rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Examples of studies analyzing the relationship between public expenditure and economic variables**

|**Dependent**<br>**variable**|**Study**|**Level of**<br>**aggregation**|**Period**|**Specification**|**Method**|**Findings**|**Limitations in the methodology**<br>**used**|
|---|---|---|---|---|---|---|---|
||Aschuer<br>(1989a)|US National|1949 to<br>1985|Cobb-<br>Douglas;<br>Log levels|OLS|Strong positive relationship between output per unit of capital<br>input, the private labor-capital ratio, and the ratio of the public<br>capital stock to the private capital input. The increase in the<br>ratio of public to private capital stocks raises total factor<br>productivity by 0.39%***|Estimates at the national level is too<br>large to be credible; Common trends<br>induced<br>spurious<br>correlation;<br>Reverse<br>causality;<br>Production<br>function estimate is inadequate;<br>Endogeneity; Measurement error|
||Munnell (1990)|US National|1948 to<br>1987|Cobb-<br>Douglas;<br>Log levels|OLS|One percent increase in public capital would raise productivity<br>by 0.31 to 0.39 percent|Common trends induced spurious<br> <br> <br>|
|**ational GDP)**|Munnell and<br>Cook (1990)|US States|1970 to<br>1986|Cobb-<br>Douglas;<br>Log levels|OLS|States that have invested more in infrastructure tend to have<br>greater output, more private investment, and more employment<br>growth. The coefficient on public capital is 0.15***|correlation;<br>Reverse<br>causality;<br>Endogeneity; Measurement error|
|**ut or reginal/n**|Evans and<br>Karras (1994)|US States<br>(Private<br>Nonagricultu<br>ral Sectors)|1970 to<br>1986|Cobb-<br>Douglas;<br>Log levels|FE|There is no evidence that government activities are productive<br>except investment in education. Government capital often has<br>negative productivity (-0.048)|Reverse<br>causality;<br>Endogeneity;<br>Measurement error|
|**ut (sector outp**||US Regions|||OLS<br>FE|Region-level estimates are essentially identical to those from<br>state data (FE estimate is -0.12*), suggesting no quantitatively<br>important spillover effects across states||
|**Outp**|Holz-Eakin<br>(1994)|US States|1969 to<br>1986|Cobb-<br>Douglas; Log<br>levels|OLS<br>FE<br>IV|Only OLS estimates of state production functions find<br>substantial productivity impacts (0.203***). Estimates of<br>production functions that use FE reveal no role for public-sector<br>capital in affecting private sector productivity (-0.0517*)|Production function estimate is<br>inadequate; Differencing out useful<br>information; Measurement error|
||Wylie (1996)|Canadian<br>National|1946 to<br>1991|Cobb-<br>Douglas;<br>Log levels|OLS|Aggregate production function method yields estimates of high<br>returns to infrastructure investment in terms of goods-sector<br>productivity in Canada over the 1946-1991 period. Total<br>infrastructure capital stock per hour worked is estimated to be a<br>significant contributor to goods-sector output per hour, with a<br>productivity elasticity of 0.517*|Endogeneity;<br>Common<br>trends;<br>Reverse<br>causality;<br>Measurement<br>error|

16
# Research Components of Uortd Bank Education Projects, since 1982.

|Area|CountryandLoanC<br>utnberI|Approve|<br>Research Components|Current StatusofStudies,inctudina<br>Compteted Documnts/Data|
|---|---|---|---|---|
||||I foltowing projects: (i) tracer system for<br>vocational training graduates (due Feb 1990),<br>(ii) industrial trade specifications<br>(June 1988<br>(iii)job creation investment<br>possibilities<br>(Feb<br>19 W9),<br>I<br>(iv) employment counseting<br>system for voc.<br>ltraining<br>(Feb 1989),<br>(v) manpower needs in agriculture,<br>constructio<br>(Feb 1989), tourism (Feb 1990). handicraft<br>(Jure 1990), textile, and fisheries sectors<br>(Feb 1989).|industry: ongoing for oth-r sectors.<br>"Etudes sectoriels: etude textile habittment;<br>bitan technique et financier;<br>etude artisarant"<br>(1989) (doc. # H20393);<br>(iii) and (iv) not yet initiated;<br><br>|(v)compteted in construction, textite,<br>administration.<br>Other sectors under preparation.<br>"Etude deltadequationentre la formation<br>n professionelle<br>et tlemploi" (1985) (doc. # H21310)|
|ENEA|Morocco LN 3026-NOR|1989|Rural Primary Education Project.<br>(i) measure the inpact of textbooks;<br>(ii) measure the impact of teacher training<br>on learning- using standardized learning tests<br>as a medium of interpolation.<br>(iii) School mapping project also<br>to be established (2.23 p.18).|Loan became effective 11/89.|
|ENEMA|Oman LM 2419-ON|1984|Second Education. No research component.|Confirmed.|
|ENEMA|Oman Lt 2799-Cm|1987|Third Education Project. No research component.<br>Strengthen management information<br>capacity;<br>Evatuation report.|Evaluation report not found.|
|ENEMA|Pakistan CR 1602-PAK|1985|Second Primary Education. (i) Pre-investment<br>studies;<br>(ii) review of pre-service<br>teacher training;<br>(iii) project preparation<br>studies;<br>(iv) completion study (para. 2.13 p.10).|Partially completed. FCU/PNE Wing, HOE (1988)<br>Research evaluation studies under PEP2<br>(doc. # H22574)|

53
of underlying individual sources agree about the direction of the change. These examples underscore the importance of carefully examining the factors underlying changes in the aggregate governance indicators in particular countries. In order to facilitate this, on the WGI website users can retrieve the data from the individual indicators underlying our aggregate indicators and use this to examine trends in the underlying data as well as changes over time in the composition of data sources on which the estimates are based.

# **3.3  Trends in Global Averages of Governance**

We conclude by reviewing the available evidence on trends in global averages of governance over the expanded time period that we now cover. As we have already noted, the WGI are not informative about trends in global averages because we assume that world averages of governance are zero in each period, as a convenient choice of units. While the aggregate indicators are of course informative about the relative performance of individual (or groups of) countries over time, in order to assess trends in global averages of governance we need to return to our underlying individual data sources.

In Table 7 we summarize trends in world averages in a number of our individual data sources. Most of the sources in this table are polls of experts, with data extending over the whole period 1996-2007 covered by the aggregate WGI. Other than expert polls, only one of them, GCS, is a survey with sufficiently standard format to enable comparisons over a reasonable period of time, in this case from 2002 to 2007. The first column reports the number of countries covered by the source in each of the periods shown, and the next three columns present the average across all countries of each of the sources in each of the indicated years. The underlying data have been rescaled to run from zero to one, and for each source and governance component, we report the score on the same question or average of questions that we use in the aggregate indicator. The next two columns report the standard deviation across countries for each source. The final column reports the change in the global average of each indicator over the longest period for which it is available, together with a t-statistic associated with a test of the null hypothesis that the world average score has not changed.

23
after about ...ve quarters.

All the results reported so far refer to ...scal multipliers computed for the “median” expansion and recession. We also looked, however, at what happens in “extreme”booms and recessions. For industrial countries, we ...nd that the multiplier in extreme recessions is much larger than in typical recessions (almost 70 percent higher). But perhaps the most striking result when considering extreme recessions is that lower government spending reduces output for any time horizon and, more importantly, the multiplier is always larger than one. In particular, the long-run multiplier (i.e., after 4 semesters) reaches 1.69. In terms of the current debate on austerity in the Eurozone, our results would indicate that the remedy could be worse than the disease in the sense that debt-to-GDP ratios would actually increase (even after 2 years) as a result of a ...scal contraction.

The paper proceeds as follows. Section 2 discusses the data and our methodology that follows the direct projection method mentioned above. Section 3 presents our estimates in the following order: (i) single (or non-linear) multipliers; (ii) multipliers in expansions and recessions; (iii) multipliers when government spending is going up and down; and (iv) multipliers taking into account both recession/expansion and government spending going up or down. Section 4 presents our estimates for extreme recessions or booms and compares them with our previous estimates (which apply to the median recession or boom). Section 5 concludes.

# 2 Data and methodology

## 2.1 Data

For OECD countries we use semiannual data on seasonally-adjusted real government spending, seasonally-adjusted real GDP, and government spending forecast errors (FE<sup>G</sup> ) computed as the di¤erence between forecast series prepared by professional forecasters and actual, ...rst6
Factor Endowments, Technology, Capital Mobility and the Sources of Comparative Advantage in Manufacturing<sup>_∗_</sup> Shushanik Hakobyan Daniel Lederman Fordham University World Bank .

**JEL Classifications** : F11; F14

**Keywords** : Factor Endowments; Rybczynski; Specialization; Productivity

> _∗_ The authors gratefully acknowledge financial support from the World Bank’s Research Support Budget and from the Office of the Chief Economist for Latin America and the Caribbean of the World Bank. The authors thank two anonymous referees who provided comments on the initial research proposal and to Francisco Carneiro for his patience and insightful comments.
The key challenge in identifying the causal effect of the bailout is the difficulty of constructing a valid counterfactual. Because cross-sectional exposure to the debt relief is a function of loan delinquencies prior to the program, one might worry that estimates of program impact are endogenous to pre-existing economic trends. To address this concern, our identification strategy takes advantage of plausibly exogenous cross-sectional variation in program exposure, generated by the Adwdrs program eligibility rules. We additionally validate our credit market estimates using bank-district data, which allows us to exploit within-district variation to explicitly control for local shocks to credit demand.

We find that the bailout had a significant and economically large effect on post program credit allocation and loan performance. A one standard deviation increase in bailout exposure led to a 4-6% decline in the number of loans and a 13-16% decline in the amount of credit outstanding. The results also reveal a significant reallocation of credit away from districts with high exposure to the bailout. While districts with high (above median) program exposure received 36 cents of new lending for every 1 dollar of credit written off under Adwdrs, districts with low (below median) program exposure received 4 dollars for every 1 dollar of debt relief. Despite this reallocation of new bank lending to observably “less risky” districts, we document a significant decline in loan performance in districts with high bailout exposure, concentrated among borrowers that had previously been in good standing.

In the second part of our analysis, we examine the effect of debt relief on the real economy. Supporters of Adwdrs, including India’s government at the time, cited debt overhang and investment constraints as one of the key motivations for the program and argued that debt relief could not only provide immediate relief to households, but also improve agricultural investment, with positive implications for rural productivity, wages and consumption. We use detailed, regionally disaggregated data to test for the effect of the bailout on rural productivity, wages and employment. Our results identify a precise zero for each of these outcomes. This is consistent with household-level evidence on the impact of Adwdrs (see 2
be permitted to dispose of the property because the secured creditor is more likely than the enforcement officer to be familiar with the resale market for the property. The secured creditor is also motivated to sell it for a price that will cover the outstanding obligation.

Another aspect of enforcement is the method of disposition of collateral. In many countries the collateral must be sold by public or judicial auction, often court-controlled, with cumbersome procedures, and minimum bids. Court auctions are generally the least effective method of selling and maximizing the value of pledged assets.<sup>39</sup> The preference under modern practice is for the secured creditor to be allowed to dispose of the property through a private sale. Requiring an auction not only delays the sale, thus allowing devaluation of the assets, it also may not be appropriate for the type of assets involved—for example, there may be a very small market, or niche market, such as for biotech assets. Further, often minimum bids are required based on ―market value‖ of the asset. Evaluations to determine minimum bids are of questionable reliability, and fail to account for the auction price, which is lower than fair market value. An auction that fails to bring a minimum bid not only delays the sale; it also allows the asset value to deteriorate.<sup>40</sup> Therefore, it is important in a modern secured transactions regime to allow the creditor to decide on the method of disposition. Another option, more commonly used, is to allow the creditor to take the collateral in satisfaction of the debt, which leaves the creditor free to dispose of it as he sees fit.<sup>41</sup> In MENA, the disposition of secured assets is one of the major constraints to financial institutions (see Figure 3). The data provided in Figure 3 is for both immovable assets and movable property. However, while both represent a constraint, movable property is clearly more difficult to sell (71% of banks consider it a major constraint) after a default and a repossession of the asset, than immovable assets (49% consider it a major impediment). The figure also illustrates how the process of recovering a debt secured with collateral becomes more challenging at the enforcement and execution phases, which is clearly one of the reasons why secured transactions has not properly developed in the MENA region.

> 39 _Secured Transactions Systems and Collateral Registries_ , IFC, January 2010, 2.2.5 Enforcement of a security interest 3. Disposition of the secured asset/s, page 57:  Disposition is important, and it is preferred that the secured creditor, rather than a court enforcement officer, dispose of the collateral.

> 40 The problems with mimimum bids and appraisals and repeated sales have been recounted by reformers in several countries during interviews with experts assessing the systems of collateral execution.

> 41 The option to allow the creditor to take the collateral in satisfaction of the debt has been reported by various countries to interviewers assessing the collateral execution systems.

31
# **Table 7: Does treatment heterogeneity explain gender differences?**

Dependent Variable: Real profits adjusted for own hours

||FE|FE|FE|FE|FE|FE|FE|FE|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Amount**|**7.78*****|**7.86*****|**7.07****|**7.38*****|**7.88****<br>*****|**7.41****|**7.90*****|**6.77****|
||(2.81)|(2.87)|(2.82)|(2.83)|(2.77)|(2.89)|(2.90)|(2.95)|
|**Amount*Female**|-8.60**|**-8.54****|**-7.40***|**-7.75****|**-8.35****|**-7.62***|**-8.92****|**-6.09**|
||(3.97)|(4.00)|(4.17)|(3.86)|(3.95)|(4.04)|(3.99)|(4.24)|
|Amount*Ever had a formal loan||-7.85||||||-8.82|
|||(5.76)||||||(5.66)|
|Amount*Number of Wage Workers|||-3.16|||||-1.86|
||||(2.46)|||||(2.48)|
|Amount*Household Asset Index||||-1.81||||-2.82**|
|||||(1.16)||||(1.17)|
|Amount*Years of Education|||||1.18**|||1.61***|
||||||(0.49)|||(0.56)|
|Amount*Digitspan recall||||||2.62||1.42|
|||||||(1.66)||(1.56)|
|Amount*Risk aversion|||||||-0.61|-0.13|
||||||||(1.14)|(114.00)|
|Observations|3697|3697|3697|3697|3697|3655|3697|3655|
|Number of enterprises|365|365|365|365|365|356|365|356|

Robust Standard errors in parentheses, *, ** and *** indicate significance at the 10%, 5% and 1% levels respectively. All variables used in the interaction apart from gender are standardized to have mean zero by subtracting off their mean.

39

-  -
# **5.- MODEL**

## _5.1 Selection biases_

Consider a general model for firm _i_ in time _t_ which relates outcomes _Y_ to observable firm attributes _X_ and an indicator variable for participation in a program _D_ : where ε is made up of a time-invariant firm-specific component _v_ and a randomly distributed error term _u_ . If firms are randomly assigned to the treatment and control groups, then the treatment and the control groups have similar distributions of the non-observed attributes. In this case, OLS can be used to estimate (1) from post-program cross-sectional data to get an unbiased measure of α , the net effect of the program.

The challenge is to estimate the net impacts of program participation α free of bias from self-selection of firms into programs based on their observable and unobservable productivity attributes. To see this, rewrite (1) separately for the treatment and control groups and difference the two equations to get an expression for α as in (2): The differenced equation in (2) <u>identifies two potential sources of bias</u> from non-random assignment, one due to differences between groups in observed attributes _X_ , ( _X it_ 1 − _X it_ 0 ) another due to differences in the non-observed attributes _u_ , _E_ ( _ui_ 1) ≠ _E_ ( _ui_ 0 ) . The first source of bias can be minimized by careful matching of the control group to the treatment group in terms of observables _X_ . However, the second source of bias due to u remains. One partial solution is to include a lot of observable variables that are correlated with the outcome of interest. This reduces the residual variance, and allows one to determine how much selection on unobservables there would have to be in order to overturn the direction of program impact obtained when assuming selection on observables (Altonji, Elder and Taber, 2005). Evaluation studies sought to address these potential selection biases through the combined use of propensity score matching and difference in difference (DID) methods. This paper uses similar methods to accommodate the specific nature of our panel data, which is discussed below.

22
summarized here (Martin and Winters, 1996; UNCTAD/WTO, 1997; OECD, 1997). Broadly speaking, MFN tariffs on imports of manufactures into the major industrial countries markets were reduced by an average of 40 percent from a trade weighted average of 6.3 percent before the Round to 3.8 percent after the Round, with the reductions to be phased in over five years and the first installment to be put in place on January 1, 1995.<sup>19</sup> Countries have reduced their tariff rates accordingly since then. Moreover, in 1997, following the Information Technology agreement, the duty on a number of products in this sector was reduced to zero on an MFN basis (Finger and Schuknecht, 1999). Canada undertook a further unilateral liberalization of about 1,800 lines of MFN tariffs in 2008-2009 (WTO TPR, Canada 2011). Applied rates are even lower, averaging 3.2 percent reflecting various preferential arrangements (Table 3).

MFN tariffs on products other than agriculture range, for example, from an average of 0.8 percent in Japan to 3.7 percent in the US and 4.0 percent in the EU (UNCTAD/WTO, 1997, Annex Table 1). But the rates are higher on products of interest to developing countries such as textiles and shoes (Hertel and Martin, 1999). At the same time, for the Quad countries (Canada, EU, Japan, US), one third of all MFN tariff lines are duty free.

The tariffication of various measures of support and protection in the agricultural sector resulted in substantial increases in the initial tariffs on a wide range of agricultural products in some major markets. Thus, the average applied MFN rate for agricultural commodities (production weighted) in 1996 ranged from 7.9 percent in the U.S to 10.7 percent in the EU (OECD, 1997, Table 3.1). Subsequently, developed countries were to reduce agricultural tariffs by 36 percent across the board, at the same time as access for agricultural products would be enhanced by reductions in domestic support measures.

39
**Appendix 4: Test of balancing of covariates between poor and non-poor households, 2010-2013, rural Malawi**

||NON PO|OR|POO|R|t-test difference|
|---|---|---|---|---|---|
|Variables|Mean|SD|Mean|SD|p-value|
|**Treatment variables**||||||
|Non-farm wage employment (0/1)|18.94|39.19|8.65|28.12|0.000|
|Non-farm self-employment (0/1)|24.84|43.22|16.08|36.75|0.000|
|**Outcome variables**||||||
|HHPCE|76,672.85|49,807.94|25,833.13|7,228.67|0.000|
|Log of HHPCE|11.13|0.45|10.11|0.33|0.000|
|Poverty incidence|0.00|0.00|100.00|0.00||
|Poverty gap||||||
|Poverty severity||||||
|Food insecurity (0/1)|25.07|43.35|45.24|49.78|0.000|
|Food consumption adequacy||||||
|1|33.02|47.03|55.69|49.69|0.000|
|2|58.29|49.32|41.21|49.23|0.000|
|3|8.69|28.18|3.11|17.36|0.000|
|Seeds purchase decision (0/1)|42.10|49.38|39.26|48.84|0.177|
|Fertilizer purchase decision (0/1)|45.93|49.84|27.30|44.56|0.000|
|Inputs purchase decision (0/1)|63.68|48.10|54.34|49.82|0.000|
|Seed purchase per acre (1000MKW)|0.61|1.94|0.39|1.16|0.000|
|Fertilizer purchase per acre (1000MKW)|4.57|11.40|1.99|6.00|0.000|
|Inputs purchase per acre (1000MKW)|5.15|11.59|2.38|6.28|0.000|
|Land cultivated (acres)|4.93|62.06|3.60|30.21|0.599|
|**Covariates**||||||
|Age of the household head|43.51|16.88|44.93|15.87|0.025|
|Male headed-household|77.49|41.77|73.91|43.93|0.026|
|Highest level of formal education acquired<br>by household head||||||
|<br>None|71.53|45.13|86.69|33.98|0.000|
|PSLC|11.34|31.71|7.82|26.86|0.000|
|JCE|8.72|28.22|3.74|18.98|0.000|

44