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

- <mark>Altonji, J. G., and R. K. Mansfield. 2018. Estimating Group Effects Using Averages of Observables to Control for Sorting on Unobservables: School and Neighborhood Effects.</mark> _<mark>American Economic Review</mark>_ <mark>, 108(10):2902–2946.</mark>

- <mark>Altonji, J. G., and S. D. Zimmerman. 2017. “The Costs of and Net Returns to College Major.” (No. w23029). National Bureau of Economic Research.</mark>

<mark>Altonji, J. G., P. Arcidiacono, and A. Maurel. 2016. “The Analysis of Field Choice in College and Graduate School: Determinants and Wage Effects.” In</mark> _<mark>Handbook of the Economics of Education</mark>_ <mark>5:305–96. Elsevier.</mark>

- <mark>Altonji, J. G., E. Blom, and C. Meghir. 2012. “Heterogeneity in Human Capital Investments: High School Curriculum, College Major, and Careers.”</mark> _<mark>Annual Review of Economics</mark>_ <mark>4(1):185–223.</mark>

- <mark>Andrews, R. J., S. A. Imberman, and M. F. Lovenheim. 2017. “Risky Business? The Effect of Majoring in Business on Earnings and Educational Attainment.” (No. w23575). National Bureau of Economic Research.</mark>

- <mark>Andrews, R. J., S.A. Imberman, M. F. Lovenheim, and K. M. Stange. 2022. “The Returns to College Major Choice: Average and Distributional Effects, Career Trajectories, and Earnings Variability” (No. w30331). National Bureau of Economic Research.</mark>

- Bahr, P. R. 2016. “The Earnings of Community College Graduates in California.” (CAPSEE Working Paper). New York, NY: Center for Analysis of Postsecondary Education and Formal Employment.

- Bailey, T., Bashford, J., Boatman, A., Squires, J., Weiss, M., Doyle, W., Valentine, J. C., LaSota, R., Polanin, J. R., Spinney, E., Wilson, W., Yeide, M., & Young, S. H. 2016. _Strategies for postsecondary students in developmental education – A practice guide for college and university administrators, advisors, and faculty_ . Washington, DC: Institute of Education Sciences, What Works Clearinghouse

- Barrera-Osorio, F., and H. Bayona-Rodriguez. 2019. “Signaling or Better Human Capital: The Causal Effect of Prestigious University Attendance on Educational Trajectory and Labor Market: Evidence from Colombia.” _Economics of Education Review_ 70:20–34.

- Belfield, C. R., and T. Bailey. 2017. “Model Specifications for Estimating Labor Market Returns to Associate’s Degrees: How Robust Are Fixed Effects Estimates?” (CAPSEE Working Paper). New York, NY: Center for Analysis of Postsecondary Education and Formal Employment.

- <mark>Belfield, C. R., and T. Bailey. 2011. “The Benefits of Attending Community College: A Review of the Evidence.”</mark> _<mark>Community College Review</mark>_ <mark>39(1):46–68.</mark>

- Bettinger, E., and A. Soliz. 2016. “Returns to Vocational Credentials: Evidence from Ohio's

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& Carry outlet in Bucharest. It took another two years before other large European retailers entered Romania. Only since year 2000, Romania has seen rapid expansion of foreign retailers, including Carrefour from France, REWE from Germany, and Cora from Belgium (see Table 2). In 1999, there were only 5 outlets of 3 global retail chains operating in Romania. From 1999 to 2001, the number of outlets increased fivefold. From 2001 to 2005, the number again tripled and reached a total of 86 outlets. The total selling space of global retail chains increased 10 times from 43,000 square meters in 1999 to 463,000 square meters in 2005 (see Table 3).

Following the trend observed in other transition economies, foreign chains have become dominant players in the Romanian retail sector in which there are few significant domestic players. In 1999, they employed around 1,400 workers, invested 44 million dollars in capital stock and generated 3.2% of total retail sales. In 2005, they had a total workforce of more than 18,900, a total capital stock of 844 million dollars and generated 3.27 billion dollars in sales, accounting for about 22.2% in total retail sales. (See Table 2) The expansion of global retail chains in Romania was not uniform across regions. The area around the capital city Bucharest, especially its outskirts, was the initial focus of their entry. The Western region, close to Hungary, also attracted a lot of entry in the initial period (see Table 4).<sup>7</sup> In 2005, the regional distribution of outlets was still uneven. There were 16 and 19 outlets in Bucharest and West, respectively, but only 4 outlets in Northeast and 3 in Southwest (see Figure 1).

The expansion strategy depended on the history and the nature of each chain’s activities. Cash & carry market has a longer history in Romania, starting with Metro’s entry in 1997, and it sells to private traders/stores as well as households. Such chains have expanded into large and medium-sized cities. The hypermarket format was first introduced into Romania in 2001 by Carrefour and is the largest of all formats targeting households. Hypermarkets, therefore, concentrate in cities with population more than 300,000.<sup>8</sup>

> 7 The regional classification is based on the community nomenclature of territorial units for statistics (NUTS) defined by Eurostat.

> 8 Hypermarkets are defined as retail outlets selling groceries and non-food merchandise with a retail sales area of over 2,500 square meters. They are frequently located in out-of-town sites or as the anchor store in a shopping center.

9
auditing requirements lower bank corruption and ease firms financing constraints. Beck, Demirguc-Kunt, and Maksimovic (2004) suggest that the probability of firms reporting finance as a major obstacle increases with the intensity of the activity restrictions.<sup>49</sup>

- **Supervision:** According to Cull, Demirguc-Kunt, and Morduch (2011), the cost of compliance with regulations in the form of adapting to regular on-site supervision leads micro-finance institutions to curtail their outreach so as to maintain financial self-sufficiency. As Beck, DemirgucKunt, and Levine (2011) demonstrate however, supervision in the form of private monitoring improves firms’ access to finance.

- **Deposit Insurance:** While Barth et al. (2009) find that the existence of deposit insurance schemes raise the probability that a firm will report bank corruption as a major obstacle to its growth, Allen et al. (2012) show that the probability of households owning and using a bank account increases with the coverage of the deposit insurance schemes.

# • **Institutions**

- **Creditor Rights and Credit Registries:** Institutions matter for increasing firms’ and households’ access to finance, as they lower the monitoring costs incurred by formal financial institutions. Galindo and Micco (2005) find that the higher the creditor rights, the easier the access of small firms to finance. Beck, Demirguc-Kunt, and Martinez Peria (2007) find that the higher the credit information index, the higher the penetration of bank branches across the country. Barth et al. (2009) similarly argue that the existence of private credit bureaus lowers corruption in lending but that public credit bureaus have no significant association. An important distinction emphasized by Love and Mylenko (2003) is that while public registries may be associated with financial depth, they are less likely to be associated with access to finance. In their empirical study, they confirm this hypothesis and find a negative association between the presence of private credit registries and financing constraints perceived by firms. Brown, Jappelli, and Pagano (2009) study a similar hypothesis using firmlevel panel data covering transition countries and present more robust results. They document positive and significant effects of information sharing on access to finance, but this effect is more pronounced for opaque firms and countries with weaker protection of creditor rights.

- **Bank Branch and ATM Penetration:** Allen et al. (2012) and Beck, Demirguc-Kunt, and Martinez Peria (2007) study the impact of distance barriers and pro-access policies on the probability of households owning a bank account and firm managers reporting finance as an obstacle to firm

> 49 Not only firms but also households are negatively affected by activity restrictions.

23
Based on both economic magnitudes and statistical significance: marrying off children appears to be the most effective strategy employed by households. This strategy soaks-up or dampens the negative impact of shocks in more folds than the other candidate significant strategies. This holds across both poverty and consumption expenditure outcomes. Engaging in migration and spiritual activities suggest opposite results in some cases. For instance, the effect of engaging in spiritual activities on the damaging effects of shocks on households’ absolute poverty status —a non-economic strategy– is significantly positive at the 10% level. This would imply more sensitivity of households to shock realizations following their dependence on spiritual activities. Cultural arrangements and social norms can potentially skew households to rather more ineffective strategies —as illustrated here via the channel of spiritual activities.

The results for migration are similar. Engaging in migration as a coping mechanism implies more sensitivity of households’ consumption and poverty to shock realizations. The result that migration of one or more members of the household as a strategy leads to worse household outcomes can be explained by theories of asymmetric information/behavior between migrants and their families, including others.<sup>12</sup> In a recent study, Joseph, Nyarko & Wang (2015) provided evidence that increase in migrant earnings is associated with decrease in remittances over time. This discrepancy between earnings and remittance becomes narrow in situations where the migrants have more co-workers from the same location who are likely to inform their families’ members. This is suggestive of asymmetric information driving remittances. Our knowledge about whether or not asymmetric information between migrants and their families leads to inefficient outcomes is crucial and remains an open research question (see e.g., Joseph, Nyarko & Wang 2015; Seshan & Zubrickas 2015 for related discussions). While we do not fully provide rigorous analyses about this, our results are suggestive: engaging in migration as a shock-coping strategy makes households’ consumption and poverty inclinations worse.

> 12 Such results may also reflect the nature of the labor market conditions (i.e., in terms of unfavorable wages and working conditions) at the destination of migrants who are mostly unskilled (see e.g., United Nations 2003 for related discussions).

19
charge of all land-related issues, i.e., to receive, validate and notarize transactions, disseminate information, and help implement land use plans at sector local level. This was expected to help decentralize land services. All SLMs received some basic training and were sworn so they could also notarize transactions. By the end of 2015, 367 of the 416 sectors in Rwanda had a SLM in place. Deploying so many staff at very short notice raised logistical and managerial challenges but also prompted a range of innovations. For example, RNRA developed a web-based interface for SLMs to directly initiate transactions in LAIS instead of relying on paper records that have to be transported to the DLO and can be altered more easily. SLMs are also equipped with GPS-enabled tablets so they can conduct subdivision surveys although clarification of legal provisions on this subject may be needed to allow these to be effectively deployed.

Table 4 provides descriptive statistics on roll-out of SLMs and land week events at cell level: coverage with SLMs increased from 18% in batch one (in the third quarter of 2014) to 73% and 82% in subsequent batches (2<sup>nd</sup> and 3<sup>rd</sup> quarter of 2015). Local land week events were conducted in a total of 1,076 cells (614 in land week I and 462 in land week II). We also note that the mean distance to the DLO ranges from some 7 km in Kigali to some 15 km in the East and that the urban population share, which ranges between 9% in the East and 19% in the West and South, increases to more than 200% in Kigali city.

# **3.3 Assessing the impacts of decentralization and awareness campaigns**

To see if measures to improve awareness and decentralize service delivery affected the volume of registered land transfers, we exploit quarterly data from 2014 and 2015 on the number of registered transactions for the country’s 2,148 cells. Locally weighted regressions of the number of registered transactions per quarter by distance to the DLO and year or presence of SLMs point towards a clear increase in volumes of registered sales but not non-monetary transfers over time (figure 3) and with the presence of a SLM (figure 4).

Cell-level quarterly data for 2014 and 2015 also allow us to explore determinants of registered transfers of agricultural or residential land. We denote cells by _c_ , quarters by _q_ and year by _t_ to estimate a regression of the form where _Tcqt_ is number of registered residential or agricultural land transfers (total, monetary or nonmonetary) for cell _c_ in quarter _q_ in year _t_ ; _Dc_ is the distance of cell _c_ from the DLO in km, _Scqt_ is an indicator variable for whether or not cell _c_ is located in a sector where a SLM had been appointed at _q_ in _t_ ; _Lcqt_ is a dummy indicating past coverage of the sector where cell _c_ is located by land week events at _q_ in _t_ ; _Rc_ is the number of registered agricultural or residential land parcels in _c_ ; _Uc_ is the share of urban population at sector level from 2012 census estimates; _Pt_ is a province-year dummy; _εcqt_ is a random error term, and _β_ s are coefficients to be estimated.

10
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# **8.  References**

Bris, A., W. Goetzmann, and N. Zhu (2003) “Short Sales in Global Perspective,” Mimeo, Yale School of Management.

Credit Lyonnais Securities Asia (CLSA) and Asian Corporate Governance Association (ACGA). Various years. “Corporate Governance Watch: Annual Survey of Corporate Governance in Asia.” De la Torre, A. and S. Schmukler (2005), “Whiter Latin American Capital Markets?” Mimeo, World Bank.

Djankov, S., R. La Porta, F. Lopez de Silanes, and A. Shleifer. 2005. “The Law and Economics of Self-Dealing” NBER Working Paper No. W11883.

Endo, T. and G. Rhee (2005) “Margin Purchases and Short Sales in Emerging Markets: Their Rationales and Design Variables,” Mimeo, The World Bank.

Fama, E.F. (1970) “Efficient capital markets: a review of theory and empirical work”, _Journal of Finance_ 25:383-417.

Fama, E.F. (1991) “Efficient capital markets II”, _Journal of Finance_ 46:1575-1617. Fratzscher, O. (2006) “Emerging Derivative Markets in Asia”, Mimeo, The World Bank. Hess, D. and G. Impavido. 2003. “Governance of Public Pension Funds: Lessons from Corporate Governance and International Evidence”, _World Bank Policy Research Working Paper_ , No. 3110.

Impavido, G. and D. Sourrouille (2006) “The role of pensions in capital market development in East Asia,” Mimeo, The World Bank.

La Porta, R., F. Lopez-de-Silanes, and A. Shleifer. 2006. “What Works in Securities Laws?” _Journal of Finance_ Vol. LXI, No. 1 pp. 1-32.• February 2006.

Lesmond, D. (2005) “Liquidity of emerging markets,” _Journal of Financial Economics_ 77:411-452.

Lesmond, D., C. Trzcinka, and J. Ogden. 1999. “A New Estimate of Transaction Costs,” _Review of Financial Studies_ 12 (5): 1113-1142.

Morck, R., B. Yeung, and W. Yu (2000), “The Information Content of Stock Markets: Why Do Emerging Markets Have Synchronous Stock Price Movements”, _Journal of Financial Economics_ , 58: 215-260.
**Table 3: Impacts of mental health on refugee partners’ and children’s outcomes**

|Dependent variable:|Partner<br>employment<br>status|Partner life<br>satisfaction|Child mental<br>health scores|Child school<br>performance<br>below average|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Standardized mental health<br>scores|-0.004|-1.018*|3.170*|0.016***|
||(0.005)|(0.570)|(1.756)|(0.005)|
|_First stage of 2SLS (depende_|_nt variable is stand_|_ardized mental heal_|_th scores)_||
|Trauma experience*Year<br>after arrival|-0.023***<br>(0.006)|-0.228*<br>(0.130)|||
|Trauma experience|||0.537***<br>(0.183)|0.415***<br>(0.133)|
|Kleibergen-Paap F stat.|35.644|28.205|8.766|9.710|
|AR 95-CIs|[-0.014, 0.004]|[-2.406, -0.465]|[-0.582, 8.685]|[0.001, 0.476]|
|Dep. Mean|0.200|7.608|8.954|0.061|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|5,297|1,663|351|587|
|Numberof individuals|1,384|986|351|587|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Columns (1) and (2) show results of IV-Logit-FE (marginal effects) and IV-FE models, columns (3) and (4) show results of IV model using data from wave 3 of the BNLA. Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one.

29
# C. REGIONAL COOPERNTION'

Regional cooperation started on a modest scale and developed into a set of "empires" with more than 300 enterprises around the country serving kibbutzim, moshavim, and private farmers. The regionals have played a major role in the development and growth of agriculture in the last 20 years, but they were also among the main causes of the current crisis.

# 1. Secondary Cooperation

In terms of a modern economy, the moshav is a small unit. To increase scale of operation in farm services, the moshavim establishrl secondary regional cooperative ventures. These include regional supply ("purchasing") cooperatives and a variety of service establishments such as fruit and vegetable sorting and packing operations, storage facil.ities, poultry meat processing and packing plants, machinery maintenance centers, and feedstuff elevators. Some ventures with litt:Le or no connection to agriculture were also established to increase employment and-income in the regions.

-The regionals, both the supply cooperatives and the service enterprises, are associations of associations owned and operated by participating moshavim; individual farmers are not directly members in these second order cooperatives. The regional supply cooperatives are almost always the supplier of inputs to the service enterprises, often they are also partners in the ownership of the regional ventures. Like the moshav associations, the supply cooperatives are also active in financial intermediation--channeling credit to the moshavim and to the service enterprises in their regions.

The mode of operation of the supply cooperative is also similar to that of the moshav association: it extends credit to the moshavim, mostly in conjunction with the supply of inputs; it relies on mutual liabilities; it requires all its members to channel the proceeds from the marketed farm product through its offices to facilitate monitoring "This section draws on Zusman (1988, Chapter K) and on Cohen (1984), Kadishay (1985), and Kislev, Marvid (1988).

12
11 weights providing the maximum discrimination between households in the sample, in terms of their ownership of these particular assets ( **_x_** ).<sup>13</sup> McKenzie (2005) lists a number of reasons why an asset index such as this might in fact be preferable to consumption or income as a basis for inequality measurement, including the likelihood that recall bias might be smaller for asset ownership questions than for some income or expenditure questions. But he also highlights two potential pitfalls in using asset indices, namely the possibilities of truncation and clumping. Whereas truncation would most likely arise from not observing assets capable of distinguishing either the very poor from those just above them, or the very rich from those just below them, clumping might be caused by using too few assets, leading to “false modes” in the distribution, arising from insufficient discriminating power in the index. Figure 1 plots the superimposed histogram and kernel density estimate for our main asset index, revealing the absence of both truncation and clumping.

The second method we propose to circumvent the missing data problem relies on a simple statistical procedure for combining information on circumstances from the TDHS with information on consumption from the HBS. Ultimately, since the link between the two surveys is provided largely by components of the asset index (and a few additional covariates), this second exercise can be seen as an alternative way of using information on assets to measure inequality of opportunity in Turkey. Our approach here closely follows McKenzie (2005) in imputing consumption from the HBS into the TDHS, using a bootstrap prediction method.<sup>14</sup> This procedure consists of combining a direct prediction based on a regression model, with a repeated draw of residuals comparable to a bootstrap. The relationship between wealth indicators _X_ and per capita consumption _c_ is estimated, on sample _S a_ (from the auxiliary HBS survey), using a log-linear regression model:

> 13 The TDHS data files contain a pre-constructed asset index, supposedly also given by (6). As the survey documentation does not describe the details of how that index is constructed, best research practice generally involves computing the index from the underlying data, as we have done here. The correlation coefficient between our main index and the TDHS index is 0.98, and the kernel density functions for both indices are very similar, although the kernel for our main index is considerably smoother. 14 This approach is a simplified version of the consumption imputation procedures proposed by Elbers, Lanjouw and Lanjouw (2003).
||**Extremepoor**|**Poor**|**Middle class**|**Richest**|
|---|---|---|---|---|
|**Total**|‐0.144**|‐0.089**|‐0.057**|0.074**|
|**_Endowments_**|0.066**|0.082***|0.145***|0.205***|
|Demographic Structure|0.014**|0.018***|0.021***|0.041***|
|Education|0.008**|0.002|0.009**|0.022***|
|Wage work|‐0.06**|‐0.037**|‐0.024**|‐0.026|
|HH business|‐0.062|‐0.047*|‐0.031|‐0.037|
|Assets|0.112***|0.095***|0.089***|0.056***|
|Land|‐0.001|‐0.001|‐0.002|0.001|
|Access local markets|‐0.024|‐0.012|0.023***|0.081**|
|**_Returns_**|‐0.209***|‐0.17***|‐0.203***|‐0.13***|
|Demographic Structure|‐0.03|0.03|‐0.147|‐0.196|
|Education|0.12**|‐0.072*|0.016|0.025|
|Wage work|‐0.039**|0.001|0.009|‐0.005|
|HH business|‐0.181**|0.071|0.105|0.019|
|Assets|0.064|0.078**|0.028|‐0.014|
|Land|0.007|‐0.001|0.006|‐0.034**|
|Access local markets<br>_Note:_ _Extreme poor_are popul<br>decile;_middle class_are in the f<br>_Source: NPS_2008/09 and 201|‐0.047<br>ation groups in the bo<br>ifth decile, and the_ric_<br>2/13.|‐0.028<br>ttom 10 percent<br>_hest a_re in the top|0.046**<br>of the distribution; th<br>decile.|‐0.042<br>e_poor_are in the third|

45