rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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- Agarwal, Sumit and Robert Hauswald, 2010, Distance and Private Information in Lending, _Review of Financial Studies_ 23(7), 2757-2788 _._

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- Agarwal, Sumit, Wenlan, Qian, Amit Seru and Jian Zhang, 2015a, Disguised Corruption: Evidence from Consumer Credit in China, ABFER Working Paper

- Agarwal, Sumit, Tomek Piskorski, Amit Seru, Tienfoo Sing and Jian Zhang, 2015b, Information Leakage and Insider Trading in the Housing Market, SSRN Working Paper _._

- Almeida, Heitor, Murillo Campello, Bruno Laranjeira, and Scott Weisbenner, 2012, Corporate Debt Maturity and the Real Effects of the 2007 Credit Crisis, _Critical Finance Review_ 1, 3-58 _._

- Aparicio, Francisco Javier, and Joy Langston. Committee leadership selection without seniority: The Mexican case. Centro de Investigación y Docencia Económicas (CIDE), División de Estudios Políticos, 2009.

Ballinas, Victor and Andrea, Becerril, 2012, Integration of 59 Ordinary Commission in the Senate, _Politica._

- Barth, James R., Chen Lin, Ping Lin, and Frank M. Song, 2009, Corruption in Bank Lending to Firms: Cross-country Micro Evidence on the Beneficial Role of Competition and Information Sharing, _Journal of Financial Economics_ 91, 361-388.

- Baharth, Sreedhar, Sandeep Dahiya, Anthony, Saunders and Anand Srinivasan, 2011, Lending Relationships and Loan Contract Terms, _Review of Financial Studies_ 24, 1141-1203.

- Barberis, Nicholas, Maxim Boycko, Andrei Shleifer, and Natalia Tsukanova, 1996, How does Privatization Work? Evidence from the Russian Shops. _Journal of Political Economy_ 104(4), 764-790.

- Battaglini, Marco, and Stephen, Coate, 2008, A Dynamic Thoery of Public Spending, Taxation and Debt. _American Economic Review_ 98(1), 201-236.

- Beck, Thorsten, Aslı Demirgüç-Kunt, and Ross Levine, 2006, Bank Supervision and Corruption in Lending, _Journal of Monetary Economics_ 53, 2131-2163.

- Beck, Thorsten, Robert Cull, and Afeikhena Jerome, 2005, Bank Privatization and Performance: Empirical Evidence from Nigeria, _Journal of Banking and Finance_ 29, 2355-2379.

- Berger, N. Allen, Philip Molyneux, and John O.S. Wilson, 2014, _The Oxford Handbook of Banking_ Oxford: Oxford University Press.

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Figure 9: Private debt after global recessions

# A. Global private debt

<!-- Start of picture text --> Index, t = 100<br>140<br>Previous global recessions 2020<br>120<br>100<br>80<br>t-2 t-1 t t+1 t+2 t+3 t+4 t+5<br>C. Private debt in EMDEs<br>Index, t = 100<br>140<br>Previous global recessions 2020<br>120<br>100<br>80<br>t-2 t-1 t t+1 t+2 t+3 t+4 t+5<br><!-- End of picture text -->

# B. Private debt in advanced economies

<!-- Start of picture text --> Index, t = 100<br>140<br>Previous global recessions 2020<br>120<br>100<br>80<br>t-2 t-1 t t+1 t+2 t+3 t+4 t+5<br>D. Countries with higher private debt five<br>years after global recession<br><!-- End of picture text --> <!-- Start of picture text --> Percent of countries<br>100<br>World Advanced economies EMDEs<br>75<br>50<br>25<br>0<br>1975 1982 1991 2009<br><!-- End of picture text --> Source: Kose et al. (2021).

A.-C. Swathe indicates the evolution of private debt in percent of GDP, indexed at 100 in the year of the global recession (year “t”). The previous global recessions include four global recessions: 1975, 1982, 1991, and 2009. Data are available for up to 184 countries, including 38 advanced economies and 146 EMDEs.

D. Chart shows the share of countries with private debt higher by 1 percentage point of GDP or more in five years after a recession (e.g., 1975 refers to the share of countries with higher private debt in 1980 than in 1975).

29
perceive that in utility or welfare metrics, its advantages outweigh its drawbacks (Binswanger, Deininger, and Fe 1995). Yet to our knowledge, this has received little attention in the literature.

In this article, we contribute to the literature on the economic consequences of land fragmentation, taking these three fundamental issues seriously. Building on insights found in Ali, Deininger, and Ronchi (2018) and Veljanoska (2016), we address issues of endogeneity by exploiting a unique natural experiment, Ethiopia’s history of land reform and allocation which we argue represents an exogenous source of household-level land fragmentation. Like Ali, Deininger, and Ronchi (2018), we are able to construct multiple complementary fragmentation measures, reflecting number of parcels, size and geographical dispersion. Unlike most studies, however, our data source, Ethiopia’s Living Standards and Measurement Study-Integrated Survey on Agriculture (LSMS-ISA), contains information on household food security outcomes allowing us to assess the impact of land fragmentation on welfare metrics in terms of food security.

We find that in Ethiopia, land fragmentation reduces food insecurity. To understand why, the paper unpacks the risk diversification mechanism. Consistent with Shaw’s (1963) conjecture, land fragmentation mitigates the adverse effects of low rainfall on food security. Households with diverse parcel characteristics in terms of slope, elevation and wetness can grow a diverse set of crops. By allowing farmers to create a diverse crop portfolio, land fragmentation helps mitigate weather risks, improving food security outcomes.

# **The evolution of access to land in Ethiopia**

Our identification strategy hinges on our assertion that land access and fragmentation are exogenous to farmer ability. Justifying this claim first requires understanding the evolution of access to land in the diverse regions of Ethiopia.

3
On average, private investment could have reached 17.7 and 18 percent of GDP in the 1980s and the 1990s (compared to 11.6 and 12 percent observed) if governance institutions and structural reforms had been improved at the same time (see Table 3). If we add the indirect impact of human capital (0.4 to 0.5 percent of GDP, through the amelioration of the governance institutions), private investment in Egypt could have been stimulated by 48 and 77 percent during the 1980s and the 1990s, and could have reached 18.7 and 15.7 percent of GDP (compared to 12.6 and 8.9 observed), respectively. This percentage increase would have been of 64 and 54 percent in Iran (with a private investment ratio of 15.7 and 19.5 percent of GDP in this case), 51 and 68 percent in Morocco (with a ratio of private investment of 18.1 and 16.9 percent of GDP), and 45 percent in Tunisia (19.7 and 21.7 percent of GDP for the private investment ratio). These figures are in line with the performances of the East Asian economies which have achieved, on average for our sample of countries, an investment ratio of 17.5 and 19.9 percent of GDP during the two sub-periods<sup>41</sup> .

**Table 3. Private Investment to GDP**

||**Observed**|**1980s**<br>**Predicted**|**Observed**|**1990s**<br>**Predicted**|
|---|---|---|---|---|
|**Egypt**_(1)_|**12.6**|18.7|**8.9**|15.7|
|_(2)_||_(48)_||_(77)_|
|**Iran**_(1)_|**9.6**|15.7|**12.7**|19.5|
|(2)||_(64)_||_(54)_|
|**Morocco**_(1)_|**12.1**|18.1|**10.1**|16.9|
|(2)||_(51)_||_(68)_|
|**Tunisia**_(1)_|**13.6**|19.7|**14.9**|21.7|
|(2)||_(45)_||_(43)_|
|**Average **_(1)_|**12.0**|18.0|**11.6**|18.5|

_<u>Source</u>_ <u>: Authors’ calculations</u> _Notes_ : (1): % GDP ; (2): % increase Our simulations also give a more precise diagnostic of which specific governance institutions would improve private investment performances in MENA. Contributions are 34
countries as opposed to developed ones. In other words, economic growth has on average been less polarizing in developing countries than in developed ones.

These findings indicate that the external effect of real trade openness on internal spatial inequality appears to have had a more polarizing effect in developing countries than economic growth. The important question in this context is, of course, what are the underlying structural factors behind the observed differences in the trade effect. As noted in Regression 9 in Table 2 above, the diminishing size and lack of significance of the development dummy interaction after controlling for spatial structure, government intervention, and sector differences point to these structural factors as part of the reason. This line of reasoning is confirmed in Table 4 in which the variable averages are collapsed across different country groups.

# **Insert Table 4 around here**

In Table 4 all the identified conditioning country characteristics appear to be working against developing countries. This is especially pronounced after disaggregating countries into high middle and low income clusters, especially when taking into account current existing degrees of global integration, on one side, and levels of spatial inequality, on the other. This implies that, as highlighted by Rodríguez-Pose and Gill (2006), the room for growth in spatial inequalities is much greater in the developing than in the developed world as a) developing countries tend to be characterized by structural features that potentiate the polarizing effect of trade openness, b) they already have much higher existing levels of spatial inequality, and c) their level of trade openness is, on average, still only a fraction of the one among developed countries.

# **5.2. Dynamic analysis**

Table 5 presents the results of the dynamic panel regressions. The results were computed using the xtabond2 command in STATA (Roodman, 2006). Reported results correspond to the 1<sup>st</sup> difference Arellano-Bond GMM estimation. The reason for this is that the usually preferred Arellano-Bover system GMM was repeatedly 22
confirmed by analysis on supercycles, which utilized a smaller data set (15 instead of 27 commodities) but for a longer time period (1900-2020 instead of 1970-2020).

A number of policy implications stem from our analysis. Heterogeneity of shocks across commodities implies that policies should be tailored to the terms-of-trade faced by each country by considering the export and import commodity mix rather than applying one-size-fits-all policies. For example, countercyclical macroeconomic policies can help buffer the impact of transitory shocks. Countries that depend on exports of highly “cyclical” commodities that are buffeted by frequent transitory shocks may want to build fiscal buffers during the boom phase and use them during the bust period in order to support economic activity. Policy makers could reduce the duration of the medium-term cycle in mineral commodities by shortening the period between discovery and the production process. In contrast, in countries that rely heavily on commodities that are subject to permanent shocks, structural policies may be needed to facilitate adjustments to new economic environments. For example, low income countries that depend on exports of agriculture as a source of revenue should embark on diversification efforts to limit the effects downward price trends. The procyclicality of policy interventions and commodity agreements is still an important aspect in the oil market where OPEC along with some nonOPEC oil producers engage in supply management. While such management could be beneficial at times and stabilize markets (such as the intervention in the early stages of the pandemic), it is important to be mindful of the fact that such interventions could have long lasting impacts. More broadly, the dominance of medium-term cycles in industrial commodities highlights the importance of investment cycles (and hence investment policies and climate) in commodity price movements.

Our research can be extended in several ways. First, our understanding of the sources of heterogeneity and similarities within and across commodity groups can be enhanced by assessing the degree and the evolution of synchronization in commodity cycles at different frequency domains. Second, although identification of future turning points and the medium-term path of commodity prices is beyond the scope of the current analysis, updating the results regularly with new observations could inform investmentand policy-making decisions. Third, from an econometric perspective, the difference of the permanent component’s shares based on the two samples certainly deserves further analysis.

_— 17 —_
5 Table 1: Foreign Direct Investment Inflows, 1990.

|Country|$ millions|
|---|---|
|Industrial Countriac|151,970|
|Developing Countries|32,473|
|Argentina|2,036|
|Bermuda|819|
|Brazil|2,118|
|Chile|*595|
|China|3,489|
|Colombia|501|
|Egypt|947|
|Hong Kong|783|
|Indonesia|964|
|Korea|715|
|Malaysia|2,902|
|Mexico|2,632|
|Nigeria|588|
|Philippines|530|
|Saudi Arabia|572|
|Singapore|4,808|
|Taiwan|1,330|
|Thailand|2,376|
|Turkey|697|
|Venezuela|451|

Source: United Nations, **_World_** _Investment Report 1992: Transnational Corporations as Engines of Growth_ (New York: United Nations, 1992), Annex Table 1, pp. 312-316.

macroeconomic instability. One cannot escape the iih.pression that Korea, under the impact of abundant external capital, might lose its competitive exchange rate, overborrow, and ultimately become once again a problem debtor. Korea's investment rate is more than 30 percent of GNP. There is little to suggest that capital imports are necessary because capital is in short supply.

The question which I now address is whether FDI flows to developing countries have increased
risk neutral investors with different exogenous holding periods and limited capital trade securities subject to fixed transaction costs. Amihud and Mendelson show that transaction costs result in a clientele effect, with investors who have longer holding periods selecting to hold illiquid stocks in equilibrium. Amortized transaction costs of investors in each _liquidity clientele_ group determine the liquidity premia for illiquid securities.

The static model with exogenous holding periods has been extended to incorporate dynamic decisions of investors. In models where the holding period decision is determined endogenously (Constantinides 1986, Vayanos 1998, Vayanos and Vila 1999, Heaton and Lucas 1996), the resulting liquidity premium is much lower. In these models, the marginal utility from trading is low and investors respond to transaction costs by turning over their portfolio less frequently. These models predict a liquidity premium on asset prices that is a magnitude smaller than transaction costs, but they also predict unrealistically low levels of trading activity and volume. In models where investors are forced to trade frequently (Huang 2003, Lynch and Tan 2007, Lo, Mamaysky and Wang 2006) the resulting liquidity premium can be large.

In all these models, the magnitude of the relationship between holding periods and transaction costs determines the liquidity premium in the market.<sup>5</sup> Using individual trade data, I test for the relationship between holding periods and transaction costs after controlling for a number of investor and stock characteristics. I also analyze the magnitude of the impact of transaction costs on holding periods, and compare the results to calibrated values in the models of Vayanos (1998), Constantinides (1986) and Lo, Mamaysky and Wang (2005). The first hypothesis is thus:

**H1a:** _Holding periods are positively related to measures of fixed transaction costs after controlling for investor and stock characteristics._

Previous studies have shown that, on average, households‟ stock investments perform poorly. Odean (1999), for instance, reports that individual investors‟ purchases under-

> 5 Although in this study I only focus on a subset of investors in the market, namely retail investors, a number of papers have shown that correlated trading by retail investors impact returns (Kumar and Lee 2006, Barber, Odean and Zhu 2006, and Hvidkjaer 2008).

5
- Surkan P., A. Ettinger, S. Ahmed, C. Minkovitz, D. Strobino. 2012. Impact of maternal depressive symptoms on growth of preschool- and school-aged children. Pediatrics, 130: e847–e855.

- Surkan P., A. Ettinger, S. Ahmed, R. Hock, Saifuddin Ahmed, D. Strobino, C. Minkovitz. 2014. Early maternal depressive symptoms and child growth trajectories: a longitudinal analysis of a nationally representative US birth cohort. BMC Pediatrics, 14:185.

- Taylor, C. A., Guterman, N. B., Lee, S. J., and Rathouz, P. J. (2009). Intimate partner violence, maternal stress, nativity, and risk for maternal maltreatment of young children. American journal of public health, 99(1), 175-183.

- Teti, D. 1999. Maternal Depression/Mental Health and Home Visiting. Presentation at the Workshop on Revisiting Home Visiting, sponsored by the Board on Children, Youth, and Families of the National Research Council and Institute of Medicine. March 8-9, 1999, Georgetown University Conference Center, Washington, DC

Tibshirani, R., 1996. Least Absolute Shrinkage and Selection Operator. Software.

Thomas, Duncan, Victor Lavy, and John Strauss 1996. “Public Policy and Anthropometric Outcomes in the Cote D’Ivoire.” Journal of Public Economics, 61(2): 155-92.

Victora, C.G., Mercedes de Onis, Pedro Curi Hallal, Monika Blössner, and Roger Shrimpton. 2010. “Worldwide Timing of Growth Faltering: Revisiting Implications for Interventions.” Pediatrics, 125(3):473-480.

World Health Organization (WHO). 2020. Maternal and Child Mental Health Topic. Available at - <u>https://www.who.int/mental_health/maternal child/maternal_mental_health/en/</u> Wolf, A, I. De Andraca, and B. Lozoff. 2002. Maternal depression in three Latin American Samples. _Social Psychiatry and Psychiatric Epidemiology,_ 37, 169-176.

25
Gregorio Impavido

> associated with a larger share of asset portfolios in stocks, but the presence of plan members' representatives has little significant impact on any of the investment strategies.

**Table 4. The most important single determinants of ke investment strategies**

||**nvestmientaftategy**|**M**<br>**bst'importanitgovemiancedetenninantvt-***|
|---|---|
|<br>Allocationoffundsintoequities<br>Placementoffundsinequity indices|<br>Independentperformanceevaluation<br>Boardresponsibleforinvestments|
|<br>Long-terminvesting<br>Tacticalinvesting<br>Extemalmanagementportfolio<br>Atleastsomeintemationalinvesting<br>Investoractivism<br>Sociallimiting<br>Economictargeting|<br>Boardresponsibleforsettingassetallocations<br>Govemorselectedbyplanparticipantsandinvestmentrestrictions<br>Numberofgovemors<br>Independentperformanceevaluation<br>Govemors electedbyplan participants<br>None<br>None|
|Source:UseemandHess(2001).||

Useem and Hess (2001) obtain similar results using surveys of U.S. state and **local** retirement systems conducted during the 1990s. They identify seven determinants of govemance that are consistently associated with seven key investment strategies, as shown in Table 4.

**Table 5. Impact of governance on public pension fund performance**

||**Depende**<br>**Rateofret**|**ntvarlable:**i<br>**urnonassrn**|
|---|---|---|
|Explanatoryvariable|**(1)**|**(2),**|
|**GovernancePolicies**|||
|Investmentrestrictions|-0.23(0.59)|-0.16(0.59)|
|Independent performanceevaluation|0.81(0.67)|0.43(0.72)|
|**Boardpurview**|||
|Boardsetsassetallocations|-0.72(0.71)|-0.48(0.76)|
|**Board**isresponsibleforinvestments|0.18(0.51)|0.43(0.55)|
|**Boardcomposition/size**|||
|Numberofgovemors|-0.08(0.06)|-0.15(0.07)*|
|<br>Shareofplanparticipants|-1.16(10.6)|-1.16(1.07)|
|**InvestmentStrategies**|||
|Tactical investing ofassets|1.69(0.65)*|1.25 (0.69)|
|Equitiesasapercentageoftotalassets|0.07(0.02)*|0.07 (0.22)*|
|Extemalmanagementofallassets|0.58(0.58)|0.71(0.58)|
|<br>Intemationalinvestmentofsomeassets|2.17(0.52)*|1.87(0.56)*|
|OtherControls|||
|Investmentriska||0.05(0.09)|
|Systemassets(In)||0.41(0.30)|
|MultipleR<sup>2</sup>|0.38*|0.41**|

Note: ***p** < 0.05, ** p < 0.01. Regression is based on 104 retirement systems; linear regression is for return on assets. Standard errors are in parentheses. **a.** Standard deviation of annual return on assets for 1988-92. Source Useem and Mitchell (2000).

Although governance indicators have a direct impact on investment strategies, Useem and Mitchell (2000) do not find a direct impact on performance, measured as the annual rate of return on assets one year later and shown in Table 5. Only investment strategy variables are found to explain a relatively large variability of fund performance, and these

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the HFPS, the occupation variables had not been codified before. The responses to the occupation questions are open, and hence difficult to assign to a few categories.

We find 9,223 different answers in all the occupation variables included in the Latin American HFPS. In this study we make an effort to codify these answers in a few categories. To that aim we map the open individual responses to the occupation questions in the HFPS to the 41 groups in the 2-digit International Standard Classification of Occupations version 08 (ISCO-08). The ISCO categorization, proposed by the International Labour Organization (ILO), is currently adopted by most countries in the world, including many in Latin America, to classify the data on occupations collected in surveys, census and administrative records. We select the 2-digit grouping since it provides enough heterogeneity for the analysis, and at the same time avoids problems of lack of observations (Vosters 2018).

The codifying of the occupation variables in the HFPS implies a painstaking process of assigning each answer to an ISCO group, following the guidelines of the ISCO codebook, which includes specific information of the names, characteristics and activities of each occupation. In particular, we use the Spanish version of the codebook, consistent with the official language of the 13 Latin American countries included in the analysis. We were able to match 98 percent of the answers to the HFPS surveys to an ISCO group.<sup>6</sup> The orange bars in Figure 1 show the share of respondents in each of the ten occupation groups in the 1-digit ISCO classification before the start of the pandemic. On average, for all countries, the group with the largest share is Services and Sales Workers (21 percent) followed by Professionals (19 percent), Elementary Workers (14 percent), and Craft and Related Trades Workers (12 percent). There are some groups with a share between 6 and 9 percent (Technicians and Associate Professionals; Clerical Support Workers; Skilled Agricultural, Forestry and Fishery Workers; and Plant and Machine Operators and Assemblers). Finally, the number of observations is small for Managers (3 percent) and negligible for Armed Forces Occupations (0.27 percent).

# **3.3 Comparison with National Household Surveys**

In this section we compare the structure of occupations in the HFPS before the pandemic with the one constructed with microdata drawn from national household surveys (NHS). To that aim we take advantage of the Socioeconomic Database for Latin America and the Caribbean (SEDLAC), a large project of harmonization of national household surveys that has been carried out by CEDLAS-UNLP and The World Bank since 2002. In particular, we use the NHS of the 13 Latin American countries included in the HFPS project for circa 2019.

The occupation variables are already codified in the household surveys by the national statistical offices although, unfortunately, the codification is not uniform across countries. Latin American countries use different systems of occupation codes: they use different

> 6The codes that carry out the matching are available upon request.

7