rafmacalaba/datause-encoder-data huggingface dataset:
households without electricity themselves. And we show that ignoring these external effects, and latent geographic heterogeneity, substantially biases impact estimates toward over-estimating the gains from rural household electrification. In particular, the use of village placement as an instrument for household placement is also seen to be a source of sizeable bias in some past estimation methods. Indeed, when compared to our preferred estimator, ignoring endogenous assignment across households appears to yield a less biased estimate than obtained by assuming that village placement only alters outcomes via household placement.

After reviewing the literature in the following section, we examine the impacts of electrification in a model of household time allocation. We then discuss our data (Section 4) and method specification (Section 5). Our results are presented in Section 6. Section 7 summarizes the lessons learnt from the study.

# **2. Arguments and evidence from the literature**

The evidence on the economic gains from rural electrification is somewhat mixed. World Bank (2004) and Mathur and Mathur (2005) point to large differences in time allocation between rural Indian households with electricity and those without it, although concerns about endogenous selection clearly cloud the inferences that can be drawn. Some papers have reported evidence of seemingly large impacts of household electrification on household consumption, income and other dimensions of welfare in developing countries (Khandker et al. 2009a, b, 2012, 2013). Using data for India, Khandker et al. (2012) claim proportionate impacts of electrification on income of the order of 25-50%.<sup>5</sup> Other studies, including Bensch et al. (2011) using data for Rwanda, do not find such impacts.

Labor supply responses have figured prominently in past arguments. One popular hypothesis on the benefits of household electrification is that by relieving time burdens spent in collecting and preparing fuel, household electricity results in rural women engaging in marketbased work (Wu, Borghans and Dupuy 2009; Dinkelman 2011; Kohlin et al. 2011). A number of studies using US data show that the introduction of household electrical appliances—by raising women’s productivity in domestic work—can account for a large share of the increase in married American women’s labor force participation in the 20<sup>th</sup> century (see, for example,

> 5 These are coefficients on household electrification in regressions for the log of income, using village electrification rates as the instrumental variable. Estimates vary with the conditional quantile. Impacts were somewhat lower for consumption, but still sizable for the upper consumption groups.

4
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 6. Large hidden costs associated with ENE**

||Power<br>billings<br>(consu-<br>mers +<br>EDEL)<br>(Gwh/<br>year)|System<br>losses<br>in<br>distri-<br>bution<br>to<br>consu-<br>mers<br>(%)|Collectio<br>Consu-<br>mers|ns (%)<br>EDEL|Cost-<br>recovery<br>bench-<br>mark<br>($/kWh)|Average r<br>(US$/<br>Consu-<br>mers|evenue<br>KWh)<br>EDEL|Average e<br>tariff (US<br>Consu-<br>mers|ffective<br>$/kWh)<br>EDEL|Total<br>hidden<br>costs ($ million/<br>year)|Total<br>hidden<br>costs<br>(%<br>reve-<br>nues)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|2007|2,374|23|70|27|0.16|0.04|0.02|0.04|0.02|366|542|
|2008|2,920|23|70|27|0.16|0.04|0.02|0.04|0.02|450|542|
|2009|3,236|23|70|27|0.16|0.04|0.02|0.04|0.02|498|542|

_Source:_ Derived based on Briceño-Garmendia, Smits, and Foster (2009); Angola calculations derived from IFC staff estimates and World Bank (2010a).

_Note:_ kWh = kilowatt-hour.

**Table 7. Increasing hidden costs at EDEL in terms of monetary value**

||Power<br>billings<br>(GWh/year)|System<br>losses<br>(%)|Implicit<br>collection<br>ratio<br>(%)|Cost-<br>recovery<br>benchmark<br>($/kWh)|Average<br>revenue<br>($/kWh)|Average<br>effective<br>tariff<br>($/kWh)|Total hidden<br>costs<br>($ million/year)|Total<br>hidden<br>costs<br>(%<br>revenues)|
|---|---|---|---|---|---|---|---|---|
|2005|1001|46|54|0.06|0.02|0.04|59|239|
|2006|1252|40|61|0.06|0.03|0.04|67|203|
|2007|1475|36|61|0.07|0.03|0.04|91|214|
|2008|1814|36|68|0.07|0.03|0.04|102|171|
|2009|2200|36|68|0.06|0.03|0.04|120|164|

_Source:_ Derived based on Briceño-Garmendia, Smits, and Foster (2009); Angola calculations derived from IFC staff estimates and World Bank (2010a).

_Note:_ kWh = kilowatt-hour; GWh = gigawatt-hour.

**Figure 6. Massive hidden costs in the power sector in Angola**

<!-- Start of picture text --> a) ENE b. EDEL<br>600% 300<br>500% 250<br>400% 200<br>150<br>300%<br>100<br>200%<br>50<br>100%<br>0<br>0% 2005 2006 2007 2008 2009<br>2007 2008 2009<br>Losses Underpricing Collection Inefficiencies Losses Underpricing Collection Inefficiencies<br>Source: AICD calculations.<br>revenue revenue<br>Hidden costs as a shre of Hidden costs as a shre of<br><!-- End of picture text --> The magnitude of hidden costs in Angola’s power sector, at about 400 percent of revenues, is second only to the Democratic Republic of Congo (figure 7). ENE’s hidden costs are 542 percent of its revenue and EDEL’s hidden costs are 164 percent of its revenue (figures 6a and 6b). In the Democratic Republic 16
**Table 1: Land use in Orissa**

|**Land use**|**Share of total area**(%)|
|---|---|
|Agricultural land (gross cropped area), of which:|**59**|
|Net area sown|_41_|
|Tree crops|6|
|Grazing land|_4_|
|Cultivable wasteland|_3_|
|Uncultivable wasteland|_3_|
|Fallow land|_2_|
|Forest land, of which:|**36**|
|Reserved forest area|_17_|
|Protected forest area|_10_|
|Other (e.g._panchayat_land & village forests)|_9_|
|Non-agricultural land|5|
|Total land area (15,540,000 ha)|100|

Source: Directorate of Economics & Statistics, Bhubaneswar Forest land makes up 36 percent of the state land area (Table 1). Over a fifth of the state population are tribal people whose livelihoods are traditionally derived from forest products. For administrative and management purposes, forests in Orissa are divided into three categories: reserve (representing almost half of the total forested area), protected (just over a quarter of forested area), and other (a quarter of the forested area), including village forests. Reserve forests are fully under the control of the Forest Department and are managed under various silvicultural systems. In protected forests, only the forest crop (i.e. trees) is managed by the Forest Department; the land is owned and controlled by the Revenue Department. Rights and privileges of local communities vary by the type of forest -restricted in reserved forests and more liberal in protected forests. Village forests are generally treated as open access lands with no investment from government, and are generally extremely degraded except where community protection has started (Saxena, 1996; Singh, 1995).

There are now 30 districts in Orissa (see Map). The process of district re-organisation is described in Box 1.

Three districts were selected for intensive study. The objective was to capture some of the intra-state variation in land tenure, agricultural and rural livelihood systems. Initially, it was assumed that much of this variation would stem from the historical legacy of the different land revenue systems prevalent in the state at the time of Independence. Consequently, one district was selected from each of: the former Bengal revenue system (Khurda district); Madras revenue system (Ganjam); Central Province system (Sambalpur). Dhenkanal district was also selected to represent the former princely states of Orissa, but had to be dropped from the field investigation owing to time constraints. As the study progressed, it became clear that there were indeed many differences among the three selected districts in the extent and nature of private land transactions, but these differences did not obviously or directly result from the legacies of different land revenue and tenure systems. Rather, they may be attributed to a combination of social, economic, and topographical factors. For instance, there is some evidence that land fragmentation is a serious problem in the coastal plains. At the same time, there are many similarities with respect to land markets. The land sales market is more or less uniformly depressed throughout the state. Tenancy is widely prevalent in spite of being banned throughout the state.

9
**Table 13: Performance of Medical Insurance, 1997-2002**

||1998|1999|2000|2001|2002|
|---|---|---|---|---|---|
|Gross Premium Income (GPI) (mn JD)|14.50|14.95|16.46|23.62|25.82|
|GPI Growth (% )||3.1|10.1|43.5|9.3|
|Reinsurance ratio %|50.8|48.2|46.9|37.0|37.6|
|NIC/NEP|85.4|89.7|85.6|85.5|78.3|
|Net Commissions (% of GPI)|-2.0|-3.8|-1.9|1.1|0.9|
|Total Expenses (% of GPI)|11.8|13.7|14.7|12.4|12.4|
|Traditional Combined Ratio (%)|95.2|99.6|98.5|99.0|91.6|
|Net Combined Ratio (% of NEP)|105.6|109.2|110.8|109.5|99.4|
|Underwriting Result (% of NEP)|-5.6|-9.2|-10.8|-9.5|0.6|
|Unearned Premium Reserves (% of NEP)|31.1|30.1|30.2|24.8|24.9|
|Outstanding Claims Reserves (% of NEP)|5.9|8.1|9.6|10.6|11.2|
|Total Reserves (% of NEP)|37.0|38.2|39.8|35.4|36.1|
|Outstanding Claims Reserves (% of NIC)<br>Source: In|6.9<br>surance Co|9.1<br>mmission|11.2|12.4|14.3|

# **G. Other General Insurance Lines**

Only 2 large companies and one medium company generate 45 percent of their premiums from fire, marine and general accident business. Most of the other companies rely on motor and/or medical insurance for two-thirds or more of their premium income. Many companies have a very small presence in the market, generating less than half a million dinars in gross premiums. The Herfindahl index of market concentration is 648 (Table 14). This is higher than in the motor branch, but lower than in medical insurance.

**Table 14: Institutional Structure of Other General Insurance, 2002**

|% of<br>Other Premiums|
|---|
|2 Large<br>24.8|
|11 Medium<br>57.0|
|13 Small<br>18.2|
|HHI<br>648|
|Source: Insurance Commission|

Large industrial and commercial risks operate with low loss ratios and benefit from negative commissions, implying high profits (Table 15). The commissions they receive from reinsurers exceed those they pay to their agents. This is normal practice in business with developing countries since reinsurers need to compensate local primary insurers for the general expenses they incur in generating their business. However, 2002 was an unusual year in the sense that the level of negative commissions exceeded the level of general expenses. In previous years, negative commissions covered 80 percent of general expenses.

16
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38
concentrated one-third of the market in 2007. These strong entries corresponded to Sociedad Agricola Drokasa in 1998, Camposol in 1999 –two of the largest exporters- and a Consortium of fruit producers in 2001.<sup>19</sup> Figure 8 presents the average number of exits according to the age of the exporting spell. We observe a drastic decrease in the average number of exits after the first year of exporting. In particular, we observe that in 667 exporting attempts, exporters cease to export after their first year of operation. Then, for spells that lasted at least two years, on average, only 271 came to an end after their second year of operations.

If we translate the exits into the fail rate by age group (Figure 8b), we observe that the decrease in the fail rate remains, although it is less abrupt. For instance, a one-year old spell has a 34% probability of failure (exiting the market), a two-year old spell has a 27% chance of failure. This declining trend continues as the attempts last longer.

Who are these exits? A large part of them are occasional exporters that try with only one shipment. Figure 9 shows the distribution of the annual number of shipments exported by firms that lasted only one year. Fifty-six percent of these single-year firms exported only one shipment.<sup>20</sup> In addition, the model suggests that lower quality entrepreneurs are more likely to exit. To examine this hypothesis, we develop a binary variable for the entrants that is one if the firm exited after one year. We expect exits to occur more frequently among low

> 19 According to the export transaction data from SUNAT, Camposol exported for the first time using that name in 2002. However, based on information obtained from the company's website and during an interview with a representative of the company, we observed that Camposol started to export in 1999 under the name of Sol Produce (and a different id number), previous name of the company and one of the brand names that the company uses today for its exports of packed asparagus. We took note of that fact and we combined the export transaction data from both companies and treated them as one under the name of Camposol.

> 20 We made a similar calculation for the group of individuals with single-year entries and obtained an even larger percent: 60% of the individuals that lasted one year exported only one shipment.

25
**Table 10: Break falsification test. Ackerberg et al. TFP Measure**

||Banking<br>break|Banking<br>break|Banking break<br>(Rajan-<br>Zingales)|Banking break<br>(Rajan-<br>Zingales)|Telecom break|Telecom break|Insurance<br>break|Insurance<br>break|Transport<br>break|Transport<br>break|
|---|---|---|---|---|---|---|---|---|---|---|
||2001|2001|2001|2001|2002|2002|2002|2002|1997|1997|
|Break|2.610***|2.480***|0.528***|0.558***|9.125***|9.794***|5.198**|3.890|8.053***|7.427***|
||(0.662)|(0.706)|(0.084)|(0.091)|(2.528)|(2.605)|(2.345)|(2.417)|(2.635)|(2.633)|
|Falsification test: 1|-0.070||0.180||4.565*||-0.099||0.381||
|year prior to break|(1.171)||(0.129)||(2.763)||(1.836)||(1.259)||
|Falsification test: 2||-0.330||0.161*||4.070||-3.378*||2.700*|
|years prior to break||(0.854)||(0.095)||(2.765)||(1.961)||(1.397)|
|Tariffs(t-1)|0.000|0.000|0.003|0.003*|0.001|0.000|0.000|0.000|-0.000|-0.000|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Input Tariffs(t-1)|-0.004|-0.004|-0.004|-0.004|-0.003|-0.002|-0.003|-0.003|-0.010|-0.010|
||(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.009)|(0.007)|(0.007)|
|Foreign Dummy|0.029*|0.029*|0.029*|0.029*|0.033**|0.033**|0.035**|0.036**|0.043***|0.044***|
||(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.017)|(0.016)|(0.016)|
|Observations|22,558|22,558|22,558|22,558|22,558|22,558|22,558|22,558|22,558|22,558|
|R-squared|0.030|0.030|0.035|0.035|0.030|0.030|0.028|0.028|0.032|0.033|
|Break coeff = year(s|) prior coeff||||||||||
|F-stat|5.21|10.74|7.2|17.09|2.91|4.39|5.02|7.57|6.59|2.36|
|p-value|0.023|0.001|0.008|0.000|0.089|0.037|0.026|0.006|0.011|0.126|

Notes: The dependent variable is the log TFP estimated using the Ackerberg et al. method for each of the 11 industries listed in Table 2. All specifications include firm and year fixed effects. Robust standard errors, clustered at the industry-year level, are reported in parentheses. *** denotes significant at the 1 percent level, ** at the 5 percent level, * at the 10 percent level.

45
0.:

# Notes to Chapter 2

1. The treatment given here understates the revaluation effect since it ignores the fact that, in the presence of a trade deficit, a devaluation will reduce nominal GDP.

2. Joan Robinson said that such a model would be as useful as a map with a scale of I in 1.

3. It has recently been argupd that (Bhaduri _et al.,_ 1993) that aid depresses domestic demand, so Griffin's point is reinforced not negated by incorporating aid's impact on income. These arguments are pursued in Chapter 3.

4. There is an obvious limitation to this approach given the restricted length of most developing country data sets. For an modified approach using developing country data see White and Wignaraja (1992).

5. For example, the apparent presence of autocorrelation in either time series or cross-section data is often symptomatic of incorrect functional form.

6. The concept of robustness (and its converse fragility) are formalized in the extreme bounds analysis (EBA) propounded by Leamer (1983). Pagan's (1987) examination of GSP and EBA shows there to be many points of overlap, and where they differ that GSP probably has the upper hand.

7. His argument is discussed more extensively in Chapter 3.

- B. Time series analysis of Colombian data gave a coefficient of -0.84.

9. White (1992d) discusses the factors behind the size and direction of the bias resulting from Griffin's misspecification.

10. This report is not yet in the public domain.

11. These results are from the simple regression. Despite the fact that the report contains the data for the analysis I was unable to replicate the regression results in the report (but got close for the macro policy variable). The slope coefficient on that variable is quite robust to changes in model specification (but not choice of sample).

12. The problem here may also be one of imposing an invalid restriction on the data by inappropriate pooling, which may be dealt with by adding dummy variables. (Invalid pooling is a _forn_ of omitted variable bias). Re-estimation of equation 2.3 with a slope dummy for EDUEXP (equal to EDUEXP for observations 36 to 38 and zero for all others) gives a siilar result to that from omitting these observations: the slope coefficient for EDUEXP is higher but insignificant.

24
**_<u>________________________________________________________</u>_**

# **Table of Contents**

|Introduction............................................................................................................................... 1|
|---|
|1.1<br>Impact of The Trade Facilitation on Competitiveness.................................................. 2|
|1.2.<br>The New Security Dimension....................................................................................... 3<br> <br>|
|2.<br>Existing Data Sets and Analytical Tools ...................................................................... 5|
|2.1<br>General Economic Indicators........................................................................................ 5|
|2.2<br>Trade Indicators .......................................................................................................... 10|
|2.3<br>Customs and Border Crossing Times ......................................................................... 16|
|2.4<br>Transport Infrastructure Indicators ............................................................................. 23|
|2.5<br>Services and Information Technology Indicators....................................................... 33|
|2.6<br>Development Indicators.............................................................................................. 37|
|Conclusions............................................................................................................................. 40|
|Annex A. World Business Environment Survey .................................................................. 42|
|Annex B. Investment Climate Assessments (ICAs)- Logistics Module............................... 43|
|Annex C. Infrastructure Indicators ....................................................................................... 49|
|Annex D. Module-Based Project Guidelines for Trade Facilitation..................................... 50|
|Annex E. Country Ranking by WMO Trade Facilitation Indicators.................................... 53|
|Annex F. TTFSE Performance Indicators ............................................................................ 55|
|Annex G. Trade Logistics Template..................................................................................... 57|
|Annex H. Sample of Infrastructure/Facilitation Indicators................................................... 59|
|Annex I. Summary of Main Data Sources.......................................................................... 62|
|Selected Bibliography............................................................................................................ 66|

iii ____________________________________________________________________________________________________________
Figure 1 (a): Indonesia Skill Premiums – basic controls <!-- Start of picture text --> Indonesia: Skill Premium and Skilled Workforce - Basic controls only<br>1<br>0.9<br>0.8 Skilled-1: At least<br>0.7 Upper Secondary<br>0.6 Skilled-2: Tertiary and<br>0.5 above<br>0.4 Skill premium-1<br>0.3<br>0.2 Skill premium-2<br>0.1<br>0<br>1994 1999 2001 2003 2005 2007<br>Year<br>of skilled workforce<br>Skill premium and proportion<br><!-- End of picture text --> Figure 1 (b): Indonesia Skill Premiums – industry controls <!-- Start of picture text --> Indonesia: Skill Premium and Skilled Workforce - With Industry controls<br>0.9<br>0.8<br>Skilled-1: At least<br>0.7<br>Upper Secondary<br>0.6<br>Skilled-2: Tertiary and<br>0.5 above<br>0.4 Skill premium-1<br>0.3<br>0.2 Skill premium-2<br>0.1<br>0<br>1994 1999 2001 2003 2005 2007<br>Year<br>of skilled workforce<br>Skill premium and proportion<br><!-- End of picture text --> 22
<!-- Start of picture text --> 7 8 9 10 11 12 13<br>Log per Equivalent Adult income<br>kernel = epanechnikov, bandwidth = 0.1028<br>.6<br>.4<br>Density<br>.2<br>0<br><!-- End of picture text --> Note: This figure shows the density function of per equivalent adult income and two poverty lines (the vertical lines). The dashed poverty line corresponds to the situation after the hypothetical elimination of SPS measures in Guatemala.

Source: Authors’ computation using the 2011 Guatemalan HHS.

27