rafmacalaba/datause-encoder-data huggingface dataset:
However, one can see that having prior experience with a product is largely decreasing the odds of experiencing termination (versus continuation, as expected from Table 2), but it is decreasing the odds of upgrading even more (both are statistically significant). As explained above, this kind of effect may or may not be beneficial: firms which have exported the product in the past will continue exporting it but stand less of a chance of starting to export something superior.

Overall, this exploratory setup of a multiple-exit, competing risks model seems justified by the results. The impact of the variables of interest identified in the main analysis becomes more nuanced when looking at heterogeneity among outcomes (i.e. not all deaths are the same).

# 5.4 **Robustness Checks**

The results presented in section 5.1 have been tested for robustness by running the full model from Table 2 with slightly altered specifications. The results of these robustness checks are presented in Table 6 alongside the reference (i.e. specification (3) from the main analysis). The alterations pertain either to a specific assumption made in the analysis or to a particular sub-sample. The model used is the random effects logit since this was judged as the most appropriate one.

**Table 6 – Robustness checks on the main specification with five different setups**

|||RE logit<br>Vl|N||Dth|Dth|
|---|---|---|---|---|---|---|
||Main|aue<br>>$1000|o<br>Thailand|Textiles|ea<br>6 mths|ea<br>18 mths|
|Firm sold prod TH first|0.79**|0.86|0.66**|0.80*|1.01|0.77***|
|No prior importing (ref)|1|1|1|1|1|1|
|Prior imports other dest.|0.77**|0.75**|0.76**|0.66**|0.92|0.84**|
|Prior imports curr. dest.|0.91*|0.78*|0.49***|0.66**|0.92|0.85*|
|Prior experience w. prod|0.38***|0.39***|0.52***|0.36***|0.58***|0.54***|
|Prior experience w. dest.|0.59***|0.60***|0.64***|0.62***|0.86**|0.73***|
|Familiarity with prod|0.91***|0.91***|0.94***|0.90***|0.96***|0.94***|
|Familiarity with dest.|0.98***|0.98***|0.95***|0.97***|0.99***|0.99**|
|Peers|0.89***|0.83***|0.86***|0.92**|0.98***|0.91***|
|N|11706|10981|6262|7027|12462|10710|

_* p<0.10 ** p<0.05 *** p<0.01. Source: Own calculations based on Lao PDR customs data. Exponentiated coefficients. The time dependence of the hazard is introduced as log(time) in all models. All models include the gravity variables, RCA and initial value, but are omitted for clarity._ The first alteration excludes from the sample all monthly transactions which are below USD1000, to ensure that they are indeed commercial transactions and not gifts, samples or personal items. Next is an exclusion of all exports to Thailand (a significant share in the dataset) to isolate the impact of this very large, developed and culturally similar trade partner, where different effects could be at play that are not captured in 27
|**Appendix table 2: S**|**ale and lea**|**se transact**<br>|**ion with bu**<br>|**yer inform**<br>|**ation**||||||
|---|---|---|---|---|---|---|---|---|---|---|
||||**Individuals**||||**Le**|**gal entities**|||
||**Total**|**2021**|**2022**|**2023**|**2024**|**Total**|<br>**2021**|<br>**2022**|<br>**2023**|**2024**|
|**Panel A: Leases**|||||||||||
|No. of transactions|112,940|38,862|18,782|30,580|24,716|1,861,379|715,755|359,149|521,526|264,949|
|Area transacted|365,261|131,994|54,378|97,514|81,376|3,974,276|1,560,635|708,784|1,112,153|592,704|
|Price median|51.5|49.7|53.2|52.7|51.6|91.9|92.7|87.9|94.9|90.3|
|Price mean|86.0|84.3|94.4|85.4|82.7|119.3|117.3|129.9|116.2|116.8|
|# unique tenants|18,761|8,815|4,215|6,734|5,455|25,187|16,506|12,837|14,972|13,019|
|Mean area/tenant|19.5|14.97|12.90|14.48|14.92|157.79|94.55|55.21|74.28|45.53|
|Median area/tenant|7.20|6.99|7.01|7.09|7.93|19.38|24.52|17.89|20.60|15.22|
|Max area/tenant|694.7|694.7|278.1|377.9|303.9|35,106|35,106|14,605|13,952|5,905|
|Share w. data||||||0.73|0.65|0.74|0.77|0.78|
|Reg. area tenant||||||1,031|1,072|1,107|946|1,013|
|Cul. area/tenant||||||895|933|988|803|874|
|**Panel B: Sales**|||||||||||
|No. of transactions|369,267|89,204|74,003|119,331|86,729|20,077|870|822|384|18,001|
|Area transacted|853,455|236,896|165,845|258,639|192,075|56,637|1,511|1,516|1,069|52,540|
|Price median|969|1,133|990|935|920|1,249|1,134|779|2,074|1,259|
|Price mean|1,650|2,320|1,458|1,524|1,515|1,777|3,742|2,142|6,336|1,651|
|# unique buyers|100,085|25,904|26,477|41,268|30,597|1,532|141|98|126|1,275|
|Mean area/buyer|8.5|9.15|6.26|6.27|6.28|37.0|10.72|15.47|8.48|41.21|
|Median area/buyer|1.48|2.00|1.78|1.00|1.00|6.00|2.00|2.35|1.01|7.42|
|Max area/buyer|3,195|286|179|285|3,195|2,233|154|570|521|2,233|
|Share w. data||||||0.59|0.33|0.37|0.32|0.67|
|Reg. area buyer||||||2,102|2,480|1,398|1,410|2,144|
|Cul. area/buyer||||||1,850|2,103|1,228|1,328|1,887|
|_Source:_Own comput<br>_Note:_Figures on area<br>entities and 18,761 in<br>unique buyers (1,532<br>individuals).|ation based<br>are in hect<br>dividuals)<br>legal entit|on parcel-le<br>ares throug<br>who, on ave<br>ies and 100|vel statistic<br>hout. Over t<br>rage, rented<br>,085 indivi|s from Ukra<br>he four-year<br>98.7 ha (15<br>duals) who|ine registry<br>period, the<br>7.8 ha for l<br>bought on|of rights on p<br>re were a tota<br>egal entities a<br>average 9 ha|roperty.<br>l of 43,948 u<br>nd 19.5 ha fo<br>(37 ha for le|nique tenant<br>r individual<br>gal entities|s (25,187 leg<br>s) and 101,61<br>and 8.5 ha fo|al<br>7<br>r|

30
# **7. Conclusion**

In this paper, we first extended the basic Todaro-type model of rural-urban migration to the case of migration from rural areas to two potential destinations, secondary town and big city, and derived conditions under which a poverty gradient from rural to town to city will exist as an equilibrium phenomenon. We then addressed the motivating question: Should public investment be targeted to big cities or to small towns, if the objective is to minimize national poverty?

We have shown that the answer to this question is not self-evident and depends on the migration response (which in turn depends on the migration cost and the difference in the informal-formal wage gaps between the town and the city), where the poverty line lies relative to incomes in the three locations, and at times also the poverty index. We developed sufficient statistics for the policy decisions based on these income parameters and illustrated the empirical relevance of this model with an example of a long running panel from rural Kagera, Tanzania, which represents a natural migration equilibrium, and thus an ideal platform to give empirical content to the model and explore its relevance in practice. Further, we showed that the structure of the sufficient statistics is maintained in the case where the model is generalized to introduce heterogeneous workers and jobs.

Together these results show that the common consideration of urban areas in the aggregate foregoes important dimensions of these urban areas, which can have an important bearing on the poverty outcomes of urban investment. More analysis of the distributional effects of urban investment, which explicitly incorporates such disaggregation, is called for. This paper has taken one step in that direction.

20
# **References**

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- Aggarwal, S., D. Jeong, N. Kumar, D.S. Park, J. Robinson, A. Spearot, 2020. Did COVID-19 market disruptions disrupt food security? Evidence from households in rural Liberia and Malawi.

- Alderman, H. Chiappori, P. A., Haddad, L., Hoddinott, J. and Kanbur, R. 1995. Unitary versus collective models of the household: Is it time to shift the burden of proof? _World Bank Research Observer_ , 10, pp. 1–19.

- Amare, M., K.A. Abay, L. Tiberti, and J. Chamberlin. 2020. Impacts of COVID-19 on food security: Panel data evidence from Nigeria: Intl Food Policy Res Inst.

- Amjath-Babu, T. S., Timothy J. Krupnik, Shakuntala H. Thilsted, and Andrew J. McDonald. 2020. Key indicators for monitoring food system disruptions caused by the COVID-19 pandemic: Insights from Bangladesh towards effective response. _Food Security_ : 1-8.

- Arndt, C., Davies, R., Gabriel, S.,  Harris, L., Makrelov, K., and Robinson, S., Anderson, L. 2020. Covid-19 lockdowns, income distribution, and food security: An analysis for South Africa. _Global Food Security_ . https://doi.org/10.1016/j.gfs.2020.100410.

- Banerjee, A., M. Faye, A.Krueger, P. Niehaus,T. Suri, 2020. Effects of a universal basic income during the pandemic. Working Paper.

- Barrett, C. B. 2020. Actions now can curb food systems fallout from COVID-19. _Nature Food_ , 1– 2.

- Berhane, G., D. O. Gilligan, J. Hoddinott, N. Kumar, and A. S. Taffesse. 2014. Can Social Protection Work in Africa? The impact of Ethiopia’s Productive Safety Net Programme. _Economic Development and Cultural Change_ 63 (1):1-26.

- <mark>Berhane, G., Golan, J., Hirvonen, K., Hoddinott, J., Kim, S., Taffesse, A.S., Abay, K. Assefa, T.W., Habte, Y., Abay, M.H., Koru, B., Tadesse, F., Tesfaye, H., Wolle, A., and F. Yimer, 2020. Evaluation of the nutrition-sensitive features of the fourth phase of Ethiopia's Productive Safety Net Program. ESSP Working Paper 140. Washington, DC: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/p15738coll2.133685</mark>

19
- 11 -

Romania's lower level of GDP per capita, it is expected that comparatively lower levels of investment would be forthcoming. Yugoslavia's housing investment has, similarly, exceeded considerably the norm for countries with similar income levels. Thus on average Poland appears to have been investing relatively less than both countries with similar income levels and countries that have had similar housing deficits in the recent past.

# B. **Is There Labor Market Disequilibrium?**

We argue that labor markets are distorted if there is evidence that (1) internal migration is less than should be expected, and (2) wage rates are distorted. In this section, we examine evidence on Polish internal migration and wage rates relative to those of other countries, and find some support for the existence of labor market disequilibrium. The next section examines the evidence more closely based on econometric models of both migration and wage rates.

# Is The Flow of Labor Affected?

Poland's labor market, like that in many centrally-planned economies, has often shown signs of excess demand. The Polish labor force grew more slowly than population in the 1970s and 1980s. The World Bank (1987) analysis of the labor market argues that labor turnover rates of around 20 percent in the 1980s have been relatively low given significant labor shortages, and suggests that the low rate of turnover may be due to housing shortages that limit labor mobility and to modest wage differentiation between most industrial sectors which results in little incentive to change jobs.

Limited labor mobility in Poland has not been the result of explicit policies limiting migration. Administrative controls on migration to large cities were put into effect in 1954 and rescinded as late as 1984 in Warsaw, but analysts argue the controls were unenforceable and ineffective in stemming migration (Ciechocinska, 1987). A policy of both active and passive decentralization of industrial establishments from large cities to towns and rural areas, attempted during the mid-1950s until 1970, was also ineffective, as many workers declined to move to their establishments' new locations because of insufficient housing in the new locations, among other reasons (Ciechocinska, 1987).

Policies labeled "lagged urbanization" (Jedraszko, 1983) or "economizing on urbanization" (Ofer, 1977) have provided the government of Poland with the instruments of an implicit migration policy. Ofer (1977) argues that socialist countries restrict urban investments in order to maximize investment resources allocated to industry. The planners thus restrict the levels of urban infrastructure and services
- [11] Crucini, M. J. and G. D. Hess. 2000. “International and Intranational Risk Sharing,” in Intranational Macroeconomics, Gregory D. Hess and Eric van Wincoop, editors, Cambridge, U.K.: Cambridge University Press, 2000, 37-59.

- [12] Davis, S. 2014. “Financial Integration and International Business Cycle Co-movement.”, Journal of Monetary Economics, 64, 99-111.

- [13] Deaton, A. 1987. “Life-Cycle Models of Consumption: Is the Evidence Consistent with the Theory?”In Advances in Econometrics: Fifth World Congress, Vol. 2. Edited by Truman F. Bewley. New York: Cambridge University Press, 1987.

- [14] Fratzscher M. and J. Imbs J. 2009 “Risk sharing, finance and institutions in international portfolio.” J Financial Econ 94:428—447

- [15] Galí, J., Lopez-Salido, J. D. and J. Valles, 2007 “Understanding the Effects of Government Spending on Consumption” Journal of the European Economic Association 5, 227-250.

- [16] Gao, X., Hnatkovska, V., and V. Marmer. 2014. "Limited Participation in International Business Cycles Models: A Formal Evaluation." Journal of Economic Dynamics and Control 39, 255—272.

- [17] Hadri, K. 2000. “Testing for stationarity in heterogeneous panel data.” Econometrics Journal 3: 148-161

- [18] Im, K. S., M. H. Pesaran, and Y. Shin. 2003. “Testing for unit roots in heterogeneous panels.”, Journal of Econometrics 115: 53-74.

- [19] Jappelli, T., and L. Pistaferri. 2010, The Consumption Response to Income Changes, Annual Review of Economics 2(1), 479—506.

- [20] Kollmann, R. 2012. “Limited asset market participation and the consumption-real exchange rate anomaly”, Canadian Journal of Economics, 45 (May (2)) (2012), pp. 566—584

- [21] Kollmann, R. 2015. "Exchange Rates Dynamics with Long-Run Risk and Recursive Preferences." Open Economics Review, 26: 175-196.

- [22] Kose, M. A., E. S. Prasad, and M. E. Terrones. 2009. "Does Financial globalization promote risk sharing?," Journal of Development Economics, Elsevier, vol. 89(2), pages 258-270, July.

- [23] Lane, P. R. and G. M. Milesi-Ferretti. 2007. "The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970—2004", Journal of International Economics 73, November, 223-250.

- [24] Levin, A., C.-F. Lin, and C.-S. J. Chu. 2002. “Unit root tests in panel data: Asymptotic and finite-sample properties.”, Journal of Econometrics 108: 1-24.

- [25] Lewis, K. K., 1996. “What Can Explain the Apparent Lack of International Consumption Risk Sharing?” Journal of Political Economy, Vol. 104, No. 2, pp. 267—297.

- [26] Lewis, K. K. 1997. “Are Countries with Offi cial International Capital Restrictions Liquidity Constrained?”, European Economic Review, 41, 1079 - 1109.

- [27] Mace, B. “Full Insurance in the Presence of Aggregate Uncertainty.” Journal of Political Economy 1991, 928-956.

14
<!-- Start of picture text --> 100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Asia Latin America Sub-Saharan Average Asia Latin America Sub-Saharan Average<br>Africa Africa<br>Rural Urban Male Female Poor Non-poor Educated Non-educated<br><!-- End of picture text --> _Note:_ Individuals who have completed at least primary education are grouped as “educated”; individuals who are in the bottom 40 percent of the national income distribution are classified as “poor.” Individual weights are applied in the calculation.

The demographic profiles of mobile internet users and nonusers are notably different (figure 2). Across the countries covered in the sample, 54 percent of the nonusers are female, and 68 percent of them are living in rural areas, while the majority of mobile internet users are male and urban residents. The average age of mobile internet users is 29.6 while that of the nonusers is 37.1, indicating that the elderly lag behind in adopting new technologies. Mobile internet users tend to be more educated and wealthier than nonusers. The average number of years of formal education (12.0 years) of mobile internet users is almost twice that of the nonusers (6.1 years). Furthermore, 92 percent of mobile internet users have at least completed primary school, while more than half of the nonusers have either no formal education or have not completed their primary education. The monthly income of mobile internet users is $303.4, more than double that of nonusers at $123.6.

# **Figure 2. Demographic profiles of mobile internet users and nonusers**

<!-- Start of picture text --> 100% 40.0 350.0<br>90% 35.0 300.0<br>39%<br>80% Male46% rural 30.0 250.0<br>70% 65% 68% 25.0<br>Male rural 200.0<br>60%<br>20.0<br>50% 150.0<br>15.0<br>40% 100.0<br>10.0<br>61%<br>30% 54% urban 5.0 50.0<br>female<br>20% 35% 32%<br>0.0 0.0<br>female urban<br>10% mobile internet user non-mobile internet user<br>0%<br>years of formal educaiton age<br>mobile non-mobile mobile non-mobile<br>internet userinternet user internet userinternet user monthly income (usd)<br>usd<br><!-- End of picture text --> _Note:_ Individual weights are applied in the calculation.

6
typical smaller changes in our governance estimates, there is relatively more disagreement across individual sources about the direction of the change than there is for large changes. Nevertheless, even for these smaller changes, typically the majority of underlying individual sources agree about the direction of the change. These examples underscore the importance of carefully examining the factors underlying changes in the aggregate governance indicators in particular countries. In order to facilitate this, on the WGI website users can retrieve the data from the individual indicators underlying our aggregate indicators and use this to examine trends in the underlying data as well as changes over time in the composition of data sources on which the estimates are based.

# **3.3  Trends in Global Averages of Governance**

We conclude by reviewing the available evidence on trends in global averages of governance. As we have already noted, the aggregate WGI measures are not informative about trends in global averages because we assume that world averages of governance are zero in each period, as a convenient choice of units. While the aggregate indicators are of course informative about the relative performance of individual (or groups of) countries over time, in order to assess trends in global averages of governance we need to return to our underlying individual data sources.

In Table 7 we summarize trends in world averages in a number of our individual data sources. Most of the sources in this table are polls of experts, with data extending over the period 1998-2008. Only one of them, GCS, is a survey with sufficiently standard format to enable comparisons of global averages for a large set of countries over a reasonable period of time, in this case from 2003 to 2008 (prior to 2003 country coverage for GCS was substantially lower). The first column reports the number of countries covered by the source in each of the periods shown, and the next three columns present the average across all countries of each of the sources for 1998, 2003, and 2008. The underlying data have been rescaled to run from zero to one, and for each source and governance component, we report the score on the same question or average of questions that we use in the aggregate indicator. The next three columns report the standard deviation across countries for each source. The remaining columns 22
significantly;" income changes also enter positively, though insignificantly; and, inflation enters negatively and significantly. On the basis of the regression results in Tables 3 and 4, one would conclude that explicit deposit insurance has typically been _nlegatively_ correlated with financial development. The next section analyzes the decision to create an explicit program in order to explain this negative partial correlation.

**Table 4: Explicit Deposit Insurance and Changes in Financial Depth**

|**Explanatory**<br>**Variable**|**Dep Var: Change**<br>**to G**|**in the Ratio of M2**<br>**DP**|**Dep Var: Chan**<br>**Quasi-M**|**ge in the Ratio of**<br>**oney to GDP**|
|---|---|---|---|---|
||**(1)**|(2)|(3)|(4)|
|Constant|2.268|1.800|2.259|2.137|
||(3.15)|(2.01)|(3.27)|(2.48)|
|Per Capita Inc|0.247|0.276|0.199|0.245|
||(2.29)|(2.21)|(1.93)|(2.04)|
|Explicit Dep Ins|-2.980|-2.867|-1.581|-1.738|
||(3.58)|(2.85)|(1.98)|(1.80)|
|Avg Inflation|-0.084|-0.080|-0.065|-0.065|
||(5.71)|(4.70)|(4.60)|(3.99)|
|% Change, Real||0.276||0.134|
|Per Capita GDP||(1.39)||(0.91)|
|Adj. R-Squared|.52|.46|.37|.34|
|Observations|43|40|43|40|

Notes: t-statistics in parentheses. Per Capita Income measured in thousands of 1985 $US (Source: Summers-Heston). Inflation measured as average 3-year percentage change in consumer price index. For countries with explicit programs, inflation wvas averaged over the three-year period just after inception. For those with implicit insurance, inflation was averaged over all 3-year periods from 1980-95 for which data were available. Countries with inflation rates higher than 80% were excluded from these calculations.

# **III. The Decision to Create an Explicit Deposit Insurance Scheme**

From a methodological perspective, isolating the link between explicit deposit insurance and financial depth is difficult because the decision to create an explicit scheme likely depends on many factors that also drive future financial depth. Accounting for the This is little surprise given the summary statistics in Table 2. Again, however, the theoretical link between income levels and changes in financial depth is not well established.

10