rafmacalaba/datause-encoder-data huggingface dataset:
# Table 9. Portfolio at Risk(>30days) w/ Interaction term (MFI-level regression)

P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

## - <u>Panel A: Overall Score Nominal (0 100)</u>

||V|<br>ersion 1(Ful samp|<br>Depvar: Portfolio at<br>les)|Risk(>30days)<br>Version 2|<br>(DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Overall Score Nominal|-0.0003|-0.0007***|-0.0006**|-0.0003|-0.0007***|-0.0007**|
||[0.455]|[0.001]|[0.047]|[0.458]|[0.001]|[0.020]|
|NBFI dummy * Overall Score Nominal (0-100)|0.0004|0.0007**|0.0008*|0.0006|0.0006**|0.0008*|
||[0.355]|[0.025]|[0.079]|[0.199]|[0.026]|[0.093]|
|Combined group dummy * Overall Score Nominal (0-100)|-0.0007|0.0002|0.0003|0.001|0.0005|0.0007|
||[0.407]|[0.563]|[0.523]|[0.190]|[0.157]|[0.132]|
|NBFI dummy|-0.0108|-0.0315**|-0.0359|-0.0196|-0.0298**|-0.0385|
||[0.697]|[0.029]|[0.134]|[0.492]|[0.025]|[0.112]|
|Combined group dummy|0.055|0.0004|-0.0015|-0.0288|-0.0229|-0.0289|
||[0.186]|[0.979]|[0.947]|[0.494]|[0.175]|[0.204]|
|Observations|627|627|598|572|572|546|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.074|0.208|0.165|0.071|0.25|0.172|
|Adjusted R-squared|0.0447|0.183|0.137|0.0391|0.224|0.143|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.000104|-0.000018|0.000177|0.000303|-0.0000575|0.000134|
||[0.806]|[0.946]|[0.709]|[0.490]|[0.815]|[0.783]|
|Combined group dummy|-0.000993|-0.000487*|-0.00031|0.000659|-0.00017|0.0000706|
||[0.161]|[0.094]|[0.515]|[0.316]|[0.598]|[0.883]|

49
**<u>Table 14: Ginasio Carioca & IDERio Middle Schools</u>**

||(1)<br>IDERio Mid<br>(SD)|(2)<br>IDERio Mid<br>(SD)|(3)<br>IDERio Mid<br>(SD)|(4)<br>IDERio Mid<br>(SD)|
|---|---|---|---|---|
|Carioca Middle School * Post|1.044<br>[0.092]***|0.681<br>[0.100]***|0.376<br>[0.120]***|0.861<br>[0.174]***|
|Single-Shift * Post|0.022|-0.096|-0.042|-0.340|
||[0.129]|[0.114]|[0.105]|[0.187]*|
|School of Tomorrow|0.151|0.108|-0.066|0.202|
||[0.079]*|[0.081]|[0.119]|[0.129]|
|More Education|0.074|0.063|0.067|0.111|
||[0.035]**|[0.034]*|[0.068]|[0.049]**|
|Mean items in the house||0.093|0.105|0.088|
|||[0.021]***|[0.035]***|[0.033]***|
|% Mothers w/ HE||0.017|-0.551|-0.042|
|||[0.196]|[0.314]*|[0.262]|
|Participation rate-Prova Rio 7th<br>year||0.861|0.801|0.744|
|||[0.183]***|[0.379]**|[0.280]***|
|Age-grade distortion||-0.014|-0.004|-0.014|
|||[0.003]***|[0.005]|[0.004]***|
|Constant|-1.088|-1.784|-2.165|-1.721|
||[0.026]***|[0.214]***|[0.409]***|[0.321]***|
|Observations|2345|2204|190|1113|
|Number of schools|335|315|38|159|
|R-squared|0.73|0.74|0.83|0.75|
|School FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Restricted Sample<br>Robust standard errors in brackets|No|No|Yes|Yes|

<mark>* significant at 10%; ** significant at 5%; *** significant at 1%</mark> Notas: IDERio in Middle Schools from 2009 to 2014. Column (3) refers to only schools that eventually become Single-Shift until 2015, in years 2009 through 2013. Column (4) refers to only schools with low age-grade distortion in 2010 (prior to the program). The info on number of goods in the house and mother education is from Prova Brasil (and imputed in the years without Prova Brasil, starting in 2009). Standard errors clustered in school level.

34
_IMPACT OF WEDP LOANS_ _7_ Where: _Yi_ is the outcome variable measured in 2016. _Loani_ is a dummy variable taking the value of one if the firm received a loan at any point as reported in either 2014 or 2016 surveys. _Post_ 2016 is a dummy variable taking the value of zero in 2014 and one in 2016. _Xi_<sup>_′_isavectorofcontrolvariablesthatcouldchange</sup> from the 2014 to 2016 (for example, age of owner, marital status and household size). _β_ 1 will measure the average level differences of firms that receive a WEDP loan versus those that did not. Propensity score matching will again be utilized such that the control group outcome is matched to a treated observation using characteristics of the entrepreneur and firm size (as described above).

Before presenting our impact results, we first describe the observed take-up of the different WEDP programs and present characteristics of firms that were actually able to borrow.

# **III. Take-Up of the WEDP Services**

Female firm owners register for WEDP in Ethiopia via One Stop Shops in their city and their business is required to be a registered firm for at least six months. The WEDP-registered entrepreneurs are not guaranteed a loan – both the entrepreneurs can choose which program services (loan or training) they want to take-up and the MFIs will determine creditworthiness of the borrower before lending. During the follow-up surveys, we asked the business owner to specify which WEDP program services they utilized. The self-reported WEDP loan and training status were reconciled with administrative data collected by WEDP and information in the access to finance section of the survey. Table 1 shows that at the time of the follow-up survey 34% of firms in the sample received a WEDP loan and 78% took WEDP training that was provided through Technical and Vocational Education and Training (TVET) colleges.

|**WEDP Program Status**|**Number of frms**|**Percentage**|
|---|---|---|
|Loan Only|164|8%|
|Training Only|1,118|52%|
|Both loan and training|551|26%|
|No WEDP Services|306|14%|
|Total|2,139|100%|

Table 1—Number of firms that received the different WEDP services These proportions are in line with admistrative data of overall WEDP-registered clients that stand at 19,431 firms in 2017. We find 35% received a WEDP loan and 54% received training in the full database of registered WEDP firms.
# **Natural disasters and human capital accumulation**<sup>1</sup>

**Jesus Crespo Cuaresma**<sup>2</sup>

**Keywords:** Natural disasters, human capital, education, school enrollment, Bayesian model averaging.

## **JEL Classifications:** Q54, I20, E24, C11.

1 This study was prepared as a background paper to the joint World Bank - UN Assessment on the Economics of Disaster Risk Reduction. Funding from the Global Facility for Disaster Reduction and Recovery is gratefully acknowledged. The author would like to thank Apurva Sanghi, the coordinator of the assessment, for many intellectually challenging discussions that helped shape the form of the paper. The paper has also profited from very helpful comments by Jos é Miguel Albala-Bertrand, Jed Friedman, Samir KC, Norman Loayza, Reinhard Mechler, Paul Raschky, Gallina Vincelette and participants at the Brown Bag Lunch Seminar at the World Bank. All remaining errors should be exclusively attributed to the author.

2 Department of Economics, University of Innsbruck; World Population Program, International Institute of Applied Systems Analysis (IIASA) and Austrian Institute for Economic Research (WIFO). Postal address: Universitätstrasse 15, 6020 Innsbruck, Austria. E-mail address: jesus.crespo-cuaresma@uibk.ac.at
OECD. Column 3 relates this simulated value to the actual. It is first clear that structure is not everything. Norway’s predicted level is within 10% of its actual suggesting that the fact that its RDI is double that of Chile is significantly due to low investment rates within existing sectors. However, it is also clear that structure matters. On average, simulated OECD aggregate investment rates are just under 60% of those observed and Finland and Germany, are roughly 30% of their actual.

Table 3 asks which sectors are most responsible for these very large disparities by applying the average OECD sectoral investment rates to the difference between Chile’s and the aggregate OECD sectoral participation rates. Virtually the entire difference can be accounted for by Chile’s very low participation in the electronics and transport sectors, both of which show very high average investment rates. The fact that Chile has not added Nokia to its forestry industry the way Finland did explains the vast difference in the two countries simulated rates.

In summary, Chile’s low R&D comes in part from its specialization in sectors with low R&D intensity, but this is not the whole explanation, as there is also a significant gap that comes from lower R&D investment rates at the sector level. In the next section we explore whether such lower R&D investment rates can be seen as consequences of a specific innovation shortfall or of a broader problem of low accumulation of all kinds of capital.

# **3. A model of knowledge capital accumulation**

There is a long literature that tries to understand the relative contribution of capital accumulation and productivity growth to economic growth. More recently, research has focused on what is sometimes called “development accounting,” the goal of which is to understand the determinants of income differences across countries at a particular point in from 1970-1990.

11
-27-

**TABLE 2.7**

**US** Eaployment and Productivity in 1975 According to Annual Survey of Manufactures and National Accounts

|E|ASM<br>mployment<br>(1000)|Nat. Accounts<br>Employment<br>(1000)|ASM Gross<br>Value Added<br>Per Person<br>Employed|National<br>Accounts<br>Adjusted GDP<br>per Person<br>Employed|
|---|---|---|---|---|
|Food & Kindred Products|1,321|}1,687|}31,538|}18,657|
|Beverages|204|}|}|}|
|Tobacco Products|66|73|56,388|37,342|
|Textile Mill Products|835|872|17,663|11,172|
|Apparel, Other Textile|||||
|Products|1,214|1,258|12,149|8,810|
|Lumber & Wood Products|588|684|17,612|14,914|
|Furniture & Fixtures|396|425|15,884|11,464|
|Paper & Allied Products|589|643|30,465|21,449|
|Printing & Publishing|1,070|1,129|23,029|15,883|
|<br>Chemicals, Allied Products|<br>842|1,C25|53,416|27,394|
|Petroleum & Coal Products|<br>141|189|74,465|32,931|
|Rubber, Miscellaneous|||||
|Plastic Products|585|603|23,247|15,372|
|Leather, Leather Products|<br>240|247|13,280|9,769|
|Stone, Clay, Glass|||||
|Products|589|641|25,210|17,417|
|Primary Metal Industries|1,089|1,144|27,885|23,770|
|Fabricated Metal Products|<br>1,417|1,475|24,138|18,185|
|Machinery, Except Electric|1,967|2,096|25,950|19.786|
|Electric, Electronic|||||
|Equipment|1,524|1,705|22,864|16,616|
|Motor Vehicles &|||||
|Equipment|699|787|30,709|26,522|
|Other Transportation|||||
|<br>Equipment|906|918|26,348|21,458|
|Instruments,<br>Related|||||
|<br>Goods|500|551|28,315|17,261|
|Miscellaneous<br>Manufactured|||||
|Goods|393|442|19,288|13,532|
|Total|17<sup>,</sup>174<sup>a</sup>|18,594|25,765|18,240|

a) excludes 1,128,400 employees with a payroll of **US$** 19,014.9 in administrative offices and auxiliaries located outside establishments.

Source: National Income and Product Accounts of the US 1929-76 and sources cited in table 2.6
<!-- Start of picture text --> Caregiver Norm: Women's most important role is to take care of her home and children<br>Personal Beliefs and Perceived Community Norms<br>Mexico : : : . : : : : : : - : : : : : : : :<br>Guatemala : : : : : a : : : : : :<br>El SalvadorSpain : : -—_S_: : 010,00Ss a OSS: ————s: : : : : .: : :<br>Argentina : : : : : ——_—Wa WH, * : : : : : : :<br>Slovakia : : : : ee| : : : : : : : :<br>Eelsiom : : : ° - : : : —— : : : : : : :<br>Poland : : : : : [ee : : : : : : :<br>United Kingdom . . . - a . . . . . . . . .<br>Sorbia : : : : : : : ‘° ° : : : : : :<br>Slovenia ‘ ‘ : : : i2| : ‘ : ‘ : : : ‘<br>Switzerland : : : : : a> * : : : : : : : : :<br>Czech HondurasRepublic : : : : : : : asesa : : : : : : :<br>United States of America : : : : : : _ : : : : : : : :<br><!-- End of picture text -->

<!-- Start of picture text --> aRole :: :: :: :- :: :_E :: :ee: : : : :: :: :: :- :: ::<br>BelarusMS :: :: :: :: :: :: :: :: :SC;- : : :: :: :: :: :: ::<br>wazzea : : : : : : : : : : : : : ‘ : : : : :<br>tabaE : : : : : : : : : : re_ : : : : :<br>Malaya : : : : : : : : : : : —_———*o—_—____—__- : : : : :<br>MoroccoLatvia : : : : : : : : : —_—*“+4 : : : : : : :<br>aaa2 : : : : : : : : : —— : : : : : : :<br>SmbamePalestine205 :: :: :: :. :- :: :: :: :: :: /-oe :-4 —_—s: :: :: :. :: ::<br>has : : : : : : : : : : : : _-— : : : : :<br>Piulpaines : : : : : : : : : : - : : eo : : : :<br>Uanet ampSuratSS :: :: :: :: :: :: :: :: :: :: :- :: [es, ou :: :: :. :: ::<br>oedageacar :: :: :: :- :- :: :: :: :: :: :- :: aLegaoe * :: :: :- :: ::<br>‘aaa : : : : : : : : : : - : ‘ee T* : : . : :<br>Uganda : : : - - : : : : : - : : eo : : - : :<br>Suan : : ; : : : : : : ; : : . - ; : : :<br>Myanmar : : : : : : : : : : : : .: : : : : : :<br>Afghanistan_, mene : : : : : : : : : : : : : 7 s : : : : :<br>Unbelastm -—<br>Maunitania —<br>Jordan : : : : : : : : : : : : _ : : : : :<br>Algeria eo<br>Lesotho : : : : : : : : : : : : : : —e - : : :<br>Buin : : : : : : : : : : : : es : : : : :<br>Libya : : : : : : : : : : : : [-_— : ; : : :<br>Banzladeth «#——<br>Pakistan : : : : : : : : : : : : : : eo : :<br>Ezypt e+<br>sorwnne : : : : : : : : : : . : : +: ———— : :<br>oo|<br>0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 TS 80 85 90 95 100<br>e Community © Individual<br><!-- End of picture text -->
# Appendix Table 2D

||Domain 4 Ind<br>(1)<br>Agree/Stro|ex: Student E<br>(2)<br>nglyAgree|ngagement<br>(3)<br>Strongl|(4)<br>yAgree|(5)<br>Incl.|(6)<br>Neutral|
|---|---|---|---|---|---|---|
|Treatment|‐0.0126|0.0118|‐0.286|‐0.232|0.149|0.145|
||(0.170)|(0.156)|(0.221)|(0.191)|(0.163)|(0.169)|
|Effort Median|0.139|0.182|0.303|0.377**|‐0.0743|‐0.0546|
||(0.164)|(0.149)|(0.182)|(0.174)|(0.242)|(0.229)|
|Treatment X Effort Median|0.0843|0.0743|0.192|0.155|‐0.0241|‐0.0253|
||(0.247)|(0.245)|(0.261)|(0.246)|(0.273)|(0.281)|
|Male||‐0.192||0.124||‐0.120|
|||(0.172)||(0.139)||(0.195)|
|Part‐Time||0.134||0.250||0.00607|
|||(0.143)||(0.148)||(0.106)|
|No Training||0.0760||‐0.0537||0.237*|
|||(0.291)||(0.325)||(0.132)|
|Less than University Degree||0.320**||0.249||0.129|
|||(0.146)||(0.318)||(0.134)|
|University Degree||‐0.0458||0.0340||0.0362|
|||(0.114)||(0.137)||(0.161)|
|>= 60 students in class||0.221**||0.358*||0.127|
|||(0.106)||(0.189)||(0.154)|
|Age||0.00187||‐0.0207**||0.0103|
|||(0.0113)||(0.00989)||(0.0138)|
|Number of Subjects teaching this Term||‐0.0696||0.0985||‐0.123|
|||(0.115)||(0.0770)||(0.153)|
|Constant|‐0.0863|‐0.0757|‐0.0578|‐0.0414|‐0.0324|‐0.170|
||(0.117)|(0.348)|(0.174)|(0.382)|(0.116)|(0.489)|
|Observations|348|348|348|348|348|348|
|R‐squared|0.008|0.036|0.036|0.085|0.005|0.019|

Standard errors in parentheses, clustered by school. *** p < 0.01, ** p < 0.05, * p < 0.10.

36
Cooke, R.M. and Goossens, L.H.J. (2000), “A Procedures Guide for Structured Expert Judgment,” EUR 18820, European Commission Report.* Cooke, R.M. and Goossens, L.H.J. (2000), “Procedures Guide for Structured Expert Judgment in Accident Consequence Modeling,” Radiation Protection and Dosimetry, Vol. 90, No. 3, pp.303309.* Cooke, R.M., and Goossens, L.H.J. (2000), “Expert Judgment Elicitation in Risk Assessment,” Delft University of Technology (“Abstract NATO workshop, Lisbon, 1-4 October 2000)* Cooke, R.M. (1995), “UNICORN: Methods and Code for Uncertainty Analysis,” AEA Technologies.* Cooke, R. M. (1991), “Experts in Uncertainty: Opinion and Subjective Probability in Science,” Oxford University Press, Oxford.* David, H.A. (1963), “The Method of Paired Comparisons,” Charles Griffin, London DeGroot, M.H. (1974), “Reaching a Consensus,” _Journal of the American Statistical Association_ , Volume 69, pp. 118-121.* DeGroot, M.H. and Mortera, J. (1991), “Optimal Linear Opinion Pools,” _Management Science_ , Volume 37, Issue 5, pp 546-558.* Delbecq, A., Van de Ven, A., and Gusstafson, D. (1975), “Group Techniques for Program Planning,” Glenview, III, Scott-Foresman.* Eliashberg, J. and Winkler R.L. (1981), “Risk Sharing and Group Decision Making,” _Management Science_ , Volume 27, Issue 11, pp.1221-1235.* French, S. (1986), “Calibration and the Expert Problem,” _Management Science_ , Volume 32, Issue 3, pp 315-321.* French, S. (1981), “Consensus of Opinion,” _European Journal of Operations Research_ , 7, pp. 332-340.* French, S. (1980), “Updating of Belief in the Light of Someone Else’s Opinion,” _Journal of the Royal Statistical Society: Series A_ , Volume 143, pp. 43-48* Genest, C. and Zidek, J.V. (1986), “Combining Probability Distributions: A Critique and an Annotated Bibliography, _Statistical Science_ , Volume 1, Issue 1, pp. 114-135.* Goicoechea, A., Hansen, D.R. and Duckstein, L. (1982), “Multiobjective Decision Analysis with Engineering and Business Applications,” Wiley.* 13
Economic Review 95: 437-449.

Bloom, David E., and Killingsworth, Mark R. (1985), “Correcting for Truncation Bias caused by a Latent Truncation Variable”, Journal of Econometrics, 1985, pp. 131-135.

Bowles, Samuel (1972), “Schooling and inequality from generation to generation”, Journal of Political Economy 80: 219-251.

Chetty, R, and N. Hendren, P. Kline, and E. Saez, (2014). “Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States,” The Quarterly Journal of Economics, Oxford University Press, vol. 129(4), pages 1553-1623.

Cohen, A (1991), _Truncated and Censored Samples: Theory and Applications_ , CRC Press.

Corak, M and A. Heisz (1999), “The Intergenerational Earnings and Income Mobility of Canadian Men: Evidence from Longitudinal Income Tax Data, Journal of Human Resources. Volume 34, Number 3 (Summer), pages 504-533.

Deaton, A (1997), _The analysis of household surveys: A microeconometric approach to development policy_ . Oxford University Press.

Emran, M. Shahe and F. Shilpi (2011). “Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam”, Journal of Human Resources, issue 2, 2011.

Emran, M. Shahe and F. Shilpi (2015). “Gender, Geography and Generations : Intergenerational Educational Mobility in Post-reform India”, World Development, Vol. 72, 362-380.

Emran, M. Shahe and Yan Sun (2015), Magical Transition? Intergenerational Educational and Occupational Mobility in Rural China: 1988-2002, World Bank Policy Research Working Paper 7316.

Greene, W (2012), Limited Dependent Variables - Truncation, Censoring, and Sample Selection, Chapter 19, _Econometric Analysis_ , Pearson.

Hausman, J and D. Wise (1977), “Social Experimentation, Truncated Distribution, and Efficient Estimation”, Econometrica, May 1977.

32
-2-

level of interest rates. Thus, the risk that higher interest rates may exacerbate the f4s a2' imbalance and/or the fiaancial difficulty of the business sector and, thereby, of the banking sector, cannot be discounted.

Such macro and regulatory concerns need, however, to be viewed against several areas of potentisl and long-term gain in investment efficiency, resource mobilization, and conduct of credit policy which could materialize with the process of interest rate reform. These gains are not trivial in the case of Egypt, where investment efficiency is known to be drastically low, currency substitution in foreign currencies has tended, in re^ent years, to account for an increasing share of total depositsV -- with eroding influence on monetary base -- and where credit allocation has been traditionally effected through a rigid system of quantitative ceilings imposed on each bank's asset portfolio.

The purpose of this paprr is to discuss interest rate policy in Egypt with the aim of providing a perspective on its role in the country's stabilization and adjustment programs. Section II describes briefly some of the structural problems facing the Egyptian economy over the past decade and policy initiatives undertaken. Section III reviews Egypt's financial sector, including the institutional setting and the current structure of interest rates. Section IV examines the role of interest rate policy in the adjustment process, including its irpact on the attraction of workers remittances, domestic residents' holdings of foreign deposits, and investment efficiency. Section V addresses the vexing question of how interest rate reform may affect the economy and focuses on its potential impacts on the investment behavior of the business sector and the solvency of the banking sector. Finally, Section VI summarizes the main conclusions and offers some policy recommendations.

**I** The role of interest rates for enhancing investment efficiency in developing countries is central to the interest rate liberalization arguments of McKinnon (1973),. and Shaw (1973). For more recent studies see Balassa (1982); IMF (1983); Lanyi and Saracoglu (1985); and Roe (1982).

v Currency substitution has been intensively discussed in the context of Latin American countries; see, for instance, Ramirez-Rojas (1985); Ortiz (1983); and Ortiz and Solis (1982).
Interpretation of the coefficients is straightforward. As all variables are in percentage changes, the coefficients are the (partial) elasticity of poverty with respect to our measure of employment-intensive growth or productivity-intensive growth. Because of the way the decomposition is performed, _β_ s will indicate the percentage change in the headcount poverty ratio that is likely to accompany a 1 percent increase in our employmentintensive measure in sector _s_ . Analogously, the coefficient _γs_ will indicate the percentage change in the headcount poverty ratio for a 1 percent growth in our measure of productivity-intensive growth in sector _s_ .

# **_2.3 Which are the determinants of the growth profile?_**

As mentioned previously, if we find that the profile of growth matters for poverty reduction, it is interesting to explore whether there are economic policies or labor market conditions that are correlated with particular growth patterns.

To estimate the impact of these underlying determinants, we estimate the following equation: Where _~~x~~ js_ is our measures of employment/productivity-intensive growth in country _j_ in sector _s_ , _Zj,t_ =0, is a vector of possible explanatory variable in country _j_ at the beginning of the spell ( _t=_ 0) and _Xj,_ are country specific control variables.

The _Zj,t_ =0 vector will include regulation variables and traditional variables that have been associated with growth and that might affect the investment climate in a country or TFP growth. By using the explanatory variables at the beginning of the spell, we hope to be able to better assess causality.

# **_2.4 The Data_**

To analyze whether employment matters for poverty reduction we make use of data on total and sectoral GDP, poverty, population and employment. The analysis covers the period between 1980 and 2004, and uses a sample of 39 developing countries, and 106 growth spells.<sup>19</sup> Data on aggregate GDP come from the World Bank’s World Development Indicators. To construct sectoral GDP we use data from the U.N. National Accounts, which give the

> 19 The Annex lists the growth spells.

12