rafmacalaba/datause-encoder-data huggingface dataset:
|and potential determinants<br><br>Share of largest line<br>(_D7_)<br>(_D8_)<br>(_D9_)|-0.250<br>-0.197<br>-0.140<br>(0.951)<br>(0.934)<br>(0.891)<br>-0.373<br>-0.372<br>-0.376<br>(0.138)_∗∗∗_<br>(0.137)_∗∗∗_<br>(0.135)_∗∗∗_<br>0.240<br>0.241<br>0.249<br>(0.134)_∗_<br>(0.133)_∗_<br>(0.127)_∗_<br>0.112<br>0.086<br>(0.384)<br>(0.375)<br>0.051<br>(0.138)<br>-3.390<br>-2.795<br>-1.540<br>6.023<br>(5.761)<br>(1.706)<br>58<br>56<br>58<br>0.47<br>0.18<br>0.17<br>f largest line computed as largest export<br>nitial values, unless otherwise reported.<br>indicates signifcance at 5 percent level,|
|---|---|
|export diversifcation<br>**ation measure (% change)**<br>Herfndahl<br>(_D5_)<br>(_D6_)|-0.178<br>-0.207<br>(0.624)<br>(0.595)<br>-0.252<br>-0.249<br><br>(0.103)_∗∗_<br>(0.101)_∗∗_<br>0.298<br>0.294<br>(0.095)_∗∗∗_<br>(0.091)_∗∗∗_<br>-0.042<br>(0.246)<br>-2.678<br>-3.305<br>(3.782)<br>(1.161)***<br>56<br>58<br>0.18<br>0.17<br>2-digit sectoral level. Share o<br>re (natural) logarithms of i<br>ance at 10 percent level, _∗∗_|
|between sectoral <br>**Diversifc**<br>ucts<br>(_D3_)<br>(_D4_)|1.197<br>-0.221<br>(0.861)<br>(0.638)<br>-0.709<br>-0.253<br>(0.131)_∗∗∗_<br>(0.103)_∗∗_<br>0.261<br>0.298<br>(0.123)_∗∗_<br>0.096_∗∗∗_<br>-0.024<br>(0.253)<br>0.038<br>(0.092)<br>0.752<br>-3.129<br>(1.647)<br>(3.966)<br>58<br>58<br>0.18<br>0.49<br>fcation measures at the <br>t sector.<br>All variables a<br>heses.<br>_∗_indicates signifc<br>ent level.|
|artial correlations <br>2 and 2007 _†_<br>Number of prod<br>(_D1_)<br>(_D2_)|1.646<br>1.516<br>(0.900)_∗_<br>(0.887)_∗_<br>-0.725<br>-0.727<br>(0.130)_∗∗∗_<br>(0.130)_∗∗∗_<br>0.31<br>0.306<br>(0.127)_∗∗_<br>(0.126)_∗∗_<br>-0.54<br>-0.476<br>(0.362)<br>(0.356)<br>-0.126<br>(0.130)<br>9.192<br>7.73<br>(5.680)<br>(5.474)<br>58<br>56<br>0.50<br>0.19<br>es used to generate diversi<br>git line within each 2-digi<br>ors are reported in parent<br>ates signifcance at 1 perc|
|Table 8: P<br>thereof, 200|Density<br>RCA<br>Trade volume<br>PRODY<br>Tarif<br>Constant<br>N<br>R-squared<br>_†_ 6-digit HS lin<br>share of 6-di<br>Standard err<br>and _∗∗∗_indic|

25
**Table A18: First stage: Log Refugees at 50km with decay function (i.e. index generated using 1)distance to closest cluster, 2)square of distance to closest cluster and** **<u>3) square root of distance to closest cluster) instrumented by distance-based IV.</u>**

||Log|of refugees (50|km) (with dista|nce)|Log of refugees (50k|m) (with sq dist|ance)|Log o|f refugees (50k|m) (with sqrt di|stance)|
|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)<br>(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Log of Distance IV (mean border distance)|9.24792***|9.24997***|8.71514***|8.68000***|3.59668***<br>3.59728***|3.56997***|3.55512***|12.45550***|12.45850***|11.62387***|11.58091***|
||(2.10281)|(2.10376)|(2.27960)|(2.30744)|(0.83500)<br>(0.83525)|(0.95432)|(0.96117)|(2.91265)|(2.91397)|(3.11908)|(3.16094)|
|HH head age||-0.00264|-0.00001|-0.00347|-0.00147|-0.00032|0.00075||-0.00340|-0.00019|-0.00760|
|||(0.00363)|(0.00323)|(0.01550)|(0.00222)|(0.00177)|(0.00760)||(0.00432)|(0.00390)|(0.01984)|
|Age squared||0.00003|-0.00000|0.00002|0.00001|0.00000|-0.00002||0.00003|-0.00000|0.00006|
|||(0.00003)|(0.00003)|(0.00016)|(0.00002)|(0.00002)|(0.00008)||(0.00004)|(0.00004)|(0.00021)|
|HH head sex (male)||-0.00493|-0.00571|0.00350|-0.00427|-0.00648|-0.02940||-0.00492|-0.00487|0.01811|
|||(0.01102)|(0.01611)|(0.08026)|(0.00556)|(0.00881)|(0.04476)||(0.01426)|(0.02032)|(0.10108)|
|Household size|||0.00070|||0.00113||||0.00043||
||||(0.00160)|||(0.00105)||||(0.00193)||
|Married monogamously|||-0.10517|||-0.05740||||-0.12797||
||||(0.07852)|||(0.03487)||||(0.10126)||
|Married polygamously|||-0.11545|||-0.06754**||||-0.13711||
||||(0.07481)|||(0.03337)||||(0.09640)||
|Separated (divorced/widowed)|||-0.07393|||-0.04820||||-0.08434||
||||(0.07726)|||(0.03461)||||(0.09953)||
|Completed primary sch|||0.00050|||-0.00200||||0.00298||
||||(0.01293)|||(0.00745)||||(0.01557)||
|Commercial farming|||0.00052|||0.00188||||-0.00066||
||||(0.00885)|||(0.00470)||||(0.01145)||
|Wage employment|||-0.01362|||-0.00372||||-0.01978||
||||(0.02168)|||(0.01155)||||(0.02606)||
|Non-agricultural self-employment|||0.00461|||-0.00012||||0.00770||
||||(0.01590)|||(0.00821)||||(0.01967)||
|Remittances & Others|||0.00852|||0.00238||||0.01199||
||||(0.01517)|||(0.00811)||||(0.01913)||
|Property income and investments|||0.00629|||-0.00493||||0.01241||

27
privatization that was undertaken.<sup>9</sup> Mass privatization led to ownership structures that were initially highly dispersed because the entire adult population of the country, or all insiders to each firm, were allocated vouchers with which to purchase the shares of the company. Mass privatization was also argued to hinder the establishment of effective corporate governance, especially when long "agency chains" were created by the emergence of financial intermediaries holding privatization vouchers (John Coffee, 1996; Stiglitz, 2002). It probably also hindered the development of secondary capital markets and in many countries it also initially resulted in majority ownership by insiders (Estrin, 2002).

Whether as a consequence of institutional weakness and/or the methods of privatization, the EBRD Transition indicators show that capital markets in transition economies developed less quickly than other market economy structures such as liberalized price setting or openness to trade. Indeed, stock markets in transition economies during the 1990s were often characterized by insufficient regulation, institutional fragility and weak minority shareholder protection (EBRD 1998; John Bonin and Paul Wachtel, 2003).

# _2.4 Selection of firms to be privatized_

Whatever the privatization methods used, it is likely that firms are not assigned for privatization at random. This has important implications for econometric work assessing the impact of privatization on company performance because it implies that studies that treat the allocation of firms for privatization as random or do not adequately control for the non-random selection may potentially overstate the positive effect of privatization on performance. D-M for instance indicate that 47 percent of pre-2003 studies that they survey do not control for this non-random selection.

> 9 For example, though retained state shareholdings were small in some of the leading transition economies in CEE, the state continued to own significant shareholdings in others, especially in the CIS. Thus in a 1999 survey of privatized firms, the EBRD found that in 20 of the 23 countries, the state had retained some shares in around 20% of privatized firms, with more than a 20 percent shareholding in around 12 percent of the firms. The state kept a share of more than 15 percent of privatized firms in eight countries and more than 30 percent in a further four (Bennett, Estrin and Maw, 2005). Retained state ownership has been a factor in recent Chinese privatizations (Lihui Tian and Estrin, 2008). Governments have also issued golden shares to retain influence over some of the privatized SOEs.

12
as follows, assuming the market conduct parameter is constant to simplify the exposition: _∂pj_ where marginal surplus _msji ≡ qj ∂qi_<sup>.</sup> By Cramer’s rule, the quantity responses can be written as follows: where _det_ ( _·_ ) is the determinant of a matrix and **Ci** is the matrix formed by replacing the _i_ ’th column of **C** by **d** .

The pass-through of product _i_ can be written as a function of the quantity responses as follows: We write out the pass-through formula for product 1 when the number of products _n_ = 2 as follows: Note that there are now three additional terms in the pass-through formula, which relate to the cross-marginal-revenue effects, the inverse cross-price effects, as well as the effect of benefits on the marginal revenue of the other product.

We can further extend equation (42) by using equations (38) and (39). Note that since _pi_ = _pi_ ( _q_ 1 _, q_ 2 _, b_ ), by total differentiation, we can write<sup>_∂_</sup> _∂b_<sup>_<u>pi</u>_=</sup><sup>_−d_</sup> _db_<sup>_<u>q</u>_</sup><sup><u>1</u></sup> _∂q∂p_ 1 _<u>i</u>_<sup>_−d_</sup> _db_<sup>_<u>q</u>_</sup><sup><u>2</u></sup> _∂q∂p_ 2 _<u>i</u>_<sup>,sowecan</sup> estimate<sup>_∂_</sup> _∂b_<sup>_<u>pi</u>_asafunctionofMPCsandinversedemandslopes.Therefore,thepass-through</sup> 64
present a much bigger puzzle, especially because commitment crowded in liquid savings _during_ the commitment period. Although optimally chosen commitment can increase the savings of time inconsistent agents, theory predicts that this will be at the expense of liquidity held elsewhere. Hence, our light overdrafter results underscore that commitment savings products can benefit consumers for reasons unrelated to time inconsistency.

What remains less clear is _why_ commitment products benefit savers for other reasons. While we cannot isolate a single mechanism for our effects, we do identify several potential channels, including habit formation and learning. Ultimately, additional research is needed to explore the multiple pathways through which illiquid savings products transform individuals’ lives.

# **References**

- Angeletos, G.-M., D. Laibson, A. Repetto, J. Tobacman, and S. Weinberg (2001). The Hyperbolic Consumption Model: Calibration, Simulation, and Empirical Evaluation. _The Journal of Economic Perspectives 15_ (3), 47–68.

- Banerjee, A. V. and E. Duflo (Winter 2007). The Economic Lives of the Poor. _The Journal of Economic Perspectives 21_ (27), 141–167.

- Bank of Ghana (2014). Statistical Bulletin: March 2013. Technical report, Bank of Ghana Statistics Department.

- Bank of Ghana (2017). Monthly Statistical Bulletin: December 2016. Technical report, Bank of Ghana Statistics Department.

- Becker, G. S. and K. M. Murphy (1988). A Theory of Rational Addiction. _The Journal of Political Economy_ , 675–700.

- Benjamin, D. J. (2003). Does 401 (k) Eligibility Increase Saving?: Evidence from Propensity Score Subclassification. _Journal of Public Economics 87_ (5), 1259–1290.

- Beshears, J., J. J. Choi, D. Laibson, B. C. Madrian, and W. L. Skimmyhorn (2017). Borrowing to Save? The Impact of Automatic Enrollment on Debt.

- Brune, L., X. Gine, J. Goldberg, and D. Yang (2016). Facilitating Savings for Agriculture: Field Experimental Evidence from Malawi. _Economic Development and Cultural Change 64_ (2), 187–220.

14
that, on the basis of a panel gravity model, ceteris paribus a landlocked country traded about 28% less than a coastal one. Estimates of similar magnitudes were found in other studies. Clearly, these estimates could explain only a small part of the CACs’ under-trading with the rest of the world. Indeed, few of those papers focused explicitly on the impact of landlockedness on trade. One notable exception was Raballand (2003) who analyzed the effect of landlockedness on trade in the case of Central Asian countries. Using a restricted sample of 46 CIS countries, 18 of which landlocked, over a period of 5 years (1995-1999), he found landlockedness to reduce trade by more than 80%. This very large impact, substantially out of step with usual estimates, was at least partly due to the fact that his landlockedness variable was equal to one only for bilateral trade between _two_ landlocked countries, while the landlockedness variable in other studies usually marks the bilateral trade of landlocked countries with all their partners, landlocked or not.

Dummy variables in a gravity equation are black boxes that do not tell us much about how landlockedness affects trade. A limited number of studies have shed light on the channels through which its impact is felt. In his study of Central Asian trade, Raballand introduced two other components of landlockedness ---shortest distance to a major port facility and number of borders crossed in transit, and found them to have significant effects. Using CIF/FOB data from the IMF for 97 developing countries, 17 of which landlocked, Radelet and Sachs (1999) estimated that transport and insurance costs were twice as high for landlocked countries as for coastal countries. They concluded that “geographic isolation and higher shipping costs may make it much more difficult if not impossible for relatively isolated developing countries to succeed in promoting manufactured exports”. Limao and Venables (2001) found a large effect of infrastructure quality on trade costs, in particular for landlocked countries, on the basis of a cross section of countries with controls for the quality of transit-country infrastructure. Poor infrastructure accounted for 40% of predicted transport costs for coastal countries and up to 60% for landlocked countries. The high impact of remoteness and infrastructure on trade costs was also evidenced by Brun et al. (2005). Using IMF estimations of freight payments as a percentage of imports, Stone (2001) found a comparable transport cost burden. The transport costs were lower in transit states than in landlocked countries in 75% of the cases, from the 64 possible comparisons between landlocked countries and transit countries.

3
13 Using the 1992 household survey, average household income per capita is estimated at Leva 16,809, about US$ 709 (see Table 2). The average household income per capita in the rumral sector is Leva 19,151 (about US$ 808), and it is 26 percent higher than the urban average (Leva 15,090 or about US$ 637). Furthermore, for each income decile, rural household income is higher than urban one (see Table 2). The difference in income levels between the two sectors is statistically significant.

Income distribution is measured by groups (decile, quintiles, etc.) ranldng households by their income/consumption expenditure. The distribution of household income per capita by income decile is shown in Table 2. The Gini coefficient, an index measuring the inequality of income distribution, which is equal to zero in the case of perfect equality and to 100 percent in the case of total inequality, is 25.8 percent. While the Gini index in a typical middle-income country had ranged between 40 and 54 percent, in Central and Eastern Europe it oscillated between 20 and 29 percent, i.e., values even lower than those prevailing in Western market economiesel. This comparatively low income inequality, although changing rapidly, is still an important characteristic of most economies in transition. Table 2 shows that the rich (top decile) receive nearly 22 percent of total income, a share that exceeds their population share by more than 50 pent. In contrast, the poor (the bottom 20 percent) receive less than 10 percent of total income, i.e., a share that falls short of their population share by about 50 percent. Another way of viewing the concentration of income in the upper income groups is to calculate the _decile distribution ratio,_ that is, the share of the bottom 40 percent in relation to the share of the top 20 percent. Table 2 10/ Hassan and Peters (1994).
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 9. Rail networks in Angola**

|Company|Port|Region|Lines (km)<br>Total|Operating|%<br>operating|Gauge<br>(mm)|
|---|---|---|---|---|---|---|
|Caminhos de Ferro de Benguela (CFB)|Lobito|Central|1,333|246|18|1,067|
|Caminhos de Ferro de Luanda (CFL)|Luanda|Northern|479|181|38|1,067|
|Caminhos de Ferro de Moçamedes<br>(CFM)|Namibe|Southern|907|425|47|1,067|
|Amboim|Amboim|Central|122|0|0|760|
|Total|||2,841|852|30|-|

_Source:_ Bullock 2009.

# **Challenges**

Angola will need to rehabilitate or rebuild the 60 percent of its current rail network that is currently out of operation. This is a tremendous task, since in many cases it requires expensive works to remove mines and complete replacement of the obsolete or deteriorated rails. In addition, the performance of existing lines is very poor. Freight density and labor productivity are low in Angola in comparison to other African countries, and will need to be tackled through institutional reform (table 10).

**Table 10. Railway indicators for Angola and selected countries, 2000–05**

||CFM (Angola)|BR (Botswana)|CEAR (Malawi)|Nacala Railroad<br>(Mozambique)|Beira Railroad<br>(Mozambique)|Ressano Garcia Line<br>(Mozambique)|Transnamib (Namibia)|Spoornet (South Africa)|RSZ (Zambia)|NRZ (Zimbabwe)|
|---|---|---|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state run (0)|0|0|0|1|1|0|0|0|1|0|
|Freight density (1,000 tonne-<br>km/km)|469|827|90|270|663|364|475|2,427|406|902|
|Passenger density (1,000<br>passenger-km/km)|—|—|38|103|44|44|33|60|92|166|
|Labor productivity (1,000 traffic<br>units per employee)|580|722|131|710|281|—|484|3,308|502|390|
|Locomotive productivity (million<br>traffic units per locomotive)|30|41|3|25|13|—|25|33|25|8|
|Carriage productivity (1,000<br>passenger-km per carriage)|4,046|2,391|1,176|3,333|750|—|—|—|3,286|—|
|Wagon productivity (1,000 net<br>tonne-km per wagon)|950|987|82|260|476|—|805|913|377|195|
|Freight yield (US cents/tonne-km)|—|—|6|5|3|3|—|—|4|—|
|Passenger yield (US<br>cents/passenger-km)|—|—|1|0.9|0.5|1|—|—|1|—|
|<br>_Source:_Bullock 2009. Derived from A<br>_Note:_* With 2.5 passenger-km equiv<br>— = Not available.|ICD rail op<br>alent to 1 tr|erator dat<br>affic unit, 1|abase(ww<br>tonne-km|w.infrastru<br>equivale|ctureafric<br>nt to 1 traf|a.org/aicd/t<br>fic unit.|ools/data<br>)|.|||

23
a small modification to this model is made in order to cover the case of unit roots with a stochastic growth process, which appears to be present in the US data.

# **3.3.1 Motivating the Choice of Stochastic Processes**

We choose our shock processes to examine two plausible scenarios in the detection of technology shocks:

1. There are confounding low-frequency but less persistent processes in the data other than technology;

2. There are high or business cycle frequency processes in the data.

As described earlier, our NAMS approach has been designed to deal with the first case, while the spectral identifications are targeted at the second. Before we turn to the simulations, we briefly describe the specifications that can generate the two described processes. This includes covering the frequency domain properties of AR(1) and AR(2) processes.

Our choice of driving processes is motivated by the following well-known spectral density factoid. Consider the white noise process _εt_ , with variance _γ_ (0) = _σ_<sup>2</sup> and autocovariance function _γ_ ( _h_ ) = 0 for _h_ = 0.

Therefore, the spectrum is Now consider the AR(1) process _vt_ = _ρvt−_ 1 + _ε_ with autocovariance function _γ_ ( _h_ ) = _σ_<sup>2</sup> _ρ_<sup>_|h|_</sup> /(1- _ρ_<sup>2</sup> ). The associated spectrum is,

Notice when _ρ >_ 0 it is obvious that the spectrum is dominated by low-frequency shocks, and in the case of negative autocorrelation, _ρ <_ 0, the spectrum is dominated by high frequency components. This simple factoid shows that a specification of a simple AR(1) process for _b_ allows us to generate a confounding low-frequency process.<sup>13</sup> A business cycle frequency shock requires an AR(2) process.

We replicate a sinusoidal business-cycle shock process with a specific frequency ( _f_ ) using the following AR(2) process: Here we set _f_ such that the shock process for _b_ has a periodicity of 8-quarters (2 years).

In our application, to ensure the cyclical process degrades over time (and avoids a unit root), we multiply both coefficients ( _ρb,_ 1 and _ρb,_ 2) by 0.9. We also calibrate

> 13A negatively-signed AR coefficient would allow us to include a confounding high frequency shock. For a more detailed discussion of the data-generating processes behind a range of spectral densities, see Medel (2014).

16
# **_Table A4.8c Relationship between project design and IEG binary outcome rating, weighted by original commitment (in millions, constant 2015 $)_**

|||**De**|**pendent vari**|**able: IEG 1-6**|**outcome rat**|**ing**||
|---|---|---|---|---|---|---|---|
|**Design index components**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Number of project|-0.010|||||||
|components|(0.057)|||||||
|Number of project||-0.030**||||||
|sub-components||(0.013)||||||
|Number of PDO indicators at|||-0.058|||||
|entry|||(0.020)|||||
|Number of intermediate||||-0.005||||
|indicators at entry||||(0.011)||||
|PDO rating|||||0.031|||
|(1 high - 4 low)|||||(0.132)|||
|Results framework rating||||||-0.242**||
|(1 high – 4 low)||||||(0.109)||
|Extent of changes to project|||||||-0.291**|
|components|||||||(0.119)|
|Observations|120|120|120|120|120|120|120|
|Pseudo R-squared|0.131|0.173|0.191|0.133|0.131|0.169|0.176|
|||**De**<br>|**pendent vari**<br>|**able: IEG 1-6**<br>|**outcome rat**|**ing**||
||**(8)**|**(9)**|**(10)**|**(11)**||||
|**Design index**|0.120**<br>(0.060)|||||||
|Project Characteristics Index<br>||0.191***||||||
|for comparison<sup>1</sup>||(0.052)||||||
|CPIA at Approval|||0.444**<br>(0.204)|||||
|Change in CPIA over Project||||-0.0685||||
|Lifetime||||(0.278)||||
|Observations|120|120|120|120||||
|Pseudo R-squared|0.164|0.226|0.14|0.104||||

Notes: This table provides estimates of IEG Outcome Ratings regressed on common design features and quality ratings. Using Principal Component Analysis , an Index is created for Design based on these features and ratings. (1) For comparison, an Index is created for Project Characteristics based on the log of original commitment, preparation cost, first and second half supervision cost, the change in project size from approval to closing, and the time it takes projects to move from one key milestone in the project lifecycle to another. The table also provides the estimates of regressing IEG Outcome on two country variables, CPIA score of project country at approval and the change in the score over the project lifetime.

62
# **Tables**

Table 1: Descriptive Statistics for Employed

||All Employed|Informal|Formal|I/F Ratio|
|---|---|---|---|---|
|Sample Size|16,405|10,446|5,959|1.75|
|Population|7,974,924|4,799,786|3,175,138|1.51|
|E(_lnWs_)|-0.73|-1.11|-0.48|2.30|
|SD(_lnWs_)|2.01|1.85|2.06|0.90|
|E(_tes_)|88.51|92.33|82.77|1.12|
|SD(_tes_)|106.22|113.70|93.60|1.21|
|E(_tnes_)|6.90|7.34|6.33|1.16|
|SD(_tnes_)|13.13|13.63|12.45|1.09|
|E(_Y Ss_)|10.31|9.13|12.15|0.75|
|SD(_Y Ss_)|4.17|3.82|4.20|0.91|

Author’s calculations based on ECH, Second Quarter 2003, 13 Metropolitan Areas. All statistics weighted using expansion factors. _Y Ss_ : years of schooling. Employment and non-employment duration in months. Informality using SP(health and pension) definition. Log of Hourly Wages in current dollars of 2003. I/F ratio is different in the sample than in the population because of sample weights.

Table 2: Decomposition of Variance in Log Hourly Wages

||Informal Sector|
|---|---|
|Mean of Log Wages (_M_1)<br>Variance of Log Wages (_σ_1)<br>Proportion of Employed in Sector (_P_1)|-1.11<br>3.42<br>0.39|
||Formal Sector|
|Mean of Log Wages (_M_2)|-0.48|
|Variance of Log Wages (_σ_2)|4.27|
|Proportion of Employed in Sector (_P_2)|0.61|
||Economywide|
|Mean of Log Wages (_P_1_M_1+_P_2_M_2)|-0.73|
|Sum of Within-Sector Variance (_P_1_σ_1+_P_2_σ_2)|3.94|
|Between-Sector-Variance _P_1_P_2(_M_1_−M_2)<sup>2</sup>|0.09|
|Total Variance|4.04|

Author’s calculations based on ENH, Second quarter of 2003, 13 Metropolitan areas. All statistics weighted using expansion factors. Informality using SP (health and pension) definition. Log of nominal hourly wages in current 2003 dollars (Exchange Rate =2877.65 Pesos per dollar - Source: World Bank) 37