rafmacalaba/datause-encoder-data huggingface dataset:
# **1. Introduction**

The movement of labor between sectors is a core aspect of structural change in economies throughout the world.<sup>1</sup> The effects of changes in prices, technology, and policies on production, trade and living standards are strongly influenced by the extent to which labor is able to relocate. In a literature survey, Hertel and Reimer (2005, p. 400) find that “the poverty impacts of trade reform often hinge crucially on how well the increased demand for labour in one part of the economy is transmitted to the rest of the economy.” Drawing on a global CGE analysis, van der Mensbrugghe (2007) indicates that the treatment of labor market segmentation and sectoral wage differences has a strong impact on simulated welfare impacts of the Doha round of trade reform, especially for developing countries. The findings reported in Porto and Hoekman (2010) indicate that labor market frictions in developing countries are substantial, giving rise to costly labor market adjustments and limiting labor reallocation. Similarly, even though the analysis has typically been done from a firm (as opposed to sector) perspective, labor adjustments costs are a major focus in the labor economics literature (for example, see Hamermesh 1989, Cooper and Willis 2009, and Cooper et al. 2015).

The Computable General Equilibrium (CGE) literature includes a variety of approaches to mobility, the most common being to assume that mobility between any pair of sectors is either impossible or unconstrained. In this paper, we present and test an innovative approach that, in contrast with this dichotomy, views intersectoral labor mobility as falling along a continuum. It offers a means of making sure that simulations with CGE models more closely approximate empirical patterns for labor movements and/or wage differences between workers of different origins (including incumbent workers) that are employed in a sector. The approach is inspired by the classic transportation model of the linear programming literature (for references, see Gass and Assad 2005, pp. 51-52) and the product-space (PS) literature due to Hausmann and coauthors (Hausmann and Klinger 2006; Hausmann et al. 2011).

The essence of our approach is that workers are viewed as having capabilities that to varying degrees permit them to be efficient in different sectors. As a consequence, reallocation of workers between sectors comes at a cost in the form of lost efficiency: labor that comes from another sector is less efficient than the labor that already works in the receiving sector. These efficiency losses are related to proximity between pairs of sectors, a key concept in the PS literature. It is valued between 0 and 1 and here used to measure the degree to which a worker in the 1 We use the two terms “sector” and “activity” interchangeably with the same definition as “industry” in the System of National Accounts, i.e. a grouping of establishments (or producers) engaged in the same or similar kinds of activities classified in the same International Standard Industrial Classification of All Economic Activities (ISIC) code (United Nations 2003, p. 74).

2
# ANNEXES

I GROWTH. DEBT RELIEF AND THE REAL EXCHANGE RATE IN MEXICO II SECONDARY MARKET PRICING AND THE VALUATION OF THE RECAPTURE CLAUSE: AN OPTION PRICING APPROACH ' III CHOICES MADE BY MEXICO'S CREDITORS _IV_ This annex is based on van Wijnbergen (1990), "Growth,<sup>Debt Relief and the</sup> Real Exchange Rate in Mexico', mimeo, World Bank.

JA/ This Annex is based on "Secondary Market Prices<sup>under Alternative Debt</sup> Redliction Strategies: An Option Pricing Approach", by Claessens and van Wijnbergen (1989).
<!-- Start of picture text --> HB Consumption(9.14,9.78]<br>[33] 27 (7.87,8.50]B® (8.50,9.14]<br>(22) (6.59,7.23](7.23,7.87]<br>a- : fs) (6.95,6.59]<br>rawa: p25, 3 1) (5.31,5.95](4.68,5.31]<br>we = (4.04,4.68]<br>° a BB (3.40,4.04](2.76,3.40]<br>39 56 4 4a<br>60<br>@o° oO. 6 = r::)<br>& ><br>31Ove or On s<br>xe52oy5° ofPam2 48 °<br>me JT 26 7<br>)me. “ ©er °°<br>proportional to number of number of of ELCs per per thousand children children<br>HB Consumption 0399.78)<br>B oe . = (9.00,9.39](8.61,9.00]<br>BB 22861<br>6 Fo BBHB css223caszes<br><a WB 7057.44<br>is . 3 ke HB 66,7.05)<br>bad DDBB eese2627.600)<br>n 6 BB 6495.28)<br>1 e:105.49)<br>WD) @715.10<br>- it Cc—~— 725 2 16 - 30 1)/ @s2473)(3.93,4.32)(3.54,3.93]<br>25 < A917 =~ (3.15,3.54)(2.76.15)<br>5 11 {10}7 10 ‘ ° cf —_ A \ i 8OFj 32 he13 re. | 10 cs= v7 10 4<br>é0 | =-<br>~<br>‘ 6 13<br>lgA25 \ 9 Q 13 22<br>) 10<br><!-- End of picture text -->

NOTE: Point size proportional to number of number of of ELCs per per thousand children children NOTE: Point size proportional to number of kindergartens per thousand children
# **Table** **<u>9: Alternative dispute mechanism initiatives in MENA</u>**

|**Country**|**Mechanism **|
|---|---|
|Syria|Insurance dispute resolution committee within supervisor with ombudsmanpowers|
|Qatar|The Ministryof Business and Trade has ombudsmanpowers.|
|Oman|The supervisor has issued code ofpractice for insurers and acts as mediator.|
|Lebanon|Insurance arbitration council for motor and medical claims under US$50,000.|
|Jordan|Insurance Disputes Resolution Committee has ombudsmanpowers upto US$35,000|

Source: World Bank country visits and AXCO Reports

# _Tax policy_

**Insurers in MENA are subject to direct taxes, levies to support supervisors (generally significantly less than 1% of turnover) and levies to fund guarantee funds/ nominal defendants.** Tax regimes are illustrated in Table 10.  While the taxes on non life classes are high in some cases, they are not out of alignment with those in industrial countries.  A more serious issue is the turnover taxes applied to life insurance premiums and zakat applied to policyholder funds.  These are a direct penalty on the savings components of mainstream products such as endowment assurance, and the whole of life and universal life.  The economic reasons for introducing such taxes are hard to fathom.  Algeria applies a combined VAT and premium tax of 19% to all insurance classes, including life. This more than any other factor probably accounts for the virtual non existence of a genuine long term life insurance sector in that country. Similarly a 3.25% tax on capitalization policies in Morocco has effectively killed that market.

**Table 10:  Significant Premium Taxes in MENA (Selected Cases)**

|**Country**|**VAT/ GST **|**Stamp Duty**|**Premium Tax **|**Other **|
|---|---|---|---|---|
|Algeria|17% on all classes-<br>includinglife|5% prem. MTPL|2% on all classes – including life||
|Egypt|||1% life, 5% non life except mandatory<br>insurance||
|Jordan|16% of all non life<br>except PA/ med|1% prem. all<br>classes|||
|Lebanon|||5% non marine, 3% marine|6% municipal<br>tax - non life|
|Syria||3% prem. all<br>classes||5% prem. all<br>classes|
|Tunisia|||10% prem. all non life except export<br>focused, agriculture and decennial<br>5% on marine and aviation||

Source: AXCO and country visits

# _5.2. The Supervisory Framework_

**Many insurance supervisors in MENA do not enjoy adequate levels of legal, administrative and budgetary independence** .  As shown in Table 11, insurance supervision is still conducted by units inside government ministries in eight MENA countries.  Very rarely, these units or departments operate with sufficient administrative autonomy and have the conditions to attract and retain qualified personnel.  Morocco is probably one of the few exceptions in this regard, but Morocco is also passing a new law introducing a new and independent insurance supervisor

22
F igure 4 Drought impacts in Malawi <!-- Start of picture text --> 0<br>‐0.2 1 2 3<br>‐0.4<br>‐0.6<br>‐0.8<br>‐1<br>‐1.2<br>Y ears<br><!-- End of picture text --> C ase 1: Direct effect; C ase 2: Maximum educ = 8 years; C ase 3: Household size = 10 F igure 5 Public assistance and ex‐ante actions in Bangladesh <!-- Start of picture text --> 0<br>‐0.2 1 2 3 Water<br>depth<br>‐0.4 effect<br>‐0.6 Duration<br>effect<br>‐0.8<br>‐1<br>Y ears<br><!-- End of picture text --> C ase 1: Direct effect; C ase 2: VGF = 761.4974; C ase 3: Maximum education = 8 years 22
_P_ ( _State_ = 1| μ _i_ 1) = _P V_ ( _i_ 1 f _Ui_ 2, _Vi_ 1 f 0 | μ _i_ 1) = Substituting: η _i_ 11 = ε _i_ 2 − ε _i_ 1, η _i_ 12 = − ε _i_ 1 = _P_ ( η _i_ 11 p _Zi_ φ 1 − _Zi_ φ 2, η _i_ 12 p _Z_ φ _i_ 1 | μ _i_ 1) (A.6)

where η 1 _j_ and η 2 _j_ are distributed as: After normalization (A.7) can be expressed as a standard bivariate normal: % 1 % 1 1 _P_ ( _State_ = 1| μ _i_ 1) = Φ ( η _i_ 1, η _i_ 2, ρ ) (A.9)

Then, a contribution to the likelihood function of the observation _i_ in State _k_ is: However, (A.9) and (A.10) are different in every state. Log likelihood is formed as the sum of individual log-likelihoods (A.10) over all observations and all states: where _I_ is an indicator function for a migration state. To improve the fit of our estimation we use the Box-Cox transformation of the continuous dependent variables in our model (Heckman and Sedlacek 1990). The “Box-Cox parameter” _λ=-0.4_ provides best fit in terms of minimization of the sum of square residuals of the continuous part of the model.

The log-likelihood function (A.11) is maximized using a standard NewtonRaphson algorithm of Maximum Likelihood procedure in Stata. The maximization routine relies on analytical gradient and analytical Hessian that we programmed to improve convergence properties and speed of the estimation. The performance of maximization algorithm is crucial for the jackknife simulations we conduct in the paper<sup>∗</sup> .

> ∗ The Stata code that implements the Full Information Maximum Likelihood algorithm is available from authors on request.

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Figure 2: Impulse responses of seleceted variables following a positive one-standard deviation shock to the price of resource exports: Comparison across regimes <!-- Start of picture text --> Impulse responses of private consumption Impulse responses of private investment Impulse responses of total exports<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>All consumption All investing All consumption All investing All consumption All investing<br>All savings All balanced All savings All balanced All savings All balanced<br>Impulse responses of imports Impulse responses of GDP at market prices Impulse responses of government transfers to households<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>All consumption All investing All consumption All investing All consumption All investing<br>All savings All balanced All savings All balanced All savings All balanced<br>Impulse responses of domestic absorption Impulse responses of domestic sales Impulse responses of real exchange rate<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>All consumption All investing All consumption All investing All consumption All investing<br>All savings All balanced All savings All balanced All savings All balanced<br>Impulse responses of domestic good price Impulse responses of consumer price Impulse responses of wage rate<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>All consumption All investing All consumption All investing All consumption All investing<br>All savings All balanced All savings All balanced All savings All balanced<br>Impulse responses of foreign savings Impulse responses of foreign debt Impulse responses of sovereign funds<br>0 50 100 150 0 50 100 150<br>periods periods 0 50 100 150<br>All consumption All investing All consumption All investing periods<br>All savings All balanced All savings All balanced All savings All balanced<br>.3 1<br>.8<br>.25<br>.6 .5<br>.2<br>.15 .4 0<br>.1 .2<br>% deviation from steady state % deviation from steady state % deviation from steady state<br>.05 0 −.5<br>1 .3 10<br>.8 .2<br>5<br>.6 .1<br>.4 0 0<br>% deviation from steady state % deviation from steady state % deviation from steady state<br>.2<br>−.1 −5<br>.3 .1 2<br>.05<br>.2 0 1<br>.1 −.05 0<br>% deviation from steady state % deviation from steady state −.1 % deviation from steady state<br>0 −.15 −1<br>1.5 2.5<br>2<br>2<br>1.5 1<br>1.5<br>1<br>1<br>.5<br>.5 .5<br>% deviation from steady state % deviation from steady state % deviation from steady state<br>0 0 0<br>.5 2 150<br>0 0 100<br>−.5 −2<br>50<br>deviation as % of steady state GDP deviation as % of steady state GDP % of intial government revenue<br>−1 −4<br>0<br><!-- End of picture text -->
**Table 5.  US Imports: Value of Preferences, 2003**

||**Value**|**Value**|**_Non-Ag_**|**_Ag_**|**Value**|**Value**|**_Non-Ag_**|**_Ag_**|
|---|---|---|---|---|---|---|---|---|
|**_Beneficiaries of:_**|_(% of Total_<br>_Exports to US)_|_(% of D_|_utiable Export_|_s to US)_|_(% of Total_<br>_Exports to US)_|_(% _|_of Dutiable Exp_|_orts to US)_|
|**AGOA & GSP**|||||||||
|**_All beneficiaries_**|1.4|1.8|1.7|0.1|1.1|1.4|1.3|0.0|
|**_Botswana_**|11.8|21.8|21.8|0.0|10.7|19.9|19.9|0.0|
|**_Swaziland_**|17.7|18.5|18.3|0.2|15.8|16.5|16.4|0.2|
|**_Kenya_**|14.4|17.4|17.4|0.1|13.4|16.3|16.2|0.1|
|**_Mauritius_**|15.6|16.6|16.6|0.0|8.0|8.6|8.5|0.0|
|**_Namibia_**|6.2|13.1|13.1|0.0|4.8|10.3|10.3|0.0|
|**AGOA-LDC & GSP**|||||||||
|**_All beneficiaries_**|9.7|14.4|13.7|0.7|9.1|13.6|13.0|0.5|
|**_Cape Verde_**|17.3|22.8|22.8|0.0|14.6|19.2|19.2|0.0|
|**_Lesotho_**|18.8|18.8|18.8|0.0|17.9|17.9|17.9|0.0|
|**_Uganda_**|1.0|18.8|18.8|0.0|0.9|16.9|16.9|0.0|
|**_Madagascar_**|8.6|16.5|16.5|0.0|8.2|15.8|15.8|0.0|
|**_Ethiopia_**|1.6|15.6|14.9|0.7|1.6|15.5|14.8|0.7|
|**_Malawi_**|13.0|14.5|6.7|7.9|10.9|12.3|6.5|5.8|
|**_Mozambique_**|7.7|8.1|5.8|2.3|7.7|8.1|5.8|2.3|
|**_Tanzania_**|0.9|7.4|6.6|0.8|0.8|6.8|6.0|0.7|
|**_Zambia_**|0.3|5.7|2.7|3.0|0.2|3.4|2.7|0.7|
|**_Mali_**|1.8|5.1|5.1|0.0|0.8|2.1|2.1|0.0|
|**GSP-only**|||||||||
|**_All beneficiaries_**|0.9|1.4|1.3|0.1|0.7|1.1|1.0|0.1|
|**_Tonga_**|0.2|6.2|1.1|5.0|0.2|5.1|0.1|5.0|
|**_St Helena_**|0.6|5.7|5.7|0.0|0.0|0.0|0.0|0.0|
|**GSP-LDC only**|||||||||
|**_All beneficiaries_**|0.4|0.4|0.4|0.0|0.4|0.4|0.4|0.0|
|**_Vanuatu_**|0.3|6.6|1.5|5.2|0.1|1.5|1.5|0.0|

36
30 industrial and developing countries shows that, on average, real interest rates or after-tax real rates of return do not have a significant effect on the share of private income that is savedt9 However, financial liberalization that allows real interest rates to rebound from very negative to near-zero levels often has a positive impact on measured private saving in financially unstable, high-inflation countries. This effect is attributable to reduced flight into consumer durables and foreign assets after the interest rate has risen.

Public policies to raise public saving and hence national saving have a key role in structural adjustment. The evidence shows that the private sector does not reduce its saving one-for-one with tax increases or public spending cuts; it follows that reductions in budget deficits increase national saving. This fact has profound implications for Bank-supported adjustment lending. It was concluded earlier in Section III that after controlling for the effect of other factors, the domestic saving rate rose 4 percentage points more in EIAL countries than in any other group of countries. Thus, adjustment programs supported by the Bank have played a significant role in raising aggregate domestic saving and changing its composition in favor of public saving.

# C. Increasing Investment Efficiency

Resources for investment are scarce, and it is therefore vital to increase the efficiency of investment. Further, because more efficient investment has a higher rate of return, increasing the efficiency of 19 Among the studies presenting growing evidence on the interest insensitivity of saving in developing countries see, for instance, A. Giovannini, (1985). For an alternative view, see M.J. Fry (1988), and the survey by B. Balassa (1989).
(2004) and Becker et al. (2015, 2018), parent’s income is determined as follows: So the returns to education is _R_<sup>_pj_</sup> in the parental generation in location _j_ = _r, u_ , with _r_ for rural and _u_ for urban. We assume that the parents with zero year of schooling earns _Y_ 0<sup>_p>_0.Thefocushereisonhowthehouseholdpermanentincomechangeswiththe</sup> education of the parent _Ei_<sup>_p_.The“returnstoeducation”herethusrefertoahousehold’s</sup> permanent income, not an individual’s labor market income in the survey year which has been the focus of the Mincerian literature on returns to education.

The parent allocates income _Yi_<sup>_p_betweenownconsumption</sup><sup>_C_</sup> _i_<sup>_p_andinvestmentinthe</sup> child’s education _Ii_ . The budget constraint is: An important assumption in this specification of the budget constraint is that there is no credit market where the parents can borrow to finance children’s education. This is a plausible assumption in the context of developing countries where the student loan market (public or private) is underdeveloped or nonexistent (Chapman and Suryadarma (2013)).

Following Becker et al. (2015), we assume that the education production function exhibits the following features: (i) diminishing returns to financial investment, (ii) complementarity between the financial investment and parental education, (iii) the direct effect of parent’s education capturing the non-financial aspects such as role model effects, and (iv) a higher ability of a child has an additively separable impact and also is complementary to financial investment. We augment the specification to allow for possible effects of a child’s ability on the curvature of the production function with respect to the financial investment by the parents.

_Ei_<sup>_c_=</sup><sup>_δ_0+</sup><sup>_α_1</sup><sup>_φi−α_2(</sup><sup>_φi_)2 + (1 +</sup><sup>_ω_1</sup><sup>_φi_)</sup><sup>_δ_</sup> 1<sup>_jIi−_(1</sup><sup>_−ω_2</sup><sup>_φi_)</sup><sup>_δ_</sup> 2<sup>_jI_</sup> _i_<sup>2+</sup><sup>_δ_</sup> 5<sup>_jIiE_</sup> _i_<sup>_p_+</sup><sup>_δ_</sup> 3<sup>_jE_</sup> _i_<sup>_p−δ_</sup> 4<sup>_j_(</sup><sup>_E_</sup> _i_<sup>_p_)2(3)</sup> 7
|**Table 4: Diagnosis i**|**n thepublic an**|**dprivate sec**|**tors(unstable a**|**ngina and asth**|**ma cases onl**|**y)**|
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
||**Repr **|<br>**esentative sa**|<br>**mple**|**Dua**|<br>**lpractice sa**|<br>**mple**|
|||Correct|Correct||Correct|Correct|
||Gave<br>diagnosis|diagnosis<br>(conditional)|diagnosis<br>(unconditional)|Gave<br>diagnosis|diagnosis<br>(conditional)|diagnosis<br>(unconditional)|
|**Panel A: SP and case fixed effe**|**cts**||||||
|Is a private provider|0.168***|-0.014|0.016|0.095|-0.050|0.018|
||(0.052)|(0.057)|(0.022)|(0.068)|(0.105)|(0.053)|
|R-squared|0.130|0.121|0.075|0.130|0.114|0.054|
|Number of observations|440|178|440|201|88|201|
|Mean of public|0.263|0.150|0.039|0.380|0.395|0.150|
|Mean of private|0.431|0.135|0.058|0.495|0.380|0.188|
|Mean of sample|0.418|0.135|0.057|0.438|0.386|0.169|
|**Panel B: SP, case and market/d**|**istrict fixed ef**|**fects**|||||
|Is a private provider|0.188***|-0.019|0.023|0.089|-0.067|0.018|
||(0.072)|(0.093)|(0.031)|(0.069)|(0.109)|(0.054)|
|R-squared|0.218|0.301|0.145|0.149|0.176|0.066|
|Number of observations|440|178|440|201|88|201|
|**Panel C: SP, case and market/d**|**istrict fixed ef**|**fects**|||||
|Is a private provider|0.149*|-0.046|0.031|0.083|0.005|0.037|
||(0.081)|(0.111)|(0.035)|(0.072)|(0.122)|(0.058)|
|Has MBBS|-0.092|0.108|0.008||||
||(0.093)|(0.134)|(0.039)||||
|Has some qualification|0.023|-0.010|-0.012||||
||(0.074)|(0.075)|(0.028)||||
|Age of provider|-0.002|-0.005*|-0.002|0.002|-0.001|0.000|
||(0.003)|(0.003)|(0.001)|(0.004)|(0.008)|(0.003)|
|Gender of provider (1=Male)|-0.089|0.272*|0.079*|-0.125|-0.054|-0.086|
||(0.126)|(0.145)|(0.041)|(0.109)|(0.182)|(0.079)|
|Patient load during visit|-0.003|-0.017|-0.005|-0.018|-0.004|-0.005|
||(0.014)|(0.011)|(0.004)|(0.018)|(0.034)|(0.013)|
|R-squared|0.222|0.362|0.159|0.185|0.217|0.096|
|Number of observations|423|173|423|183|80|183|

Notes: *** Significant at 1%, ** Significant at 5%, * Significant at 10%. Robust standard errors clustered at the market level are in parenthesis. All regressions include a constant. Observations are standardized provider-patient interactions. Market fixed effects are used for the representative sample, and district fixed effects for dual practice sample.

36
that the GL allows closed form solutions for equilibrium levels of quasi-fixed inputs, which facilitates computation of substitution elasticities and their standard errors.

The model is estimated using a panel of monthly time series observations from January 2005 through March of 2008 for a cross section of twelve European countries. The relatively large number of observations and considerable variation in the data allows the estimation of variable returns to scale and input biases from technological change. For our purposes, an important advantage is that the model can be used to test whether the carbon cap-and-trade regime induced carbon saving technological innovation. Moreover, the study provides explicit measures of the degree of carbon abatement, such as carbon emissions per unit of electricity output, under the EU ETS and, most importantly, explains how this abatement was achieved.

Specifically, the degree and nature of fuel switching induced by carbon pricing and relative fuel prices is estimated. This empirical assessment of carbon substitution possibilities sheds light on whether carbon pricing significantly increases the demand for less carbon intensive fuels, such as natural gas, at the expense of carbon intensive fuels, such as coal. These substitution possibilities ultimately determine whether the demand for carbon is price inelastic, which would imply significant adjustment costs to a low carbon society, or whether carbon demand is elastic, facilitating a less costly path to achieving significant reductions in carbon emissions.

The next section provides some additional background on the European program. Section 3 presents the economic framework, discussing the theoretical underpinnings for the empirical model. The parametric specification of the econometric model is then presented in the fourth section along with a discussion of the estimation techniques. Section 5 provides an overview of the data sample, including descriptive statistics by country on electricity generation by type, net imports, total generation, and the composition of so-called combustible fuels, including natural gas, coal, and petroleum. Trends in the carbon intensity of electricity and in fuel shares in the sample are also identified and discussed. The sixth section of the paper discusses the econometric results and the 4