rafmacalaba/datause-encoder-data huggingface dataset:
sum of that rate and that of A2,t. The last step is to check that the urban sector is indeed growing faster than the rural sector, thereby verifying that dualism has come to an end. All this will be done in Section 4.

# 3 Calibration

Ethiopia’s turbulent history in the second half of the 20th Century and the paucity of relevant data pose formidable challenges to all forms of calibration and rule out any that are heavily based on historical series.<sup>11</sup> The method adopted here involves anchoring the whole system to the observed values of key variables in the year 2000, which is the starting point of all the simulations in the sections that follow.<sup>12</sup> This is a minimum requirement; but with a plethora of parameters to be determined, it is far from sufficient. It is therefore supplemented by ‘guesstimates’ based on what appear to be reasonable values or regularities in cross-country data.

## 3.1 The rural sector

The values of the following variables in 2000 are observed: enrollments, average family landholding, and all demographic magnitudes. In the absence of any estimates of the years of completed schooling for this cohort, we have used USAID’s (2005) estimate of the net attendance ratio among children of primary school age, namely, e<sup>p</sup> 1,1<sup>=0.243.</sup> The average family landholding, l1, is 1.02 hectares (OPM, 2004: 3). We define as

> 11In particular, it is difficult to estimate the key relationship between changes in human capital and economic performance. For example, enrollment rates increased by roughly 15% under both the imperial regime, which was brought down by a coup in 1974, and the junta that followed (Negash, 1990), but GDP per capita increased under the first and decreased under the second. Similar difficulties arise in evaluating the contribution of investment in physical capital. An examination of the data set assembled by Nehru and Dhareshwar (1993) reveals that the growth rate of GDP per capita was negatively correlated with the level of average gross domestic fixed investment.

> 12The choice of base year naturally involves some compromises. Our choice was heavily influenced by the availability of demographic, health and educational data. For example, 2000 is the only year for which data on rural and urban attendance ratios are available.

15
The EEU’s tariff structure for electricity services has three components: a minimum monthly charge, a fixed monthly charge, and a volumetric charge. From a global perspective, EEU’s tariff structure is unusual because both the minimum monthly charge and the fixed monthly charge vary as a function of the customer’s monthly electricity use: both charges increase if monthly electricity use exceeds the quantity thresholds shown in Table 2.<sup>14</sup> The AAWSA’s tariff structure for water services is a single-part tariff with a volumetric charge; there is no fixed charge or minimum bill. The first 7 m<sup>3</sup> used are billed at $0.08/m<sup>3</sup> , and any water consumption in excess of 7 m<sup>3</sup> (but no more than 20 m<sup>3</sup> ) is billed at $0.18/m<sup>3</sup> . A household that uses more than 20 m<sup>3</sup> per month (but no more than 40 m<sup>3</sup> ) is billed at $0.23/m<sup>3</sup> for the amount over 20 m<sup>3</sup> , and so on. This IBT structure for water services is unusual in three respects. First, the second and third blocks are very wide. Second, there is only a modest increase in the volumetric charge from the second to the third block. Third, there are several (four) blocks above 40 m<sup>3</sup> per month, which is quite a large volume of water for a single household to use in just one month.

# **3. Data collection, sampling strategy, and fieldwork**

# **_Collection of customer billing records from electricity and water utilities_**

We obtained the complete set of monthly billing records from the Ethiopian Electricity Utility and the Addis Ababa Water and Sewerage Authority for their residential customers in metropolitan Addis Ababa for the one-year period from April 2015 through March 2016. Data from EEU included

> 14 The minimum monthly charge and the fixed monthly charge are both part of the algorithm used to calculate the monthly electricity bill for a connection. A fixed charge plus a charge for volumetric use are calculated for every connection (both private and shared). The total amount is then compared to the minimum charge associated with the volumetric use. If this total is _greater_ than the minimum charge, then the monthly water bill is the total (i.e., the minimum monthly charge does not affect the monthly bill). Yet if the total of the fixed charge and volumetric charge is _less_ than the minimum bill, then the connection’s owner is billed the minimum charge for that month.

7
Table 7. Model With Interaction Between Inequality and Initial Income (Alternative Instrument)

|Dependent Variable is:|||ln(yit)||||
|---|---|---|---|---|---|---|
|Sample:|All C|ountries|Excluding<br>Cou|Transition<br>ntries|Excluding<br>Cou|East Asian<br>ntries|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Inequalityit|9.67***|9.13***|9.21***|9.29***|9.04**|6.98***|
||(3.66)|(2.16)|(2.11)|(1.79)|(3.82)|(2.21)|
|Inequalityit<br>*ln(yit-1)|-1.16***<br>(0.46)|-1.10***<br>(0.28)|-1.19***<br>(0.31)|-1.13***<br>(0.24)|-1.07**<br>(0.48)|-0.82***<br>(0.29)|
|ln(yit-1)|0.61***<br>(0.06)|0.61***<br>(0.05)|0.61***<br>(0.06)|0.61***<br>(0.05)|0.60***<br>(0.06)|0.61***<br>(0.05)|
|Hansen J, p-value||0.98||0.86||0.77|
|Kleibergen Paap<br>F-Statistic|13|18|13<br>First Stag|17<br>e: Inequalityit|13|13|
|SWratioi*Oil pricet-2|0.44***|0.09*|0.32***|0.06|0.45***|0.08|
||(0.14)|(0.05)|(0.14)|(0.05)|(0.14)|(0.05)|
|SWratioi*Oil pricet-2|-0.05***|0.01**|-0.05***|0.01**|-0.05***|0.01|
|*ln(yit-1)|(0.01)|(0.00)|(0.01)|(0.0)|(0.01)|(0.01)|
|Residual Inequalityit||0.73***<br>(0.05)||0.72***<br>(0.05)||0.79***<br>(0.05)|
|Residual Inequalityit||0.02***||0.02***||0.01**|
|*ln(yit-1)||(0.01)||(0.01)||(0.00)|
||||First Stage: In|equalityit*ln(yit-1|)||
|SWratioi*Oil pricet-2|4.10***|1.27***|3.27***|1.04**|4.19***|1.23***|
||(1.11)|(0.47)|(1.16)|(0.48)|(1.16)|(0.05)|
|SWratioi*Oil pricet-2|-0.37***|0.15***|-0.36***|0.16***|-0.40***|0.12***|
|*ln(yit-1)|(0.10)|(0.05)|(0.10)|(0.05)|(0.10)|(0.05)|
|Residual Inequalityit||4.29***<br>(0.38)||4.23***<br>(0.38)||4.80***<br>(0.41)|
|Residual Inequalityit||0.25***||0.26***||0.19***|
|<br>*ln(yit-1)||(0.04)||(0.04)||(0.04)|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|487|487|472|472|463|463|

Note: The method of estimation is two-stage least squares. The inequality variable is the market Gini from Solt (2015). _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . Bootstrapped standard errors are shown in parentheses. *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

32
policies in the past decade are Ecuador, Uruguay, Indonesia, Tunisia, Thailand, Colombia, Costa Rica and Pakistan (decrease by at least 5 points on average). Underlying these patterns of overall liberalization across economies are differences in the sectoral pattern of liberalization which are discussed in greater detail below.

**Figure 7 Changes in STRI 2008-11/2016 and level of income, 55 economies**

<!-- Start of picture text --> IND PANPAN<br>IDN<br>THA<br>IND IDN<br>CHN<br>PHLPHL MYSMYS<br>TUN THA<br>EGY<br>BGD LKALKAEGY CHN GRC FRA FINBEL DNK<br>VNM TUN CRI URYARGARG PRT OMN<br>KENKENBGD PAKPAKVNMNGANGA UKRUKR COLCOLZAFZAFDOMDOM BRABRACRI MEXMEXURY TURTURCHLCHLKAZKAZ POLPOLRUSRUSHUNHUNLTULTUPRTGRCCZEKORKORESPESPNZLNZLITAITAFRAGBRGBRFINBELCANCANOMNDEUDEUAUTAUTSWESWEDNKNLD USAUSAIRLIRL<br>PERPER CZE JPN AUSAUS NLD<br>ECU JPN<br>ECU<br>8 9 10 11<br>Avg income per capita (2008-16)<br>Note: STRI 2008-11 in blue, STRI 2016 in orange.<br>70<br>60<br>50<br>40<br>STRI 2008-11, 2016<br>30<br>20<br><!-- End of picture text --> The broad trend towards liberalization described above is also evident when considering changes in the STRI by sector and income group (Table 2). Green shaded cells highlight a reduction in the level of restrictiveness, with dark green pointing to bigger reductions. Grey cells point to insignificant changes in STRI levels.

**Table 2:** Change in STRI by sector and income group:

|Sector|Low|Lower Mid<br>Income|Upper Mid<br>Group|High|
|---|---|---|---|---|
|Distribution|-1.69|-4.45|0.24|-2.78|
|Financial|-6.42|-3.84|-0.90|-0.02|
|Telecom|-10.05|-1.68|-1.36|-3.21|
|Professional|-1.89|-6.31|-5.14|-8.06|
|Transport|-1.76|-7.87|-3.07|-22.37|

Notes: Subsectors aggregated by weighted average into the five broad services sectors displayed in column 1 (based on relative shares of these services sectors in total value added, see table 6.1 in Annex 6 in Borchert et al. (2019). Colour coding: deep green: <-5 STRI-points; light green: (-5, -1); grey: (-1, 1). Grey shading indicates that policy restrictiveness has remained qualitatively unchanged.

All sectors have been liberalized but by varying degrees. Telecommunications, professional and transport services have been liberalized by economies at all levels of development. In professional services, liberalization has been particularly significant in lower middle-income and high-income economies. Liberalization of the transport sector, especially in rail and road transport, by highincome economies stands out. For other sectors, the level of liberalization is generally milder, and may be linked to the initial level of openness. In this context it is important to note that low-income countries, that have since graduated to middle-income, liberalized substantially their financial and 17
41 128. To lessen systemic risk as Mexico's financial sector and real sectors are opened to competition will require a consistent set of structural microeconomic reforms that will have to be carefully sequenced. These will have to include:

- (a) concerted efforts to rapidly improve supervision of financial conglomerates. This action would need to be coupled with passage of more modern accounting. valuation and capital regulations that force financial groups to adopt a higher minimum standard in respect to systems for measuring and managing different forms of risk. Actions to quickly assess the quality of bank portfolios would also be essential in addition to improvements in processes for bank failure resolution and in the system of deposit insurance.

- (b) changes to the legal and regulatory framework to increasingly create greater incentives for true self-policing by a broad set of self-regulatory associations as defined above. This could greatly enhance the quality of information and its analysis through development of better practices by auditors, accountants, actuaries, appraisers, financial analysts or rating agencies etc. This would. in turn. create greater prospects for development of much greater volumes of initial public offering by companies (See part (iv) below). Simultaneously, objective standards for entry into such organizations should be developed that can be monitored by supervisory agencies.

- (c) changes in the legal and regulatory framework in respect to secured transactions or bankruptcy and in judicial processes that will broaden the set of borrowers that can access formal credit markets and reduce costs of bankruptcy or reorganization. These efforts will also improve the base of information on all borrowers and the degree of security in different forms of collateral, if coupled with actions too improve and automate the public registries inclusive of commercial and civil registries.

- (d) actions to increase the liquidity of the private securities markets by increasing both the supply and demand for securities. To do this will require a set of consistent reforms in such areas as social security (inclusive of old age security and health) or insurance which will result in a greater demand for securities through development of a broader class of institutional investors to complement existing Mexican "mutual funds". At the same time non-financial corporations will need to be subject to enough competition to begin to feel the need to raise capital through public debt or equity offerings, rather than employ retained earnings or bank borrowing. Finally, brokerage firms and banks could be permitted to engage in repurchase agreements with private securities as collateral. for reasons examined in Section IV.A above.

- (e) when the cash markets are more well developed and liquid and when supervision and self-regulatory processes have been substantially improved it will become important to introduce a greater variety of derivative instruments. Similar arguments can be made in the case of asset-backed securities denominated in local currency for the reasons noted above.
Table 9, the coefficients on the interaction terms are 10 to 15 times greater in magnitude. Since the interaction term captures the association between foreign exchange reserves specifically in times of financial contractions (when _Lag financial contraction_ dummy takes on the value 1), this indicates that the positive effect of reserves on real sector growth is especially prominent during the episodes of sharp financial contraction. This finding suggests that countries in which the severity of the financial shock is magnified by financial openness rely on foreign exchange reserves to mitigate the adverse impact of such capital flight on the real sectors. The non-linear impact of reserves is most prominent in the sectors identified as most vulnerable to financial contractions: for construction sector, a 1 percentage point higher reserves to GDP ratio is associated with 0.2 percent higher value added growth rate on average, but a 2.8 percent higher growth rate in times of financial contractions (hence partially offsetting the effect of financial contractions).<sup>7</sup>

# Figure 2 [about here]

Figure 2 illustrates the degree to which foreign exchange reserves have served to mitigate the impact of sharp financial contractions in financially open economies on real sector value added growth rates. The four panels in the figure focus on sectors most affected by financial contractions: construction, manufacturing, wholesale and retail, and transportation. The figure plots predicted contribution of financial openness (solid line) and financial openness plus reserves (dotted line) to real value added growth rates of each sector during financial contractions episodes. Predicted contribution is calculated by multiplying the coefficients from panel regression in the full specification reported in Table 9 by the values of financial openness and reserve to GDP ratio (conditioning on financial contraction dummy taking on the value of

> 7 Regression coefficients on financial openness and the reserves-to-GDP ratio have different interpretations because of the way the data have been normalized: reserves-to-GDP ratio is in integer percentage points (1 unit increase in the variable represents a 1 percentage point rise) as in the WDI database, whereas other variables are in decimal percentage points (1 unit increase in the variable represents a 100 percentage point rise). The coefficient of 0.028 on interaction with the reserves-to-GDP ratio indicates that a 1 percentage point higher reserves relative to GDP during contractions is associated with 2.8 percent higher growth rate of construction sector real value added. In contrast, the coefficient of -0.233 on the interaction term with financial openness indicates a 1 percentage point greater de-facto openness during contractions is associated with  0.2 percent lower growth rate of construction sector real value added. The economic significance of these offsetting effects depends not only on the elasticities, but on the average values of each variable as discussed later in the text.

13
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datasets used in this paper. The inputs are far too numerous to mention but include a range of satellite inputs as well as gauge datasets. There are a wide range of outputs as well, including 2-meter air temperature and rainfall, available at sub-daily intervals and differentiated vertically. ERA5 is coarser spatial resolution than the global and regional merged rainfall datasets, but more detailed than MERRA-2.

The sheer number and complexity of outputs can be a deterrent to the use of weather variables from assimilation models. Uncertainty or lack of understanding about inaccuracies associated with individual output variables of assimilation models, compared to other types of models, is another reason to carefully consider their suitability for particular research (Parker, 2016). Nevertheless, reanalysis datasets are used in a broad range of applications and even outperform other gridded climate datasets in some settings (Zandler et al., 2020).<sup>6</sup> The second reanalysis dataset used in this analysis is MERRA-2, a product of NASA’s Goddard Earth Observing System, version 5 (GEOS-5) assimilation model. Specifically we make use of the variables T2MMEAN from the statD daily statistics collection, and PRECTOTLAND from the Land Surface Diagnostics collection.<sup>7</sup> Last, we consider an EO data product produced primarily from gauge data, using only spatial interpolation techniques to produce a continuous surface from observed measurements. The NOAA Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Daily Precipitation and Temperature datasets were created using all information sources available at CPC and undergoes extensive pre-processing and cleaning, including comparison with contemporaneous data from satellite and other sources (Chen et al., 2008).

NOAA’s Climate Prediction Center (CPC) Unified Gauge-based (CPC-U) datasets for daily temperature and precipitation do not incorporate EO data in the estimation of weather variables. Instead, an optimal interpolation (OI) technique is used on gauge data for precipitation, and Shepard’s algorithm for temperature. CPC-U provides systematic global datasets for validation and climate monitoring. GTS is a primary input data source, with some national collections, but density is most sparse over Africa.

As to be expected, even though the OI interpolation performs better than other techniques, a cross-validation exercise shows performance to degrade significantly with increasing distance to nearest station (Chen et al., 2008). As a result, this dataset may not be suitable for analysis in some parts of Africa, with high spatial variation and low density of stations.<sup>8</sup>

> 6 Data and technical documentation are available for download from `https://cds.climate.copernicus.eu` . 7 Data and technical documentation are available for download from `https://disc.gsfc.nasa.gov/` .

> 8Data and technical documentation are available for download from `https://psl.noaa.gov/data/gridded/data. cpc.globalprecip.html` for precipitation and `https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html` for temperature.

9
26 dispersion. A more intuitive measure of inequality is the decile ratio, which can be thought of as the ratio of high wages (upper decile) to low wages (bottom decile). This ratio is 2.8, which again indicates a modest level of inequality.

**Table 21 Summary of Earnings Distribution, 2001**

||Mean|mM<br>decile|edian|Top<br>decile|PlO|P90|ratio<br>|Gini<br>coeff.<br>(*100)|pay|High-pay|
|---|---|---|---|---|---|---|---|---|---|---|
|||Litas|||||%||||
|Allworkers|639|357|600|1004|59.5|167.3|2.8|27.3|22.5|<br>17.2|
|_Sector_|||||||||||
|Publicsector|734|367|640|1100|57.3|171.9|3.0|26.6|16.4|<br>21.5|
|Private sector|646|350|520|1,000|67.3|192.3|2.9|27.4|27.1|14.0|
|_Firmnsize_|||||||||||
|-10|647|346|500|1,000|69.2|200.0|2.9|..|34.8|12.5|
|11-19|625|357|500|1,000|71.4|200.0|2.8|..|26.6|<br>13.6|
|20-49|635|358|560|1,000|63.9|178.6|2.8|..|23.9|<br>12.2|
|50+|719|365|600|1,100|60.8|183.3|3.0|..|17.6|<br>20.6|

> Note: PlO denotes the earnings of thebottom decilerelative to the median, expressed as a percentage. The incidence of

> low (high) pay = low(high) paid workers as a percentage of all full-time wage and salary workers.

> _Source. LaborForce Survey, May 2001, Bank staffcalculations_

Another dimension of wage inequality is the "distance" between the low paid worker (at the bottom decile) and the median worker, as well as a distance between the median worker and the high paid worker (at the upper decile). In line with earlier results, these distances are not very high by standards of transition economies. The bottom decile worker earns about 60 percent of the median wage (PlO ratio), which means that his/her relative earnings position is relatively good. On the other hand, the top decile worker earns 67 percent more than the median worker (P90 ratio), implying somewhat limited premium for high skills (productivity). In most transition economies the relative earnings

> 26 This figure, calculated using the household based LFS data is significantly lower than the Gini coefficient calculated

> using results of an employer based survey of wages and reported m Rutkowski (2001). This is quite common, however

> renders assessing the "true" degree of inequality, and in particular international comparisons, difficult. We have more

> confidence in the value reported here than in that reported earlier in Rutkowski (2001) since the latter was calculated usmg aggregate data, with a high proportion of workers concentrated m the bottom wage bracket (which is likely to

> lead to the overestimation of the degree of inequality). On the other hand, the figure reported here is probably

> somewhat biased downward due to bunching of reported wages around some particular values (multiplies of 100),

> which is typical of household based surveys of wages.

58
# **Table** **<u>6: Land market restrictions and wages</u>**

||Log(Real<br>Annual<br>wage)|Log (real per capita<br>household expenditure)|
|---|---|---|
|Proportion of Area Under LDO|-0.964**|-0.644*|
||(-2.164)|(-1.794)|
|First stage- regressions|||
|Historical Malaria|0.00307***|0.00346***|
||(4.420)|(4.795)|
|**Relevance of Instruments**|||
|Kleibergen-Paap/Angrist-Pischke F|19.72|22.99|
|Stock-Yogo 10% max. rel. IV bias|9.08|9.08|
|Individual/head’s characteristics|Yes|Yes|
|Household characteristics|Yes|Yes|
|Area characteristics|Yes|Yes|
|Observations|6,428|11,091|

All regressions include individual's age, education, marital status, and dummies for household's religion/ethnicity and a full set of area controls including travel time to cities, disease environment, land and labor productivity indicators and ethnic composition. Robust t statistics in parentheses. Standard errors corrected for clustering at the sub-district level (DSD) * significant at 10%; ** significant at 5%; *** significant at 1%