rafmacalaba/datause-encoder-data huggingface dataset:
income elasticity of import demand as evident in equation (1).<sup>9</sup> However, if the increase in the import propensity is matched by a larger increase in the incremental import-GDP ratio,  _M_ /  _Y_ , then the elasticity will indeed rise. Figure 5 (right-hand panel) below shows that the US incremental import-GDP ratio was higher (in absolute value terms) in the last two business cycles than previously and that during the recent downturn the incremental import-GDP ratio was larger (again in absolute value) in the past two years than ever since 1970.

**- -** **<u>Figure 5: US Income Elasticities and Incremental Import GDP Ratios, 1970:Q1 2008:Q3</u>**

Source: Own calculations. Data US: Bureau of Economic Analysis, National Income and Product Accounts Table, Gross Domestic Product, seasonally adjusted at annual rates. NB: Gray bars correspond to US business cycles recessions according to the definition of the NBER.

An important issue is whether the recent downturn has resulted in a structural break in historical elasticity patterns. To judge whether that history is a useful guide in the current context, we estimated long-run income elasticities of import demand in the US for 16 countries and compared these to the current experience. We refer to the latter as “crisis elasticities”. The long-term elasticities for goods and services separately were estimated using bilateral, quarterly trade data for the period 1999 to 2008. We apply an autoregressive distributed lag (ARDL) approach to co-integration, which yields consistent estimates of the long-run coefficients regardless whether the regressors are I(1) or I(0) and thus does not require pre-testing for unit root (Pesaran et al., 2001). The ARDL approach adds short-run dynamics to a long-run estimation equation as given in (3): where _M_ designates real import demand for imports from country _j_ , _Y_ real domestic income, and _E_ real bilateral exchange rate between the US and country _j_ at time _t_ . _E_ is defined as the amount of units of foreign currency per US Dollar and _εt_ denotes the random error term. The lagged level variables constitute the so-called lagged error-correction term, which should be retained or excluded from the equation based on the F-statistics. _Appendices 5_ and _6_ give full details of the estimation procedures, sample and the estimation results. The long-run relations are given by the estimates of _ν3_ in _Table A2_ (column 4). Only six countries show significant results, while others miss the 10%-level narrowly. The highest elasticities are for China and India. They imply that a

> 9 In fact, there is some debate over Freund’s elasticity estimates.  Escaith et al. (2010) find that trade elasticities rose in the 1990s compared to the previous decade, but then were lower in the 2000s compared to the 1990s.

9
|**_Table7a cont_**|**_inued: 15%_**|**_replac_**|**_ement r_**|**_ate_**|**_._**||||||||_l_|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||m<br>lZI||dy=0%||d|y = 1%|_|d|Y.1.5%<br>|<br>|d|Y2=|-|
|Contribution|**age**|18|21|25|18 ||21|25|18|21|25|18|21|25|
||_Deathl||l|||||||||||
|Real i = 4%||||||__l_|_|||||_|_|
|Retire at 55|80|4|5|5|5|5|6|5|6|_7_|6|6|7|
|,______|81|4|5|5|5|5|6|6|6|7|6|6|7|
|,__________<br>.|_<br>83|4|5|6|5|6|6|6|6|7|6|7|7|
||85|5|5|6|5|6|7|6|6|7|6|7|8|
|Retire at 60|80|3|3|4|4|4|4|4|4|5|4|**5**|5|
||81|3|3|4|4|4|4|4|4|5|4|5|5|
||83|3|3|4|4|4|5|4|5|5|5|5|5|
||85|3|4|4|4|4|5|4|5|5|5|5|**6**|
|Retire at 65|80|2|2|3|3|3|3|3|3|3|3|3|4|
||81|2|2|3|3|3|3|3|3|3|3|4|4|
||83|2|3|3|3|3|3|3|_3_|4|**4**|4|4|
||85|2|3|3|3|3|4|3|4|4|4|4|4|
|Retire at 70|80|1|2|2|2|2|2|2|2|2|2|2|3|
|.______|__ 81|2|2|2|2|2|2|2|2|2|2|2|3|
||83|2|2|2|2|2|2|2|2|3|3|3|3|
|L__________|85|**2**|2|2|2|2|3|3|3|3|3|3|3|

33
Figure 5 – France, by survey <!-- Start of picture text --> France<br>Relative inequality of opportunity Return to education<br>1980 1985 1990 1995 2000 2005<br>year of survey<br>st.deviation logs mean log dev. 1980 1985 1990year of survey 1995 2000 2005<br>Parental networking Intergenerational persistence in education<br>1980 1985 1990 1995 2000 2005 1980 1985 1990 1995 2000 2005<br>year of survey year of survey<br>Regressors include gender, age, age2 and foreign citizenship<br>Parental education is absent and is replaced by dummy indicating middle-high parental occupations<br>.46 .3 .065<br>.44 .28<br>.06<br>.42 .26<br>st.deviation logs .4 .24 mean log dev. .055<br>.38 .22<br>.05<br>.36 .2<br>.045<br>5<br>.2<br>.15 4.5<br>.1 4<br>.05<br>3.5<br><!-- End of picture text --> Figure 6 – France, age-cohort decomposition <!-- Start of picture text --> France<br>Inequality of opportunity - Standard deviation of logs personal incomes Return to education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Parental networking Intergenerational persistence in education<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Age Age<br>. .<br>1920 1940 1960 1980 2000 1920 1940 1960 1980 2000<br>Born in... Born in...<br>. .<br>Regressors include gender, age, age2 and foreign citizenship - Age and cohort effects obtained from Deaton's decomposition<br>Parental education is absent and is replaced by dummy indicating middle-high parental occupations<br>.1 .04<br>.03<br>.05<br>beta1 beta1 .02<br>0 .01<br>-.05 0<br>.2 .06<br>.04<br>.15<br>beta1 beta1 .02<br>.1<br>0<br>.05 -.02<br>.1 -.2<br>.05 -.4<br>0<br>beta1 -.05 beta1 -.6<br>-.1 -.8<br>-.15 -1<br>.2 2<br>.1 1.5<br>beta1 0 beta1 1<br>-.1 .5<br>-.2 0<br><!-- End of picture text --> 16
_Ingo Borchert and Mattia Di Ubaldo_ agreements that feature a standstill or ratched clause, that provide for comprehensive dispute settlement (ISDS and state-to-state dispute resolution), or agreements that employ a definition of market access as in US FTAs. These effects are driven by agreements between countries from the same income per capita bracket, i.e. similar economies in terms of development stage. These findings on the impact of individual aspects are robust to controlling for non-services agreement depth.

Table 3: Trade Effect of Services PTAs, 2000-16

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Services PTA|0.0036|0.0036|0.0038|0.0038||
|Comprehen dispute|0.1166<sup>∗∗</sup>|||||
|Comprehen dispute Equal||0.1340<sup>∗∗</sup>||||
|Comprehen dispute H2L||0.1054||||
|Comprehen dispute L2H||0.0106||||
|Ratchet/Stand|||0.0932|||
|Ratchet/Stand Equal||||0.1010<sup>∗</sup>||
|Ratchet/Stand H2L||||-0.0069||
|Ratchet/Stand L2H||||0.0400||
|MA US-FTA|||||0.1514<sup>∗</sup>|
|MA GATS|||||0.0035|
|MA Other|||||0.0041|
|EU Pair|0.2476<sup>∗</sup>|0.2476<sup>∗</sup>|0.2479<sup>∗</sup>|0.2479<sup>∗</sup>|0.2478<sup>∗</sup>|
|Observations|235,876|235,876|235,876|235,876|235,876|
|Exporter-year FE|Yes|Yes|Yes|Yes|Yes|
|Importer-year FE|Yes|Yes|Yes|Yes|Yes|
|Country-pair FE|Yes|Yes|Yes|Yes|Yes|

_Source_ : Authors’ estimations using WB-DTA 2.0 and ITPD-E data. Dependent variable: bilateral services trade _Xijt_ . PPML estimation with three-way clustered standard errors.

33
**<u>Table A.2: Effects of Market Integration:  Market Access Measure Based on   trade elasticity 3.8</u>**

||(1)<br>ln(Landgini, 2012)|(2)<br>ln(Landless, 2012)|(3)<br>ln(Tech use, 2012)|(4)<br>ln(Land Sale, 2012)|
|---|---|---|---|---|
|**ln(MA1996),θ = 3.8**|**0.092****|**0.306*****|**0.182*****|**0.775**|
||**(0.04)**|**(0.11)**|**(0.07)**|**(1.07)**|
|ln(Slope)|-0.003|0.079|0.019|-0.210|
||(0.04)|(0.14)|(0.08)|(1.25)|
|Elevation|-0.560**|-1.117|-0.438|0.339|
||(0.27)|(0.75)|(0.42)|(7.08)|
|Crop Suitability|-0.003*|-0.008*|-0.002|-0.009|
||(0.00)|(0.00)|(0.00)|(0.05)|
|ln(Rain)|-0.099|-0.303|-0.257*|-2.334|
||(0.07)|(0.24)|(0.15)|(2.73)|
|ln(Temperature)|-1.452**|-3.052|-1.285|-7.621|
||(0.71)|(1.94)|(1.12)|(19.96)|
|ln(Rain CV)|0.403*|1.146*|0.880**|12.487**|
||(0.21)|(0.64)|(0.38)|(6.34)|
|ln(Temp. seasonality)|-0.133|-0.357|-0.265|-5.270|
||(0.09)|(0.29)|(0.17)|(3.37)|
|Pop. density, 1961|0.006|-0.018|-0.034|-0.241|
||(0.01)|(0.04)|(0.02)|(0.38)|
|Constant|7.254*|13.753|5.086|40.458|
||(3.78)|(10.45)|(5.94)|(113.36)|
|First Stage|||||
|Weak Identification|9.06|10.94|10.94|10.94|
||0.003|0.0011|0.0011|0.0011|
|Observations|200|212|212|212|
|R-squared|-0.126|-0.147|0.161|0.165|

Notes: Estimated by 2SLS using predict market access as the instrumental variable. Predicted market access is estimated from a zero stage including railway length, distance to GQ, and the IVs chosen by Lasso (distance to GQ x Pop. density, 1961; and railway length x Crop suitability). All columns are estimated by 2SLS and include state fixed effects. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
Figure 7: The Macro-micro Linkage Effects of Reallocation of Water from Rural to Urban Uses <!-- Start of picture text --> Reallocate Water From Rural to<br>Urban Sector (Urban Shock)<br>Decrease Surface Water<br>Increase Urban Water<br>In Three ORMVAs<br>Supply<br>Increase Shadow<br>Price of Water, Macroeconomic Effects:<br>Push Resources Out of Increase Manufacturing Increase Household Real GDP Rural Income <br>Irrig. Crops Increase Ground & Service Water Small Farm Income <br>Water Use Output Consumption Med. FarmIncome <br>Large Farm Income <br>Decrease Production of Crops in Three ORMVA Lower Factor Price in Agriculture Increase Urban Wagesand Urban Income Ag. Trade Deficit Non-Ag Trade Deficit  <br>Capital Return<br>Decrease Production of<br>Some Dairy, Meat,Other Increase Crop Decrease Farm<br>Processed Food Prod. in Non-Irrg. Income<br>& Some Non-<br>Water Withdrawal<br>Irrig. Areas<br><!-- End of picture text --> Source: Roe et al. (interim reports to the World Bank during 2003-2005) Figure 8: The Macro-micro Linkage Effects of Drought Resulting in Reduction of Surface Water Available for Irrigation Source: Roe et al. (interim reports to the World Bank during 2003-2005) 28
- [6] Aghion, P., Bloom, N., Blundell, R., Griffith., R, and P. Howitt, 2005, ”Competition and Innovation: An Inverted-U Relationship,” The Quarterly Journal of Economics, 120(2) 701-728.

- [7] Aitken, B., Gordon, H., and A. Harrison, ”Spillovers, Foreign Investment, and Export Behavior, 1997, ” Journal of International Economics, 43(1-2), 103-132.

- [8] Baily, M., and H., Gersbach, 1995, ”Efficiency in Manufacturing and the Need for Global Competition,” Brookings papers on Economic Activity: Microeconomics, 307-358.

- [9] Bartelsman, E. and W. Gray, 1996, ”The NBER Manufacturing Productivity Database”, N.B.E.R, Technical Paper 205.

- [10] Bernard, A., and J. Jensen, 2004, ”Why Some Firms Export,” The Review of Economics and Statistics, 86(2), 561-569.

- [11] Bils, M. and P. Klenow, 2001, ”Quantifying Quality Growth,” American Economic Review, 91(4) 1006-1030.

- [12] Blunderl, R., Griffith, R. and Van Reenen, J., 1995, ”Dynamic Count Data Models of Technological Innovation,” Economic Journal, 105 333344.

- [13] Broda, C., and D. Weinstein, 2006, ”Globalization and the Gains from Variety,” Quarterly Journal of Economics, 121(2) 541-585.

- [14] Broda, C., and J. Romalis, 2008, ”Inequality and Prices: Does China Benefit the Poor in America?, Working Paper Chicago Graduate School.

- [15] Caballero, R., and A. Jaffe, 1993, ”How High are the Giants’ Shoulders: An Assessment of Knowledge Spillovers and Creative Destruction in a Model of Endogenous Growth,” N.B.E.R Macroeconomics Annual, 1574.

- [16] Cassiman, B,. and R. Veugelers, 2006, ”In Search of Complementarity in the Innovation Strategy: Internal _R_ & _D_ and External Knowledge Acquisition,” Management Science, 52(1), 68-82.

26
<!-- Start of picture text --> Outputs from FTT FLEX<br>Electricity price<br>Other FTT feedbacks:<br>* Power sector Further impacts on<br>FURS. investment price, government<br>¢ Fiscal revenues MFMod balance, potential<br>(carbon tax and fuel outputs,..<br>tax) and spending<br>(subsidies)<br>Inputs to FTT FLEX<br>Electricity demand<br>(int + final)<br><!-- End of picture text -->
-7-

where w,g1<sup>is the "factor inequality</sup> weight" of the i-th source in overall inequal 1<sup>ty; and g, is the relative</sup> concentration coefficient of the i-th source in overall inequality.

As noted by Lerman and Yitzhaki (1985), the Gini correlation ratio (R) in equation (7) has propertie **s**<sup>imilar</sup> to the Pearson correlation. Like Pearson's, the Gini correlation ranges between -1 and +1. R will equal 1 (-1) when an income<sup>source is an increasing</sup> (decreasing) function of total income. When the income source<sup>is a</sup> constant, then R will equal 0, implying that the source's share of the Gini is 0.

# 2. Data Set

Data come from a three-year survey of 734 ho eholds in three provinces in rural Pakistan.4 Sir,ce the goal of this survey was to analyze the determinants of rural poverty, the survey was not designed to be representative of the rural population as a whole in Pakistan. In each province the poorest district was selected on the basis of a production and infrastructure index elaborated by Pasha and Hasan (1982). The selected districts included Attock (Punjab province), Badin (Sind province) and Dir (Northwest Frontier province). Since rural poverty also exists in relatively prosperous areas, a fourth 5 district Faisalabad (Punjab province) was added to the survey.

Surveying of the households continued over a three-year perikd, 1986-87, 1987-88 and 1988-89. Of the total 734<sup>households,</sup> 7
# **Introduction**

**‘Red flags of corruption’ are one tool being utilized by World Bank task teams to attempt to uncover potential issues regarding governance failure, collusion or corruption in projects.** They range from the specific –such as falsification of documents— to the indirect –long negotiation times or overruns. Other examples of red flag indicators include unsupported conclusions in recommendations to create a project, to decide specifications, or to shortlist or award a contract, very similar bids on contracts from different firms and slow processing of procurements and contract payments.

**It is becoming an increasingly common practice to include lists of red flags in project documents to be monitored during implementation.** In many cases, ‘red flags’ are seen as a first-line indicator of potential corruption, and the triggering of the flag is presented as grounds for further investigation or a request for the involvement of the World Bank’s Integrity Department (INT) in an investigation.

**The red flag approach is utilized outside the World Bank, as well** . For example, the Asian Development Bank, the Inter-American Development Bank and the European Bank for Reconstruction and Development have all developed red flag lists and methodologies, and Transparency International encourages the compilation of red flag lists as part of anticorruption strategies.

**Red flag lists can be extensive, but the flags listed are often indirect measures.** One recent publication contained 67 red flags that it proposed task teams looked out for during project preparation and implementation.   Flags are often linked to procurement or implementation outcomes that can have multiple causes.  Slow contract processing might be the result of extended negotiations over the corrupt division of rents from an inflated bid.  Or it might be the result of limited capacity, competing priorities or the sheer complexity of the process itself.

**Given the inclusion of a range of indirect indicators, it is perhaps unsurprising that existing analyses suggest that red flags may be fairly ubiquitous in World Bank projects** .  An analysis of transport contracts from the Africa region regarding the distribution of twelve red flags found a total of 213 red flags across 185 contracts.  It is also worth noting that, overall, red flags were about as common in contracts where there had been a complaint by stakeholders about the contracting process as where there had not.  Contracts which had involved a complaint took considerably longer to negotiate, bidding competition was lower and costs per km2 were higher.  On the other hand, procurement delays were comparatively rare on complaint contracts, more firms who bought bid documents actually bid, contract price variation from bid opening was lower and large variation orders were less frequent (Alexeeva et. al., 2008).

**The potential ubiquity of red flags suggests a concern with the idea of using the presence or number of red flags as an automatic trigger for operational decisions.** Seeing some red flags in many projects might reflect the ubiquity of corrupt projects or

3
# **Table A2 OLS Estimations based on country specific variables**

|**PRESTIGE INDEX**||**1985 arrival**<br>**1990-2000**|||**1980-1990**|**1975 ar**|**rival**|**1990-2000**||
|---|---|---|---|---|---|---|---|---|---|
||**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|**predicted**<br>**(lnOP1980)**|**Δpredicted**<br>**(lnOP1980-1990)**|**(1/10) Δpredicted**<br>**(lnOP1980-1990)**|**predicted**<br>**(lnOP1990)**|**Δpredicted**<br>**(lnOP1990-2000)**|**(1/10) Δpredicted**<br>**(lnOP1990-2000)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|**(9)**|
|**Log of GDP per capita**|**0.0681*****<br>**[0.0130]**|**-0.0222*****<br>**[0.00571]**||0.0344<br>[0.0212]|**-0.0179***<br>**[0.00936]**||0.0126<br>[0.0147]|-0.00309<br>[0.00460]||
|**Openness Index**|-0.00018<br>[0.000431]|0.00026<br>[0.000188]|0.00002<br>[1.05e-05]|-0.00017<br>[0.000643]|-0.00006<br>[0.000275]|-0.00002<br>[2.19e-05]|-0.00028<br>[0.000500]|0.00012<br>[0.000150]|0.00001<br>[1.42e-05]|
|**Log of Distance to the US**|**0.0610*****<br>**[0.0218]**|**-0.0128****<br>**[0.00552]**|**0.000740***<br>**[0.000406]**|0.023<br>[0.0256]|-0.0077<br>[0.0112]|0.000924<br>[0.000834]|**0.0395***<br>**[0.0233]**|-0.00532<br>[0.00569]|0.000242<br>[0.000525]|
|**English**|0.0399<br>[0.0247]|-0.0128<br>[0.00783]||**0.0559***<br>**[0.0325]**|0.0109<br>[0.0150]||0.0318<br>[0.0260]|-0.0063<br>[0.00615]||
|**Military conflict**|**-0.0692****<br>**[0.0285]**|0.0125<br>[0.00841]|-0.000638<br>[0.000782]|0.00504<br>[0.0525]|-0.00659<br>[0.0230]|0.00039<br>[0.00136]|-0.0212<br>[0.0353]|-0.00802<br>[0.00814]|-0.00113<br>[0.000891]|
|**Communism index**|0.0218<br>[0.0304]|0.0148<br>[0.0138]|**0.00243****<br>**[0.00117]**|0.0241<br>[0.0489]|0.0049<br>[0.0275]|0.0000863<br>[0.00174]|-0.0174<br>[0.0449]|0.000972<br>[0.0101]|-0.0000822<br>[0.000838]|
|**Predicted Log of OP in 1990**|||**-0.0279*****<br>**[0.00378]**||||||**-0.0188****<br>**[0.00864]**|
|**Predicted Log of OP in 1980**||||||**-0.0339*****<br>**[0.00503]**||||
|**Constant**|5.101***<br>[0.221]|0.377***<br>[0.0701]|0.175***<br>[0.0216]|5.747***<br>[0.301]|0.255*<br>[0.132]|0.209***<br>[0.0304]|5.866***<br>[0.226]|0.101<br>[0.0747]|0.119**<br>[0.0522]|
|**Observations**|71|71|71|72|72|72|72|72|72|
|**R-squared**|0.649|0.525|0.672|0.203|0.157|0.463|0.153|0.059|0.217|

***** p<0.01, ** p<0.05, * p<0.1 Robust standard errors in brackets**

47
- 20 -

||1291/1953|
|---|---|
|UN Unit Value Index|4.804|
|NBER Index for Exports||
|of Manufacturss by Developed||
|Countries, Unadjusted for||
|Quality Change, 1986 Weights<br>NBER/UN|4.505<br>.938|

Source: Appendix Tables 2 and 8.

of almost .4 per cent per year measured from beginning to end years, or .35 per cent estimated from an equation, might be the result of many factors, some possibly offsetting, but in any case, hard to identify. The difference between unit values and specification prices is there, as well as differences in weights, in country coverage, and in index number formulas.

It is impossible to explain the differences between these price indexes and the unit value series reliably because the commodity composition of the individual-country unit value series is not known. The country indexes are combined into a world index using Paasche (current-year) weights, and many of the country indexes are of the same type. However, some important countries use other types of index numbers such as Laspeyres (United Kingdom) or Fisher (The Netherlands and, until recently, the U.S.). In addition, for eight countries, price indexes, rather than unit value indexes, are used to produce the world unit value indexes (United Nations, 1991, p. 42).

As can be seen in the most recent description of the UN indexes (United Nations, l991a), the unit value indexes cover more countries than those presented here. They include 25 developed countries, instead of the 8 in the price indexes here. However, these 8 countries included here accounted for about three quarters of the total exports by the 25 in 1989.

To judge by the unit value data, the use of the 8 countries in our list
Table 8: _TFP_ Effects of Dispersion in Marginal Returns to Real Inputs

|100*_{_(_YEffective/YActual_)-1_}_|
|---|
|sd(log<br>~~T~~FPR<br>~~r~~eal)<br>192.7697<sup>_∗∗∗_</sup>|
|(9.5454)|
|Observations<br>60,815|
|Country fxed efects<br>Y|
|Time fxed efects<br>Y|
|Industry fxed efects<br>Y|

Note: Standard errors in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 As expected, table 8 reports a strong positive coefficient for the relationship between dispersion in marginal returns and counterfactual gains in aggregate productivity. For instance, reducing _SDTFPRreal_ from its median value (0.559) to the 25<sup>_th_</sup> percentile (0.399) would imply an aggregate productivity gain of 31%.<sup>7</sup> We are now ready to answer the question of what is the economic significance of the of misallocation on real misallocation.<sup>8</sup> Consider a reduction in the dispersion of marginal returns to financial inputs from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution of standard deviations, i.e. a reduction of 0.160.<sup>9</sup> According to our estimates in table 7, such a decline reduces the standard deviation of marginal returns to real inputs by 0.027 in the average industry, and by 0.033 in industries with high external finance depen-

> 7The aggregate productivity gain is computed as follows: (0.559-0.399)*192.7697 = 30.84%.

> 8As a reminder, every time we refer to financial misallocation, we mean the within-industrycountry dispersion in the log of marginal returns to financial liabilities, demeaned by the industrycountry average, i.e. log( _TFPRf in_ <u>/</u> _TFPRfin_ ). Likewise, with real-input misallocation we mean the dispersion in log( _TFPRreal_ / _TFPRreal_ ).

> 9The reduction in the dispersion of marginal returns is computed as follows: 0.559-0.399 = 0.160.

31
# **Can Economics Become More Reflexive?**<sup>**1**</sup>

# **Exploring the Potential of Mixed-Methods**

## Vijayendra Rao

JEL Codes: B4, O1, J71, B55 Keywords: Mixed-Methods, Narrative Economics, Empathy, Participation, NLP

> 1 This paper has been written as a contribution to the Handbook on Economics of Discrimination and Affirmative Action, Ashwini Deshpande (editor). I am grateful to Harold Alderman, Monica Biradavolu, Irene Bloemraad and Berk Ozler for valuable discussions and feedback, and to Paromita Sanyal and Michael Woolcock for our many years of collaboration that have helped refine these ideas.

1