rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Descriptive Statistics: Commerce sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|2008|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computers|18.89|33.25|0.00|0.00|0.00|25.00|85.00|8,333|
|Share of labor that uses Internet|14.38|27.78|0.00|0.00|0.00|15.00|60.00|8,333|
|Stock of computer equipment/worker|9.99|36.62|0.11|1.37|4.06|9.35|19.60|8,333|
|**Employment and wages**|||||||||
|Number of white-collar workers|8.63|16.90|2.00|3.00|4.00|9.00|17.00|8,333|
|Number of blue-collar workers|24.02|32.85|7.00|10.00|16.00|25.00|45.00|8,333|
|Annual real wage white-collar workers|94.47|78.30|31.33|48.75|73.67|118.00|164.00|8,333|
|Annual real wage blue-collar workers|55.61|43.32|22.39|31.44|45.43|63.60|95.33|8,333|
|White/Blue-collar workers|0.32|0.25|0.09|0.15|0.25|0.42|0.67|8,333|
|White/Blue-collar wages|1.90|1.18|0.80|1.08|1.63|2.40|3.29|8,333|
|**Firm characteristics**|||||||||
|Capital/worker|149.30|375.16|6.31|22.78|67.18|160.86|329.33|8,333|
|Total employment|34.74|45.57|13.00|16.00|22.00|35.00|62.00|8,333|
|Firm age|21.35|14.25|8.00|12.00|18.00|26.00|38.00|8,333|
|Share of FDI|1.86|13.12|0.00|0.00|0.00|0.00|0.00|8,333|
|2013|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computers|44.68|42.08|0.00|0.00|30.00|95.00|100.00|8,333|
|Share of labor that uses Internet|42.30|41.54|0.00|0.00|30.00|90.00|100.00|8,333|
|Stock of computer equipment/worker|28.66|401.15|0.08|1.31|4.71|10.92|25.50|8,333|
|**Employment and wages**|||||||||
|Number of white-collar workers|9.35|19.70|2.00|3.00|5.00|9.00|18.00|8,333|
|Number of blue-collar workers|24.63|33.11|7.00|10.00|15.00|26.00|48.00|8,333|
|Annual real wage white-collar workers|113.52|298.11|31.50|47.98|75.83|118.29|165.44|8,333|
|Annual real wage blue-collar workers|53.59|57.03|21.71|28.13|39.71|61.69|92.53|8,333|
|White/Blue-collar workers|0.32|0.24|0.09|0.15|0.24|0.41|0.67|8,333|
|White/Blue-collar wages|2.10|1.35|0.85|1.18|1.75|2.60|3.92|8,333|
|**Firm characteristics**|||||||||
|Capital/worker|271.57|2139.37|2.83|25.45|79.27|200.13|429.00|8,333|
|Total employment|37.30|47.17|13.00|16.00|22.00|39.00|71.00|8,333|
|Firm age|19.70|13.24|7.00|10.00|17.00|26.00|36.00|8,333|
|Share of FDI|2.41|14.74|0.00|0.00|0.00|0.00|0.00|8,333|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 18
# **3.  Effect on the Durability of Private Capital**

Good public infrastructure may have a positive effect on growth by improving the durability of private capital. This has important implications for spending on maintenance and the quality of public capital<sup>11</sup> . Lack of public spending on infrastructure maintenance has been a recurrent problem in many developing countries. According to the World Bank (1994, p. 1), technical inefficiencies in roads, railways, power, and water in developing countries caused losses equivalent to a quarter of their annual investment in infrastructure in the early 1990s. Paved roads, in particular, deteriorate fast without regular maintenance. Insufficient maintenance of a railroad system will cause frequent breakdowns and lower its reliability, creating potentially severe losses for users. Thus, increasing maintenance spending, by reducing power losses, telephone faults, and so on, would help to enhance the productivity effects of public infrastructure on private production. For instance, in Vietnam, the World Bank (1999, p. 44) estimated that reducing a road’s roughness from 14 IRI (International Roughness Index) to 6 IRI would save between 12 and 22 percent in vehicle operating costs. A reduction from 14 IRI to 3 IRI would save from 17 to 33 percent in those costs. More dramatically perhaps, Gyamfi and Guillermo (1996, p. 5) estimated that for Latin America and the Caribbean, each dollar not spent on road maintenance leads to a $3.0 increase in vehicle operating costs as a result of poor road conditions. Thus, to the extent that public expenditure on maintenance affects the durability, as well as the quality, of private physical capital, it may have a sizable impact on growth.

A formal analysis of the impact of public infrastructure maintenance on private investment and growth is provided by Agénor (2005 _c_ ), who developed an endogenous growth framework in which maintenance expenditure not only increases the durability of public capital, as in Rioja (2003) and Kalaitzidakis and Kalyvitis (2004), but also raises the efficiency and durability of private physical capital. The key

> 11Hulten (1996) argued forcefully for paying more attention to the quality of infrastructure capital in the growth process; and Calderón and Servén (2004) found a link (albeit weak) between indicators of infrastructure quality and the rate of economic growth in a cross-country study. Appendix B discusses issues associated with the measurement of the quality of public infrastructure.

16
|Rank on|Author|%<br>publications|_I_<sup>3</sup> =0<br>Publication|Rank on_I_<sup>3</sup><br>_with_=0|_I_<sup>3</sup> =0.5|42<br>Rank on_I_<sup>3</sup><br>_with_=0.5|_I_<sup>3</sup> =1<br>Total|Rank on_I_<sup>3</sup><br>_with_=1|_h_-index (_I_<sup>1</sup>)|_I_<sup>2</sup><br>Quadratic|Rank on_I_<sup>2</sup>|Maximum<br>citations|Rank on<br>maximum|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|h-index||not found in<br>Google<br>Scholar|count||||citations|||influence<br>function|||citations|
|95|Frans F. H. Rutten|25%|24|137|97|147|825|191|13|0.373|180|199|544|

Note: The starting point was our database of 33,000 publications in EconLit with a health JEL code. Google Scholar citation data were obtained for as many as possible (80 percent) in June 2011. Each listed author was given full credit for a publication in the case of a co-authored publication. An _h_ -index of 10 means that the author has 10 publications to his or her name each of which has been cited at least 10 times. The quadratic influence function is defined in eqn (1). The measure _I_<sup>3</sup> is defined in eqn (2). All authors were included in calculating the figures however few publications in health economics they had to their name.
<!-- Start of picture text --> Cost Escalation in Percent<br>Upper histogram: 43 nonUK rail projects<br>Lower histogram: 3 UK rail projects<br>20 Benchmark UK<br>ito<br>=<br>2<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>5<br>0<br>UK | 1 |<br>T |<br>-60 -40 -20 [e) 20 40 60 80 100 120 140<br>Cost increase (%)<br><!-- End of picture text -->
inhabitants differently from provincial and district centers. Yet it adds to the uncertainty of the comparison.

It goes without saying that the reversal of internal net-migration from 1865-75 to 1975-80 must have seriously changed the employment situation, tending to increase unemployment in rural areas, relieving the sit'iation in urban areas. The remarkable increase, both absolutely and relatively, in rural unemployment from 1973 to 1985, as reported by the official labor force surveys may be related to the reversal of internal net-migration.

ii) External Migration. Measured as a percent of the total labor force the stock of labor abroad increased from 0.1 percent in 1962 to 3.9 percent in 1972. From 1972 to 1977 it remained unchanged at 4.1 percent (new estimate) and increased then further to 5.3 percent in 1984. The net flow was thus almost half-a-percent annually of the labor force 1962-72, zero 1972-77, and thereafter less than 0.2 percent of the labor force annually. How the slowdown of external migration affected rural and urban areas is not well understood but probably the main direct impact was upon urban areas, partly, however, shifted backwards to rural areas. How the combined impact of external and internal migration has affected urban and rural unemployment and its distribution is hard to say.

- 35 -
**<u>Table 2: Kenya Insurance Sector Ownership Shares, by Region (4 of 7)</u>**

||||<br>**Income**<br>**(million**|<br>**Company**|**Market Sh**|**are by Region**|**(%)**|
|---|---|---|---|---|---|---|---|
|||**Owner**|**KSH**|**Market**||**COME**||
|**Insurance Company**|**Shareholder(ISO Country Code)**|**ship %**|**2007)**|**KE**<br>**Share**|**GB**<br>**EU**|**EAC**<br>**SA**|**US**<br>**ROW**|
|General Accident Insurance|||682|2.08% 2.08%||||
||Rapun Limited (KE)|49.00||||||
||J S Insurance Limited (KE)<br>|49.00<br>||||||
||Shantilal Shah (KE)|2.00||||||
|Heritage All Insurance Company|||1505|4.58%||||
||CFC (GB)|64.08|||2.94%|||
||African Liason Consultant Services (KE)<br>|35.92||1.65%||||
|Insurance Company of East Africa|First Chartered Securities Limited (KE)|100.00|1,173|3.57% 3.57%||||
|Intra Africa Assurance Company|||402|1.22%||||
||Robert T. Gachecheh (KE)|10.50||0.18%||||
||Archibald Githinji (KE)|7.50||0.13%||||
||Mahendra Chandulal (KE)|5.00||0.09%||||
||Upenra Ambalal Patel (KE)|5.00||0.09%||||
||Jitenra Ambalal Patel (KE)|5.00||0.09%||||
||Dinesh Chandulal Patel (KE)|10.00||0.17%||||
||Henry Mkangi (KE)<br>|3.00||0.05%||||
||Bharat Kumar Patel (KE)|5.00||0.09%||||
||Joseph Muriu (KE)|5.00||0.09%||||
||Premji Ratna (KE)<br>|5.00<br>||0.09%<br>||||
||Ranjaben Suresh Patel (KE)<br>|5.00||0.09%||||
||Eleyo Saw Mills (??)|20.00||||||
||Praful C Patel (KE)|5.00||0.09%<br>||||
|Invesco Insurance Company|||958|2.92%||||
|Jubilee Insurance Company|||2,450|7.46%||||
||Jubilee Holdings Ltd (KE)|100.00||7.46%||||
||Kenneth Hamish Wooler Shah (KE)|0.00||||||
||Neville Patrick Gibson Warren (IN)|0.00||||||
|Kenindia Assurance Company|||3,028|9.22%||||
||Life Insurance Corp. Of India (IN)|10.00|||||0.92%|
||General Insurance Corp Of India (IN)|9.00|||||0.83%|
||New India Assurance Co. Ltd. (IN)|9.00|||||0.83%|
||Oriental Insurance Co. Ltd. (IN)|9.00|||||0.83%|
||United India Insurance Co. Ltd. (IN)<br>|9.00|||||0.83%|
||National Insurance Co. Ltd. (IN)|9.00|||||0.83%|
||Pv Karia (IN)<br>|1.39|||||0.13%|
||M N Mehta (KE)<br>|0.00||0.00%||||
||M P Chandaria (KE)<br>|0.00||0.00%||||
||Sadasiv Mishra (KE)|0.00||0.00%||||
||Simeon Nyachae (KE)|7.00||0.64%||||
||Chandaria Foundation Trustees (KE)|7.01||0.65%||||
||Mehta Group Of Companies (KE)<br>|6.02||0.55%||||
||Lex Holdings (KE)<br>|3.66<br>||0.34%<br>||||
||Others (KE)|20.00||1.84%||||

99
Notes: i) Authors’ estimation from VHLSSs using province FE models, ii) Robust standard errors in parentheses clustered at the province level, iii)*** p<0.01, ** p<0.05, * p<0.1.

**<u>Table A.6: Relation between income growth, poverty, and income inequality</u>**

|||D|ependent variable is the g|rowth of per capita incom|e||
|---|---|---|---|---|---|---|
|||FE|||GMM||
|Explanatory variables|||||||
||Model 1|Model 2|Model 3|Model 4|Model 5|Model 6|
||(1)|(2)|(3)|(4)|(5)|(6)|
||-0.169***|-0.589***|-0.579***|-0.156***|-0.119|-0.456***|
|Lagged log of per capita income|(0.025)|(0.050)|(0.055)|(0.037)|(0.082)|(0.091)|
|Lagged Gini index|-0.298|-0.258|-0.224|-0.347**|-0.607**|-0.607***|
||(0.222)|(0.180)|(0.167)|(0.170)|(0.270)|(0.218)|
|Lagged poverty gap|-0.236***|-0.032|0.027|-0.240**|-0.091|-0.217|
||(0.070)|(0.096)|(0.099)|(0.094)|(0.152)|(0.210)|
||||-0.002|||-0.003|
|Lagged log of other State spending|||(0.002)|||(0.002)|
||||0.005|||0.004|
|Lagged log of investment spending|||(0.007)|||(0.009)|
|Lagged log of population density|||-0.279***|||0.054***|
||||(0.072)|||(0.013)|
||||0.001|||0.003***|
|Lagged share of urban population|||(0.001)|||(0.001)|
||||-0.124|||-0.024|
|Lagged share of population with high-school diploma|||(0.317)|||(0.305)|
||||0.001|||-0.000|
|Lagged share of ethnic minority population|||(0.000)|||(0.000)|

44
**Table 3: Pooled Multinomial Transition Estimation Results (Marginal Effects at Average) for All Transitions (2006–07/2007–08/2008–09/2009–10/2010–11/2011–12)**

|Panel (a)|**Transitions fr**|**om Formal Em**|**ployed (FE)**|||
|---|---|---|---|---|---|
||**FE to FE**|**FE to IE**|**FE to U**|**FE to OLF**|**Avg. X**|
|Avg. P.|0.9211|0.0307|0.0214|0.0266||
|HH Income (log)|0.0210***|-0.0112***|-0.0079***|-0.0018|9.96231|
|Aggregate Social Assistance|-0.0216***|0.0154***|.0052793|.0008393|0.1010|
|Number of Obs.|16531|LR Chi2(57)|1310.41|P>Chi2|0|
|Log Likelihood|-6.924|||||
|Panel (b)|**Transitions fr**|**om Informal E**|**mployed (IE)**|||
||**IE to FE**|**IE to IE**|**IE to U**|**IE to OLF**|**Avg. X**|
|Avg. P.|0.0438|0.8322|0.0234|0.1004||
|HH Income (log)|0.0069***|0.0025|-0.0056***|-0.0038|9.3525|
|Aggregate Social Assistance|-0.0201***|0.0194***|0.0067***|-0.0060|0.3646|
|Number of Obs.|19545|LR Chi2(57)|2531|P>Chi2|0|
|Log Likelihood|-13087|Pseudo R2|0.0882|||
|**Panel (c )**|**Transitions fr**|**om Unemploye**|**d (U)**|||
||**U to FE**|**U to IE**|**U to U**|**U to OLF**|**Avg. X**|
|Avg. P.|0.1881|0.2322|0.2826|0.2968||
|HH Income (log)|0.0567***|-0.0376***|-0.0078|-0.0112|9.25886|
|Aggregate Social Assistance|-0.1164***|0.0955***|0.0034|0.0174|0.3352|
|Number of Obs.|3242|LR Chi2(57)|1310.41|P>Chi2|0|
|Log Likelihood|-4017|Pseudo R2|0.1003|||
|Panel (d)|**Transitions fr**|**om Out-of-Lab**|**or Force (OLF)**|||
||**OLF to FE**|**OLF to IE**|**OLF to U**|**OLF to OLF**|**Avg. X**|
|Avg. P.|0.0438|0.8322|0.0234|0.1004||
|HH Income (log)|0.0009**|-0.0080***|-0.0028***|0.0099***|9.39764|
|Aggregate Social Assistance|-0.0028***|0.0244***|0.0018|-0.0234***|0.2339|
|Number of Obs.|35679|LR Chi2(57)|3081.50|P>Chi2|0|
|Log Likelihood|-14952|Pseudo R2|0.0934|||

_Notes:_ Control variables: Year dummies for the period of 2007–12, age, gender, marital status, education, number of children, home ownership, and family status (extended family in household). Coefficients are significant at 1 percent (***), 5 percent (**), 10 percent (*). Avg. X: the average values of explanatory variables. Due to the nonlinear nature of the multinomial logit model, the effects of explanatory variables on dependent variables vary along the explanatory variable vector X. Therefore, we estimated the effect coefficients around the sample average of X.

Table 3 compares the transition behavior of social assistance beneficiaries and non-beneficiaries. First, the fourth column of panel (d) sheds light on the case of Turkey in the ongoing discussion about 15
- Mellon, J., Bailey, J., Scott, R., Breckwoldt, J. and Miori, M. (2022), ‘Does gpt-3 know what the most important issue is? using large language models to code open-text social survey responses at scale’, _Using Large Language Models to Code Open-Text Social Survey Responses At Scale (December 22, 2022)_ .

- Rao, V. (2023), Can economics become more reflexive? exploring the potential of mixed-methods, _in_ ‘Handbook on the Economics of Discrimination and Affirmative Action, A. Deshpande Editor’, Springer.

- Small, M. L. and Calarco, J. M. (2022), _Qualitative Literacy: A Guide to Evaluating Ethnographic and Interview Research_ , University of California Press.

- Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E. and Lample, G. (2023), ‘Llama: Open and efficient foundation language models’.

- Veselovsky, V., Ribeiro, M. H. and West, R. (2023), ‘Artificial artificial artificial intelligence: Crowd workers widely use large language models for text production tasks’, _arXiv preprint arXiv:2306.07899_ .

- Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022), ‘Chain-of-thought prompting elicits reasoning in large language models’, _Advances in Neural Information Processing Systems_ **35** , 24824–24837.

12
Inpres schools in section (6) below.

# (5.3) Robustness Checks and Other Concerns

We check the robustness of our estimates above in a number of ways. First, we use mother's schooling instead of father's schooling as an indicator of children's socioeconomic status. This is motivated by substantial evidence that mother's inuence is stronger on daughters (Torche (2019), Emran and Shilpi (2011)). The evidence, however, suggests that the results and the conclusions do not change in any signicant manner. Please see subsection (OB.1.1) in the online appendix.

The second robustness check uses alternative comparison groups, by excluding the oldest birth cohorts who might be less comparable. Again, the conclusions are robust. Please see online appendix subsection (OB.1.2) for the details.

The third robustness check addresses the issue of potential biases in the estimates from nonrandom sample truncation owing to coresidency in the census data. We provide evidence on this issue from two approaches, and the evidence taken together suggests that our conclusions are unlikely to be driven by coresidency bias. First, we use rich data from Indonesia Family Life Survey (IFLS) which includes information on nonresident parents and children, and thus do not suer from cooresidency bias. A comparison of the census estimates of the mobility equations with those from IFLS shows that the estimates are in general close, and the degree of downward bias in the census estimates is small. Second, we implement correction for possible truncation bias (even if small) by using inverse probability weighting (IPW). Nicoletti and Francesconi (2006) provide evidence that IPW performs better than Heckman selection correction when dealing with such coresidency bias in intergenerational mobility analysis. They suggest house rental cost for identication, as house rental is the largest cost for children planning to leave parental home. Since house rental rates are not available for our study period, we use the recently available data on built-up density. Built-up density in 1975 in a district is used as a measure of house rent: a higher built-up density in a district implies lower rent and a lower coresidency rate (borne out by a negative coecient on built-up density in the selection equation). The IPW corrected estimates are similar to the unweighted estimates reported above. We provide an explanation for the evidence that selection correction does not alter the estimates substantially. This apparently surprising nding can be understood better when we 20
1986—the precise year of their export surges. While their currencies were depreciating in the years up to 1985, they appreciated sharply as soon as it was clear they would enter the EC and continued to appreciate for the next several years—suggesting that depreciation did not play a major role in the surges. In South Africa, apartheid was dismantled in 1990-91 and democratic elections took place in 1994. The end of apartheid was rewarded with the removal of sanctions by the industrial countries that had been limiting exports. The currency appreciated in the years leading up to the surge. While moderate depreciation accompanied the surge, strong export growth was likely more related to the regime shift and greater openness to South Africa’s exports in major markets.

One noticeable feature of the data on export surges is that a number of developing countries experienced surges in 1986 or 1987. This is not a coincidence. The debt crisis, which started with Mexico’s default in 1982, led to large real devaluations in nearly all of the highly indebted developing countries on the order of 30 to 90 percent from the early 1980s to 1986. And these large devaluations resulted in export surges. What is especially interesting in these cases is that because of the crises, trade liberalization initiatives were most often reversed. Among the most drastic reversals were Argentina and Philippines, both of which experienced export surges. In Argentina, non-tariff barrier coverage was low before the crisis; however, after it 62 percent of tariff lines had non-tariff barriers. Similarly, in the Philippines, NTB coverage increased from 40 to 100 percent of tariff lines. In addition, because of increased exports from many developing countries, developed countries increased import barriers during this period (Laird and Nogues 1988). This suggests that even when trade barriers are on the rise, a competitive 25
**-** **<u>Table 1: Key household characteristics by macro region</u>**

||**All**||**By r**|**egion**||
|---|---|---|---|---|---|
||**farmers**|**Coastal**|**Central**|**Mountain**|**Tirana**|
|**Household characteristics**||||||
|Household size|4.646|4.265|4.400|5.201|5.394|
|Members 14-60 years old|2.721|2.469|2.722|2.928|3.394|
|Dependency ratio|0.419|0.428|0.390|0.439|0.380|
|<br>Head's age|52.272|53.467|51.522|51.912|50.152|
|<br>Head's education (years)|7.874|8.258|8.016|7.327|8.697|
|<br>Max. educ. In household|9.817|9.995|9.853|9.575|10.485|
|Toilet|0.508|0.555|0.570|0.387|0.849|
|TV|0.083|0.147|0.077|0.028|0.061|
|Share of poverty|0.179|0.115|0.190|0.232|0.182|
|<br>Cons. per capita (US $)|915|1049|888|802|1044|
|**Income composition (%)**||||||
|from agric. (crop & livestock)|0.437|0.419|0.398|0.508|0.121|
|<br>from non-ag. Wage|0.181|0.182|0.201|0.151|0.402|
|<br>from non-ag. Self|0.063|0.078|0.063|0.039|0.228|
|from remittances|0.316|0.318|0.333|0.301|0.248|
|**Asset ownership**||||||
|Area of land owned (ha)|0.848|1.252|0.804|0.526|0.283|
|Area cultivated (ha)|0.629|0.895|0.620|0.409|0.071|
|<br>No. of plots owned|2.998|3.172|3.380|2.574|1.303|
|<br>Any plough|0.130|0.019|0.140|0.235|0.000|
|Any cattle|0.690|0.551|0.620|0.912|0.242|
|Any horse|0.333|0.277|0.296|0.440|0.000|
|<br>Value of all animals (US $)|859|720|715|1161|171|
|**Land tenure**||||||
|Any privatized land|0.697|0.934|0.747|0.448|0.179|
|<br>Any inherited land|0.306|0.113|0.222|0.578|0.121|
|Any purchased land|0.069|0.056|0.114|0.010|0.750|
|<br>Deed from 1991|0.743|0.904|0.755|0.608|0.152|
|<br>Deed from 1946|<br>0.213|<br>0.080|<br>0.139|<br>0.411|<br>0.143|
|**Land use**||||||
|Any irrigation|0.644|0.528|0.631|0.799|0.061|
|<br>Area irrigated|0.298|0.338|0.284|0.287|0.001|
|<br>Annual cropland rainfed (ha)|0.282|0.460|0.289|0.116|0.027|
|Annual cropland irrig. (ha)|0.279|0.312|0.261|0.279|0.001|
|<br>Perennial land rainfed (ha)|0.052|0.097|0.051|0.007|0.104|
|Perennial land irrig. (ha)|0.018|0.026|0.023|0.008|0.000|
|<br>Pasture land (ha)|0.090|0.127|0.072|0.076|0.015|
|**Ag. output&productivity**||||||
|Total value of ag. output (US $)|1004.4|1318.3|888.8|849.7|184.9|
|<br>... share from livestock|0.484|0.348|0.445|0.642|0.344|
|Total profit/ha (US $)|1269.2|768.7|1008.8|2049.1|203.2|
|<br>Used crop extension|0.210|0.322|0.106|0.196|0.212|
|<br>Used vet service|0.282|0.393|0.228|0.229|0.212|
|**Community characteristics**||||||
|<br>Distance to nearest bank (km)|3.964|4.678|3.815|3.621|0.000|
|<br>Migration share in comm. (%)|20.386|22.695|17.042|21.096|22.182|
|<br>Shortae of labor in comm|0368|0516|0384|0230|0000|
|g .<br>Share of hhs with credit|.<br>0.347|.<br>0.418|.<br>0.385|.<br>0.245|.<br>0.303|
|<br>**Fallowing**||||||
|<br>Any land idle?|0.176|0.241|0.233|0.064|0.121|
|<br>Size of idle land (ha)|0.098|0.162|0.096|0.037|0.116|
|,,,, less than 5 years|0.364|0.357|0.362|0.410|0.250|
|,,,, 5 years or more<br>|0.661<br>|0.657<br>|0.685<br>|0.590<br>|0.750<br>|
|Rented out land (ha)|0.029|0.069|0.013|0.005|0.020|
|<br>No. of obs.|1796|593|558|612|33|

_Source:_ Own computation from 2005 Albania LSMS 21