rafmacalaba/datause-encoder-data huggingface dataset:
- 39 -

few costs seem to be associated to that function<sup>23</sup> . In the case of invalidity administrative costs should be presented on a per-worker basis.

For the federal pension system in the U.S., total reported administrative costs were the following:

# Table 12 _Annual Administrative costs in US Social Security (mUilions of current dollars)_

|Year|Invalidity<br>Administrati<br>Cost|Number of<br>ve Covered<br>Workers(m.)|Invalidity<br>cost per<br>worker*|Old-Age<br>& Surviv.<br>Adm. Costs|Number of<br>Pensions<br>(million)|Average<br>Cost per<br>pensioner*|
|---|---|---|---|---|---|---|
|1988|737|129.6|6.7|1,776|34.54|60.2|
|1989|754|132.1|6.4|1.673|35.01|53.8|
|1990|707|133.6|5.7|1,563|35.56|47.5|

: 1992 dollars per year. assuming that inflation was 4% per year. Sources: Social Security Bulletin. Annual Statistical Bulletin 1990, page 129; EBRI Databook on Employee Benefits. 1992. pages 161 and 164. This table does not consider the Supplemental Security Income program which provided means-tested pensions, nor the Black Lung program. both administered by the SSA.

The cost figure for invalidity is a bit higher than Chilean charges, specially when considering that it does not include coverage for survivorship. However, direct comparisons are of limited use given the differences in accident rates and actual coverage.

The cost figure for pensioners must be transformed into a cost per year in active life to make an appropriate comparison with the other figures presented in this paper. The adjustment reduces the figure because of two reasons: first the number of active years (close to 40) is higher than the average number of years as pensioner (close to 17 in 1989). Second. the real rate of return on contributions, adjusted by risk, is probably positive in the United States. The appropriate rate of return of a pay-as-you-go financed pension system is the sum of the rate of growth of real wages and of the

> 23 Unfortunately this practice was followed in the first draft of this paper. On the other hand, it is possible that the "benefit function' includes the costs of maintaining earnings records. This function should be of low cost because they are not used until pensioning unless the person requests a projection of his pension. As this right has been available only since 1989 these requests are infrequent Still. it would be desirable to brak down SSA costs further, to separate the funcion of maintenance of records.
|**Table 7. Sector Exports as a Percent of**|**Total Exports**|**of the Fede**|**ral Okrug**|||||Sector|
|---|---|---|---|---|---|---|---|---|
|**Sector**<sup>**b/**</sup>|Northwest|Central|South|Volga|Urals|Siberia|Far East|total|
|Railway transportation|3.4|3.4|3.0|2.9|1.0|3.5|2.9|2.5|
|Truck transportation|0.5|0.5|0.4|0.3|0.1|0.3|0.4|0.3|
|Pipelines transportation|0.4|0.0|1.2|1.2|2.2|0.2|0.3|1.1|
|Maritime transportation|2.8|2.7|3.2|3.1|2.1|2.4|2.4|2.6|
|Air transportation|2.4|6.1|3.5|2.1|0.9|1.8|2.4|2.4|
|Other transportation|0.5|0.7|0.6|0.6|0.2|0.4|0.4|0.4|
|Telecommunications|0.3|0.8|0.5|0.3|0.2|0.3|0.3|0.3|
|Science & science servicing|0.2|0.4|0.3|0.2|0.1|0.2|0.2|0.2|
|Financial services|0.3|0.6|0.3|0.3|0.1|0.2|0.3|0.3|
|Ferrous metallurgy|9.7|9.6|3.9|2.1|5.4|3.8|0.6|5.2|
|Non-ferrous metallurgy|8.2|3.7|4.2|3.8|5.9|28.7|27.8|9.7|
|Chemical & oil-chemical industry|5.3|6.2|4.5|10.6|0.6|5.0|0.2|4.6|
|Mechanical engineering & metal-working|10.6|21.2|8.4|10.5|2.3|7.8|8.0|8.9|
|Timber & woodworking & pulp & paper industry|10.4|0.7|0.5|1.2|0.1|4.7|6.8|2.4|
|Construction materials industry|0.2|0.5|0.3|0.3|0.1|0.3|0.1|0.2|
|Food industry|3.6|5.4|7.4|1.1|0.2|0.8|7.8|2.4|
|Other industries|0.8|2.9|0.9|0.9|0.1|0.3|0.2|0.8|
|Public services, culture and arts|0.1|0.2|0.1|0.1|0.0|0.1|0.1|0.1|
|Agriculture & forestry|0.2|0.6|1.1|0.5|0.1|0.4|0.3|0.4|
|Coalmining|0.6|0.0|0.4|0.0|0.1|3.8|1.7|0.7|
|Housing and communal services|0.1|0.3|0.1|0.1|0.0|0.1|0.1|0.1|
|Construction|1.0|2.8|2.7|1.4|0.3|0.7|0.4|1.1|
|Electric industry|0.4|0.7|0.4|0.4|0.2|0.3|0.3|0.3|
|Gas|0.1||0.4|0.3|2.9|0.0|0.1|1.0|
|Crude oil extraction|5.5||15.6|15.6|26.1|1.8|3.1|13.0|
|Oil refining and processing|6.5|7.7|9.7|14.9|0.9|8.2|6.9|6.9|
|Other goods-producing sectors|0.5|0.6|0.4|0.5|0.2|0.3|0.4|0.4|
|Post|0.1|0.3|0.2|0.1|0.1|0.1|0.1|0.<br>1|
|Wholesale and retail trade|24.6|19.8|25.0|24.2|47.4|23.1|24.6|30.8|
|Textiles and apparel|0.6|1.5|0.8|0.5|0.2|0.4|0.6|0.6|
|Okrug Total:|9.3|14.8|6.6|18.5|31.5|14.6|4.7||

41
# **1 Introduction**

Land is a fundamental component of household and personal wealth in rural areas and is the key factor of production in smallholder production systems. As such, the data on parcel and cultivated plot areas are the heart of economic research linked to agriculture and the design and implementation of land registration, titling and redistribution programs. Furthermore, the Sustainable Development Goal (SDG) Target 2.3 require doubling of agricultural productivity and incomes of small-scale food producers by 2030, and the monitoring of the progress towards this target rely on land area information sourced from household or farm surveys.

While large-scale household and farm surveys in low- and middle-income countries have traditionally relied on farmer reporting to elicit information on land areas, this can be problematic, particularly in the African context, which is characterized by the high incidence of smallholder farming and the fragmentation of farms into multiple parcels with irregular shapes and without formal titles. Several reasons may contribute to the inaccuracy in self-reported land areas. First, farmers may knowingly overstate or understate their landholdings for strategic reasons that may relate to access to development programs and/or taxation. Second, there is a natural tendency to round off numbers and provide approximations, which leads to heaping of the data around discrete values. Third, geography, particularly slope, can influence the way farmers assess distance and area. Fourth, the use of non-standard measurement units and within-country variation in the type and standard unit equivalence of these units complicate the compilation of conversion factors for land area measurement. In fact, methodological research has shown that self-reported land areas are subject to systematic measurement error with direct implications for the accurate measurement and analysis of land productivity (Carletto, et al., 2013, 2015).

These reasons, combined with (i) the validated accuracy of GPS-based land area measurement in household survey experiments in Ethiopia, Nigeria, and Tanzania (Zanzibar) (Carletto, et al., 2017), and (ii) the ever2
strategy; innovation activities; profile of human resources dedicated to innovation activities; results of the innovation efforts; sources of information and innovation objectives; sources of finance for innovation activities; obstacles to innovation; linkages; employment management capabilities and training policy; organization of labor; and knowledge management capabilities.

# _Sectoral and Regional Data_

Table A.1 of appendix A presents all indicators considered in the analysis as proxies of contextbased complementary factors. They are organized according to our conceptual framework, presented in figure 5. These indicators were built from various data sources listed in the last column of the table.

# **4.2 Empirical Strategy**

# _4.2.1 Introducing Innovation Complementarities in the Production Function_

In order to estimate the average returns to innovation across all firms in our sample, we first estimate the knowledge production function specified in equation [1]. Following Griliches (1979), this baseline equation is a production function in per worker units: that is, extended by knowledge. The dependent variable is the aggregate value<sup>19</sup> of firm at year Inputs are labor (L), capital stock (C) and knowledge stock (I). We measure all variables in natural logarithms, assuming a log linear relationship between inputs and aggregate value. Firm and time fixed ii tt. effects are included in order to control for firm and time invariant unobserved heterogeneity. <u>i</u> i <u>i i</u> i . [1] i i i YY CC II ii ii LL = αα+ ββLL + γγ LL + δδ LL + uu + ττ + vv Given that we aim to measure the effect of context-based complementary factors on innovation returns, the baseline model expressed in equation [1] is expanded to include interactions between knowledge r ) and market r ) factors—defined at the sectoral ( ) and regional ( ) level— and investment in innovation. This allows us to recover the influence of the ii ii (KK (MM different factors over the innovation returns coefficient (equation [2]). ss rr <u>i i i i i i i i i i</u> 0 1 r 2 r 3 r 4 r . i i i i i i i i i i YY CC II II II LL = αα+ ββLLiir ii + γγ LL + δδ LL + δδ MM ii + δδ [2] KK ii + δδ MM ii LL + δδ KK ii LL + uuii + ττii + eeiir ii Therefore, 0, 3 , and 4 are our main coefficients of interest because they estimate the marginal effect of market and knowledge complementary factors on innovation returns. Standard panel data procedures are used to produce estimates for coefficients. δδ δδ δδ

# _4.2.2 Endogeneity of Investment in Innovation_

The likely presence of omitted variables and simultaneity issues raises a challenge for the precise identification of the coefficients of the knowledge production function. In this section, we focus on the endogeneity of the investments in innovation variable, given that its returns over productivity are our main concern. We are aware that capital and labor variables are

> 19 Results are robust when we use sales as the dependent variable and include expenditure on intermediate goods in the regression.

14
Table A9. Thailand. Farm Earnings

||Farm|Profits|
|---|---|---|
||2000|2009|
|Head with 6-11 yrs of Educ.|0.0660<br>|0.0472<br>|
||(0.0554)|(0.0387)|
|Head with >11 yrs of Educ.|0.260**|0.0813|
||(0.105)|(0.0565)|
|Spouse with <6 yrs of Educ.|0.206***|0.343***|
||(0.0740)|(0.0429)|
|Spouse with 6-11 yrs of Educ.|0.290***|0.422***|
||(0.0888)|(0.0497)|
|Spouse with >11 yrs of Educ.|0.773***|0.630***|
||(0.154)|(0.0724)|
|26 to 35 yrs old|0.183|0.371**|
||(0.167)|(0.170)|
|36 to 45 yrs old|0.434***|0.575***|
||(0.167)|(0.165)|
|46 to 55 yrs old|0.465***|0.613***|
||(0.169)|(0.167)|
|56 to 65 yrs old|0.558***|0.513***|
||(0.170)|(0.168)|
|Female|-0.313***|-0.157***|
||(0.0759)|(0.0388)|
|North|-0.699***|-0.411***|
||(0.0616)|(0.0443)|
|Northeast|-0.958***|-1.002***|
||(0.0564)|(0.0413)|
|South|-0.0330|0.282***|
||(0.0672)|(0.0508)|
|Urban|-0.289***|-0.210***|
||(0.0692)|(0.0485)|
|HH owns land|0.152|0.167***|
||(0.103)|(0.0585)|
|Share of members older than 18|-0.162*|-0.0814|
||(0.0946)|(0.0686)|
|Constant|7.691***|7.759***|
||(0.206)|(0.177)|
|Observations|5,801|10,118|
|R-squared|0.107|0.141|
|Sigma|1.392|1.349|
|Standard errors in parentheses|||
|*** p<0.01, ** p<0.05, * p<0.1|||

59
= price of formal downstream firms ( _fjl_ 1) is still equal to 1: = The pass-through of taxes to the price of informal downstream firms ( _fjl_ 0) can be written as: where _sj_ 0 _F_ is the share of formal inputs in firm _j_ 0’s total production costs:

# **D Calibration Appendix**

This sub-section how we calibrate tax rates under the three sce- explains optimal policy narios defined in expressions (6), (8) and (9). Table 4 summarizes our choice of calibration parameters.

First, we calibrate several parameters directly from our data: we use the observed budget shares described in Section 3, total household expenditure to proxy for household income and the the observed informal shares of consumption for each good and country. We relax our theoretical assumptions that Engel curves are log-linear and that economic development does not affect within-country inequality, using instead the observed budget shares and income distributions in each country. Note that our model calls for using budget shares observed under a counterfactual ’no tax or transfers’ scenario. We do not attempt to adjust observed budget shares to take into account the fact that they are affected by current tax systems as this would require an in-depth understanding of the tax and transfer system in each country in our sample which is beyond the scope of this paper.

We similarly use our data to obtain estimates of income elasticities for all goods and varieties.To obtain an estimate of the income elasticity of demand for the formal variety, _ηj_ 1 we use our estimates of the slope of the formal Engel curve for good _j_ , _βj_ 1, and the expression _ηj_ 1 = 1 + _<u>βsjj</u>_ 11<sup>.We similarly obtain income elasticities</sup><sup>_ηj_using</sup><sup>_ηj_= 1 +</sup> _<u>βsjj</u>_<sup>.</sup> Second, we use existing literature to calibrate the remaining parameters.There are no estimates of the cross-price compensated elasticity of demand between formal and informal varieties _ε_ ̃<sup>_C_</sup> _α_ so we use estimates of the of substitution in _j_ elasticity consumption across stores of different types available in the literature. The cross-price elasticity 89
# _Nigeria_

The sample consists of applicants to a nationwide business plan competition, described in McKenzie (2014). The first round data consist of 1725 individuals surveyed between November 2012 and April 2013 who were operating businesses at that time. The sample consists of applicants who had been selected for a business plan training workshop, or had scores close to the threshold for being selected. A second followup survey was conducted between October 2013 and February 2014, allowing measurement of survival over a 1 year horizon, and of the stability of business practices. The business practice questions asked did not include F5-F8 (financial statements).

# _Sri Lanka_

The business practice questions were asked in three separate surveys undertaken in Sri Lanka, and we use the data from all three.

The first sample comes from the Sri Lankan Longitudinal Survey of Enterprises (SLLSE), a long-term panel survey of microenterprises designed by the authors in collaboration with Suresh de Mel (de Mel et al, 2010, 2013). The baseline survey was intended to be a representative survey of male microenterprises with 2 or fewer employees in the Colombo, Kandy, and Galle-Matara areas of Sri Lanka, and was conducted between April and October 2008. This gives 1562 individuals with business practice scores. These enterprises were then subsequently re-interviewed in 10 additional follow-up rounds, in April and October of 2009, 2010, 2011 and 2012, and in April of 2013 and 2014. This enables us to examine survival over periods of as long as 5.5 years.

The second sample consists of a representative sample of current female business owners operating in greater Colombo and greater Kandy areas, who had monthly profits of 5,000 Rs or less (US$43). These 628 women were first interviewed in January 2009, and form the current enterprise sample for a business training intervention described in de Mel et al. (2014). Four rounds of follow-up surveys were conducted in September 2009, January 2010, September 2010, and June 2011. We use the January 2010 follow-up to measure the one-year survival rates and stability of business practices over a one-year horizon.

The third sample consist of small and medium enterprises that were a booster sample to the baseline of the SLLSE, which were surveyed in order to generate a larger sample of firms of this size (de Mel et al, 2010). The baseline sample consisted of 610 of these firms, but did not ask business practice questions. We use the 471 firms that were reinterviewed and still surviving in April 2009.

25
we nd it helpful to summarize the model of Anderson et al. (2019). We do this in Section 2.1 for two reasons. First, as demonstrated in Section 2.2, it will enable us to describe and decompose several partial and GE channels through which DTAs impact FDI. In addition, in Section 2.3, we will capitalize on the structural equations for bilateral trade ows and FDI in order to specify the corresponding estimating equations, which in turn will deliver our key estimates of the direct impact of DTAs on trade and FDI.

# 2.1 Theoretical Foundations

To motivate and perform the empirical analysis, we rely on the theoretical framework of Anderson et al. (2019), who derive a multi-country dynamic model of trade, investment in physical capital and FDI under the following assumptions.<sup>6</sup> Each country ( _i, j ∈ N_ ) produces a single tradeable good (dierentiated by place of origin, Armington, 1969), which, subject to iceberg trade frictions ( _tij,t ≥_ 1 ), can be used for consumption ( _Cj,t_ ) and to build countryspecic physical capital ( _Kj,t_ ) in any other country. In addition, each country invests in non-rival technology capital ( _Mj,t_ ),<sup>7</sup> and the technology capital of one country can be used in all other countries subject to investment frictions ( 1 _≥ ωij,t ≥_ 0 ).<sup>8</sup> The decisions on aggregate consumption ( _Cj,t_ ), aggregate investment in physical capital ( Ω _j,t_ ), and aggregate investment in technology capital ( _χj,t_ ) in each country are made by representative agents who maximize the present discounted value of their lifetime utility subject to a sequence of constraints, as captured by the following consumer optimization

> 6We refer the reader to Anderson et al. (2019) for motivation behind some of the assumptions and for details on all derivations. For the convenience of the reader, we enclose the online Appendix from Anderson et al. (2019) along with this paper.

> 7The modeling of FDI in form of non-rival technology capital is in the spirit of Markusen (2002), McGrattan and Prescott (2009, 2010, 2014) and McGrattan and Waddle (2017). One interpretation of technology capital is akin to the notion of knowledge capital, and possible examples include patents, blue-prints, management skills/practices, etc.

> 8If _ωij,t_ = 1 , then country _j_ is totally open to the use of foreign technology of country _i_ capital at time _t_ within its borders. If _ωij,t_ = 0 , no foreign technology from country _i_ can be used in country _j_ at time _t_ .

7
Table 3: Geographical, cultural, and economic distances

|_Dependent Variable:_||Migratio|n Flows||
|---|---|---|---|---|
||WGI CC <br>(1)|WGI GE<br>(2)|CPI<br>(3)|ICRG<br>(4)|
|WGI CC in _o_|0.16***<br>(0.01)||||
|WGI CC in _d_|–0.47***<br>(0.01)||||
|WGI GE in _o_||0.28***<br>(0.02)|||
|WGI GE in _d_||–0.66***<br>(0.02)|||
|CPI in _o_|||0.16***<br>(0.01)||
|CPI in _d_|||–0.45***<br>(0.01)||
|ICRG in _o_||||0.13***<br>(0.01)|
|ICRG in _d_||||–0.38***<br>(0.01)|
|Contiguous border|0.29***<br>(0.08)|0.30***<br>(0.08)|0.29***<br>(0.08)|0.27***<br>(0.09)|
|Common language|0.92***<br>(0.09)|1.04***<br>(0.09)|0.96***<br>(0.09)|0.92***<br>(0.10)|
|Distance (_log_)|–0.95***|–0.87***|–0.93***|–0.97***|
||(0.04)|(0.04)|(0.04)|(0.05)|
|Common religion|0.96***<br>(0.07)|0.96***<br>(0.08)|0.98***<br>(0.08)|0.96***<br>(0.09)|
|Common currency|0.41***<br>(0.05)|0.43***<br>(0.05)|0.42***<br>(0.05)|0.40***<br>(0.05)|
|Time zone diference|–0.21*** <br>(0.04)|–0.23*** <br>(0.04)|–0.23*** <br>(0.04)|–0.20***<br>(0.05)|
|Size of _o_ (_log_)|0.65***<br>(0.01)|0.65***<br>(0.01)|0.66***<br>(0.01)|0.66***<br>(0.01)|
|Size of _d_ (_log_)|0.54***<br>(0.02)|0.51***<br>(0.02)|0.53***<br>(0.02)|0.50***<br>(0.02)|
|Constant|–1.58*** <br>(0.34)|–1.66*** <br>(0.35)|–1.59*** <br>(0.35)|–0.97**<br>(0.39)|
|Year FE|Yes|Yes|Yes|Yes|
|Adj. R-Square<br>N|0.55<br>6053|0.54<br>6053|0.54<br>6053|0.54<br>4813|

Robust standard errors clustered by county in parenthesis. ***, **, and * indicate statistical significance at the 1 _%_ , 5 _%_ , and 10 _%_ levels, respectively.

17
|Phone|(8)|-0.132|[.079]*|-0.172|[.090]*|-0.120|[.077]|3759 / 239|. Standard errors are|asures on an indicator|e is an estimate of the|ed via bootstrapping.||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Expenditures<br>Video|(7)|-0.079|[.117]|-0.167|[.121]|-0.125|[.113]|3761 / 238|ck fxed efects|d validation me|. The diferenc|error is calculat||
|All (0-2)|(6)|-0.205|[.143]|-0.334|[.148]**|-0.239|[.137]*|3763 / 239|icators and blo|the survey an|tered by block|the standard||
|Homeless|(5)|0.001|[.077]|0.007|[.087]|-0.156|[.082]*|3765 / 239|treatment ind|the diference in|robust and clus|e estimates and||
|haviors<br>Gamble|(4)|0.072|[.093]|-0.131|[.104]|-0.198|[.095]**|3763 / 238|h outcome on|regression of|rd errors are|iference of th||
|ally sensitive be<br>Marijuana|(3)|0.058|[.074]|-0.031|[.066]|-0.048|[.071]|3762 / 238|and regress eac|nt come from a|round. Standa|as the linear d||
|Potenti<br>Steal|(2)|-0.014|[.088]|-0.041|[.082]|-0.122|[.075]|3764 / 238|rvey rounds|from treatme|each endline|is calculated||
|All (0-4)|(1)|0.121|[.196]|-0.190|[.202]|-0.516|[.194]***|dation<br>3765 / 239|estimates pool all su|Estimates of the bias|block fxed efects and|g for observed bias. It||
|||||||||vali|TE|al.|for|tin||
|E||||||cash||survey/|-based A|individu|trolling|ter adjus|* p<0.1|
|Adjusted AT||Cash only||Therapy only||Therapy and||Observations,|_Notes:_ The survey|robust and clustered by|for treatment arms, con|true treatment efect af|*** p<0.01, ** p<0.05,|

lviii
1— Br-4 Br-1 VP) = be +o ~*(P) pn Yeit-1(P) t-1 t p Vet-1(P) pth ~~—_—~~ Yeit-1(P) Mejt-1 (Pp) = Elytir-1@)] = Ue + Br-1%1-1 ~*(p) 2 Ofir-12 2 2 2 = E |(veie1) - Heje-1(p)) | = 0¢ — PE19¢-1

Yejt-1(P) Br-1 Br-1 He oF Be-1 Ye-1(P) = Me-1 + O%r-1 ~*(P) Mrjt-1@) — Vt-1(p) = (Me — Me-1) + Bi-1 — DOt-1 ~*(p) pth t—1 He — He-1
<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>General Technical Engineering<br>@ Private Unaided @ Private Aided<br>@ Local Body m@ Government<br><!-- End of picture text --> <!-- Start of picture text --> 100% ' 0.8<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>General Technical Engineering<br>m= Not Known mw Private Unaided<br>m Private Aided w Local Body<br>= Government<br><!-- End of picture text -->
# Disasters and Economic Welfare: Can National Savings Help Explain Post-disaster Changes in Consumption?

## **Reinhard Mechler**<sup>1</sup>

International Institute for Applied Systems Analysis (IIASA)

## **JEL:** E01, Q54, Q56

**Keywords:** Natural disasters, macroeconomic consequences, welfare effects, genuine savings.

> 1 Support is gratefully acknowledged by the World Bank-UN project “Economics of Disaster Risk Reduction.” We would like to thank the team leader of this project, Apurva Sanghi, and Sebnem Sahin of the World Bank for ongoing support and stimulating discussions, as well as Norman V. Loayza, Kirk Hamilton, Alejandro Fuente, Jesus Crespo Cuaresma, Stefan Hochrainer and two anonymous referees for extremely helpful and stimulating comments.
9 Figure 2 Relatboship Between Total Factor Productivity Growth and Total Factor Input Growth <!-- Start of picture text --> 8<br>Gv 8 Gv 10 Gv 12<br>7<br>6 Gv6 4<br>ai<br>c \ ~~~~~ong<br>2\> ltl Germany Israel<br>orway a<br>_ AS w oi = jjtal..nc<br>I- 3 ede<br>3r a Netherl\<br>I Uniited Staa\<br>Argentn<br>\ Venezuela\\<br>2 3 4 5 6 7 8<br>Total factor input growth (percent)<br><!-- End of picture text --> Notes: G, refers to value-added growth. Total factor input growth includes the contribution of labor and capital. Periods for calculations vary from country to country. In most cases the period covers 1955 -15.

<!-- Start of picture text --> Key: m Developed country<br>A Developing country<br>13 Hongkong, Israel, Korea, Spain, Taiwan<br><!-- End of picture text --> Source: Chenery, Robinson and Syrquin (1986, figure 2.2)