rafmacalaba/datause-encoder-data huggingface dataset:
# **8. Heterogeneity in trends of norm-related outcomes**

This sections discusses in detail the marginal effects of wealth, location, and education (the independent variables) on outcome variables (the dependent variables, see for more details the equations and explanations of page 10 in the main text). We estimate the marginal effects by regressing the outcome variables on wealth, location (rural versus urban), higher education (secondary and tertiary education versus no or primary education), their interactions with year of birth, and state fixed effects. Standard errors are clustered by state.

For variables that have a life cycle, i.e., variables that change as an individual ages, for example employment, we control for age in the regression. We run the heterogeneity analysis for Bangladesh, India, Nepal, and Pakistan. Data in Afghanistan (and Maldives) were collected only in one (and two) surveys, and this does not permit to trace cohorts as they age.

As the independent variables are categorical, the marginal effects are represented as the impact of moving from the reference category to one of the other categories. In the case of wealth, the reference is the poorest quintile; for the urban group, the reference is rural; for education, the higher education group is compared against the lower or no education group. Thus for example, if moving from the poorest quintile (the reference category for the wealth variable) to a richer quintile does not produce any effects, then the plotted marginal effect will be at the zero level on the vertical axis, meaning that there is no statistically significant difference between one category with respect to the reference category.

For each of the outcomes variables the marginal effect figures include four panels, one for each country. Each panel provides information about the level of the marginal effect (the position of the line) and whether this is different for different cohorts (the slope of the line). More in detail, moving from left to right allows to compare marginal effects between cohorts born in the past and more recent cohorts.

In extreme synthesis, a common finding across different outcomes is that wealth seems to have some marginal effect, but that urban location and education do not tend to be associated with better outcomes, as pointed out in stylized fact 2. However, there is quite a bit of heterogeneity across outcomes and countries. The details are discussed in the remainder of this section.

62
Table 5: Effect of savings default on savings

||(1)<br>NBS<br>account|(2)<br>Formal<br>savings|(3)<br>Informal<br>savings|(4)<br>In-kind<br>savings|(5)<br>Total fnancial<br>assets<br>(2)+(3)|(6)<br>Total<br>savings<br>(2)+(3)+(4)|
|---|---|---|---|---|---|---|
||Panel A: On|e week after|transfer||||
|Savings Default|1669.683**<br>(828.824)|1277.934<br>(1581.009)|-3050.113**<br>(1238.233)|-1112.016<br>(3973.808)|-647.722<br>(2366.426)|-908.143<br>(4903.924)|
|Observations|474|474|474|474|474|474|
|Mean of dependent variable in Cash group|5576.86|8917.03|14849.70|22076.32|24347.50|47799.98|
|SD of dependent variable in Cash group<br>R-squared|10141.54<br>0.30|21269.97<br>0.34|17075.07<br>0.39|48092.90<br>0.33|35367.95<br>0.46|73605.06<br>0.50|
||Panel B: Tw|o weeks after|transfer||||
|Savings Default|1691.103*|734.006|-925.735|-15013.889**|630.532|-15344.649*|
||(1018.838)|(2090.709)|(1355.932)|(7626.461)|(2687.514)|(8935.366)|
|Observations|314|314|314|314|314|314|
|Mean of dependent variable in Cash group|5308.10|9429.67|11392.61|46683.69|20852.68|68490.62|
|SD of dependent variable in Cash group|8950.40|23826.40|17025.50|88591.66|37473.69|120342.79|
|R-squared|0.27|0.36|0.41|0.29|0.53|0.46|

‘Formal savings’ is the sum of balances at NBS, any other bank or microfinance institution, and employee savings accounts. ‘Informal savings’ is the sum of balances in ROSCAS, village savings clubs, cash at home or in a secret hiding place, cash given to someone else for safe keeping. ‘In-kind savings’ is the sum of advance purchases of farm inputs, business inventory, bags of maize. Panel A shows regressions with outcome variables measured one week after the transfer, Panel B uses the same outcomes measured two weeks after the transfer. The sample for Panel B is smaller since two-week follow-up data were only collected for respondents who received transfers immediately or with one day delay. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in <u>parentheses.</u> * _<u>p <</u>_ 0 _._ 1, ** _<u>p <</u>_ 0 _._ 05, *** _<u>p <</u>_ 0 _._ 01.

33
Uganda is still a predominant rural country, it was estimated by Carruthers et, al. (2008) that with a base scenario, Uganda should spend almost four percent of its GDP annually on roads (rural and non-rural) (see Table 1).

Despite investments, the RAI has not reached yet 30 percent in Uganda. Therefore, thousands of additional km of rural roads would need to be built in Uganda to achieve a rural access index of 100 percent.

# **Table 1:  Level of Investment Needed to Meet Transport Targets of Base and Pragmatic Scenarios, by Country**

|**Base scenario**||**Pragmatic scenario**||
|---|---|---|---|
|**Group**<br>**Country**|**%GDP**|**Group**<br>**Country**|**%GDP**|
|Congo,Dem.Rep.|25.1|1<br>Congo,Dem.Rep.|12.6|
|1<br>Niger|12.2|Niger|6.2|
|<br>Chad|11.1|2<br>Chad|5.5|
|Mozambique|9.4|<br>Mozambique|5.1|
|Zambia|7.5|Zambia|4.4|
|Malawi|6.3|Malawi|3.9|
|Namibia|5.6|Namibia|3.7|
|2<br>Burkina Faso|5.1|Tanzania|3.0|
|<br>Ethiopia|5.0|BurkinaFaso|2.8|
|Tanzania|4.9|3<br>Ethiopia|2.7|
|Madagascar|4.3|Benin|2.3|
|Sudan|4.0|Ghana|2.2|
|Benin|3.8|**Uganda**|**2.2**|
|Ghana|3.7|Sudan|2.1|
|**Uganda**|**3.6**|Madagascar|2.0|
|3<br>Senegal|3.5|Senegal|1.8|
|<br>Cote d'Ivoire|2.6|Cote d'Ivoire|1.8|
|Cameroon|2.6|Rwanda|1.7|
|Kenya|2.5|4<br>Kenya|1.5|
|Rwanda|2.2|Cameroon|1.4|
|Nigeria|2.0|Nigeria|1.3|
|4<br>Lesotho|1.5|Lesotho|1.3|
|South Africa|0.6|South Africa|0.4|

Source: Carruthers et al. 2008.

Based on extensive data collection in Uganda, it is demonstrated in this paper that investments in rural roads have a positive impact on farmers’ income in Uganda (consistent with findings from Fan et al. 2004). However, based on our selected districts, 12
Agreement, especially provisions on advance rulings (Articles 3) and border agency and Customs cooperation (Articles 8 and 12) should significantly increase predictability and reduce the dispersion of border clearance times. Third, supporting the modernization of public border entities other than Customs, such as SPS agencies or the Police, would significantly contribute to lowering supply chain unreliability by shortening the import process as most import clearance delays usually originate from these agencies. In particular, incentivizing them to adopt risk management systems aimed at reducing physical inspections for low-risk consignments without compromising their mandate of protecting the domestic market from phytosanitary threats or illegal trade is key to enhancing predictability in import times. Fourth, incentivizing the expansion of the Authorized Economic Operator (AEO) Program could open a fast-track and predictable clearance channel for compliant firms. Inviting public agencies other than Customs to join is key for the program to be attractive to the private sector. Accepting a wide range of private stakeholders, including importers, exporters, Customs brokers and transport and logistics services providers that comply with a list of security, tax and Customs requirements is also key to facilitating the trade process without compromising its safety.<sup>37</sup> Finally, our findings call for a revised methodology to quantify time to trade by using transaction-level trade data. Nowadays IT systems adopted by public and private operators involved in the trading process, such as ASYCUDA for Customs, Navis for port operators and GPS devices for transport operators, allow moving away from perceptions-based indicators of time to trade, usually derived from expensive surveys administered to truckers, to produce transaction-based objective measures at low cost based on time stamps collected from these IT systems for any container in movement along the logistics supply chain, from port arrival to cargo delivery at the importer’s warehouse. Constructing measures of unreliability in times to trade using Big data opens new directions of research on trade costs and their impact on firm trade performance.

> 37In exchange, benefits for AEOs include lower clearance time, higher predictability and lower logistics costs due to reduced movement of cargo within logistics terminals.

29
<u>Table 16. (cont.) Results of random effects logit model estimations</u>

|**Immigr**|**ants with**|**Danish**|**educati**|**on**|**Immigr**|**ants with**|**foreign**|**educatio**|**n**|
|---|---|---|---|---|---|---|---|---|---|
||Coeff.|p|Std.<br>err.|Marginal<br>effect<br>(per-<br>centage<br>points)||Coeff.|p|Std.<br>err.|Marginal<br>effect<br>(percent-<br>age<br>points)|
|Age|-0.012||0.10|-0.01|Age|-0.29|*|0.11|-0.68|
|Age<sup>2</sup>/100|0.073||0.12|0.05|Age<sup>2</sup>/100|0.37|**|0.14|0.87|
|Experience<br>|-0.138|***|0.03|-0.09|Experience<br>|-0.22|***|0.04|-0.52|
|Exp.<sup>2</sup>/100|0.048||0.10|0.03|Exp.<sup>2</sup>/100|0.39|**|0.14|0.92|
|YSM|0.078||0.04|0.05|YSM|0.28|***|0.04|0.66|
|YSM<sup>2</sup>/100|-0.192||0.10|-0.13|YSM<sup>2</sup>/100|-0.53|***|0.10|-1.24|
|Children(#)|-1.213|*|0.60|-0.83|Children(#)|-0.88||0.68|-2.07|
|Single|-0.399|**|0.14|-0.25|Single|0.01||0.16|0.02|
|Short highed|5.467|***|0.24|45.55|Short highed|4.83|***|0.29|62.28|
|Medhighed|3.563|***|0.24|8.55|Medhighed|8.89|***|0.30|97.15|
|Longhighed|4.415|***|0.22|21.50|Longhighed|8.50|***|0.29|96.55|
||||||Danish Track 1|-1.65|*|0.79|-1.96|
||||||Danish Track 2|1.58|***|0.35|7.44|
||||||Danish-missing|0.07||0.19|0.17|
||||||Danish-missing<br>and arrived after<br>1.1.1999|-2.57||2.32|-2.23|
|Turkey|-0.301||0.30|-0.18|Turkey|0.16||0.27|0.40|
|Pakistan|-0.142||0.30|-0.09|Pakistan<br>|-0.99|**|0.31|-1.56|
|Vietnam|-0.815|***|0.24|-0.43|Vietnam<sup>#</sup>|-|-|-|-|
|Iran|-0.918|***|0.20|-0.52|Iran|1.04|**|0.39|3.97|
|Iraq|2.154|***|0.33|4.70|Iraq|-1.50|***|0.25|-2.03|
|Ex-Yugoslavia|0.930|***|0.22|0.97|Ex-Yugoslavia|0.06||0.21|0.13|
|Somalia|-1.590|*|0.73|-0.56|Somalia|-0.74||1.14|-1.26|
|Stateless|-1.542|***|0.32|-0.58|Stateless|-0.46||0.44|-0.89|
|1996|0.212||0.17|0.16|1996|-0.10||0.19|-0.23|
|1997|0.036||0.17|0.03|1997|-0.02||0.19|-0.05|
|1998|0.007||0.17|0.01|1998|0.01||0.18|0.02|
|1999|0.213||0.17|0.16|1999|0.13||0.18|0.33|
|2000|0.395|*|0.17|0.31|2000|0.25||0.18|0.65|
|2001|0.404|*|0.18|0.32|2001|0.03||0.19|0.08|
|2002|0.648|***|0.18|0.56|2002|-0.02||0.19|-0.06|
|Constant|-7.201|***|2.12||Constant|-2.36||2.30||
|Rho|0.901||0.01||Rho|0.90|0.01|||
|LRchi2(26)|1182.7||||LRchi2(29)|2528.4||||
|Prob>chi2|0.0<br>||||Prob>chi2|0.0<br>||||
|LogLikelihd|-<br>6231.3||||LogLikelihd|-<br>5675.1||||
|Obs.|23,189||||Obs.|20,513||||

Legend: * p<0.05; ** p<0.01; *** p<0.001

# This variable has been left out of the estimation due to collinearity. Vietnamese individuals have in this case been assigned a value in the “Other non-Western” category instead. Note: (a) The estimations are performed using a sub-sample consisting of workers with either a vocational education or a short, medium, or long higher education.

(b) The reference category for ethnic origin is “Other non-Western countries”.

46
MALI’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE however, so has the share of thermal generation and hydropower now accounts for 55–60 percent of the generation mix. This level of oil-based generation is a significant cost burden for the Malian economy. Mali does not produce oil, and oil imports are subject to enormous markup caused by Mali’s flawed land transportation and condition as a landlocked state.

Despite its handicaps, Mali has recently made important strides in the power sector. Since 2000, when the concession on EDM was granted, the power utility’s sales have grown at a rate of over 10 percent (and up to 17 percent) per year. New connections grew by over 70 percent between 2000 and 2004 and 30 percent between 2004 and 2007. This strong performance is partly attributable to an accelerated expansion of electricity access in Bamako. Between 2000 and 2005, 7.7 percent of Mali’s population gained access to power compared with the benchmark of 4.4 percent for African peer low-income countries (the figure was 12.1 percent for middle-income countries) (table 10). The number of low voltage customers increased 110 percent between 2000 and 2009, with an average 15,107 new connections per year.

**Table 10. Benchmarking Mali’s power indicators**

|||**Low-income**||**Mali**||**Middle-**<br>**income**|
|---|---|---|---|---|---|---|
||**Unit**|**,**<br>**nonfragile countries**|Late 19|90s<sup>Mid-2000s</sup>|<sup>Late 2000s</sup>|<br>**countries**<br>|
|National access to electricity|% population|32.8|7.6|12.8|17|49.5|
|Urban access to electricity|% population|72.8|26.0|41.3||74.4|
|Rural access to electricity|% population|12.7|0.4|2.7||26.3|
|Population gaining access|% population/year|<br>4.4||7.7||12.1|
|||||Mid-2000s|Late 2000s||
|Installed power generation capacity|MW/mil. people|20||21|23|799|
|Power consumption (residential)|kWH/capita|107||69.2|76.4|4479|
|Power outages|Day/year|124.5||—|52.2|70.6|
|Firms’ reliance on own generator|% consumption|21||30|16|11|
|Firms’ value lost due to power outages|% sales|6||2.7|2|2|
|Delay in obtaining an electrical connection|Days|41|||36|12|
|Collection ratio|% billings|93||95|96|100|
|System losses|% production|24||23|22|20|
|Cost recovery ratio|% total cost|89||57|50|85|
|Total hidden costs as % of revenue|%|88.4||131|113|140.6|
|Effective power tariffs (US cents/kWh)|**Mali**|Countries<br>predominantl<br>generati|with<br>y hydro<br>on|Countries w<br>predominantly t<br>generatio|ith<br>hermal<br>n<br>Othe|r developing<br>regions|
|Residential at 100kWh/month|21.0|10.7||15.7|||
|Commercial at 900kWh/month|18.6|12.9||19.0|5|.0 – 10.0|
|Industrial at 100 kVA|—|9.3||13.0|||

Source: Eberhard and others 2009.

Derived from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data Other source include: Access data coming from Demographic and Health Surveys 1996 and 2001. Utility data from AICD electricity database downloadable from http://www.infrastructureafrica.org/aicd/tools/data **.** Data referring to outages is coming from the 2003 and 2007 Enterprise Surveys.

25
**<u>Table 5: Determinants of participation in non-market land transaction</u>**

||**Type of i**<br>|**nheritance**<br>|
|---|---|---|
||**Out**|**In**|
|Agricultural ability|-0.011|0.045*|
||(0.84)|(1.89)|
|Household size|0.002**|0.004***|
||(2.42)|(3.20)|
|No. of unmarried sons below 25 in 1981|0.010***|-0.032***|
||(5.09)|(6.00)|
|Dynasty land endowment|0.001***|-0.001|
||(3.96)|(1.11)|
|Landless dynasty dummy|-0.022**|-0.090***|
||(2.22)|(6.01)|
|Total asset value (log)|0.001|0.013**|
||(0.49)|(2.36)|
|Years of independence in 1999|0.002***|-0.003***|
||(3.00)|(4.05)|
|Dummy for ST and SC castes|0.007|0.017|
||(0.96)|(1.32)|
|Observations|2816|4280|

Absolute value of z statistics in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

23
Table 2: SVARMA(AICc) results at PM2 _._ 5 _>_ 6, _R_<sup>ˆ2</sup> = 0 _._ 737, 41 estimated parameters on ( _N −_ max( _p, q_ ) _× T_ ) _×_ 3 = 4068 data points with 372 fixed demeaning components. _AICc_ = _−_ 7390 _._ 091.

|Pollution<br>(pol)|Bottom Expenditures<br>(bot)|Expenditures<br>(exp)|
|---|---|---|
|_φ polt−_1<br>-0.068**|-0.092***|-0.047***|
|(-2.57)|(-2.866)|(-2.391)|
|_φ polt−_2<br>-0.070***|-0.063***||
|(-4.381)|(-2.969)||
|_φ polt−_3<br>0.026*<br>||0.054**<br>|
|(1.652)||(2.507)|
|_φ bott−_1|-0.108*|0.089***|
||(-1.94)|(2.577)|
|_φ bott−_2|-0.139***|-0.129***|
||(-4.736)|(-2.791)|
|_φ bott−_3<br>-0.039**|-0.099***|-0.141***|
|(-2.119)|(-3.544)|(-3.534)|
|_φ expt−_1|0.071***|-0.374***|
||(2.964)|(-12.805)|
|_φ expt−_2|0.158***<br>(4.453)||
|_φ expt−_3|0.074***<br>||
||(3.14)||
|_M polt−_1<br>-0.515***|0.126***||
|(-15.292)|(3.233)||
|_M polt−_2|||
|_M polt−_3|-0.052*<br>|-0.082**<br>|
||(-1.814)|(-2.131)|
|_M bott−_1<br>-0.038*|-0.253***||
|(-1.94)|(-4.296)||
|_M bott−_2||0.252***|
|||(4.313)|
|_M bott−_3||0.128**<br>|
|||(2.203)|
|_M expt−_1|||
|_M expt−_2|-0.134***|-0.387***|
||(-3.621)|(-10.705)|
|_M expt−_3||-0.147***|
|||(-4.273)|
|_ρ_<br>0.812***|0.305***|0.327***|
|(27.765)|(3.177)|(2.976)|
|_b_<br>0.088|0.18|0.229|
|_σ_<br>0.119|0.129|0.176|
|_ν_<br>2.004|7.313|4.703|
|4-lag white-noise _p_<br>1.000|0.129|0.176|

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 _Constant omitted, t-statistics in parenthesis for the SARMA components._ xvi
# **FIGURE 2 Distribution of Net Benefits of Fifty-four U.S. Regulations, 1990 to Mid-1995 (in Billions of 1994 Dollars)**

<!-- Start of picture text --> 16-<br>(negative net benefits) (positive net benefits)<br>14<br>12<br>.2 10<br>8<br>26<br>4<br>2<br>0<br>-100 to <-1O -10Oto <-1 -l to < O to < IlIto < 0 1 lOto <100<br>Present Value of Net Benefits<br><!-- End of picture text -->

**Source:** Hahn (1996)
|ML_dml_|_−_0.002<br> (0.061)<br>0.038|(0.079)<br>0.052|(0.045)|Yes|386|ard<br>were<br> 100|
|---|---|---|---|---|---|---|
|ML_b_|_−_0.069 <br> (0.056) <br>0.106_∗∗_|(0.050) <br>0.049|(0.035)|Yes|386|the stand<br>rd errors <br>d conduct|
||<br>|||||nd<br>a<br>n|
|CC|0.041<br>(0.128)<br>0.221_∗∗_|(0.098)<br>0.013|(0.039)|Yes|386|ession a<br>he stand<br>le in 5 a|
|SR|_−_0.211_∗∗_<br>(0.105)<br>0.372_∗∗∗_|(0.065)<br>0.246_∗∗∗_|(0.068)|Yes|386|ia OLS regr<br>cients and t<br>lit the samp|
|ML_b_||0.131_∗∗∗_|(0.039)|No|386|computed v<br>ession coef<br> LASSO, sp|
|ields<br>CC||0.095_∗∗_|(0.045)|No|386|ients were <br>e ML regr<br>on, we use|
|sures of Y<br>SR||0.448_∗∗∗_|(0.060)|No|386|beta coefc<br>cates that th<br>his estimati|
|Mea<br>ML_b_|0.106|(0.088)||No|386|s that the <br>L_dml_ indi<br>hod. For t|
|CC|0.075|(0.157)||No|386|indicate<br>tions. M<br>ning met|
|SR|0.582_∗∗∗_|(0.136)<br>||No|386|nd CC. ML_b_<br>h 100 replica<br>achine lear<br>1|
|ML_b_|_−_0.023<br> (0.074)|||No|386|l for SR a<br>strap wit<br>) double m<br> ***p_<_0.0|
|||||||ve<br>ot<br>8<br>;|
|CC|0.139<br>(0.107)|||No|386|illage le<br>d via bo<br> al. (201<br>**p_<_0.05|
|SR|_−_0.302_∗∗_<br>(0.119)<br>s per ha|bor per ha||efect?<br>No|386|d errors clustered at the v<br>L predictions are obtaine<br>wing the Chernozhukov et<br>nifcance levels: *p_<_0.1;|
||eed|la||ed|s|dar<br>e M<br>llo<br> Sig|
||ea<br>ty S|f Hh||Fix|ation|Stan<br>or th<br>ted fo<br>ions.|
||r<br>ti|o||e|v|_:_ <br>s f<br>u<br>it|
||Plot a<br>Quan|Days||Villag|Obser|_Notes_<br>error<br>comp<br>repet|

23
# _Estimates of election effects_

If there is a political expenditure cycle, FONCODES expenditures at a particular point should be a function of the electoral calendar:

# (1) _Expt =f(Elecb zd_

where _Expt_ stands for FONCODES expenditures in time t, _Elect_ is the presence (or absence) of an election at the end of time period t, and _z,_ is a vector of exogenous variables which affect expenditures. The timing of elections is taken to be determined outside the model. Insofar as it is easier to manipulate the timing of FONCODES expenditures than the timing of elections, this is a reasonable assumption.

Table 1 reports results of OLS regressions of total expenditures and expenditures for comnunitybased and special projects on electoral dummies, a linear time trend, and quarterly seasonal dummies. Columns (i), (iii) and (v) report results from the regressions of expenditures on a single electoral dummy variable which takes on the value one for the three months immediately prior to an election, and zero otherwise.<sup>6</sup> Columns (ii), (iv) and (vi) include three separate electoral dummies to allow the effect of elections on expenditures to vary by election.

**Table 1: The impact of elections on the timing of FONCODES expenditures**

||**Community**|**-based projects**|**Specia**|**l projects**|**Total exp**|**enditures**|
|---|---|---|---|---|---|---|
||(i)|(ii|(iii)|(iv)|(v)|(vi)|
|**ELECTION**|7.05**|-|5.07**|-|12.72***|-|
||(2.03)||(2.39)||(2.78)||
|**Election 1**|-|6.08|-|0.82|-|3.73|
|||(1.14)||(0.36)||(0.57)|
|Election 2|-|4.51<br>(0.69)|-|14.74***<br>(3.53)|-|21.84**<br>(2.58)|
|**Election 3**|-|10.49<br>(1.61)|-|11.72***<br>(2.73)|-|17.93**<br>(2.11)|
|**Monthly linear trend**|0.04|0.03|0.04|-0.00|0.09|0.01|
||(0.30)|(0.15)|(0.85)|(0.09)|(0.60)|(0.08)|
|**Seasonal dummies**|**Yes**|Yes|Yes|Yes|Yes|Yes|
|**Durbin-Watson statistic (original)**|1.20|1.22|2.45|2.43|1.49|1.42|
|**Durbin-Watson statistic (transformed)**|2.13|2.10|2.00|2.05|2.03|2.06|
|**R-squared**|0.167|0.173|0.278|0.370|0.274|0.328|
|**Number of observations**|42|42|42|42|42|42|

> * indicates significance at the 10% level; **indicate **s** ignificance at the 5% level; *** indicates significance at the 1% level.

Note: Absolute t-statistics are reported in brackets, and are corrected for serial correlation using the Cochrane-Orcutt procedure. A constant was calculated but is not reported.

6 Because of the two-week time lag between approval of a project and disbursement of funds, the relevant month for the parameter _Elect_ in equation (1) is taken to be that in which the project was approved if the election took place in the second half of the month, but the month preceding it if the election took place in the first half of the month.

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suggests that the unbanked are far from a homogeneous group. While they tend to be poorer and less educated as a group than the banked, the unbanked of Bogotá also include individuals from high income groups.

# **3.2 PAYMENT SERVICES FOR THE UNBANKED**

13. **The majority of the unbanked receives their salary in cash** , while the banked are more likely to receive paychecks or have their salaries deposited directly into savings accounts (see Figure 3.7). Handling cash payments makes the unbanked more susceptible to loss and robbery than the banked. Unlike in Brazil, where the majority of respondents prefers to be paid in cash, over 90% of the banked individuals in Colombia who receive payments by check and direct deposits responded to the question how they would like to be paid that they prefer the existing system.

**Figure 3. 7  Payment of salaries to the banked and unbanked**

<!-- Start of picture text --> 0.3%<br>Other<br>0.1%<br>Deposit in checking 0.5%<br>account 4.4%<br>5.0%<br>Check<br>9.4%<br>Deposit in savings 5.0%<br>account 52.4%<br>89.2%<br>Cash<br>33.8%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Population by income level<br>Banked Unbanked<br>Source: DANE household study of Bogotá , 2003<br><!-- End of picture text --> 14. **Cashing checks is costly for the unbanked.** One third of the people paid by check are unbanked, corresponding to 5% of the unbanked. The unbanked cash their checks in banks in 83% of the cases. The remainder change the checks with family members of friends, check cashing outlets (CCO’s), commercial stores, or other unidentified methods. In addition to the fees charged by banks or check cashing outlets (between 5% and 20% of the check’s face value), the time spent in accessing check cashing locations represents an additional cost for the unbanked. As Colombian banks do not honor checks drawn on other banks except for deposit account holders, checks have to be cashed at the bank on which they are drawn. According to the survey, 60% of the unbanked live over ten blocks away from a formal bank. Access to CCO’s appears slightly easier for the unbanked as 32% of the unbanked live closer than 10 blocks to a CCO.

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