rafmacalaba/datause-encoder-data huggingface dataset:
the linear equation (1), the error term is _εi_ = _δ_ ( _Si_<sup>_p_)2+</sup><sup>_ζi_,andtheconditionalvarianceof</sup> _εi_ is a function of father's education simply because of a misspecied functional form. To check this, we estimate the mobility equation (3) and the impact of father's education on the conditional variance dened in terms of _ζi_ . The estimates for various samples are reported in Tables A.4-a.8 in the online appendix section OA.2. The evidence suggests strongly that the relations between family background and conditional variance of children's schooling uncovered in Tables 2-4 are not driven by functional form misspecication of the mobility CEF.

The next question we address is whether the estimated impact of father's education largely reects the omitted cognitive ability heterogeneity of children. For this analysis, we take advantage of the IFLS-2014 survey in Indonesia which collected data on multiple indicators of cognitive ability of a child (measurement taken in 2014 when the children are adult): raven test scores and two memory tests. We construct an index of cognitive ability in two steps. First, we construct the rst principal component of the dierent measures of cognitive ability. In the second step, we regress the rst principal component on age and age squared of a child to take out the Flynn eect. The residual from this regression is our index of cognitive ability of a child. We control for the ability index and its squared in the regression for conditional variance in equation (2) above. The estimates for the full sample are reported in online appendix Table A.4 (see online appendix section OA.3). The main message that comes out is that the estimated eects of father's education on conditional variance of schooling of children are not driven by omitted ability heterogeneity. Even though ability controls reduce the estimated coecient, the inuence of father's education still remains substantial and statistically signicant at the 1 percent level. The estimates for other subsamples are available from the authors.

# (5) Combining the Mean and Conditional Variance Eects: New (and More Complete) Measures of Relative and Absolute Mobility

The evidence presented above suggests strongly that it is important to understand the inuence of family background on the conditional variance in addition to the standard mean eects. In this section, we develop an approach that combines the mean and variance eects using standard results from the theory of decisions under uncertainty.

14
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE Mozambique-South Africa. Although freight traffic density in Tanzania was substantially lower than in South Africa, as of 2006 it was on a par with neighboring Kenya, Uganda, and Zambia (table 6).

**Table 6. Benchmarking railway indicators, 2006**

||Kenya (KRC)|South Africa<br>(SPOORNET)|Malawi (CEAR)|Tanzania (TRC)|Tanzania-Zambia<br>(TAZARA)|Uganda (URC)|Zambia (RSZ)|
|---|---|---|---|---|---|---|---|
|Concessioned (1)/ State run (0)|0|0|1|0|0|0|1|
|Traffic density, freight, 1000 ton-km/km|690|5,319|112|510|460|815|379|
|EFFICIENCY||||||||
|Staff: 1,000 UT per Staff|185|3,037|204|228|300|181|452|
|Coaches: 1,000 passenger-km per coach|1,015|596|1,285|3,157|3,120|n.a.|2,772|
|Cars: 1,000 ton-km per wagon|200|925|212|692|502|166|180|
|Locomotive availability in %|44.8|—|89.9|74.2|25.2|69.5|31.2|
|TARIFFS||||||||
|Average unit tariff, freight, US cents/ton-km|3.8|—|5.8|4.0|3.0|15.2|3.9|
|Average unit tariff, passenger, US cents/passenger-km|0.6|—|1.0|1.6|1.1|2.3|0.8|

_Source:_ Bullock 2009. Derived from AICD rail operators database, http://www.infrastructureafrica.org/aicd/tools/data. — = Data not available. n.a. = Not applicable.

## **Challenges**

Tanzania’s rail concession has run into difficulties. The Tanzania Railways Corporation (TRC) concession was awarded to India's RITES in September 2007 for a period of 25 years, giving the concessionaire a 51 percent stake in the company. The contract has since experienced labor conflicts and financial distress, due to lower than anticipated traffic flows. By the end of the first year of the concession TRC’s operational and financial performance indicators had fallen below pre-concession levels. Both the concessionaire and the government have made several proposals to renegotiate the contract, but in the end the contract was terminated.

# Air Transport

## **Achievements**

Tanzania has the fourth-largest air transport domestic market in Sub-Saharan Africa, in part due to its immense tourism attraction. The main airport in Dar es Salaam had actual passenger figures of 1.2 million in 2008 with much additional traffic related to the international airport on Zanzibar.

Tanzania stands out in the region for allowing competition in its domestic air transport market. Each of the country’s 17 domestic routes has more than one provider. As of 2007, only one other country in Sub-Saharan Africa, South Africa, permitted competition in its domestic air transport sector. Tanzania has also made significant progress with institutional reforms in the sector by establishing an independent 18
19

**Table 9:** occupations in which Non-citizens are concentrated. 1983

|Occupation|Total<br>(1)|Zambian<br>(2)|Non-Zambian<br>(3)|<br>(3) as_4I_<br>of (1)|
|---|---|---|---|---|
|Physical Scientists|430|292|138|32.1|
|Architects and engineers|<br>2,476|1,381|1,095|44.2|
|Engineering technicians|3,222|2,874|348|10.8|
|<br>Doctors|334|73|261|78.1|
|Nurses and midwives|4,104|3,798|396|9.6|
|Statisticians and|||||
|related technicians|832|625|207|24.9|
|Accountants|3,531|2,716|815|23.1|
|University and other|||||
|Higher Ed. Teachers|1,472|1,081|391|26.6|
|Secondary Teachers|3,410|2,775|635|18.6|
|Primary Teachers|14,295|13,809|486|3.4|
|Managers|2,249|1,573|676|30.1|
|Stenographers|7,120|6,921|199|2.8|
|Book-keepers|7,278|7,160|188|2.6|
|General clerks|15,669|15,430|239|1.5|
|Cooks|4,602|4,438|164|3.6|
|Protective service|||||
|workers|18,362|18,083|279|1.5|
|Farm managers|1,493|1,345|148|9.9|
|Agricultural workers|13,348|13,030|318|2.4|
|Production supervisors/|||||
|<br>foremen|8,215|7,618|597|7.3|
|Miners|15,054|14,318|736|4.9|
|Machinery fitters|11,345|10,910|435|3.8|
|<br>Electrical fitters|<br>4,587|<br>4,410|177|3.9|
|Plumbers, welders|5,841|5,672|169|2.9|
|Bricklayers, carpenters|9,270|9,011|259|2.8|
|Material handlers|6,790|6,650|140|2.1|
|Transport operators|9,846|9,421|425|4.3|
|Other labourers|29,410|28,669|741|2.5|
|sub-total|204,585|193,923|10,662|5.2|
|Other workers|||||
|included in survey|60,043|58,039|2,004|3.3|
|Total in Survey|264,628|251,962|12,666|4.8|

Source: Calculated from GRZ 1983, Appendix 1.
**Table 3:** Technology-specific data used in the numerical application

||Description|Unit|Legacy|Gas|Wind|Source|
|---|---|---|---|---|---|---|
|_C_<sup>_m_</sup><br>_i_|Nominal<br>investment costs|$/kW|1 800|1 200|2 000|IEA (2010)|
|_Xi_|Average annual<br>new capacity in<br>Europe|GW/yr|4.2|11|10|ENERDATA (2012)|
|_Hi_|Average annual<br>operating hours|h/yr|7 500|7 500|2 000|IEA (2010)|
|_Fi_|Carbon intensity|gCO2/kWh|530|330|0|ENERDATA (2012);<br>Trotignon and Delbosc<br>(2008)|

**Table 4:** General parameter values used in the numerical application

||Description|Unit|Value|Source|
|---|---|---|---|---|
|_r_|Discount rate|%/yr|5||
| ̄<br>_M_|Carbon budget (central value)|GtCO2|22|UE (2011)|
|_D_0|Electricity demand in 2008|TWh/y|1 940|ENERDATA (2012);<br>EU (2011)|
|_G_|Annual growth of demand|TWh/y|16.5|EU (2011)|
|_δ_|Depreciation rate|%/yr|3.33|IEA (2010)|
|_A_|Convexity parameter (central value)|⋅|0||

electricity only at the moments where it is windy, which expectedly happens a given number of hours per year.

We assume for simplicity that all technologies have the same depreciation rate _δ_ , calibrated as _δ_ = 1/lifetime assuming a lifetime of 30 years (IEA, 2010). We consider that Europe is price-taker on the fossil resources costs _αi,t_ , which follows the trajectory assumed in the European 2050 Roadmap (Table 2). Note that _αh_ corresponds to the average price of the resources of the legacy technology mix (Table 1).

To calibrate the cost functions, we assume that when investment equals the actual average annual investment flow in Europe between 2009 and 2011 ( _Xi_ ), the marginal investment cost _Ci_<sup>_m_</sup> is equal to the OECD median value for 2010 (as found in IEA, 2010). We write the cost function as: _A_ ∈(0 _,_ 1) is a convexity parameter, equal across technologies for simplicity. When _A_ = 1, the marginal investment cost are constant, there is no adjustment cost for production capacity, and optimal investment pathways can exhibit jumps. When _A_ = 0, the marginal cost curves are linear, capacity accumu14
# Introduction

Between 2010 and 2014, the Middle East and North Africa (MENA) region, experienced an extraordinary wave of energy and food subsidy reforms. These reforms did not achieve the objective of removing subsidies completely—far from it, but they were extraordinary in two important respects. They were unprecedented because no other period in the history of the region had seen such a wave of subsidy reforms and because they occurred during an extremely complex period from a social and political perspective—a period of war, revolutions, and social upheavals.

What triggered the reforms? Who reformed, when, how, and why? What are the pros and cons of reforms? These are some of the questions we discuss in this paper. Using the information contained in a cross-country comparative analysis and eight country case studies (Verme and Araar, 2016), this paper summarizes events and reflects on some of the choices made by policy makers and emerging (although still unfolding) lessons. A brief history of subsidies will show how their evolution followed a similar pattern across the countries of the region, a pattern mainly guided by oil prices and shifts in the dominant political views of the time. We then ask what triggered the reforms and try to pinpoint the key factors that eventually forced governments to take action on reforms. Next is a summary of the essential elements of the subsidy reforms in those countries that implemented reforms between 2010 and 2014. Last is a discussion of reforms and the challenges that remain in completing the reforms.

# A Brief History of Subsidies

Consumer subsidies are part of the history of the MENA region. In some countries, subsidies were already present during the colonial period and were part of the colonial heritage when countries became independent. Each case study in Verme and Araar (2016) briefly reviews the origin and evolution of subsidies during the post-independence period, and this section reviews some of the salient features of this history.

Consumer subsidies have evolved to serve different purposes depending on the country considered and the historical period. Most of the consumer subsidy systems as we know them today were introduced in the MENA region between the 1940s and 1970s. Price stabilization was the initial motivation for these subsidies. As countries emerged first from World War II and then from the colonial struggles, one of their main concerns was price instability and rising prices on 2
in turn may further increase public debt (to fund the recapitalization of the SOBs), and/or exacerbate the credit crunch and economic slowdown given shareholder difficulties to raise capital at the time of bank distress. In some countries, failure of private sector banks forced the public sector to take over weak banks, drastically increasing the share of SOBs in total banking sector assets.

It is difficult to assess the true fiscal position of some countries due to opacity of the fiscal position and/or uncertainty about contingent liabilities of the large public sector which may hamper access to markets in times of need. Proliferation of public‐private partnerships (PPPs) and guarantees for infrastructure financing further complicate the fiscal analysis. As the credit and the economy decelerate, the performance of SOEs, SOBs and the fiscal position of the sovereign deteriorate, further resulting in a vicious circle.

In some countries, banks reliance on public sector funding, due to concentration of Treasury or public sector resources, exposes banks to liquidity risks at time of sovereign distress, which could further exacerbate a credit crunch.

Financial sector exposure to sovereign related risks is problematic in many countries not because of the sovereign credit risk, but because the exposures are remunerated at below market rates, causing stress in the profitability and or solvency of financial institutions. This in turn can create fiscal contingent liabilities. Provision of subsidized lending by public banks in some countries compresses lending margins and banks internal capital generation, forcing the sovereign to inject capital to sustain credit growth.

Finally, vulnerabilities in the banking system can also affect public debt levels. Exposure to politically connected firms, related‐party lending and weak oversight in some countries resulted in a banking crisis and bail outs of SOBs that have substantially increased public sector debt.

<u>East Asia and Pacific</u><sup>14</sup>

**Key characteristics and trends in public debt and financial sector exposures**

In many East Asian countries, there are implicit government guarantees to the banking sector and there is an expectation that should a major banking crisis arise, the government will intervene to contain the losses and spillover effects for the whole financial system and the economy overall. Public debt remains moderate and/or manageable in EAP’s largest economies, but efforts will be needed to rebuild fiscal buffers. In terms of the evolution of

> 13 This regional section is based on substantive inputs provided by Eva Gutierrez, Raquel Letelier, and Alena Kantarovich (FCI‐ECA), and Elisa Gamberoni (MTI‐ECA).

> 14 This regional section is based on substantive inputs provided by Ana Maria Aviles (FCI‐EAP), and Richard Record (MTI‐EAP).

31
# **REFERENCES**

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- _Easterly, William (2009)._ “How the Millennium Development Goals are unfair to Africa”. World <u>Development, Vol. 37, No. 1, pp. 26-35.</u>

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- _Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2010._ <u>Africa’s Infrastructure: A Time for Transformation. Agence Française de Développement and World Bank.</u>

_IMF. 2007._ <u>Financial Programming and Policies. IMF Institute.</u>

- _IMF. 2011a._ Liberia: 2011 Sixth Review Under the Three-Year Arrangement Under the Extended Credit Facility, Request for Extension of the Arrangement, and Augmentation of Access—Staff Report; Staff Supplement; Press Release on the Executive Board Discussion; and Statement by the Executive Director for Liberia. Country Report No. 11/174. July.

- _IMF. 2011b._ Liberia: Policy Note for the Seventh Review under the Extended Credit Facility Arrangement. August 25.

- _Jack, William and Maureen Lewis. 2009._ “Health Investments and Economic Growth: Macroeconomic Evidence and Microeconomic Foundations,” pp. 1-39 in eds. Spence, Michael and Maureen Lewis. <u>Health and Growth. Commission on Growth and</u> Development.

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15
and Porto (2003) are among the few studies that employ the latter approach<sup>23</sup> . In this paper we adopt the latter approach and use VNLSS 1993 and 1998 to estimate the determinants of growth in income and especially the role of services in this growth.

There are a series of service variables that have been highlighted in the literature as critical to significant supply response. The first set of these relate to the availability of cash income for the households. Cash income, whatever its source, allows the farmer to buy inputs such as fertilizer, capital inputs and labor, thus increasing the yields and output. The variables used as a proxy for eliminating the cash constraint are the availability and magnitude of credit, and the role of cash crops.

The positive links between cash crop and food production works through two channels. The first are synergies at the household level and the second are regional or location specific spillover effects. In many cases, due to the lack of credit, extension, and other sources of cash income, food crop farmers have no access to inputs such as fertilizer and seeds that could be obtained if the farmer became a part of a cash crop scheme. Even when there are no explicit cash crop schemes, the cash and credit necessary for intensification of food crop production can be obtained through the sale of cash crops. Lump-sum investments can be paid using the receipts associated with cash crops. Finally, knowledge and extension services can be obtained through cash crop schemes. The regional spillovers take place when cash crop trading attracts investments into the region for inputs, storage, and other market institutions and infrastructure. These market institutions and infrastructures can equally be used by food producers and can increase the returns to smallholder food production. Moreover, the existence of cash crop income and credit allows the households to obtain inputs such as fertilizer and gain access to markets.

One variable that determines the probability of the household participating in commercial crop production is its physical location. In particular, agro-climatic conditions are important. Not all crops can be efficiently produced in each locality. For example, the most important cash crop in Vietnam is coffee and is only produced in the Central Highlands (and in a less intense form in Southeast). In this region the average share of cash crop income in total income is 33% (60%) in 1993 (1998), while it is only

> 23 Reimer (2002) and Goldberg& Pavcnik (2004) provide a good survey of the literature.

17
- Castilla, C. and Walker, T. (2013). Is ignorance bliss? the effect of asymmetric information between spouses on intra-household allocations. _American Economic Review_ , 103(3):263–68.

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- Cocco, G. (2013). Toiling Tunisia. _Photo Reportage: https://www.giovannicocco.it/work/toiling-tunisia/ (accessed 3rd of December 2021)_ .

- Cr ́epon, B., El Komi, M., and Osman, A. (2020). Is it who you are or what you get? comparing the impacts of loans and grants for microenterprise development. _Unpublished Working Paper_ .

- De Mel, S., McKenzie, D., and Woodruff, C. (2009). Are women more credit constrained? experimental evidence on gender and microenterprise returns. _American Economic Journal: Applied Economics_ , 1(3):1–32.

- De Mel, S., McKenzie, D. J., and Woodruff, C. (2008). Returns to Capital in Microenterprises: Evidence from a Field Experiment. _Quarterly Journal of Economics_ , 123(4):1329–1372.

- Delecourt, S. and Fitzpatrick, A. (2021). Childcare matters: Female business owners and the baby-profit gap. _Management Science_ .

- Derksen, L., Kerwin, J., Reynoso, N. O., and Sterck, O. (2021). Appointments: A more effective commitment device for health behaviors. _arXiv preprint arXiv:2110.06876_ .

- Di Maio, M. and Fiala, N. (2020). Be wary of those who ask: a randomized experiment on the size and determinants of the enumerator effect. _The World Bank Economic Review_ , 34(3):654–669.

- Duflo, E. (2012). Women empowerment and economic development. _Journal of Economic Literature_ , 50(4):1051–79.

- Fafchamps, M., McKenzie, D. J., Quinn, S., and Woodruff, C. (2014). Microenterprise Growth and the Flypaper Effect: Evidence from a Randomized Experiment in Ghana. _Journal of Development Economics_ , 106:211–226.

- Field, E., Jayachandran, S., and Pande, R. (2010). Do traditional institutions constrain female entrepreneurship? A field experiment on business training in India. _American Economic Review_ , 100(2):125–29.

24
- b) More volatility of sales is associated with higher financial savings;

- c) Higher cost of capital is associated with higher financial savings;

- d) Increase in financial savings leads to increase in physical savings (i.e. investment) in about 2 years.

Despite the data limitations, two important findings are consistent across the two very different datasets. The fact that these findings are consistent in both datasets reaffirms the usefulness and credibility of these results.

First is the finding that larger firms invest more (physical saving), which is essential for maintaining competitiveness and growth. The study points out that one important reason small firms invest less is lack of access to credit. Thus, improving access to credit for small and medium enterprises is likely to have a positive impact on investment.

Second, despite the financial deepening, the use of credit products has been declining during the last decade. This result is observed in both datasets. Specifically, usage of credit products in ICA data has declined from 23% in 2003, to 21% in 2005 and 18% in 2007 surveys. Among the listed firms, the debt levels relative to assets have declined from about 25% average (20% median) in 2001 to below 20% average (and below 15% median) in 2008. This trend raises concern and more research is needed to understand the reasons for such “deleveraging.” The reasons could be supply-side constraints, with banks unwillingness to lend at rates attractive to corporate sector or crowding out by the government sector, or demand-side weaknesses, such as lack of productive growth opportunities. Our data do not allow such analysis.

Large literature has established a solid link between access to finance, productive investment and growth on the macro and micro levels. The results of this study confirm that firms in Egypt, especially small and medium firms, would benefit from increased access to finance which is likely to support firm investment into productive assets (i.e. physical savings) and stimulate growth.

Based on this analysis, the main policy recommendation from the study reaffirms the importance of improving access to financial services in Egypt. It is recommended to undertake further studies on constraints to financial access and include data on financial savings in future 31