rafmacalaba/datause-encoder-data huggingface dataset:
34 study whether the value of financial depth in 1960 predicts the rate of economic growth, capital accumulation, and productivity improvements over the next 30 years. Table 3 summarizes some of the results. In the three regressions reported in Table 3, the dependent variable is, respectively, real per capital GDP growth, real per capita capital stock growth, and productivity growth averaged over the period 1960-1989. The financial indicator in each of these regressions is the value of DEPTH in 1960. The regressions indicate that financial depth in 1960 is significantly correlated with each of the growth indicators averaged over the period 1960-1989.22 These results, plus those from more sophisticated time-series studies, suggest that the initial level of financial development is a good predictor of subsequent rates of economic growth, physical capital accumulation, and economic efficiency improvements over the next 30 years even after controlling for income, education, political stability, and measures of monetary, trade, and fiscal policy. 2 3 The relationship between the initial level of financial development and growth is economically large. For example, the estimated coefficients suggest that if in 1960 Bolivia had increased its financial depth from 10 percent of GDP to the mean value for developing countries 22 There are an insufficient number of observations on BANK, PRIVATE, and PRIVY in 1960 to extend the analysis in Table 3 to these variables. Thus, King and Levine (1993b) use pooled, cross-section, time-series data. For each country, data permitting, they use data averaged over the 1960s, 1970s, and 1980s; thus, there are potentially three observations per country. They then, relate the value of growth averaged over the 1960s with the value of, for example, BANK in 1960, and so on for the other two decades. They restrict the coefficients to be the same across decades. They find that the initial level of financial development is a good predictor of subsequent rates of economic growth, capital accumulation, and economic efficiency improvements over the next 10 years after controlling for many other factors associated with long-run growth.

23 Financial sector development tends to Granger-cause economic performance (Paul Wachtel and Peter Rousseau (1995) and Klaus Neusser and Maurice Kugler 1996, but disagreement exists (Woo Jung 1986; and Philip Arestis and Panicos Demetriades 1995).
<!-- Start of picture text --> Figure 4. Firms' Outuput Gap<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>-2%<br>-4%<br>-6%<br>mean median<br>1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995<br><!-- End of picture text --> <!-- Start of picture text --> Figure 5 - Adjustment functions<br>Baseline estimation (all other shortages=0)<br>1,0<br>0,9<br>0,8<br>0,7<br>0,6<br>0,5<br>0,4<br>0,3<br>0,2<br>0,1<br>0,0<br>-2 -1,6 -1,2 -0,8 -0,4 0 0,4 0,8 1,2 1,6 2<br>White Collar Blue Collar Capital<br><!-- End of picture text --> 29
In this paper, we study two distinct shocks stemming from the global transmission of the crisis and their respective impact on credit availability for micro, small and medium firms. We analyze a unique dataset including information from all business loan applications and loan contracts of AccessBank Azerbaijan between 2006 and 2009. The bank provides an interesting object of study since it is foreign-owned and largely refinanced in foreign currency as are many MSME banks in the region. Due to the distortions in international capital markets, which, for instance, made the placement of a CDO impossible, the bank was hit by unforeseen delays in its refinancing pipeline in the second and third quarters of 2008 (the first shock) although its financial performance remained very strong.

At the same time, Azerbaijan’s economy proved resilient to the international turmoil up to the last quarter of 2008 when economic growth eventually slowed down (IMF (2008), Hübner and Jainzik (2009)). The timeline of events in Azerbaijan allows us to study a pure supply side shock, the _Funding shock_ , while firms had not yet been affected by the economic crisis (see also Dooley and Hutchison (2009) for this so-called decoupling of emerging markets in the beginning of the crisis). Then, we can contrast its impact on credit availability for firms of different sizes and industries from the impact of the _Real economy shock_ (the second shock). Important for our study is that AccessBank Azerbaijan faced a short period of tight refinancing at a time when the economic crisis was still absent from the country, while its refinancing pipeline strengthened again from the fourth quarter of 2008 onwards, i.e. exactly at the time when Azerbaijan’s economy started to slow down. This setting allows us to study how banks react to different shocks coming from the funding vs. the real economy side of their business and the mechanisms they have at hand.

Observing loan applications and the bank’s decision whether to approve the application, we assess which loan types (agro, micro and SME) are affected most within the two channels identified. During the economic slowdown, we expect SME loans to be affected most as their credit quality is likely to deteriorate most. SME businesses, compared to microenterprises, are involved in a broader variety of activities and are more likely to provide non-subsistence goods and services (such as furniture and household appliances or real estate services), for which demand typically decreases in economic downturns, or may be suppliers to the oil industry.<sup>3</sup> Finally, we examine whether previous bank-borrower relationships help to mitigate credit constraints.

> 3 See e.g. http://www.cdcdevelopmentsolutions.org/eastern-europe-and-caucasus#Azerbijan for efforts to increase SME capacities to win contracts with the oil industry in Azerbaijan. See IFC (2009) for a comparison of sectors in which SMEs and individual entrepreneurs are active.

3
Table 5: Satellites, bands and indices used in the analysis.

|Band /<br>Index|Name|Central wavelength / Index formula|Satellite|
|---|---|---|---|
|VV|Vertically polarized backscatter|5.5465763 cm|Sentinel-1|
|VH|Horizontally polarized backscatter|5.5465763 cm|Sentinel-1|
|RATIO|Ratio|VV / VH|Sentinel-1|
|DIFF|Difference|VV – VH|Sentinel-1|
|RDED4|Red Edge 4|865 nm|Sentinel-2|
|GCVI|Green Chlorophyll Vegetation Index|(NIR – GREEN)/1|Sentinel-2|
|NBR1|Normalized Burn Ratio 1|(NIR – SWIR1) / (NIR + SWIR1)|Sentinel-2|
|NDTI|Normalized Difference Temperature Index|(SWIR1 – SWIR2) / (SWIR1 + SWIR2)|Sentinel-2|
|NDVI|Normalized Difference Vegetation Index|(NIR – RED) / (NIR + RED)|Sentinel-2|
|SNDVI|Smoothed Normalized Difference<br>Vegetation Index|(NIR – RED) / (NIR + RED + 0.16)|Sentinel-2|

# **2.2.3 Harmonic regressions for characterizing crop phenology**

We used the multi-temporal collection of bands and indices from S1 and S2 to capture changes in crop phenology over time. To identify temporal patterns that characterize crop phenology, a harmonic regression model was fit at a pixel level to the time series of each unique band and index (Deines et al. 2020, Jin et al. 2019). See Equations 1 and 2 for Malawi and Ethiopia, the latter of which includes an additional pair of harmonic terms. The algorithm produces features that capture the seasonality of different crop types and that include harmonic coefficients, seasonal mean, and goodness of fit measures. These features are useful to map crop types because a maize pixel undergoes seasonal changes in greenness that differ from those of other crops (See Figure 2).

Equation 1 G _0 1 2_ c _2 1 3_ ( _2 1 4_ c _2 2 5_ ( _2 2_ (where _1_ = _1_ and _2_ = _2_ ) tt GGII = ββ + ββ tt+ ββ cc( ππωω tt) + ββ ccs ππωω tt) + ββ cc( ππωω tt) + ββ ccs ππωω tt) + εε ωω ωω Equation 2 G _0 1 2_ c _2 1 3_ ( _2 1 4_ c _2 2 5_ ( _2 2 6_ c _2 3 7_ ( _2 3_ tt (where GGII _1_ == ββ _1_ , + ββ _2_ = _2_ tt+ ββ, and _3_ cc(= _3_ ππωω) tt) + ββ ccs ππωω tt) + ββ cc( ππωω tt) + ββ ccs ππωω tt) +ββ cc( ππωω tt) + ββ ccs ππωω tt) + εε ωω ωω ωω 12
Policy Research Working Paper 9025

# **Abstract**

In Addis Ababa, an increasing block tariff has been used to calculate households’ monthly bills for electricity and water services. This study estimates the magnitudes of the combined water and electricity subsidies received by households with private connections to the electricity grid and piped water network in 2016, and it evaluates the distribution of these subsidies among wealth groups. Customer billing data supplied by utility companies are matched with socioeconomic information collected through a household survey. It is the first detailed analysis of the combined effects of increasing block tariffs for electricity and water in an urban area in a developing country. The results show that the combined subsidies are large. The average household receives a subsidy of US$26 per month, about 6 percent of household income. The findings also show that electricity and water subsidies under the increasing block tariff disproportionately accrue to richer households, with even less targeting when both sectors are considered jointly. The poorest quintile receives 12 percent of the total subsidies for electricity and water services, while the richest quintile receives 31 percent. The water increasing block tariff’s targeting of subsidies was somewhat worse than that of the electricity increasing block tariff.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The lead author may be contacted at helenacd@live.unc.edu. The project leader may be contacted at mtoman@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(6) where 1 and 0 are the outcomes ( ) that would be obtained by a country if it is exposed to treatments and respectively, corresponds to while is PP3 PP3 PP3 , # = 1,2, is a set of covariates, and denotes the propensity scores. The TT= 1 TT= 0 TT= 1 PP# > 0.5 TT= 0 outer expectation is over the distribution of . Table 10 presents the values of the PP# ≤0.5 WW pp(WW) ATTs. For the sake of brevity, the results of the estimation of the Probit models used to obtain (pp(WW)|TT= 1) propensity scores are given in Tables A.4 and A.5 in the appendix.

We see from Table 10 that Provision has a negative and significant effect on Participation in MENA, whilst the effect is positive and significant in OECD. As to Protection, it has a positive and significant effect on Participation both in MENA and OECD. Thus, the treatment effect estimation confirms results obtained with the IV fractional Probit. This demonstrates the robustness of the results obtained previously as to the effects of Provision and Protection on Participation.<sup>52</sup> Moreover, an important step in the PSM approach is the analysis of whether or not the covariates are balanced after the matching. To do this, in the same line as D'Agostino (1998) and Grier and Grier (2021), we rely on the t-test on the mean of each independent variable after the matching. The results are reported in Tables 11, 12, and 13.

**<u>Table 10.</u>** <u>PSM treatment effect estimation</u><sup>+</sup>

|Treatment<br>|**Table 10.**PSM treatment effe<br>MENA<br>MENA|ct estimation <br>OECD|OECD|
|---|---|---|---|
|P2 > 0.5<sup>++</sup><br>|−0.03<sup>∗∗∗</sup>|0.06<sup>∗∗</sup><br>[0.15<sup>∗∗∗</sup>]<sup>+++</sup>||
|<br>P1 > 0.5<br>|0.04<sup>∗∗∗</sup><br>||0.40<sup>∗∗∗</sup>|
|<br>Obs.<br> <br>|187<br>187<br> <br>|382<br>|382<br>|
|<br>+ ATTs reported;*:p<<br><br>|0.10,**:p< 0.05,***:p< 0.01.<br><br>|||
|<br>++For MENA, as the<br>seen, using this treatme<br>+++Value of theATTw|<br>maximum ofP2in the data is less than0.5,<br>nt for OECD as well does not change the c<br>ithP2 > 0.25|we take as a treatment <br>onclusion.|P2 > 0.25. As can be|

The t-tests for covariate balance show that after the matching, there is no significant difference in the means of the covariates between the control and treated groups. This holds for both the OECD and MENA samples, and for all the treatment effects estimations. Hence, in

> 52 Note that a competing approach to PSM commonly discussed in the literature is the pure Nearest Neighbor Matching (NNM). See Abadie and Imbens (2006, 2011), among others. In NNM, the nearest neighbor algorithm is applied based on a weighted function of the covariates for each observation, and not on propensity score as in PSM. We estimated the ATTs using NNM and found similar results. These results are available from the authors upon request.

29
For tables A17 to A20, in all regressions, the IV is generated using the mean border distance. Regressions (1) to (4) show results with the regressor (Log of refugees at 50km) constructed using distance to closest cluster. For regressions (5) to (8), the refugee index is generated using the square of the distance while for regressions (9) to (12), the square root of the distance to closest cluster is used. These represent the decay categories of the refugee index. Year and cluster fixed effects are included in regressions (1) to (3), (5) to (7) & (9) to (11). For each of the 3 decay categories of the regressor (i.e. the refugee index), the first regression is without other controls; the second regression has age, agesq & sex(male==1) controlled for; and the third regression has the more endogenous controls added (i.e. HH size, initial marital status, education, occupation, other HH income sources & ownership of land). In Regressions (4), (8) & (12), household fixed effects replace the cluster fixed effects and all the other controls are included, but the time invariant ones drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses. Significance level is: *** p<0.01, ** p<0.05, * p<0.1.

# **Table A17: Second stage: Log Refugees at 50km with decay function (i.e. index generated using 1)distance to closest cluster, 2)square of distance to closest cluster** **<u>and 3) square root of distance to closest cluster) instrumented by distance-based IV.</u>**

|||||Lo<br>|g of welfare<br>|(consumption<br>|aggregate per ad<br>|ult equivalen<br>|t)<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of refugees (50km) (with distance)|0.03226|0.03207|0.07808*|0.07384*|||||||||
||(0.04466)|(0.04459)|(0.04453)|(0.04302)|||||||||
|Log of refugees (50km) (with sq distance)|||||0.08294|0.08246|0.19062*|0.18028*|||||
||||||(0.11182)|(0.11163)|(0.10133)|(0.09788)|||||
|Log of refugees (50km) (with sqrt distance)|||||||||0.02395|0.02381|0.05854*|0.05534*|
||||||||||(0.03335)|(0.03330)|(0.03403)|(0.03286)|
|HH head age||0.00261|0.01187**|-0.00029||0.00264|0.01193**|-0.00068||0.00260|0.01188**|-0.00012|
|||(0.00510)|(0.00558)|(0.01656)||(0.00511)|(0.00558)|(0.01663)||(0.00510)|(0.00558)|(0.01654)|
|Age squared||-0.00003|-0.00008|-0.00000||-0.00003|-0.00008|-0.00000||-0.00003|-0.00008|-0.00001|
|||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)||(0.00005)|(0.00005)|(0.00014)|
|HH head sex (male)||0.00470|0.02499|-0.07482||0.00489|0.02578|-0.06926||0.00466|0.02483|-0.07556|
|||(0.03461)|(0.03745)|(0.06348)||(0.03457)|(0.03751)|(0.06376)||(0.03462)|(0.03744)|(0.06347)|
|Household size|||-0.02626***||||-0.02642***||||-0.02623***||
||||(0.00539)||||(0.00539)||||(0.00539)||
|Married monogamously|||-0.42111**||||-0.41838**||||-0.42183**||
||||(0.16636)||||(0.16609)||||(0.16638)||
|Married polygamously|||-0.36601**||||-0.36215**||||-0.36699**||

25
Figure 3. Poverty Estimates 2009-2013 Near the Syrian Border <!-- Start of picture text --> Host communities Near the Syrian Border<br>80<br>70<br>60<br>50<br>40 44.2 42.6 40.9 41.1 40.3<br>30<br>20<br>10<br>0<br>2009 2010 2011 2012 2013<br>Settled Migrant Households Near the Syrian Border (>4 years in Turkey)<br>80<br>70<br>60<br>50<br>40<br>30<br>27.5<br>23.8<br>20 21.3<br>17.7<br>12.5<br>10<br>0<br>2009 2010 2011 2012 2013<br>Recent Migrant Households Near the Syrian Border (<=4 years in Turkey)<br>80<br>70<br>60<br>50<br>46.7<br>40<br>37.0<br>30<br>27.1<br>20<br>17.5<br>15.6<br>10<br>0<br>2009 2010 2011 2012 2013<br>Poverty Rate (%)<br>Poverty Rate (%)<br>Poverty Rate (%)<br><!-- End of picture text --> _Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. Vertical line represents standard error. Poverty line is $5/day PPP per capita. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. Results displayed at the 95% Confidence Interval. _Source_ : LFS 2009-2014. World Bank author’s calculations.

22
assumptions about “true” WTP from the identified interval associated with a switch from one option to the other, by selecting either a single location or imposing a distribution of values within this interval. In many cases, either the midpoint or one end point of the interval is used, which results in artificially low variation and may introduce measurement error. In addition, observations that exhibit NSB must either be dropped from the dataset or modified to impose an arbitrary endpoint on the open interval.

The method that most closely respects the structure of the data is the interval regression approach proposed by Andersen et al. (2007), which employs a generalization of the tobit model. However, MSB observations remain a problem; the researcher has to make assumptions about the interval in which the subject’s WTP lies for MSB. Typically, the first and last observed switch are used, ignoring information from in-between switches.

In summary, existing approaches to MPL deal with choice error only incompletely. Moreover, the problem of order bias has been largely overlooked. This may be because in many contexts, uniform bias is not overly distorting: for example, in many applications of MPL, the main interest may be ordinal preferences or a simple sample split (e.g., into a more or less risk averse group). However, in WTP elicitation, the (cardinal) monetary value has meaning: a downward or upward shift of the distribution of elicited WTP due to bias may make the difference between a majority of the population expressing a positive vs. negative valuation for a service or policy.

In the next section, we propose a two-pronged approach to MPL design and estimation that uses randomization to detect order biases and a random utility model that includes fixed effects and order indicators to explicitly account for choice error and bias. In section 3.6 we use our approach to show with data from South Africa that (i) there is significant order bias in MPL data that affects estimated WTP, and (ii) many existing estimation approaches may exacerbate measurement issues.

# **3 Accounting for choice error and bias in MPL design and WTP estimation**

## **3.1 Accounting for bias: MPL design**

As discussed above, while the MPL format has many advantages, such as ease of understanding and rich data, it is plausible that it also induces some framing or anchoring effects. A challenge in examining order bias is that typical MPL implementations do not 15
Table 4b continued

||Year|Share of<br>wage<br>employment<br>in<br>agriculture<br>_ _ _ _ _ _ _|Share of<br>wage<br>employment<br>in<br>industry<br>_ _ _ _ _ _ _**_(%)**<br>**(%J)**|Share of<br>wage<br>employment<br>in<br>services<br>(NO<br>M|Working age<br>population<br>in 1995<br>(thousands)|Source|Income<br>group|
|---|---|---|---|---|---|---|---|
|MozAnbique||0.0|6.4|7.0|8,486|e|low|
|Ghana|1984|5.3|25.2|37.0|9,045|c|low|
|Uganda||_5.8_|31.5|28.4|9,970|e|low|
|Kenya||10.4|42.4|37.6|13,845|e|low|
|Sudan||10.3|42.3|37.6|15,238|e|low|
|Tanzania||1.3|20.6|19.0|15,242|e|low|
|Zaire||6.4|44.9|33.5|21,485|e|low|
|South Africa||27.5|67.6|60.0|24,994|e|middle|
|Ethiopia||0.4|I83|17.5|29,401|e|low|
|Nigeria|1986|2.3|21.0|35.1|63,988|1|low|

Note: Working age population for countries ofthe forner Yugoslavia, former Czechoslovakia, and former Soviet Union are calculated by a forward projection of the 1980-90 growth rate, see text for detail Sources: cl,b,o Census survey, Labor force survey, Household survey and Official Estimates from ILO various years; sCountry sources, see text for details; e=Estimates, see text for details 35
_4_ **_PoUsh Fisc4l Crisis_**

**Table 1: T'axes paid by the enterprise sector (as a percentage of sales at producer prices)**

||1988|<br>1989|<br>1990|1991|
|---|---|---|---|---|
|Turnover tax|7.1<br>|6.8<br>|5.0<br>|**_5.6_**<br>|
|Profit tax<br>|7.9|8.8<br>|8.4<br>|**4.5**<br>|
|Dividend||1.°<br>|1.8<br>|1.7<br>|
|**TIP**|**_0.5_**<br>|1.8<br> <br>|2.0<br>|2.8<br>|
|Payroll taxes<br>of which|6.7*<br>|<br>7.2*<br>|6.6<br>|**_8.5_**<br>|
|Wage taxes<br>|2.2<br>|**_2.5_**<br> <br>|2.1<br>|2.7<br>|
|Social security and labor fund|4.4*<br>|<br>4.7*<br>|**_4.5_**<br>|**_5.8_**<br>|
|Real estate tax<br>|_0.5_<br>|0.2<br>|0.4<br>|0.8<br>|
|All taxes above|22.6|26.2|24.2|24.0|
|Net indirect taxes<br>|-0.3|-07|1.1|2.9|
|(turnover tax minus subsidies)|||||
|Memorandum items:<br>|||||
|Sales of own productioti (bn**zi)**<br>|48541<br> <br>|153768<br>|805397<br>|1001933.<br>|
|" at "producer prices' (bn**zi)**<br>|48704<br> <br> <br>|154874<br>|796845<br>|974137<br>|
|Nominal GDP (bn**zi)**|29629<br>|118319|606726|940300|
|General government surplus/deficit|||||
|as % of GDP<br>|0.0|-6.0|3.1|-5.3|
|Industrial price index inflation<br>|<br>||||
|(% increase, December-December)|<br>75.5|653.0|192.9|35.7|

* Estimate

# III. MAMNG SENSE OF POLISH PROFTS

The main problem with interpreting the published data4 on enterprise profits is that historical cost accounts (HCA) suffer from an inflation bias, and during the period in question inflation in Poland rapidly accelerated and then decelerated (Table 1). The inflation bias results from the lag between the time at which an input is purchased and the time at which the output that ermbodies the input is sold. If the price level increases during this period, the pr duct will look like it generated large profits for the enterprise - input costs were low relative to the selling price - but this is an illusion. These are just 'paper profits", 4. The main source of data on the enterprise sector used in this paper is the Polish Central Statistical Office. Th ee d ata are supplemented with data from the Ministry of Finance.