rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Tests of _β_ -convergence conditional on public spending and CCT data, 2000–14

||**(1)**|**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
||**2000-2014**|**2000-2005**|**2005-2010**|**2010-2014**|
|||**a. All mun**|**icipalities**||
||_−_0_._025***|_−_0_._059***|_−_0_._026***|_−_0_._015***|
|_ln yit−τ_|(0.002)|(0.004)|(0.003)|(0.004)|
||0.003**|0.006**|_−_0_._010***|0.025***|
|Public spending|(0.001)|(0.003)|(0.003)|(0.005)|
||0.035***|0.067***|_−_0_._023|_−_0_._055**|
|Annual growth in CCT coverage|(0.010)|(0.014)|(0.026)|(0.024)|
|Obs.|1,957|1,957|2,106|2,035|
|R<sup>2</sup>|0.367|0.348|0.182|0.065|
|||**b. Urban m**|**unicipalities**||
||_−_0_._025***|_−_0_._060***|_−_0_._027***|_−_0_._014***|
|_ln yit−τ_|(0.002)|(0.005)|(0.003)|(0.004)|
||0.004***|0.008**|_−_0_._009**|0.029***|
|Public spending|(0.001)|(0.003)|(0.004)|(0.006)|
||0.033***|0.066***|_−_0_._023|_−_0_._055**|
|Annual growth in CCT coverage|(0.010)|(0.015)|(0.027)|(0.025)|
|Obs.|878|878|975|927|
|R<sup>2</sup>|0.369|0.364|0.197|0.072|
|||**c. Rural mu**|**nicipalities**||
||_−_0_._033***|_−_0_._095***|_−_0_._035***|_−_0_._076***|
|_ln yit−τ_|(0.002)|(0.004)|(0.005)|(0.009)|
||0.000|0.010***|_−_0_._013***|0.008|
|Public spending|(0.001)|(0.003)|(0.004)|(0.006)|
||0.011|0.058***|_−_0_._072|_−_0_._110*|
|Annual growth in CCT coverage|(0.013)|(0.011)|(0.044)|(0.057)|
|Obs.|1,079|1,079|1,131|1,108|
|R<sup>2</sup>|0.434|0.426|0.098|0.230|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of parameters _β_ and _γ_ in equation (4), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth rate in the mean per capita income of municipalities over the period. _ln yit−τ_ and public spending are for the initial year and are in log-scale and in real per capita terms in August 2014 prices. The growth rate in CCT coverage is the annualized growth rate in the number of beneficiary families in each municipality over the period. Urban (rural) municipalities are defined as those with more (fewer) than 15,000 inhabitants. The intercepts are shown in tables 3–6 and 8–11 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 16
# <u>Introduction</u>

The Egyptian power sector has been entirely under government control since the nationalization in 1962 with only a handful of IPPs developed later. For much of the period since nationalization, the sector has received both explicit and implicit subsidies, creating very little incentive for efficiency improvement in the sector. Reforms have been announced frequently but implemented only very partially and in short bursts (with long periods of relatively little reform between the bursts). This has not been very different from the pace of economic reform in the rest of the Egyptian economy.

Figure 1 presents the main reform events in the power sector since the 1970s grouped in the various elements of reforms- restructuring, regulation, private sector participation and competition. Overall, we see until very recent years the achievement of only a marginal role for private sector participation, a restructuring that has been more nominal than real (figure 2), some competition _for_ the market but none _in_ the market, a regulator that has developed considerable capacity to advise and monitor but with no independence over the critical issue of cost recovery, and cost recovery itself which has been a long way from full (even in financial terms, let alone in economic terms when the large implicit subsidies in natural gas prices as fuel to the power sector are accounted for).<sup>2</sup> The actual reform agenda today remains quite large (even though the implementation of reform has accelerated somewhat since 2014, particularly in catching up on cost recovery). It is also important to recognize that data for this study range from 1990-2015, to align with other countries as part of the study, and hence they do not capture the detailed impact of reforms undertaken since 2015. Wherever possible, the reform steps after 2015 have been mentioned to give an idea of the kinds of changes the sector is going through, but this study is primarily an attempt to understand the experience of power sector reforms in Egypt and draw lessons for the larger global community.

This paper sets outs to cover the Egyptian power sector reform story since the 1990s focusing on sector outcomes such as security of supply, access and affordability, utility performance, and sector regulation and tariff setting. The paper begins with a chronological account of the power sector reforms and the associated political economy. Then the paper considers the impact of the reform steps taken in the Egyptian power sector, by highlighting the sector performance over the years. Finally, the paper concludes by reflecting on the lessons learned from Egypt’s two decade long reform experience that can be relevant to policy makers globally.

> 2 Natural gas in Egypt has generally been priced to the power sector well below its opportunity cost.

3
1994 after becoming the “designated city”.

The interim report leaked during the plan making process of the Fourth CND failed to offer favorable prospects for the future of Tohoku, by providing nothing beyond the conventional description that Tohoku was Japan’s major supply base of food and timber and the station of tourism and recreation. On the other hand, from the viewpoint of concerns over a progressing hollowing-out of Japan’s manufacturing industry along with the shift to overseas production, it was the problem that the Tohoku region remained at a very low concentration of R&D functions in the private sector and a very high rate of outflow of S&T university graduates. Therefore, the major task of Sendai was to invite and foster R&D functions (TRRDC 1987, HTDFPC 1987).

Professors at Tohoku University, who had been conducting comparative regional studies, perceived a huge gap between the current status of the Tohoku region and the pursuit of the regional level adaptation that took place in the Western nations in response to structural changes after the 1970’s: specifically, the shift from the conventional exogenous strategy to the endogenous regional innovation strategy. Bearing the slogans: ‘The Tohoku in The World’ and ‘The Construction of The Future-oriented Industrial Society’, the TIC Plan, a bottom-up and innovation-oriented regional development project for the Tohoku region, was derived from the international symposium entitled “Tohoku in the World of the 21<sup>st</sup> Century” (Sep. 1986), which was the first occasion on which over 2000 people gathered from industries, universities and governments around seven prefectures. The 37
<u>Table 3: Estimated Yearly Percentage Point Trends for three tenure categories, 1995-2020.</u>

|<br>|<br>Less<br>|<br>than 1<br>|<br>Length of t<br>5 t<br>|<br>enure (years)<br>o 10<br>|<br>More<br>|than 10<br>|
|---|---|---|---|---|---|---|
|Sub-samples|Coeff.<br>|_Std. error_|Coeff.|_Std. error_|Coeff.<br>|_Std. error_|
|Total|0.098<sup>***</sup>|_0.018_|0.046|_0.036_|-0.269<sup>***</sup>|_0.035_|
|_Gender_|||||||
|Males|0.079<sup>***</sup>|_0.019_|0.070<sup>**</sup>|_0.033_|-0.288<sup>***</sup>|_0.036_|
|Females|0.119<sup>***</sup>|_0.017_|0.014|_0.039_|-0.240<sup>***</sup>|_0.036_|
|_Education_|||||||
|Lower-secondary|0.069<sup>**</sup><br>|_0.021_|0.063|_0.038_|-0.307<sup>***</sup><br>|_0.040_|
|Upper-secondary|0.091<sup>***</sup><br>|_0.019_|0.048|_0.034_|-0.263<sup>***</sup><br>|_0.033_|
|Tertiary|0.128<sup>***</sup>|_0.017_|0.024|_0.039_|-0.191<sup>***</sup>|_0.029_|
|_Age groups_|||||||
|30-40|0.104<sup>***</sup>|_0.018_|0.050|_0.053_|-0.265<sup>***</sup>|_0.047_|
|40-50|0.079<sup>***</sup>|_0.012_|0.113<sup>**</sup>|_0.032_|-0.324<sup>***</sup>|_0.031_|
|50+|0.056<sup>***</sup>|_0.008_|0.054<sup>**</sup>|_0.018_|-0.196<sup>***</sup>|_0.021_|
|_Sub-regions_|||||||
|Central Europe|-0.135<sup>**</sup><br>|_0.040_|0.153<sup>***</sup>|_0.033_|0.041<br>|_0.037_|
|Northern Europe|0.149<sup>***</sup>|_0.026_|0.020|_0.043_|-0.496<sup>***</sup><br>|_0.026_|
|Southern Europe|-0.002<br>|_0.024_|0.068|_0.046_|-0.156<sup>***</sup><br>|_0.036_|
|Western Europe|0.211<sup>***</sup>|_0.021_|-0.006|_0.042_|-0.302<sup>***</sup>|_0.047_|

_Note_ : Coefficients and standard errors are estimated from a linear regression of 26 marginal effects of the year dummies on the linear time trend. Marginal effects are derived from estimating the probability of a worker having one of three durations of job tenure. Estimation of the probability of a worker having less than one year of job tenure is conducted on a sample of respondents older than 20. The probability of a worker having 5–10 years of tenure is estimated on a sample of respondents 25 and older. The probability of having 10 or more years tenure is estimated on a sample of workers 30 and older. EU-LFS 1995-2020. Bootstrapped standard errors.<sup>***</sup> indicates that the coefficient is significant at 1% level,<sup>**</sup> - at 5% level,<sup>*</sup> - at 10% level.

39
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **Figure 2: Cote d’Ivoire’s Bilateral and Multilateral Real Exchange Rate,  Based on GDP Deflators**

<!-- Start of picture text --> Cote d'Ivoire: Multilateral Versus Bilateratal (France)<br>Real Exchange Rate<br>Computed with GDP Deflators Index - 1995 = 100<br>140<br>130 Multi RER-GDP Defl - 12 Countries<br>Bi RER-GDP Defl - France<br>120<br>110<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>Years - Computed With Average Annual Data<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text -->

**The CFA was overvalued prior to 1994** .  Figure 2 depicts our indices of the REER constructed with GDP deflators. In this case, the apparent over-valuation of the CFA prior to devaluation is more pronounced than with the CPI REER measures  Both the bilateral and the multilateral measures have trended down in recent years since 1996, signaling a loss of some of the competitiveness gained in the 1994 devaluation.

**The preferred measure is the MREER built from GDP deflators**<sup>**4**</sup> . The CPI includes only final household consumer goods, while the GDP deflator includes all goods produced in the economy<sup>5</sup> . The CPI and GDP deflator measures exhibit similar profiles<sup>6</sup> . As the GDP-deflator based measure includes wholesale goods, raw commodities, and industrial inputs, it would seem to be the preferable measure to use, especially in the case of a commodity exporter.  As the multilateral real exchange rate index more closely approximates “the world”, the MREER seems much preferable to the BREER.

> 4 Ideally, we would like to have a measure of trade-able goods versus non-trade-able goods.  Some authors advocate using the foreign wholesale price indices (WPI) versus the domestic CPI.  We were unable to access a full set of WPIs for 1960-2002 for all countries in the MREER index to build this measure.

> 5 Raw cocoa thus appears in the GDP deflator for Cote d’Ivoire; it dooes not appear in CPIs as it is not a final consumption product.

> 6 We present comparisons of the CPI versus GDP-deflator indices in the Annex.

4
# **_Investment and Trade Policy_**

We now move from policies that are mainly focused on improving the timeliness of trade flows within GVCs (via better infrastructure and connectivity) to policies that target the quantity and quality of trade and investment flows in a country (via openness and promotion of trade and investment flows).

Looking at the impact of backward GVC links on economic upgrading (table 4), we find that trade and investment policies play an important mediating role. On the investment side, we find that both our qualitative measure of investment freedom (column 1) and our outcome-based measure of FDI inflows (column 3) positively moderate the effect of GVC integration on domestic value added. This supports the findings from the literature review that productivity spillovers from FDI are higher when foreign firms have been targeted by investment promotion efforts (Du, Harrison, and Jefferson 2011; Harding and Javorcik 2012).

**Table 4. The role of trade and investment policies in economic upgrading through GVCs**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|(5)<br>_DVA_|
|---|---|---|---|---|---|
|**Backward links**||||||
|_FVAX_|-0.0124<br>(0.0347)|0.0735***<br>(0.0263)|0.0537***<br>(0.0101)|0.0520***<br>(0.0110)|0.0249*<br>(0.0142)|
|_FVADP_|0.271***|0.301***|0.252***|0.252***|0.251***|
|_FVAX*Investment freedom_|(0.0239)<br>0.0012**<br>(0.0006)|(0.0333)|(0.0270)|(0.0270)|(0.0270)|
|_FVAX*STRI WB_||-0.0005<br>(0.0008)||||
|_FVAX*FDI inflows_|||0.0026***<br>(0.0007)|||
|_FVAX*Services trade_||||0.0006***<br>(0.0002)||
|_FVAX*Openness_|||||0.0004***<br>(0.0001)|
|_Constant_|5.743***|5.742***|5.806***|5.904***|5.949***|
||(0.165)|(0.240)|(0.190)|(0.185)|(0.192)|
|Observations|7,177|5,390|7,156|7,156|7,156|
|R-squared|0.858|0.874|0.855|0.855|0.855|
|**Forward links**||||||
|_DVAR_|0.0943**|0.276***|0.212***|0.209***|0.186***|
||(0.0468)|(0.0375)|(0.0180)|(0.0198)|(0.0212)|
|_FVADP_|0.249***|0.279***|0.233***|0.232***|0.231***|
|_DVAR*Investment freedom_|(0.0227)<br>0.0020***<br>(0.0007)|(0.0329)|(0.0259)|(0.0259)|(0.0259)|
|_DVAR*STRI WB_||-0.0022**<br>(0.0010)||||
|_DVAR*FDI inflows_|||0.0013<br>(0.0010)|||
|_DVAR*Services trade_||||0.0004<br>(0.0004)||
|_DVAR*Openness_|||||0.0003***<br>(0.0001)|
|_Constant_|4.905***|5.068***|5.008***|5.206***|5.085***|
||(0.162)|(0.264)|(0.184)|(0.190)|(0.180)|
|Observations|7,180|5,391|7,164|7,164|7,164|
|R-squared|0.876|0.891|0.872|0.872|0.872|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

22
# Split Decisions: Family finance when a policy discontinuity allocates overseas work

Michael A. Clemens<sup>_∗_</sup> Erwin R. Tiongson Center for Global World Bank, Development AIM, and IZA December 5, 2012

## _Sector board: POV (Poverty Reduction)_

> _∗_ **JEL Classification Numbers: J61, O15, R23.** We thank the Philippine Overseas Employment Administration (POEA) for their kind assistance, especially current and former POEA officials and staff Patricia Santo Tomas, Carmelita Dimzon, Hans Cacdac, Jennifer Manalili, and Helen Barayuga, as well as Vincent Olaivar of the National Statistical Office. We thank the John D. and Catherine T. MacArthur Foundation for generous support, and Paolo Abarcar and Tejaswi Velayudhan for excellent research assistance. We received helpful suggestions from Jenny Aker, Christopher Blattman, William Easterly, Dean Karlan, Jonathan Morduch, Sendhil Mullainathan, Kevin Thom, Shing-Yi Wang, Patricia Cort ́es and seminar participants at NYU, Univ. of Michigan, Harvard Kennedy School, the Univ. of Minnesota, the Asian Dev. Bank, the Asian Institute of Management, Ateneo de Manila, and Tufts Univ., but we are responsible for any errors. The views expressed here are those of the authors only; they do not necessarily represent the views of the POEA, the Center for Global Development, the World Bank, or any of their boards and funders.
**Table 6. Guatemala: Effect of Schooling on Growth by Level of Education, 1951-2002**

||j = pr<br>|Depe<br>imary|ndent variable: Perce<br>j = sec<br>|nt change of GDP/w<br>ondary|orker<br>j = te<br>|rtiary|
|---|---|---|---|---|---|---|
||OLS|IV|OLS|IV|OLS|IV|
|Explanatory variables|(1)|(2)|(3)|(4)|(5)|(6)|
|Constant|0.087**|0.082**|0.127**|0.140**|0.096**|0.141**|
||(3.43)|(2.83)|(4.20)|(3.60)|(3.78)|(4.43)|
|Percent change of capital/worker|0.871**|0.766**|0.875**|0.757**|0.872**|0.785**|
||(28.8)|(5.14)|(29.5)|(5.12)|(28.6)|(6.02)|
|Percent change of capital/worker [-1]|0.113**|0.157*|0.128**|0.181*|0.083*|0.143*|
||(2.94)|(2.42)|(3.33)|(2.51)|(2.21)|(2.32)|
|log GDP/worker [-1]<sup>a/</sup>|-0.242**<br>(-5.51)|-0.264**<br>(-4.88)|-0.213**<br>(-5.43)|-0.234**<br>(-4.64)|-0.224**<br>(-5.20)|-0.327**<br>(-6.00)|
|log capital/worker [-1]|0.107**|0.088|0.091**|0.074|0.120**|0.155**|
||(3.47)|(1.65)|(3.22)|(1.61)|(3.72)|(3.63)|
|log participation of education levelj|<br>0.103**|0.092**|0.049**|0.052**|0.023**|0.033**|
|in labor force [-1]|(3.89)|(2.79)|(4.59)|(3.92)|(4.27)|(5.20)|
|Trend 1985|0.002**|0.002**|...|...|...|...|
||(3.38)|(3.15)|||||
|Long-run elasticity of capital|0.445|0.333|0.426|0.319|0.538|0.474|
|Long-run elasticity of schooling in education|0.426|0.349|0.230|0.220|0.104|0.101|
|levelj|||||||
|Adjusted R<sup>2</sup>|0.962|0.953|0.962|0.948|0.960|0.962|
|F-statistic|141.8|35.85|159.9|33.56|152.6|49.02|
|Durbin Watson<sup>b/</sup>|1.756|1.978|1.944|2.055|1.790|2.205|
|S.E. of regression|0.012|0.014|0.012|0.014|0.012|0.012|
|N|51|50|51|50|51|50|

Note: The regressions include a 1977 step dummy and impulse dummies for 1963 and 1982, significant at 1%. a/ Asymptotic critical values of the t-ratio are from Banerjee et al. (1998). b/ A Breusch-Godfrey test finds no evidence for the presence of first, second and third order correlation in the residuals.

t-statistics in parenthesis. ** Significant at 1%, * significant at 5%.

_Source:_ Author’s calculations.

-36-
9 producer tax **cameu** from countries such **as**<sup>Tanzania and Ghana,</sup><sup>**where**</sup> heavy export taxatlon (either lmplict or expllclt)<sup>have squeezed</sup> agricultural production, leading to a sharp<sup>fall in output, foreign</sup> exchange earnings, and tax revenue (see the<sup>World DevgloDuent</sup> ; a22t, 1986). Once again, producers lo*s<sup>because they</sup><sup>**are**</sup> producing less under the tax, but the government<sup>gain. no extra</sup> revenue from the reduced production activity.<sup>When export</sup><sup>**taxes**</sup> affect such an important component of the **economy**<sup>**(especially**once</sup> the linkages with the rest of the economy are accounted<sup>for), it</sup> seems clear that the taxation of agricultural output<sup>caused a</sup> substantial decline in national output.

One point should be made here. In many<sup>socialistic countries,</sup> there is little or no weight placed on "consumer<sup>surplus"; taxes</sup> which discourage the use of, e.g., luxury or<sup>decadent capitalistic</sup> goods are thought to be socially beneficial. Such<sup>taxes prevent</sup> citizens from placing private over social<sup>responsibilities.</sup> However, even socialistic countries should avoid distortionary producer taxes, since more (of the socially<sup>correct commodities) is</sup> still preferred to less.<sup>3</sup> These simple examples have provided illustrations<sup>of the</sup> efficiency cost of taxation. It is a further question<sup>how</sup> efficiency cost can be measured, and whether there<sup>is convincing</sup> 3I am neglecting here the issue of the incidence<sup>of the tax.</sup> Producers of decadent capitalist goods may be<sup>taxed in lieu of</sup> taxing consumers directly; the same outcome<sup>--</sup> a reduction in output of the commodity -- will occur.
4 finite samples and inefficiencies even asymptotically [Alonso-Borrego and Arellano **1996J.** To mitigate this problem, we use a _system_ estimator. Besides the _difference_ dynamic-panel equations, we simultaneously estimate the _level_ equations where the instruments are lagged values of the differenced regressors [Arellano and Bover **1995]. By** ameliorating the weak instrument deficiency, this _system_ estimator dramatically improves the consistency and efficiency of the estimator [Blundell and Bond **1997].** Thus, this paper uses two econometric procedures **-** a pure cross-sectional instrumental variable estimator and a GMM dynamic panel technique **-** to evaluate the impact of differences in banking sector development on economic growth, capital accumulation, productivity growth, and private saving.

The second major way in which this paper improves upon existing work is **by** using better measures of saving rates, physical capital, productivity, and banking sector development. Private saving rates are notoriously difficult to measure [Masson et al. **1995].** As detailed below, however, we use the results of a recent World Bank initiative that compiled high-quality statistics on gross private savings as a share of gross private disposable income for a broad cross-section of countries over the period 1971-1995[Loayza, Lopez, Schmidt-Hebbel and Serven **1998].** We also use more accurate estimates of physical capital stocks. Researchers typically make an initial estimate of the capital stock in **1950** and then use aggregate investment data to compute capital stocks in later years [King and Levine 1994; Nehru and Dhareshwar 1994]. These estimates use aggregate investment data that, for example, combine investment in residential structures with investment in equipment and machines, while employing a single depreciation rate. Recently, however, the Penn-World Tables (PWT) **5.6** constructed capital stock data based on disaggregated investment and depreciation data. While the PWT **5.6** discusses remaining measurement problems, these data suffer from fewer shortcomings than existing capital stock data. We also improve on existing measures of aggregate TFP growth.
level so that within villages the distribution would be viewed as fair and equitable. For example, everyone who lived in a village paid or received the same amount of money for using the stoves. These treatments were assigned completely randomly, but it remains important to check that the intended randomization was successful.

Table 4 Kruskal-Wallis Tests that the Six Treatment Assignments Come from the Same Site Characteristic Distributions

||Households<br>in_Kebele_<sup>_8_</sup>|Households in<br>Forest User<br>Group|Wealth Variance<br>in Forest User<br>Group|Existence of Forest<br>Rules/ Regulations|% Forest Biomass Change<br>Over 5 Years (Respondent<br>Assessed)|
|---|---|---|---|---|---|
|Χ<sup>2</sup>(5)|5.246|3.085|<br>6.166|.000|6.243|
|P<br>Value|.387|.687|<br>.290|1.000|.283|

***, **, * indicate significant at the 1%, 5% and 10% levels

Table 4 presents Kruskal-Wallis test results for differences in community characteristics by treatment. In the interest of brevity, only the test statistics are presented, but mean ranks for all characteristics by treatment are available from the authors. The table assesses 5 different key community characteristics and the randomization appears to have been successful. There are no systematic differences in group size, wealth distribution, forest change, or forest management.

Table 5 SUM Monitoring Data by Treatment and Round

|Treatment Description|Treatment<br>Number|Round 1|Round 2|Round 3|Round 4|
|---|---|---|---|---|---|
|Received 50 Birr usepayment,but no network|1|42|34|49|48|
|Received 50 Birr usepayment, plus network|2|53|51|57|55|
|Paid 25 Birr,but received no network|3|49|49|57|56|
|Paid 25 Birr for the stove, plus network|4|53|55|54|51|
|Received stove for free and no network|5|53|52|51|51|
|Received stove for free, plus network|6|50|46|50|40|
|Total Observatio|ns byRound|300|287|318|301|

Table 5 presents numbers of usable SUM observations by round and treatment out of a possible 360 SUM devices fielded in each round. SUM devices failed mainly because their temperature tolerances were exceeded, but some devices we removed by children and there was

> 8 The Amharic word _kebele_ translates as “peasant association.” It is the lowest official administrative region in Ethiopia and is typically comprised of many villages

**22 |** P a g e