rafmacalaba/datause-encoder-data huggingface dataset:
# _3.2. Economic Impact Estimation Methodology: Social Accounting Matrix_

Various methods can be used to estimate higher-order effect of disasters based on damages and/or losses data, including input-output (IO) table, social accounting matrix (SAM), and computable general equilibrium (CGE) model.<sup>15</sup> Since the cases for global aggregate include a large group of countries in different years, the data availability of method becomes one of the key issues for the selection of methodology. SAM is employed in this study because of its data availability of construction and the familiarity of use in international development community<sup>16</sup> .

Social accounting matrix (SAM) has been utilized to examine the higher-order effects across different socio-economic agents, activities, and factors. Notable studies using a SAM or one of its variants include Cole (1995, 1998, and 2004) among others. Like IO models, the SAM approach has rigid coefficients and tends to provide upper bounds of impact estimates. On the other hand, the SAM framework with certain disaggregation, as well as extended IO model and CGE model, can derive the distributional impacts of a disaster in order to evaluate equity considerations for public policies against disasters. In this paper, SAMs were constructed in an aggregated version for each country and each decade, based on the World Bank data.17 Due to the large number of SAMs that needed to be constructed, and in order to maintain the consistency of the structure and features among them, the SAMs were constructed in the most aggregated way—one sector (one value) for each principal account (see the Figure 3-1). This simple structure is also necessary to suit with the aggregation level of input data, total damages, for each case.

> 15 A summary and discussion of methodologies on impact estimation can be seen in a companion paper, “Critical Review of Methodologies on Disaster Impact Estimation.”

> 16 Please see Appendix 1 for detailed description of SAM, and a companion paper, ‘Impact Estimation Methodology: Case Studies.’

> 17 SAM structure draws upon the MAMS model (Lofgren and Diaz-Bonilla, 2008), and Lofgren’s SAM template is used to construct SAMs in this exercise (see Hans Lofgren’s course material (2008) on MAMS, for the detail).

12
to not only more jobs, but also more high-wage jobs. Evidence from the household data in Ghana and Tanzania suggests that workers in micro/small enterprises (5-10 employees) have essentially the same earnings as the self-employed, while workers in medium and large firms earn between 50 to 70 percent more (Sandefur et al. 2006).

**Third,** by making the business exit process more efficient and less costly, an improved investment climate can facilitate a more efficient allocation of capital and labor in different sectors. If this efficiency then leads to more productive firms in different sectors, it may have a greater net job creation effect: job losses due to failing firms are outweighed by jobs created by surviving firms.

When designing any potential IC reform program it is important to acknowledge that these three channels are interrelated. For instance, a difficult operating environment and costly exit regime may negatively affect the entry decision of new or existing informal businesses in the formal sector. In such a circumstance, fixing one channel alone may not have the desired effects, such as reforming the entry process may not necessarily lead to firm formalization. An isolated reform could even have an adverse effect. As discussed in the following section, in a poor operating environment, reforming the entry process can potentially lead to a low quality of new entrants, which could then lead to lack of competition, sub-optimal firm size and low level of aggregate productivity in the formal sector, all of which have negative consequences for both the quantity and quality of jobs. Similarly, the realization of the benefits of any IC reforms could be also limited by lack of reforms in other related areas, such as access to finance, infrastructure, macroeconomic policies, etc.

Against this conceptual backdrop, the following section reviews the evidence from the relevant literature on the impact of different channels of IC reforms on job creation.

# **3. IC Reforms and Job Creation: What Have We Learned?**

Rigorously measuring the effect of an investment climate reform or a package of reforms (or for that matter of any policy intervention) on job creation by firms would ideally require some 9
# TABLE OF CONTENTS

## Page

- I. Executive Summary ......... ............ 3

- II. introducton ........................... 9 III. Historical Background ........ ........... 11 IV. Problems with the Legal and Administrative Framework of the Land Reform ...... ....... 18

- V. Access to Land Use and Land Use Patterns .24

- Vt. Labor Organization and Employment .29 Vii. Access to Credit .35

- Vill. The Ejido Marketing Support Program .41 IX. Comparing the Performance of Ejldos and Private Farms .44

- X. Conclusions and Policy Recommendations .55 Tables .59 Appendix A: Interpreting the Results of the 1981 Agricultural Census .80 Blbiography .82

_The author woula like to thank GIOIA PALMIERI for her help as a research assistant._
_Rural-urban gradient in Latin America and the Caribbean_ 9

# **References**

- Balk, Deborah, F. Pozzi, U. Deichmann, G. Yetman, A. Nelson _._ 2004 _._ The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents. CIESIN.  processed.

- R.D. Bollman and J.D. Bryden, eds.  (1997). _Rural Employment: An International Perspective_ New York: CAB International.

- Chomitz, K.M. and D. Gray (1996)  "Roads, land use, and deforestation: a spatial model applied to Belize". _World Bank Economic Review 10_ (3) _,_ September 1996, 487-512.

- Chomitz, K.M. and Andrew Nelson. (2003)  “The Geographical Poverty Gradient in Nicaragua and its Implications for Rural Development Strategy.”  World Bank, processed.

- United Nations (2002). _World Urbanization Prospects: The 2001 Revision._ Publication ST/ESA/SER.A/216.  New York, United Nations.
competition in the US financial system, which may not apply in other countries and contexts. The approach I take here parallels other work in the finance-growth literature in using relative size of the financial system as a proxy for the level of development of the financial system, while employing instrumental variable techniques to mitigate endogeneity concerns.

The studies mentioned above address topics related to those addressed in this paper, however, none of these studies has addressed the issue of whether greater banking sector depth is associated with faster growth in a region, and none were conducted in a developing economy.<sup>9</sup> In this paper I address these gaps in the literature by measuring the relationship between the depth of a district’s banking sector and the growth of that district.

The results presented here demonstrate the potential gains from banking sector outreach by showing that regions with greater banking sector capacity grow faster.<sup>10</sup> I find that a district which moves from the 75th percentile of bank credit/NDP down to the 25th percentile will lose 4% growth over the decade. I also attempt to measure whether this relationship is non-linear and find evidence that it is. The measured effect of banking sector depth on growth is more than double in districts with credit/NDP below the median. This result is especially relevant in India where many of the past policies designed to foster banking sector outreach have focused in the areas with the lowest banking sector penetration (Burgess et al. (2005)). My regressions indicate that it is precisely these areas which were still the most financially constrained in the 1990s.

While there are many cross-country growth studies that investigate financial development, human capital,

> 9 The relationship between banking sector depth and growth is the one most comparable to the line of literature embodied in King and Levine (1993), which tends focus on financial depth measures to measure financial development.

> 10 Designing policies to promote banking sector outreach is an issue which Indian policy makers and others around the world continue to struggle with. Burgess, Pande and Wong (2005) document the results of the Indian social banking experiment, Kumar (2006) documents initiatives in Brazil to foster banking sector outreach. In fact, my results demonstrate the importance of geographic banking sector outreach generally, whether it be market or policy driven.

5
# Table 6: Cross-Country Regressions with Creditor<sup>Rights and Legal Efficiencv</sup>

The dependent variable is the ratio of the number<sup>of bankruptcies to the number of firms.Transition</sup> countries are excluded from all regressions because<sup>of theunavailabilityofdisaggregatedcreditor</sup> rights. The regressions are estimated using ordinary least<sup>squareswith robust standarderrors.</sup> _LN(GDPPC)_ is the log of GDP Per Capita. _GGDP_ is the 1-year growth rate of real GDP. _D_SYSTCRISIS_ is a dummy equal to 1 in the case of<sup>a systemicbank crisis(Caprio andKlingebiel,</sup> 2000). _RINTEREST_ is real interest rates. _CREDITOR_<sup>_RIGHTS_is thesumof dummies identifying</sup> Restrictive Reorganizations, No Automatic Stay<sup>onAssets,SecuredCreditorsPaid First,and</sup> Management Does Not stay in Reorganization<sup>(LaPorta,etal., 1998)</sup> _RULE OF LAW_ is an assessment of the "efficiency and integrity" of<sup>thelegalenvironmentas measure byBusiness</sup> International Corp (La Porta, et al. 1998 and Pistor,<sup>2000.)t-statisticsareinparentheses,*, *,and</sup> *** indicate significance at the 10%, 5%, and 1% respectively.

||(1)|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
||Creditor<br>**Rights**|Restrictive<br>**Reorganization**|No**Automatic**<br>**Stayon**Assets|Creditor<br>**PaidFirst**|Management<br>**Does**NotStay|
|_Constant_|-2.6197***<br>(-3.23)<br>|-2.0861***<br>(-3.09)<br>|0.2174<br>(0.24)<br>|-1.0766<br>(-1.46)<br>|-2.3879***<br>(-3.19)<br>|
|_LN(GDPPC,d)_|0.0010<br>(0.51)|0.0001<br>(0.07)|-0.0019<br>(-1.09)|-0.003<br>(-0.17)<br>|-0.0001<br>(-0.35)<br>|
|_CGDP,,_|-0.0817**<br>(-2.07)|-0.0669*<br>(-1.75)|-0.0937<br>(-2.47)|-1.011***<br>(-2.38)|-0.0609<br>(-1.52)|
|_DSYSTCRISIS,,_|0.2075<br>(0.38)|0.5557<br>(1.05)|0.2499<br>(0.48)|0.3105<br>(0.56)|0.4216<br>(0.80)|
|_RINTEREST,j_<br>,-1|0.0401*<br>~~~~~(1.8|0.0270*<br>7)<br>(1.68)|-0.0184<br>(-1.14)|0.0108<br>(0.53)|0.0339*<br>_<br>(1.87)|
|_CREDITOR RIGHTS_|1.2829*)*||||_|
|_RESTRICTIVE_<br>_REORGANIZATION_||4.3785***<br>(3.35)||||
|_NOAUTOMATICSTAY _<br>_ONASSETS_|||-2.4162***<br>(-2.95).,|||
|_SECUREDCREDITOR_<br>_PAIDFIRST_||||0.9607*<br>(1.76)||
|_MANAGEMENTDOES_<br>_NOTSTAY_<br>_._|||||4.3929***<br>_(3.35)_<br>|
|_RULEOFLAW _|0.5479***<br>(6.10)|0.4629***<br>(6.01)|0.3499***<br>(3.53)|0.4423***<br>_(5.99)_|0.5273***<br>(5.77)|
|_RULEOFLAW*_<br>_CREDITOR RIGHTS_|-0.1654***<br>(-2.89)|.||||
|_RULEOFLAW * RESTRICTIVE_<br>_REORGANIZATION_||-0.4602***<br>(-2.97)||||
|_LEALITY*NOA UTOMATIC_<br>_STAYONASSETS_|||0.1785*<br>(1.88)|||
|_RULEOFLAW*SECURED_<br>_CREDITORPAIDFIRST_||||-0.1579<br>_(-1.12)_||
|_RULEOFLAW*MANAGEMENT_<br>_DOESNOTSTAY _|||||-0.5447***<br>(-3.30)<br>|
|_YearDummies_|Yes|Yes|Yes|Yes|Yes|
|_Adj.R-Squared_|0.22|0.25|0.24|0.19|0.23|
|_Observations_|252|252|252|252|252|

32
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 B: Regulatory Framework and Practices (0 100)</u>

|||<br>De<br>Version 1(Ful sampl|<br>pvar: Average loan s<br>es)|ize /per capita G<br>Version 2|<br>NI<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)|
|Regulatory Framework and Practices (0-100)|0.0062*|0|0.0013|0.0055*|0|0.001|
||[0.058]|[0.988]|[0.411]|[0.069]|[0.967]|[0.552]|
|NBFI dummy * Regulatory Framework and Practices (0-100)|0.0042|0.0031***|0.0051*|0.0058|0.0030***|0.0054*|
||[0.409]|[0.001]|[0.091]|[0.207]|[0.001]|[0.079]|
|Combined group dummy * Regulatory Framework and Practices (0-100)|0.0098|0.0019**|0.0023|0.0255*|0.0025**|0.004|
||[0.282]|[0.044]|[0.295]|[0.052]|[0.034]|[0.288]|
|NBFI dummy|-0.2152|-0.1122**|-0.169|-0.2821|-0.1089**|-0.1893|
||[0.455]|[0.040]|[0.306]|[0.307]|[0.033]|[0.257]|
|Combined group dummy|-0.2709|0.0273|0.0521|-1.0054|-0.0384|-0.0213|
||[0.550]|[0.626]|[0.730]|[0.109]|[0.554]|[0.922]|
|Observations|641|641|616|583|583|560|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.282|0.575|0.421|0.304|0.615|0.443|
|Adjusted R-squared|0.26|0.562|0.402|0.28|0.602|0.423|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|0.0103**|0.00312***|0.0064**|0.0113**|0.00302***|0.00642**|
||[0.029]|[0.000]|[0.049]|[0.013]|[0.000]|[0.042]|
|Combined group dummy|0.0159*|0.00194**|0.00357|0.031**|0.00251**|0.00498|
||[0.054]|[0.024]|[0.145]|[0.015]|[0.021]|[0.213]|

26
|P070374|2002|2004|5/31/2004|7 Closed|Argentina|
|---|---|---|---|---|---|
|P070374|2002|2005|4/4/2005|9 Closed|Argentina|
|P070374|2002|2006|1/4/2006|10 Closed|Argentina|
|P070374|2002|2006|6/27/2006|11 Closed|Argentina|
|P070374|2002|2007|2/10/2007|12 Closed|Argentina|
|P070391|2001|2001|6/29/2001|1 Closed|Yemen,Republic of|
|P070391|2001|2002|4/3/2002|3 Closed|Yemen,Republic of|
|P070391|2001|2003|2/28/2003|5 Closed|Yemen,Republic of|
|P070391|2001|2004|6/30/2004|9 Closed|Yemen,Republic of|
|P070391|2001|2005|4/5/2005|11 Closed|Yemen,Republic of|
|P070391|2001|2006|11/22/2005|12 Closed|Yemen,Republic of|
|P070391|2001|2006|5/8/2006|13 Closed|Yemen,Republic of|
|P070391|2001|2007|12/21/2006|14 Closed|Yemen,Republic of|
|P070421|2001|2001|6/5/2001|1 Closed|India|
|P070421|2001|2002|4/5/2002|3 Closed|India|
|P070421|2001|2003|2/27/2003|5 Closed|India|
|P070421|2001|2004|2/24/2004|7 Closed|India|
|P070421|2001|2005|5/5/2005|10 Closed|India|
|P070421|2001|2006|10/31/2005|11 Closed|India|
|P070421|2001|2006|5/24/2006|12 Closed|India|
|P070421|2001|2007|11/29/2006|13 Closed|India|
|P070421|2001|2007|5/25/2007|14 Closed|India|
|P070421|2001|2008|11/14/2007|15 Closed|India|
|P070430|1999|1999||0 Closed|St. Lucia|
|P070430|1999|2000|4/5/2000|1 Closed|St. Lucia|
|P070430|1999|2001|5/2/2001|4 Closed|St. Lucia|
|P070430|1999|2002|6/26/2002|7 Closed|St. Lucia|
|P070430|1999|2003|5/30/2003|11 Closed|St. Lucia|
|P070430|1999|2004|11/22/2003|12 Closed|St. Lucia|
|P070430|1999|2020|11/6/2019|13 Closed|St. Lucia|
|P070432|2000|2000||0 Closed|Mozambique|
|P070432|2000|2001|5/7/2001|2 Closed|Mozambique|
|P070432|2000|2002|12/18/2001|3 Closed|Mozambique|
|P070441|2003|2003|4/11/2003|2 Closed|China|
|P070441|2003|2004|6/14/2004|5 Closed|China|
|P070441|2003|2005|5/12/2005|7 Closed|China|
|P070441|2003|2006|1/24/2006|8 Closed|China|
|P070441|2003|2007|12/18/2006|9 Closed|China|
|P070441|2003|2007|5/21/2007|10 Closed|China|
|P070441|2003|2008|5/21/2007|10 Closed|China|
|P070448|2006|2006|6/21/2006|1 Closed|Argentina|
|P070448|2006|2007|12/31/2006|2 Closed|Argentina|
|P070448|2006|2007|6/25/2007|3 Closed|Argentina|
|P070448|2006|2008|1/3/2008|4 Closed|Argentina|
|P070448|2006|2009|10/17/2008|5 Closed|Argentina|
|P070448|2006|2009|6/25/2009|6 Closed|Argentina|
|P070448|2006|2010|12/17/2009|7 Closed|Argentina|
|TABLE|1. School a|nd Work Participation,by<br>Males|Age,Surveyand Gender<br>Females|
|---|---|---|---|
|Age at|Baseline|First<br>Second|Baseline<br>First<br>Second|
|||(+3.5|(+3.5|
|baseline||(+1yr)<br>yrs)|(+1yr)<br>yrs)|
|||School|School|
|||enrolment|enrolment|
|||**%**|**%**|
|7|0.904|0.928<br>0.963|0.922<br>0.953<br>0.970|
|8|0.935|0.951<br>0.933|0.961<br>0.959<br>0.947|
|9|0.952|0.943<br>0.895|0.966<br>0.960<br>0.918|
|10|0.932|0.907<br>0.813|0.958<br>0.950<br>0.867|
|11|0.917|0.884<br>0.764|0.935<br>0.901<br>0.835|
|12|0.856|0.782<br>**_0.675_**|0.897<br>0.859<br>**_0.786_**|
|13|0.791|0.755<br>**_0.577_**|0.832<br>0.791<br>**_0.633_**|
|14|0.660|**_0.620_**<br>**_0.457_**|0.740<br>**_0.728_**<br>**_0.536_**|
|N|6090|5726<br>5033|5589<br>5266<br>4482|
||W|orkparticipation|Workparticipation|
|||**%**|%|
|10|0.021|0.019<br>0.083|0.010<br>0.006<br>0.045|
|11|0.031|0.050<br>0.132|0.012<br>0.025<br>0.057|
|12|0.057|0.093<br>**_0.209_**|0.029<br>0.042<br>**_0.117_**|
|13|0.109|0.148<br>**_0.284_**|0.057<br>0.086<br>**_0.143_**|
|14|0.213|**_0.285_**<br>**_0.371_**|0.091<br>**_0.169_**<br>**_0.203_**|
|N<br>_Notes:_W|3672<br>ork includes|4233<br>5022<br>full-time paid and unpaid|3265<br>3870<br>4480<br>activities and looking for work as a|

main activity. Figures in bold (italics) denote ages corresponding to post-compulsory schooling. Note that +1 yr (3.5 yrs) means 1 yr (3.5 yrs) after the baseline survey. N denotes the number of individuals (aged 7-14 at baseline) present in the survey listed at top of column. Schooling observed for children aged ≥7; work observed for children aged ≥10 years.

32
# **A. Methodological Appendix**

## **_A1.Basic Katz and Murphy (1992) supply and demand framework with two inputs_**

The methodology in this document follows the literature initiated by Katz and Murphy’s (1992) seminal work and subsequent contributions. The basic framework assumes a CES production function with two factors: where _Q_ is output; _A_ is total factor productivity; _λ_ and _ρ_ are technology parameters; and _S_ and _U_ are the two factors that represent units of skilled and unskilled labor, respectively.

Under perfect competition, the wage skill premium ( _ws_ / _wu_ ) in year _t_ satisfies the following relationship: where _σ=(1-_ ρ _)_<sup>_-1_</sup> is the elasticity of substitution between the two types of labor, and _D_ ( _t_ ) is the relative demand for these two factors measured in log quantity units. Under the assumption that the economy operates on the demand curve given by equation (A1), a given value of the elasticity of substitution between factors implies a time series of demand shifts: The greater the elasticity of substitution between the two factors, the greater must be the fluctuations in _D_ ( _t_ ) to explain any given time series of relative prices for a given time series of observed relative quantities. The two logged terms on the right hand side of equation (A3) can be constructed from observational data. Following the literature initiated by Katz and Murphy (1992), equation (A3) can be used to impute _D_ ( _t_ ) (which is not observable) conditional on a range of values for _σ_ .

An estimate of the elasticity of substitution _σ_ is needed to implement (A3). Most of the literature gets that estimation from the inverse of the coefficient of the relative supply of skilled labor in a wage gap regression controlling by proxies of the relative demand – usually a time trend, linear or of higher order. The mean value of _σ_ in our regression analysis indicates values fluctuating around 4, while Manacorda _et al_ . (2010) find values around 3 for Latin America. Goldin and Katz (2007), in the context of the United States, report values of around 1.6. The tables in the document will show results, alternatively, for _σ_ equal to 3 and 4.

## **_A2.Augmented model with three levels of skills_**

The basic Katz and Murphy (1992) framework for analyzing the impact of supply and demand factors on the wage skill premium relies on two broad aggregates of labor – skilled and unskilled.

35
Table 1c: Comparison to specialization/diversity in U.S. manufacturing

||Indian districts<br>in 2010 sample|2011 States<br>and District of<br>Columbia|2011 cities with<br>the 100 largest<br>manufacturing<br>employments|2011 cities with<br>90% threshold on<br>allocated CBP<br>employment|2011 cities with<br>60% threshold on<br>allocated CBP<br>employment|
|---|---|---|---|---|---|
|Count|429|51|100|193|746|
|Average mfg. establishments|31,162|11,593|1,875|1,081|359|
|Average mfg. employment|82,845|417,412|61,176|33,489|10,780|
|A. Specialization index||||||
|Mean value|6.65|3.73|4.58|5.95|9.96|
|Standard deviation|6.54|2.27|6.80|5.48|10.06|
|Minimum value|2.10|1.51|1.70|1.79|1.70|
|Maximum value|39.98|11.67|68.68|43.22|86.85|
|Median value|4.49|2.95|3.22|4.34|6.58|
|B. Diversity index||||||
|Mean value|1.41|2.31|1.79|1.44|0.98|
|Standard deviation|0.37|0.98|0.63|0.61|0.45|
|Minimum value|0.78|0.67|0.65|0.61|0.54|
|Maximum value|2.46|4.80|3.69|3.61|3.69|
|Median value|1.36|2.20|1.77|1.28|0.83|

Notes: Table provides comparison points of specialization and diversity from 2011 County Business Patterns (CBP) file. For smaller cities, CBP does not disclose employment values for some industries where the presence in the city is very small, due to confidentiality for individual plants. Columns 3-5 take alternative strategies for inclusion of cities based upon what share of the city's overall manufacturing employment is allocated.