rafmacalaba/datause-encoder-data huggingface dataset:
Ease of presentation, access and interpretation of DSI indicators are important to favor their use by the various stakeholders. For each of the sectors, the information on the countries’ percentile score and relative ranking for each of the sub-indices, aggregate categories and overall sector, would be provided on a DSI web platform. Figure 2 presents the DSI in education sector and their components as they would appear on a web platform.

**Figure 2: Delivering Service Indicators in Education and their Components**

<!-- Start of picture text --> At the School<br><!-- End of picture text --> <!-- Start of picture text -->
- Absence rate<br>- Number of hours children<br>are in school receiving<br>instruction<br>- Share of teachers with  Budget<br>minimum knowledge<br><!-- End of picture text -->

<!-- Start of picture text --> instruction<br>Teachers -<br><!-- End of picture text --> _Illustration of the DSI in the Education Sector_

# **6. PILOT SURVEYS AND PRESENTATION**

Following the design of the methodology for constructing the indicators, including survey instruments for collecting the data and implementation guidelines, AERC will pre-test the survey instruments in Tanzania and Senegal in the spring of 2010. Once the pre-tests are completed and the survey instruments refined, AERC will identify research teams and enumerators to pilot the first round of data collection in approximately 8 to 10 countries in Sub-Saharan Africa.

The pilot surveys should have two main objectives: (i) to field test and refine instruments that can be used to collect robust data; (ii) to identify a cost-effective and replicable strategy to collect data. After the first two pilots, the list of indicators could possibly be revised, particular in light of the financial implications of scaling up the project.

Ease of presentation, access and interpretation of DSI indicators are important to encourage their use by the various stakeholders. For each of the sectors, the information on the countries’ percentile score and relative ranking in each of the sub-indices, aggregate categories and overall sector, would be provided on a DSI web platform. Data on actual outcomes should also be presented, as well as simple breakdowns (between urban-rural location for example) of the sample.
43 Pesaresi, Martino; Ehrilch, Daniele; Florczyk, Aneta J.; Freire, Sergio; Julea, Andreea; Kemper, Thomas; Soille, Pierre; Syrris, Vasileios (2015): GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_built_ldsmt_globe_r2015b Pesaresi, M; Ehrlich, D; Ferri, S; Florczyk, A.J; Freire, S; Halkia, M; Syrris, V (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014; JRC Technical Report EUR 27741 EN. Ispra (VA), Italy. https://doi.org/10.2788/253582 Prosperi, D., Moudon, A. V., & Claessens, F. (2009). The Question of Metropolitan Form: Introduction. _Footprint_ , _3_ (2), 1-4.

Rosenthal, SS; Strange, WC (2004). Evidence on the nature and sources of agglomeration economies, in J.V. Henderson and J.-F. Thisse, eds., Handbook of Urban and Regional Economics, Volume 4, New York: North Holland, 2119-2171.

Schwarz, N (2010). Urban form revisited—Selecting indicators for characterising European cities. Landscape and Urban Planning, 96:29–47 Sorensen, A., 2000. Land readjustment and metropolitan growth: an examination of suburban land development and urban sprawl in the Tokyo metropolitan area. Prog. Plan. 53 (4), 217–330.

Squires, G.D. (2002). Sprawl: Causes and Consequences and Policy Responses. The Urban Institute Press, Washington, D.C.

Sutton, P. C., Cova, T. J., & Elvidge, C. D. (2006). Mapping ``Exurbia’’ in the conterminous United States using nighttime satellite imagery. _Geocarto International_ , _21_ (2), 39–45. https://doi.org/10.1080/10106040608542382 Sutton, K., Fahmi,W., 2001. Cairo’s urban growth and strategic master plan in the light of Egypt’s
Table 5b: Representative plant-level regressions for log expected loans

|||Including<br>Estimation with|Including<br>state-year and i|Using state-<br>ndustry-year FE||
|---|---|---|---|---|---|
||Baseline<br>estimation|district<br>covariates|district-<br>industry FE|industry-year<br>FE|Dropping<br>weights|
||(1)|(2)|(3)|(4)|(5)|
|Total output|0.531+++|0.525+++|0.476+++|0.514+++|0.492+++|
||(0.013)|(0.013)|(0.012)|(0.012)|(0.014)|
|Total employees|0.330+++|0.340+++|0.391+++|0.347+++|0.374+++|
||(0.018)|(0.018)|(0.016)|(0.017)|(0.020)|
|Indicator for young plant|0.408+++<br>(0.026)|0.405+++<br>(0.027)|0.403+++<br>(0.025)|0.417+++<br>(0.025)|0.416+++<br>(0.027)|
|Indicator for rural/urban|-0.043+|-0.067+++|-0.114+++|-0.080+++|-0.089+++|
||(0.026)|(0.024)|(0.021)|(0.023)|(0.025)|
|Observations|142639|137970|142639|142639|142639|
|Adjusted R-squared|0.450|0.452|0.492|0.467|0.445|

Notes: See Table 5a. Table shows representative regressions for determining expected loan size for ASI plants. Estimations consider the log amount of financial loans for each plant as a function of basic plant traits. Estimations include state-year and industry-year FE, with additional controls added as indicated by column headers. Actual expected loan levels for plants are determined using industry-specific regressions as described in the text.
results from analysis of EDS-RDC data and local GDP data predict qualitatively and quantitatively consistent results.

## **6.2 Robustness checks**

To check the robustness of the IVs to relaxation of the exclusion restriction, the Conley Bounds are calculated following Conley et al (2012), and reported in Tables 7. Table 7 shows the 95% confidence intervals for three endogenous variables -- transportation cost, number of fatalities near households and number of fatalities near markets – used in the estimation presented in Table 3. The 95% confidence interval suggests that the coefficient on the log of transportation remains consistently negative for the wealth index and consistently positive for the multi-dimensional poverty indicator, even if the assumption of exogeneity of the IV were not entirely accurate.

Similarly, Table 7 also shows that the coefficients on the number of fatalities near households and number of fatalities near markets remain consistently negative for the wealth index and consistently positive for multi-dimensional poverty. Taken together with the Angrist-Pischke F statistic and first stage results, these findings indicate that the instruments significantly predict the endogenous variables. The Conley bounds estimates shown in Table 7 allows us to test how sensitive the estimates are to different degrees of exogeneity and provide us the range of effects of transport cost and conflict when the exclusion restriction assumption is relaxed to some extent. To our relief, the effect of transport cost reduction remains positive on wealth index and negative on multi-dimensional poverty indicator and the effect of conflict reduction remains positive on wealth index and negative on multi-dimensional poverty as the exclusion restriction assumption is relaxed upto 1 percent<sup>26</sup> .

# **7. Conclusion and Policy Implications**

This paper presents new results on the effects of transportation infrastructure in areas of high conflict. It is widely assumed in policy circles, as indicated by the large investments, that rebuilding infrastructure that is damaged by prolonged conflict, or perhaps even constructing new infrastructure, could induce economic benefits. The results of this analysis, however, present a more nuanced story. We have shown that in areas with high conflict, the economic and social benefits of roads may be negated, and in some cases, actually reversed. While these results in the context of transport infrastructure are new, they are reminiscent of past research on the effects of foreign aid and conflict. Collier and Hoeffler (2004) show that although countries tend to receive a large influx of foreign aid in the years right after a civil war

> 26 The range of possible values for δ follows previous application of the Conley Bound method in the literature (e.g. Emran et al 2009).” (p.8)

22
**Figure 1: Socio-economic and proximate determinants of child mortality**

<!-- Start of picture text --> Socio-economic determinants Proximate determinants<br>Individual level: Maternal factors<br>- mother’s education and skills - age<br>(MDG2 and MDG3) - parity<br>- mother’s time - birth interval<br>- father’s education (MDG2) - maternal health (MDG5)<br>Environmental contamination<br>- safe drinking water<br>Household level: (MDG7)<br>- income/wealth (MDG1) - sanitation (MDG7)<br>- intra-household decision power<br>(MDG3)<br>Nutrient deficiency<br>- breastfeeding<br>Injury<br>- accidental or intentional<br>Community level<br>- disease environment<br>- health facilities<br>Personal illness control<br>- water infrastructure (MDG7)<br>- vaccination<br>- malaria prophylaxis<br>- treatment<br>Child mortaliy (MDG4)<br><!-- End of picture text --> Source: Authors’ elaboration based on Mosley and Chen (1984).

Mosley and Chen (1984) group socio-economic determinants into individual level, household level and community level variables.<sup>3</sup> On the individual level, parents’ skills, health and time are important determinants of child mortality. Skills are typically measured by formal educational achievement although ‘child-raising skills’ may also be acquired in different ways. While the father’s education may often work primarily through its effect on household income, i.e. by affecting employment and income of the main income earner (in most households), it also plays an important role in household decision-taking (e.g. on household expenditure pattern) and for the father’s personal illness control. Yet, the mothers’ characteristics are of primary importance to child survival. During pregnancy and lactation the mother’s and child’s health are of course directly linked. Mother’s health and nutritional status, her reproductive behavior and knowledge of child health care practices (vaccination, hygiene, disease treatment etc.) will be influenced by her educational attainment. Finally, the

> 3 The following short summary of transmission channels from socio-economic factors to proximate determinants and relationships between those factors is by no means exhaustive. It mainly draws on Mosley and Chen (1984) where much more detail is provided.

5
- 28 -

Singapore and Malaysia have centrally managed provident funds, which are not geared at investing in shares. In Malaysia, contractual savings institutions invested in shares from 4 to 7 percent of their financial assets during 1987-93. Singapore only recently has allowed some members to pick private managers and to determine how a portion of their Central Provident Fund balance will be invested.<sup>30</sup> Therefore, there should be no surprise that there is no causality in any direction between contractual savings and stock markets in these countries.

**Table** 7: **Shares of Stocks in Investment Portfolios: Selected Countries**

|**Country**|**Year**|**Contractual**|**Life**|**Pension**|
|---|---|---|---|---|
|||**Savigs**||**Funds**|
|**Malaysia**<br>|1993|7.01|17.86|5.17|
|**Singapore**|1996|5.67|33.50|0. 00|

**Source:** WB institutional investors database.

Another particular case is Chile, where causality for pension funds runs in both directions. This could be explained, in great part, by their investment regulations. When the system was introduced, they were quite draconian, at that time; the Government was mainly interested in preserving assets, hence, pension funds were allowed to invest almost exclusively in government securities. In addition, real interest rates on bonds and bills were very high. As the system and the market developed, the regulations allowed increasing participation of shares in pension fund portfolios. At the same time, real interest rates were declining thus demand for shares increased fueled by both effects. Obviously, regulation of investment policies of these institutions and after tax rates of return on financial instruments matters.<sup>31</sup> The cautiousness and reactive approach followed by the Chilean authorities resulted in a two-way causality.

The evidence is consistent with the direction of causality emphasized in this paper. Contractual savings promote capital market development in countries where capital markets are relatively small. Of course, in countries where capital markets are already developed, the effect is not as strong and the direction of causality is not as clear. In those countries, we expect reciprocal and weaker effects between both variables. The latter would be, in part, due to the fact that the illiquidity effect of contractual savings, as discussed above, would be diluted in countries with well-developed financial markets.<sup>32</sup>

> 30 See Asher, 1999.

31 See Srinivas, Whitehouse and Yermo, 1999. 32 The direction of causality from contractual savings to capital markets was also accepted in Impavido and Musalem (2000).
8 more consistent.

Key facets of adjustment are now correlated with the firm groupings AAA, AA, A. Owing to the small number of AA firms, the comparison will focus on the two ex.remes, AAA and A.

# 11.2 Labor Adjustment: Real Sales. Productivity and Unit Labor Cost

The "big bang" was accompanied by an immediate and sharp drop in industrial output measured by real sales. This was an economy-wide phenomenon, resulting eventually in a 12% decline in GDP in 1990. But the drop in output was not matched by labor shedding, leading to declining productivity and eventually, rising unit labor cost.

Results in the sample followed this general pattern. However, employment reduction has continued while sales have gradually stabilized. Figure 1 plots real sales for selected months for the AAA, AA and A firms, using September 1989 as the base. For AAA firms, real sales hit rock bottom in mid-91 and have been on arn uptrend since. AA firms show recovery in 1992; but A firms are on a downtrend throughout. The difference shows up in productivity, shown in Figure 2. Comparing June 1992 and September 1989, AAA firms almost maintained productivity levels (-3%), while these fell for AA firms (-15%) and dropped drastically for A firms (40%). Not surprisingly, efficiency measured by unit labor cost shows that AAA firms were far ahead of A firms.

Figure 3 plots the trend in unit labor cost. The year 1989 was marked by sharply rising wages, with an acceleration in the last quarter. The big bang of January 1, 1990 was marked by a huge increase in materials and energy prices and a more than three-fold increase in interest rates, which exceeded 50% for the month of January alone. Firms froze nominal wages as a shock absorber, leading to a sharp drop in unit labor costs. However, as fears of bankruptcy receded, real wages and unit labor costs grew rapidly, a trend that persisted until the end of 1990. Subsequently, the trend has been downward, indicating that all finrns in the sample are taking measures to control labor costs, including wage restraint, labor shedding and maintaining output; but in comparing the two extremes, the performance of AAA firms is decidedly superior to that of A firms.

Table 5 shows substantial labor shedding, in spite of Workers' Councils. For the total sample, labor was reduced by a remarkable 27%, with the labor-intensive A group leading the way. However, this group has also been plagued with the biggest marketing problems, leading to falling productivity.

Table 5: Index of Employment (September 1989= 100)

|||<br>Dec-89<br>||<br>June-90|Dec-90<br>||<br>June-91|Dec-91<br>||June-92<br>j|
|---|---|---|---|---|---|---|
|AAA|101.7|95.1|93.4|87.4|84.4|79.4|
|AA|99.7|98.7|95.2|87.3|78.5|76.5|
|A|100.1|95.9|88.5|81.9|74.0|67.1|
|ITotal|100.8|95.8|91.3|84.7|78.8|73.2|
decades, they typically have not affected the consumer end of the business. Instead in water private participation was mostly introduced in water treatment plants and in electricity in generation according to the private participation in infrastructure database of the World Bank <u>( http://ppi.worldbank.org/).</u>

**Figure 6:   Cost Recovery by Public Utilities in Developing Countries**

<!-- Start of picture text --> Ratio of Revenue<br>to Costs<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Telecom Gas Pow er Water<br><!-- End of picture text --> Source: World Bank (1994) In other infrastructure sectors prices are low relative to costs and access for citizens is limited. Where access exists quality is often low, particularly under public ownership<sup>6</sup> . Citizens, who do not have access, typically lower income groups, pay higher unit prices for non-networked substitutes than the rich pay for their networked services. It is unsurprising that studies on the incidence of subsidies in infrastructure suggest redistribution tends to favor the better off (Brook and Irwin, 2003). Among non-payers of utility bills, government authorities stick out. The few studies that link infrastructure service provision to corruption perceptions are consistent with this. Corruption is associated with low quality and low maintenance expenditures (Tanzi and Davoodi, 1998) and inefficient operations (Bo and Rossi, 2007 for 13 Latin American electricity distribution companies).

When a system does not supply users with demonstrated high willingness to pay (including the poor) and when important customers do not pay, corruption does not principally operate by exploiting the pricing power of infrastructure services. Instead of taking rents out of profit rents are taken out of cost. Low prices mean that investment decisions are heavily dependent on fiscal

> 6 Gassner et. al. (2007) report that private sector participation is associated with an increase in length of uninterrupted daily supply, an improvement in collection rates, a decline in operational losses, improved labor productivity and an expanded capital base. Results on formal service price and rollout were inconclusive perhaps reflecting that privatization alone does not solve the issue of underpriced services.

13
effects on aspirations.<sup>24</sup> However, once one allows for latent heterogeneity in subjective welfare there are other interpretations of the Knight-Gunatilaka finding, as discussed in the next section.

A number of papers have found evidence that unemployment lowers subjective welfare at given income.<sup>25</sup> This is not what the standard economic model of work-leisure choice would suggest, since unemployment at given income implies greater leisure, which is assumed to yield utility. (The welfare cost of unemployment is seen to be through the lost income.) There may well be an independent disutility of unemployment that is missing from the standard model, possibly associated with the quantity constraints on choice that involuntary unemployment entails or possibly stemming from social status roles played by employment. However, the welfare effect attributed to unemployment may also stem from latent heterogeneity in personality traits, which is one of the issues to which the next section turns.

# **5. Problems in using subjective data in measuring poverty**

Subjective data can offer a direct lens on welfare that is not available in standard objective data. But are subjective questions reliable, in the sense that one gets similar answers under similar circumstances? Studies of SWL or ―happiness‖ have found a worryingly high degree of variability. For example, Kristensen and Westergaard-Nielsen (2007) find that 20% of survey respondents in Europe gave a different answer on job satisfaction when asked twice within the same interview. Krueger and Schkade (2008) studied reported SWL for the same people interviewed two weeks apart and found the serial correlation coefficient was only 0.6.

In the present context, it is not the reliability of the subjective welfare scores themselves that is of greatest concern, but the reliability of the regressions for subjective welfare, as used in calibrating social-subjective poverty lines, or measuring welfare more generally. If the likely errors in subjective welfare data are white noise—classical measurement errors—we would not be concerned; the R<sup>2</sup> will be lower but the regression coefficients will still be unbiased under standard assumptions. However, there are good reasons to expect the errors in subjective data to be correlated with other explanatory variables of interest, as argued by Bertrand and

> 24 The authors note the endogeneity concern and provide instrumental variables (IV) estimates although it is not obvious that there is any theoretically valid IV for this problem.

> 25 Examples include Clark and Oswald (1994), Theodossiou (1998), Winkelmann and Winkelmann (1998) and Ravallion and Lokshin (2001).

15
Table 7, continued

|**Reform 2(gradual)**||_Annual ch_|_ange from_|_base_||||
|---|---|---|---|---|---|---|---|
|**2A**|**2009**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|
|GDP (share)|100.0|0.00|0.01|0.21|0.21|0.18|0.15|
|Agriculture|8.3|0.00|–1.07|–1.18|–1.35|0.16|0.12|
|Industry|38.3|0.00|–0.28|–0.37|–0.46|0.02|0.18|
|Services|53.4|0.00|0.23|0.31|0.42|0.31|0.35|
|Poverty|42.8|0.00|0.49|1.35|2.44|2.57|2.10|
|Rural|47.6|0.00|0.40|1.26|2.24|2.49|1.88|
|Nonfarm|50.4|0.00|0.73|1.60|2.98|2.78|2.68|
|Farm|42.0|0.00|0.28|0.94|1.84|2.18|1.39|
|Urban|29.9|0.00|0.63|1.91|3.05|3.13|2.89|
|Compensation required for poore<br>under2A|st one-third of p|opulation||||||
|Per household (YER)|0.0|0.00|3,083|8,687|19,142|19,249|18,601|
|Total(billion YER)|0.0|0.00|3|8|17|17|17|
|**2B**||_Annual ch_|_ange from_|_base_||||
|GDP (share)|100.0|0.0|0.0|0.0|0.0|0.2|0.2|
|Agriculture|8.3|0.0|–0.9|–1.0|–1.1|0.1|0.1|
|Industry|38.3|0.0|–0.3|–0.4|–0.5|0.0|0.1|
|Services|53.4|0.0|0.2|0.3|0.4|0.3|0.3|
|Poverty|42.8|0.00|0.22|0.26|0.98|0.76|0.74|
|Rural|47.6|0.00|0.09|–0.18|0.29|0.00|0.06|
|Nonfarm|50.4|0.00|0.58|1.42|2.79|2.74|2.55|
|Farm|42.0|0.00|0.00|–0.54|0.27|–0.08|–0.16|
|Urban|29.9|0.00|0.27|0.53|0.34|0.18|0.50|
|**2C**||_Annual ch_|_ange from_|_base_||||
|GDP (share)|100.0|0.0|0.0|0.3|0.4|0.7|0.7|
|Agriculture|8.3|0.0|0.1|0.0|–0.2|–0.2|–0.2|
|Industry|38.3|0.0|0.6|0.9|0.9|1.1|1.2|
|Services|53.4|0.0|–0.2|0.2|0.5|0.9|0.9|
|Poverty|42.8|0.00|–0.33|–0.96|–1.49|–2.82|–4.28|
|Rural|47.6|0.00|–0.36|–1.24|–2.00|–3.36|–5.00|
|Nonfarm|50.4|0.00|–0.26|–0.22|–0.14|–1.40|–2.40|
|Farm|42.0|0.00|–0.35|–1.58|–2.42|–3.79|–5.64|
|Urban|29.9|0.00|–0.37|–0.55|–1.15|–2.49|–3.70|

Source: DCGE model results.

Both reform scenarios raise poverty levels up to 3.0 (accelerated) and 2.6 (gradual) percentage points above baseline values during the reform process. Under both scenarios, poverty starts declining after reform, but it is important to note that it may take some years after reform for households to recover, if no additional measures are taken. As an aggregate group, urban households are more affected than rural households, with poverty increases of between 3.3 and 3.1 percentage points, compared with 2.9 and 2.6 percentage points for rural households. However, as a subgroup of rural households, farm households are the most affected, especially those that rely on irrigation-intensive agriculture for their 16