angadi SS. Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults’ physical activity. BMC Public Health. 2017;17(1):286 Allison, J. J., Kiefe, C. I., Cook, E. F., Gerrity, M. S., Orav, E. J., & Centor, R. (1998). The association of physician attitudes about uncertainty and risk taking with resource use in a Medicare HMO. Medical Decision Making, 18(3), 320-329.

Amos A & Haglund <mark>M. From social taboo to "torch of freedom": the marketing of cigarettes to women. Tob Control 2000;9:3–8.</mark> Asch, D. A., Troxel, A. B., Stewart, W. F
# 1 Introduction

This paper asks the following question: does the accumulation of pension funds ...nancial assets stimulate national saving? In other words, is the accumulation of pension ...nancial assets helpful to foster national saving?

Having an answer for this question might be useful for several reasons. First, due to pervasive aging problems, several developed countries are debating reforms in their pay as you go pension systems. In fact, The Economist (2004) alerts that a larger generation of old people will need support from a population of young people that is shrinking continuously in absolute size. One of the options available to face this fact is to reduce the role of the pay as you go system and encourage funding. In that scenario, a careful understanding of the macroeconomic consequences of accumulating pension funds might be of central interest.

Second, high saving rates typically go hand in hand with high and persistent investment rates. It is well known that the accumulation of productive factors is one of the key engines for a sustained growth process. In this respect, an in‡uential paper by Young (1995) even argues that behind the famous East Asian “miracle”there was no miracle at all: the key in achieving per capita gdp annual growth rates averaging almost 7 percent in a span of more than two decades was an impressive accumulation of productive factors. If one were able to show that pension funds increase national saving, then the design of pension regimes might have important policy implications.

In this paper we answer the above questions using regression analysis, by relying on what we believe is the largest cross country time series data on pension funds ...nancial assets constructed up to now. National saving regressions 2
.4|3594.1|

_Source:_ UN Comtrade Standard International Trade Classification (SITC) Rev. 1 data. To identify commodities as Consumption, Capital, and Intermediate goods, the conversion table Broad Economic Category (BEC) to SITC Rev. 1 from World Integrated Trade Solution (WITS) was used. In order to calculate constant price data, National Accounts data from United Nations Industrial Development Organization (UNIDO) Statistics Unit and a GDP deflator were applied.

While the importance of the electronics industry in GVC formation is undeniable, note that the trade statistics presented here contain no information about trade in services or the ownership of physical or intellectual assets. As a result, GVCs can exist without strong growth in intermediate goods trade.<sup>4</sup> Nevertheless, while current trade statistics cannot capture the more

> 4 For example, in the automotive industry a pattern of regional production has been intensifying since the mid-1980s for both political and technical reasons. This has undoubtedly dampened trade in both final and intermediate goods. Nevertheless, global integration has proceeded at the level of buyer-supplier relationships, especially between automakers and their largest suppliers, which have plants in multiple regions. As a result, local, national, and regional value chains in the automotive industry are “nested” within the global organizational structures and business relationships of the largest firms (Sturgeon, Van Biesebroeck, and Gereffi 2008). These relationships structure not only the flow of physical goods, but also the flow of information, instructions, payments, and investment that characterize GVC development. The stable share of automotive parts in total manufactured intermediate goods trade, despite the establishment of dozens of final assembly plants in developing countries over the period (Sturgeon and Florida, 2004), probably reflects the strong drive for local content in this industry, both for regulatory and operations reasons (see Sturgeon, Van Biesebroeck, and Gereffi (2008) for an extended discussion). Similarly, apparel GVCs are highly dynamic, extensive, and robust, even
**Figure 2.4  The United States as the Leader of transformation : Shares in total employment for selected subsectors ranked by capital-labor ratio,1958-2005 (selected from the 99 industrial sectors)**

<!-- Start of picture text --> Fabric Mills Computer & mfg<br>0.03 0.015<br>0.02 0.01<br>0.01 0.005<br>0 0<br>Aircraft and Automobile Other plastic product and<br>fiber optics<br>0.03 0.04<br>0.025 Aircraft mfg<br>0.03<br>0.02 Automobile Mfg<br>0.02<br>0.015<br>0.01 0.01<br>0.005 0<br>0<br>Labor –intensive sector: (industry 313210: Broadwoven Fabric Mills); Mid level labor/capital intensity (industry<br>334111: Electronic Computer Manufacturing). High capital intensity: (336411: Aircraft 336111: Automobile). High<br>technical intensity (Industry 326199: Other plastic products mfg including fiber lens, windshield and optics).<br>Source: J. Ju, J.Y. Lin, and Y. Wang, ―Endowment Structure, Industrial Dynamics, and Economic Growth,‖<br>manuscript, based on the NBER-CES Manufacturing Industry Data for the period 1958-2005 in two versions. The<br>1997 6-digit NAICS codes (473 industries) was used.<br>1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003<br>1958 1963 1968 1973 1978 1983 1988 1993 1998 2003<br>1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002<br><!-- End of picture text --> Figure 2.4 below shows the employment shares in the United States during 1958-2005
Egger, et al., 2021). Apart from Egger et al. (2021) who include refugee samples in their analyses, no study to the best of our knowledge has empirically investigated pre- and postpandemic labor market trends for refugees and hosts in Kenya. Although Janssens et al. (2021) and Kansiime et al. (2021) include pre-COVID-19 trends in their analyses, the authors only consider the impact of the pandemic on Kenyans. In addition, Janssens et al. (2021) conducted their analysis in rural Kenya while Kansiime et al. (2021) compared the situation between Kenya and Uganda, neither of which provides a holistic picture of the situation of Kenyan nationals together with refugees living in both urban and rural areas of Kenya. The current study aims to fill these gaps and contributes to the literature that examines the labor market implications of the COVID-19 pandemic around the world in general and in developing countries in particular.

The paper also contributes to the growing literature on refugee economies. While most rigorous empirical examinations in this field have considered the impact of refugee migration to the host community (Alix-Garcia, Walker, Bartlett, Onder, & Sanghi, 2018; DepetrisChauvin & Santos, 2018; Maystadt & Duranton, 2019; Verme & Schuettler, 2019), or have evaluated policy changes within specific refugee contexts (MacPherson & Sterck, 2021; Sterck & Delius, 2020) this study uses a comparative design to investigate the connection between a global demand-supply shock and labor outcomes of both the refugee and the national community, with results being representative at the national level for both communities.

The paper is structured as follows: In the next section we briefly describe the refugee context and COVID-19 situation in Kenya. In section 3 we elaborate on the novel high-frequency phone data used in the paper and present our empirical strategy. In Section 4 we present our main findings on how refugees experienced the pandemic over time as compared to nationals. In Section 5 we discuss the results and their policy implications.

# **
<!-- Start of picture text --> OLS & IV Estimates of the Impact of Loan on Industrial Production<br>3 E OLS<br>2.5 - - - I<br>2'<br>-05<br>-0.5<br>-I<br>months<br>< Figure 10 ><br><!-- End of picture text --> Figure 8 through 10 presents the results of the OLS and IV estimates of equations 4 and 5. Figure 10 shows that the IV estimates of the impact of bank lending on industrial production are much larger than the OLS estimates at all horizons. However, as shown in Figure 8 and 9, the responses of industrial production to money (MI and MCT) using the IV estimates appear not to be distinctively larger than those obtained using the OLS estimates; the effects implied by the IV estimates are somewhat lower for Ml and not consistently larger for MCT than those implied by the OLS estimates at most horizons.

These results would be supportive of the view that bank lending has played an important independent role in amplifying the real effects of tightened monetary policy implied by the interest rate channel in the December 1997 focal episode of restrictive monetary policy.

# **2. Test for the Bank Lending Channel Using Disaggregated Bank Data**

## **_Testable hypotheses_**

The first testable hypothesis underlying this approach provides the following prediction: if the bank lending channel is operating (i.e. when loan supply rather than loan demand effects is identified as being very important), both lending volume and the securities holding of smaller banks are expected to decline more rapidly in response to a contraction of monetary policy than do those of larger banks. Note that this prediction 13
find that the elasticity of trade with respect to income and distance increases after disintegration. For example, the coefficient on distance more than doubles in magnitude from -0.42 to -1.16. This change is hardly surprising - pre-reform trade over long distances was heavily subsidized. The coefficients in the reform period are much closer to the coefficients typically found on estimates of the gravity model.<sup>4</sup> This suggests that gravity has set in and that trade patterns in the FSU are now determined in a similar way to the rest of the world.

Since GNP is not exogenous with respect to trade we also use population to instrument for GDP. The results are shown in Table 2. The coefficients on income are higher. The reorientation within Russia is still evident from the change in significance of the Russia dummy over time. Russian regions are significantly less likely to trade with one-another in the pre-reform period and this bias disappears in the reform period.

# 3.3 Allowing **for heterogeneity across countries**

Non-tariff barriers, currency variability, and differences in wages and prices across regions and republics may affect trade. To allow for this possible heterogeneity, we split the sample into two panels-pre-reform and reform-and use country fixed effects in the estimation. We exclude interrepublic trade from the sample and estimate equation (2) separately for the pre-reform and reform periods,

## _TRADE,j,t = at_ + _I3yij + 3lYit + 32Yjt + / 3 DISTij + uij,_

where _-yij_ is a country dummy. The dummy for Armenia, for example, is one when Armenia is either an exporter or an importer. Since inter-republic trade is excluded and all republics are dummied 4 Most comparable is the Canada U.S. study since trade in North America is also land-based trade. McCallum 1995 and Helliwell 1998 find the elasticity of trade to distance on Canada-U.S. data is between -1.23 and -1.62. Helliwell finds the elasticity on OECD trade to be
# **Introduction**

Haiti has a vision to become an emerging economy by 2030, but remains for the moment one of the poorest and most unequal countries in the world. The country’s GDP per capita fell by 0.7 percent per year on average between 1971 and 2013. While overall income growth is a necessary condition to reduce extreme poverty, as in many low-income economies, it will not be sufficient in the case of Haiti. Even when experiencing periods of economic growth, the country has not been able to share equally the proceeds of this better performance among its population. Policies to ensure more inclusiveness are hence needed.

More generally, policy focus would be needed to meet the World Bank’s Twin Goals of promoting shared prosperity and ending extreme poverty by 2030.<sup>1</sup> Meeting these Twin Goals under inequality-neutral growth would require very optimistic assumptions about growth rates going forward.<sup>2</sup> This calls for a prioritizing of policies that are expected to be especially pro-poor. But what would be the measures most promising to deliver this faster growth for lower income groups? Once identified, could they be ranked to assist in setting priorities?

Detailed country data and in-depth analysis would usually be required to begin selecting a set of policy priorities, a challenge for low-income countries such as Haiti. Long time-series needed to identify such relationships are often not available.<sup>3</sup> As an alternative, taking the opportunity of a growing availability of more standardized country data on income distribution, this paper examines the drivers of the income of the two poorest quintiles, drawing on the panel data of 117 countries over the period 1967 to 2011 used in Dollar et al. (2016).

The results suggest that maintaining macroeconomic stability as well as investing in human and physical capital would not only accelerate overall economic growth, but benefit more particularly the poorest segments of the population. This paper confirms the central role overall economic growth should play in
that there should be a rigorous evaluation of the development impacts of the program. We decided on Tonga and Vanuatu as the focus of our evaluation, since it was expected that they would be the countries that participated most, and they offer an interesting contrast in previous migration history with New Zealand.<sup>7</sup> Tonga (population 100,000) has traditionally had high emigration rates to New Zealand, Australia and the United States, with most recent migration through family-sponsored categories and a special annual permanent migration quota to New Zealand called the Pacific Access Category. The 2006 New Zealand Census enumerated 20,520 Tongan-born in New Zealand.<sup>8</sup> In contrast to Tonga, Vanuatu (population 215,000) has had relatively little international emigration, with only 1.5 percent of its population abroad prior to the RSE (World Bank, 2008), and fewer than 1,000 Vanuatu-born in the 2006 New Zealand Census.

Given that recruiting of workers occurred at the employer level, the interests of employers in screening workers themselves, and the large number of employers involved, it was never going to be feasible to attempt to get employers to randomly select workers. Therefore we decided the most credible impact evaluation strategy would be a matched difference-in-differences approach. This would entail conducting a baseline survey of households which would participate in the RSE before the workers left, along with surveys of non-participating households, and then following these households over time. Non-participating households would be separated into whether or not they had a member of the work-ready pool who had applied for the program, but not been selected.

The RSE contains no country-specific quotas, so ex ante it was not known how many individuals from each country would participate in the scheme. However, the numbers likely to be involved were certainly too small for a simple random sample of households to pick up enough RSE households in a cost-effective way – at most 5% of households would be likely to participate in the program. This meant
) finds that Chinese local government policy measured by willingness to approve local ODI and investment in R&D has a positive impact on regional ODI. Another home country characteristic found to have a significant effect is lagged inward FDI. Yao et al (2016) find that a country’s lagged inward FDI stock in China is positively correlated with China’s contemporaneous ODI to that country. A case study by Hertenstein et al (2017) looks into Chinese ODI in the auto component industry and argues that business networks explain the connections between lagged inward FDI and ODI.

More recent studies of Chinese ODI explore firm-level data. Using MOFCOM project data from 1998-2009, Chen and Tang (2014) find that the ex-ante larger, more productive, and more exportintensive firms are more likely to invest abroad. They also find that ODI helps firms to achieve higher TFP, employment, and export intensity and greater product innovation. Wang et al (2016) use firm-level data from Zhejiang Province to examine the impact of financial constraints on firms’ ODI decisions and suggest that lowering a firm’s financial constraints can increase both the probability and volume of ODI. Chen, Dollar, and Tang (2016) focus on the determinants of Chinese ODI in Africa and show that Chinese ODI in Africa is shaped by firms’ profit 5 Another related literature has examined the patterns of FDI from other emerging economies. Gómez-Mera et al. (2015), for example, study the patterns of FDI from Brazil, India, the Republic of Korea, and South Africa and find that FDI from these emerging economies deals with a fundamental trade-off between market attractiveness and the transactions costs of entering distant and remote markets.

6 This “institutional advantage argument” is supported by Cuervo-Cazurra and Genc’s (2008) cross-country analysis, which argues that disadvantages at home due to weak institutions could become advantages abroad.

9
it is useful in highlighting the existence of the phenomenon and is used in this paper.

> their usufructuary traditions. Unfortunately, in many countries, indigenous peoples have seen their traditional lands eroded through mass deforestation. Mining and oil and mineral exploitation are notable problems in large swaths of Africa and Latin America.

> 6 Because many of the issues concerning ethnic minorities also affect indigenous peoples, issues related to indigenous peoples are included in the ethnic minorities survey.

> 7 The Global Gender Gap Index, World Economic Forum, http://reports.weforum.org/global-gender-gap-report-2015/the-globalgender-gap-index-2015/.

Anti-Discrimination Law and Shared Prosperity 5