rafmacalaba/datause-encoder-data huggingface dataset:
plausible with respect to agglomeration economies, we will focus our discussion on the first panel of Table 9.

The most important conclusion that we draw from Table 9 is probably that, in general, the effects of business environment and economic geography that we would obtain by estimating equation (7) by GLS as a random effects model would be seriously biased. Both the direction of bias and its magnitude vary by specific aspects of the business environment or geography. Thus it turns out that the uncorrected GLS estimates of the effect of labor regulation from column 7 of Table 5 is virtually identical to that in column 4 of Table 9. On the other hand, it is clear that the effect of power shortages as estimated in column 7 of Table 5 is seriously biased upwards. According to the selectivity corrected estimate of column 4 of Table 9, a doubling of the average reported loss of output due to power outages would reduce average labor productivity per plant by 22 percentage points, as opposed to reduction by only 3 percentage points according to the estimate in column 7 of Table 5. A similar comparison of column 4 of Table 9 with column 7 of Table 5 shows that estimates of the productivity effect of foreign market access that we obtain by estimating equation (7) as random effects model would not be selectivity biased, while corresponding estimates of the productivity effects of domestic market access would be upward biased by almost a factor of almost 2:1. In contrast, the uncorrected estimates of agglomeration effects in Table 5 are hugely downward biased due to selectivity.

We should perhaps stress again that the fact that we used region dummies in Tables 5, 6 and 9 in lieu of the city dummies of equations (11) to (14) and have used region, rather than city, selection probabilities, means that the estimates reported in column (4) of Table 9 would not necessarily eliminate selectivity bias entirely. However, we can tell the direction in which estimated in column 7 of Table 5 would be biased for corresponding true parameter values. More importantly, depending on the direction of this bias we can tell whether the estimates in column 4 in Table 9 are a lower bound or an upper bound of the true parameter values or to estimates that we would obtain by disaggregating the level 29
the sub-sample of parental proxies does not significantly change the estimates for proxy-reported statistics in Table 3 or the regression results in Table 5 discussed below. Fathers as proxy respondents report lower LFP and higher working hours of their children than do mothers, but the difference is not statistically significant. These results are available upon request.

In Table 4, we turn to the distribution of children’s main activities across broadly defined categories. Participation in domestic duties, while not included in labor force statistics, is commonly collected, particularly in a child labor context. This is usually done by including domestic duties as a possible answer to the questions regarding the individual’s main activity. Here we examine how reporting on domestic duties changes when using one overall question about any work (short module) compared with using three screening questions that require the respondent to specify wage work, farm work, and non-farm household enterprise work (detailed module). For the short module, the distribution across main categories is derived from a single question (question 4 in the short module – see Table A1, first column); for the detailed module, it is derived from question 9 (see Table A1, second column). The results in Table 4 show that the difference in questionnaire design between the short and detailed modules has a large and statistically significant impact on reports for both boys and girls. The first interesting finding is that the short questionnaire generates lower percentages of “no work” answers than the detailed questionnaire, i.e., higher percentages of individuals who classify themselves in employment.<sup>11</sup> The difference is especially large (-20.6 percentage points) and statistically significant for girls but not for boys. However, when asked about the sector of main activity, an extremely large percentage of children who define themselves as “working” in the short questionnaire indicate that they are engaged in domestic duties – the difference with the detailed questionnaire is very large and significant for both boys (+21 percentage points) and girls (+35 percentage points). The detailed questionnaire, by contrast, generates higher participation in agriculture for both boys and girls (the difference compared with the short questionnaire is about 15-16 percentage points). As for the

> 11 In the short questionnaire, “no work” corresponds to those who answer “no” to question 1 (see Table A1, column 1); in the detailed questionnaire ‘no work’ are those who answer ‘no’ to all questions 1,3,5.

15
Table 3: Contraceptive Use and Birth Spacing

|Full Sample<br>(1)|Rural<br>(2)|Urban<br>(3)|
|---|---|---|
|**Panel A: Using any contraceptive method**|||
|Fraction of female leaders<br>0.102|0.207*|-0.189|
|(0.112)|(0.123)|(0.174)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.526|0.519|0.544|
|**Panel B: Using modern contraception**|||
|Fraction of female leaders<br>0.184*|0.295**|-0.104|
|(0.108)|(0.124)|(0.132)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.463|0.457|0.478|
|**Panel C: Using traditional contraception**|||
|Fraction of female leaders<br>-0.082*|-0.088*|-0.084|
|(0.047)|(0.047)|(0.094)|
|Observations<br>62,122|43,981|18,141|
|Outcome mean<br>0.063|0.062|0.066|
|**Panel D: Birth spacing (years)**|||
|Fraction of female leaders<br>0.647|1.178*|-0.440|
|(0.678)|(0.696)|(1.075)|
|Observations<br>235,036|166,801|68,235|
|Outcome mean<br>6.962|6.711|7.575|
|State fxed efects<br>x|x|x|
|District characteristics<br>x|x|x|
|Individual characteristics<br>x|x|x|

_Notes_ : Data are from the 2015-16 National Family Health Survey (NFHS) of India. Panels A–C present 2SLS estimates as specified by equations 1 and 2 and using cross-sectional data. Panel D presents 2SLS estimates as specified by equation 3 using the retrospective woman-year panel data. Column (1) shows estimates for the full sample, while columns (2) and (3) restrict the samples to those living in rural and urban areas, respectively. We define close elections as those in which the gap in votes between the winner and the runner up is less that 3 percent of votes. The outcomes in the table are as follows: indicator for whether the woman is using any contraceptive method (Panel A), indicator for whether the woman is using a modern contraceptive method (Panel B), indicator for whether the woman is using a traditional contraceptive method (Panel C), and the number of months since the last birth (Panel D). All regressions include individual controls, district controls, and state fixed effects. Individual controls include age, years of schooling, and indicator variables for living in a rural area, religion, and caste. District-level controls include the share of female population, the share of urban population, and the share of SC-ST population in the district, and the female and male literacy rates. The regressions in Panel D also include year fixed effects and control for the number of children at the time of the previous election. Robust standard errors are clustered at the district level. ***, **, and * denote significance at the 1, 5, and 10 percent levels.

22
# **VI.** References

- Carlin, Wendy, and Michael Landesmann, 1997. "From Theory into Practice? Restructuring and Dynamism in Transition Economies," _Oxford Review of Economic Policy_ 13(2), 77-105.

- Clarke, George R.G., and Robert Cull, 1999. "Why Privatize? The Case of Argentina's Public Provincial Banks." _World Development 27 (5), 867-888._

- Clarke, George R.G., and Robert Cull, 2000a. "Getting to Yes: Privatizing Banks in Argentina." Mimeo, World Bank, Washington, DC.

- Clarke, George R.G., and Robert Cull, 2000b. "Provincial Bank Privatization in Argentina: The Why, the How and the So What." In: Harvey Rosenblum, Ed. _Bank Privatization: Conference Proceedings of a Policy Research Workshop Held at the World Bank, March 15-16, 1999._ Federal Reserve Bank of Dallas, Dallas TX (2000), 195-221.

- Dillinger, William, and Steven B. Webb, 1999. "Fiscal Management in Federal Democracies: Argentina and Brazil." Policy Research Working Paper 2121, World Bank, Washington DC.

- Downs, Anthony, 1957. _An Economic Theory of Democracy._ Harper and Row, New York.

- Frydman, Roman, Cheryl Gray, Marek Hessel, and Andrzej Rapaczinski, 1999. "When Does Privatization Work? The Impact of Private Ownership on Corporate Performance in the Transition Economies." _Quarterly Journal of Economics,_ **114,** 4: 1153-91.

- Galal, Ahmed, Leroy Jones, Pankaj Tandon, and Ingo Vogelsang, 1994. _Welfare Consequences of Selling Public Enterprises._ Oxford University Press, Oxford UK.

- Jones, Derek C., and Niels Mygind, 1999. "The Nature and Determinants of Ownership Changes After Privatization: Evidence from Estonia," _Journal of Comparative Economics,_ 27, 422-441.

- Kikeri, Sunita, John Nellis, and Mary Shirley, 1992. _Privatization: The Lessons of Experience._ The World Bank, Washington DC.

- Komai, Janos," 'Hard' and 'Soft' Budget Constraints." _Acta-Oeconomica,_ 25, 3: 231-45, 1980.

> , "The Soft Budget Constraint." _Kyklos,_ **_39,_** 1: 3-30, 1986.

- Laffont, Jean-Jacques, and Jean Tirole, 1991. "Privatization and Incentives." _Journal of Law, Economics, and Organization_ 7 (Special Issue), 84-105.

- La Porta, Rafael, and Florencio L6pez-de-Silanes, 1997. "The Benefits of Privatization - Evidence from Mexico." _Viewpoint_ 117, World Bank, Washington DC.

- L6pez-de-Silanes, Florencio, 1997. "Determinants of Privatization Prices." _Quarterly Journal of Economics_ **112** (4), 965-1025.

- L6pez-de-Silanes, Florencio, Andrei Shleifer, and Robert W. Vishny, 1997. "Privatization in the United States." _RAND Journal of Economics_ **28** (3), 447-71.

31
_8_ _Price Support at Any Price?_ EU level<sup>3</sup> . Let us note, that all the measures are slightly better when only the dynamic sector is taken into account. The main message of the figure is as following: Polish agriculture employs 4 times more farmers per hectare that the Western European agriculture. With a comparable level of output per hectare, all efficiency parameters (per farmer) are several times smaller in Poland. _Reduction of the excess employment in agriculture is the main structural problem that must be solved_ to improve farmers' income and to integrate successfully with the EU. It is also a condition for making an appropriate and efficient use of the other production factors (Poczta, 1993).

# 2.2 Income parity: what may endanger it?

The fall in the income relation (a ratio of average income per farmer<sup>4</sup> and average wage outside agriculture) in 1990s is always used as a main point while advocating a need to support the agricultural sector in Poland. One may argue, however, that the situation of Polish farmers is not as bad. _First,_ as it was mentioned above, the fall in the income relation is measured with respect to an artificially high base of late 1980s. _Second,_ a significant part of farmers' income is coming from outside farming activities (data of the IERiGZ, the Institute of Economics of Agriculture and the Food Economy, based on sampling, show that around 50% of income of farmers is coming from nonfarming activities). _Third,_ the situation differs substantially between the dynamic sector and the stagnating sector, so the average change in the income relation may be misleading.

_This paper does not discuss whether the current income relation is high enough or not. It rather tries to analyze whether there are medium- and long-term development trends in the Polish economy that may lead to deterioration of this relation._ For the analysis we use a Computable General Equilibrium model of the Polish economy POLAGR (see Box 1). The model was specially developed for evaluating effects of various agricultural policies.

Sensitivity analysis is used in order to assess the factors that may endanger the level of the income relation. We start from the base solution of the model (a steady case) for the period 19912010, and see what directions of changes in the income relation generated by the model are caused by changes in key macroeconomic factors outside agriculture<sup>5</sup> . In the base run we generate a steady growth path _without any active agricultural policy_ (i.e. without price/income supporting). The 3 The comparisons with the EU average may be misleading: a 16.5ha EU average farm size reflects both, the 70ha farms of UK (10 times a Polish farm area), and 5.5ha Greek farm (smaller than the Polish average farm).

4 It should be stressed, that the income of farmers includes both income from farming activities and from other sources (wage income, social security transfers,other transfers).

_5_ Using the year 1991 as a starting point is connected with a fact, that the PSE level in that year was on a low level close to zero (estimated at -3%).
23 _Agriculture is by far the main sector in which rural women are engaged._ Indeed nearly two-thirds of self-employed women work in agriculture-related activities (Table D). But trade is also important among this same group (19.2 percent). Self-employed males are more evenly spread across agriculture, livestock breeding, and trade (respectively 47 percent, 13.7 percent, and 24.2 percent, see Table C). The overwhelming majority of female unpaid labor works in the agricultural sector (86.3 percent). A similar pattern is observed for unpaid male labor, although livestock breeding is more sizeable (71.1 percent in agriculture and 21.4 percent in livestock). Finally, female wage earners are employed in the services sector (28.9 percent), by the administration (25.4 percent), or in other activities. Male wage earners are more likely to be involved in trade (26.8 percent), livestock breeding (16.8 percent). Administrative activities and services come next (14.4 percent and 11.6 percent).

# <u>3.2.2 Gender gaps in urban labor markets</u>

_Labor market indicators for women in urban centers are somewhat better than those for women living in rural areas_ (Table 2). Both participation in the labor force (30.1 percent compared to 26.5 percent) and employment (23.9 percent compared to 20.3 percent) are higher. Besides, unemployment for women is lower in urban centers than it is in rural areas (respectively 20.8 percent and 23.3 percent). This is at odds with males’ unemployment, which is in fact higher in urban areas than in rural ones.

**Table 13. Out of the Labor Force – Motives for Women in Urban Areas (in %)**

|||_Age group_<br>||
|---|---|---|---|
||_15-24_|_25-49_|_50-64_|
|_No work_|20.9|11.6|1.7|
|_Slack season_|0.1|0.9|0.1|
|_Student_|44|2.6|0|
|_Family/household obligations_|28.9|80.8|59.2|
|_Too young/old_|5.1|2.3|37.1|
|_Disabled_|0.5|1.6|1.8|
|_Other_|0.4|0.2|0|
|_Total_|100|100|100|

Source: EPCV 2004. Figures are displayed in percentage.

_Although overall urban women report family obligations as their main non-participation motive, those aged 15-24 mostly opt out of the labor market to study_ (Table 13). Indeed, most young women in urban areas are out of the labor force because they are in school (44 percent). Family reasons come second (28.9 percent) and the lack of work third (20.9 percent). Young women’s participation seems to be less affected by the presence of children in the household than that of women aged 25 to 49. But contrary to what was observed for rural women, the presence of children is negatively correlated with their likelihood to participate in the labor market.
**<u>Appendix Table 4: Kitagawa-Oaxaca-Blinder Decomposition of Monthly Wages by Age Group</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||Age 15-29|||Age 30-64||
|VARIABLES|Overall|Endow.|Coeff.|Overall|Endow.|Coeff.|
|Male|13,959.73***|||92,453.0***|||
||(1,159.740)|||(3,711.685)|||
|Female|3,371.458***|||10,763.5***|||
||(347.883)|||(865.377)|||
|Raw Difference|10,588.27***|||81,689.5***|||
||(1,210.793)|||(3,811.231)|||
|Difference due to|4,739.494***|||56,368.1***|||
|Endowments|(1,027.677)|||(3,224.216)|||
|Difference due to|5,848.782***|||25,321.4***|||
|Coefficients|(1,136.809)|||(3,943.294)|||
|Individual Lives in||-365.272|-2,404.8***||2,780.598|-691.542|
|an Urban Area||(278.347)|(933.410)||(1,794.628)|(2,977.77)|
|Individual Age||2,777.583|-77,927.442||-8,371.664***|228,211.60|
|||(1,907.21)|(65,164.895)||(2,897.903)|(146,997.0)|
|Individual Age||-3,255.5*|47,030.543||7,318.324***|-102,887.3|
|Squared||(1,963.13)|(35,337.165)||(2,820.320)|(73,552.45)|
|Number of Adults||-185.198|-4,073.02**||125.107|-661.317|
|in HH||(126.330)|(1,946.924)||(97.186)|(4,343.308)|
|Number of Children||259.154**|-714.440||-935.887***|4,700.16**|
|Aged 0-5 in HH||(126.034)|(607.336)||(293.134)|(2,126.934)|
|Individual owns a||1,360***|805.748||3,254.135|2,226.623|
|Financial Account||(384.069)|(519.399)||(2,054.753)|(2,517.225)|
|Individual is||-1,869.8*|3,724.135**||-1,642.309**|18,491.6**|
|Married||(1,059.8)|(1,816.016)||(720.831)|(7,741.180)|
|Individual is Divorced/||21.284|-134.503||-654.631**|802.787|
|Widowed/Separated||(27.340)|(82.746)||(329.292)|(880.664)|
|Individual Completed||120.321|-562.159||9,997.520***|-744.407|
|Secondary Schooling||(199.42)|(418.813)||(2,558.136)|(2,306.7)|
|Individual owns a Vehicle||48.063|965.395||-462.986|-2,301.716|
|(Bicycle, Motorcycle, Car)||(41.558)|(700.151)||(594.454)|(4,923.57)|
|Individual Works as||-244.35*|-577.69*||-1,140.15***|-306.23|
|Apprentice||(147.09)|(323.146)||(351.292)|(248.697)|
|Individual Works as||2,079***|38.334||20,410.16***|4,092.51*|
|Manager||(483.99)|(381.446)||(2,631.810)|(2,248.34)|
|Individual Works as||234.571|47.497||6,249.461***|-2,626***|
|Skilled Worker||(148.42)|(295.877)||(1,147.301)|(863.518)|
|Individual Works in||<br>4,162***|<br>-33,286***||<br>20,635.49***|<br>-35,64***|
|the Agriculture Sector||(932.4)|(7,088.141)||(4,125.394)|(9,079.82)|
|Individual Works in||-403.127|-2,110.82*||-1,195.082|-5,370**|
|the Service Sector||(343.14)|(1,085.986)||(3,212.446)|(2,582.52)|
|Constant|||75,028.***|||-81,972.7|
||||(27,512.4)|||(74,970.4)|
|Observations|5,903|5,903|5,903|4,688|4,688|4,688|

The reference dummies are family worker and manufacturing sector. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 46
||**Table 7: Inte**|**raction term**|**withgender**|**dummy**|||
|---|---|---|---|---|---|---|
|Dependent variable:|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Financially Constrained|||||||
|(dummy)|||||||
|<br>Incidence of Petty Corruption<br>(Cell average)*Firm Has<br>|-1.805***|-1.779***|-1.756***||||
|Female Owners Y:1 N:0|(0.515)|(0.522)|(0.508)||||
|Depth of Petty Corruption<br>Cll *Fi H||||-2.056***|-2.024***|-2.046***|
|(e average)rm as|||||||
|Female Owners Y:1 N:0||||(0.610)|(0.621)|(0.600)|
|Incidence of Petty Corruption<br>|1.294***|5.313|4.228||||
|(Cell average)|||||||
||(0.356)|(3.268)|(3.422)||||
|Incidence of Petty Corruption<br>(Cell average)*GDP per<br>capita (logs)||-0.476|-0.691*||||
|||(0.395)|(0.382)||||
|Incidence of Petty Corruption<br>(Cell average)*Annual Sales<br>(2009 USD, logs)|||0.242**<br>||||
||||(0.099)||||
|Depth of Petty Corruption<br>(Cell average)||||1.561***|5.263|3.442|
|||||(0.421)|(4.196)|(4.569)|
|Depth of Petty Corruption|||||-0.439|-0.626|
|(Cell average)*GDP per<br>capita (logs)|||||(0.511)|(0.476)|
|Depth of Petty Corruption<br>(Cell average)*Annual Sales<br>(2009 USD, logs)||||||0.288**<br>(0.131)|
|Annual Sales (2009 USD,<br>l|-0.124***|-0.125***|-0.197***|-0.125***|-0.126***|-0.200***|
|ogs)|(0.038)|(0.038)|(0.044)|(0.038)|(0.038)|(0.044)|
|Age of Firm (logs)|0.044|0.043|0.050|0.040|0.040|0.046|
||(0.072)|(0.072)|(0.072)|(0.072)|(0.072)|(0.072)|
|Exports (proportion of sales)|-0.469|-0.443|-0.487|-0.458|-0.440|-0.466|
||(0.359)|(0.356)|(0.358)|(0.358)|(0.356)|(0.351)|
|Firm Has Female Owners Y:1|0.234|0.224|0.227|0.206|0.198|0.209|
|N:0|||||||
||(0.162)|(0.161)|(0.161)|(0.160)|(0.160)|(0.160)|
|Foreign Ownership|<br>-0.045|<br>-0.049|<br>-0.031|<br>-0.048|<br>-0.050|<br>-0.031|
|<br>(rrtin)|||||||
|popoo|(0.280)|(0.280)|(0.280)|(0.280)|(0.280)|(0.280)|
|Multi-establishment Firm Y:1|-0.297|-0.294|-0.272|-0.291|-0.288|-0.262|
|N:0|||||||
||(0.194)|(0.194)|(0.194)|(0.193)|(0.193)|(0.194)|
|Firm Purchased Assets Last<br>Fiscal Year Y:1 N:0|-0.521***|-0.517***|-0.516***|-0.519***|-0.516***|-0.513***|

48
**Table 1: Sample characteristics in baseline survey**

||**Weekly visit**|**Recall**|
|---|---|---|
|Age|22.974|22.744|
||(0.514)|(0.584)|
|Male|0.493<br>|0.500<br>|
||(0.014)|(0.015)|
|Worked on household plot since start of season (age 10+)|0.550|0.582|
||(0.016)|(0.017)|
|Enrolled in school|0.405|0.421|
||(0.014)|(0.014)|
|Has no schooling (age 10+)|0.307|0.329|
||(0.015)|(0.016)|
|_N (individuals, all ages)_|_1,314_|_1,185_|
|Household size|5.814|5.086**|
||(0.228)|(0.168)|
|Male household head|0.752|0.807|
||(0.029)|(0.026)|
|Household head single/divorced/widowed|0.212|0.262|
||(0.027)|(0.029)|
|Number of children (age <10)|1.646|1.575|
||(0.100)|(0.096)|
|Number of persons who worked on household plot (age 10+)|2.226|2.043|
||(0.093)|(0.097)|
|Number of plots per household|2.571|2.348*|
||(0.085)|(0.088)|
|_N (households)_|_226_|_233_|
|Distance plot to residence (min. walking)|53.601|62.706***|
||(1.958)|(2.234)|
|Proportion of plots cultivating beans/peas|0.119|0.122|
||(0.013)|(0.014)|
|Proportion of plots cultivating cassava|0.100|0.122|
||(0.012)|(0.014)|
|Proportion of plots cultivating groundnuts|0.203|0.219|
||(0.017)|(0.018)|
|Proportion of plots cultivating maize|0.487|0.439|
||(0.021)|(0.021)|
|Proportion of plots cultivating yam|0.222|0.258|
||(0.017)|(0.019)|
|_N(plots)_|_581_|_547_|

Note: Standard errors in parentheses. T-test for difference between the two groups were done: *** denotes the difference is significant at the 1% level; ** at the 5% level; * at the10% level.

# **4. Recall bias in farm labor**

## **4.1. Main results**

In the surveys administered to the recall and weekly visit households, farm labor is reported per person and per plot. As discussed in section 2.2, our main measure of farm labor is _season-wide hours per person per plot_ . In this measure, we include only those plots for which farm labor was reported at least once during the season. We consider all individuals aged 10 years and older reporting at least one hour of labor on any household plot at any point in time during the agricultural season.

9
As a second test, we have employed measures of bureaucratic delay and contract enforceability (or relative degree to which contractual agreements are honored) from Business Environmental Risk Intelligence, Inc. (BERI) ,h Scores for the BERI indicators are available for only fourteen of our thirty-one countries and are set so thlat positive relationships with environmental Table 7 Impact of BERI Indexes on Environmental Indicators

|Dependent<br>Variable|Intercept|ln<br>ICPGDP|ln Delay||<br>in<br>Contract|Adjusted<br>R2|
|---|---|---|---|---|---|
|ln Air|1.99<br>(3.48)|0.32<br>(3.23)|0.19<br>(0.56)||0.81|
|ln Water|3.21<br>(6.19)|0.18<br>(2.04)|0.31<br>(1.00)||0.72|
|ln Land<br>l__________|3.25<br>(6.18)|0.20<br>(2.19)|0.18<br>(0.57)||0.68|
|ln Living<br>____<br>__|2.99<br>(4.87)|0.21<br>(1.99)|0.24<br>(0.64)|.|0.66|
|ln Env<br>l__________|4.29<br>(7.96)|0.22<br>(2.40)|0.23<br>(0.72)||0.74|
|lnAir|2.05<br>(2.24)|0.32<br>(2.10)||0.16<br>(0.34)|0.81|
|lnWater|3.45<br>(4.15)|0.15<br>(1.11)||0.35<br>(0.82)|0.72|
|ln Land|3.43<br>(4.12)|0.18<br>(1.26)||0.22<br>(0.52)|0.68|
|ln Living<br>l__________|3.01<br>(3.06)|0.22<br>(1.34)||0.17<br>(0.33)|0.65|
|ln Env|4.42<br>(5.13)|0.21<br>(1.47)||0.23<br>(0.52)|0.73<br>_|

6 For a discussion of these indicators, see Keefer and Knack (1993).

17