rafmacalaba/datause-encoder-data huggingface dataset:
# 6.3 Normative analysis: Long-Run Counterfactuals

In the previous sections I discussed the implication of lobbying at the business cycle frequency for output, TFP and micro-level implications. However, the model is also useful to answer questions related to the long run behavior of the economy. To that end, I propose several exercises that are relevant for policy and normative analysis.

## 6.3.1 Role of Misallocation in the Long-Run

In this experiment I try to assess the impact of lobbying on total factor productivity in the long run. Since lobbying acts as a subsidy on capital, eliminating this distortion has an impact on capital accumulation. Considering that rms can no longer get access to the preferential tax treatment that made them larger, there will be a reduction in the demand of capital. As a result, the capital stock in the economy would go down. To counterbalance this aggregate eect, and in the spirit of Restuccia and Rogerson (2008), I adjust the corporate tax rate _τ_ so that the capital stock in the new steady state is the same as the initial value. In this sense, I am focusing on the TFP eects associated with the elimination of lobbying through reallocation of capital and selection. Operationally, I take the xed cost of lobbying ( _fl_ ) to innity so that no rm can do lobbying in the new steady state and at the same time I reduce the corporate tax rate.

Column 2 in Table (12) shows selected statistics such as aggregate output, capital, consumption, wage and total factor productivity (TFP) for this experiment relative to the benchmark economy when both are in steady state. We can see that output and TFP increase by 0.7 % and 0.9 % respectively without lobbying. As discussed in section (4.2), lobbying increases the dispersion of the marginal product of capital relative to an economy without lobbying. Then, banning this activity implies gains in eciency on the production side. An important point to stress is that the gains are larger in the second sector, which is the one where lobbying is more intensive. Because of this dierential intensity of lobbying, the dispersion in the marginal product of capital is larger in that sector. Finally, notice that the combination of these policies increases the number of rms in the economy. Although wages are going up and therefore labor is more expensive, the reduction in the tax rate that the government is proposing makes running a rm more protable for a group of agents.

## 6.3.2 Institutional Reform: Banning Lobbying

In the previous exercise I discussed the eects of banning lobbying while at the same time reducing the corporate tax rate in order to keep the level of capital constant. In this counterfactual I propose to analyze the full eect of banning lobbying without adjusting the corporate tax rate.

The results for the rst experiment in the new steady state are shown in column 2. Relative to the benchmark steady state we observe that output decreases 1.2 % , capital used for production is 4 % lower, TFP has an increase of 0.8 % , and consumption goes up almost 1% .

39
<!-- Start of picture text --> ~~ eS S eS > ee > > eS AS eS » > SS<br>oY S Ss 4 S S RA S S S os’ S S S S S<br>Pe ee” Se” we” os” og<br>s ss eS ca ~ = s Ss s A er ,ss RS oe q »* xX?<br>gs es Os wT er Vv ce or ge oe”<br><!-- End of picture text --> <!-- Start of picture text --> =Growth ORedistribution 7 Annual%<br>point change<br>in poverty<br>3 = __ Moldova, 2001-10 n 39<br>5 8 Romania, 2001—09 i -5.3<br>Costa Rica, 2000-08 — -1.3<br>Honduras, 1999-2009 O -1.4<br>Thailand, 2000-09 C -1.6<br>> Peru, 2004—10 C_ 2.6<br>= Colombia, 2002-10 — 28<br>% Ecuador, 2003-10 — 26<br>- Panama, 2001-09 CL) “1.7<br>Chile, 2000-09 -1.3<br>Brazil, 2001-09 _ -1.9<br>Paraguay, 1999-2010 -1.0<br>Argentina, 2000-10 OO -1.3<br>¢ a<br>ai > Nepal, 1996-2003 [ -2.9<br>#~ — Ghana, 1998-2005 1 -2.1<br>- Bangladesh, 2000-10 — -1.7<br>-50% 0% 50% 100% 150%<br><!-- End of picture text -->
|**Variable**|**Obs**|**Mean**|**Std. Dev.**|**Min**|**Max**|
|---|---|---|---|---|---|
|lypc|121|8.05|1.17|5.25|10.03|
|rule|162|-0.04|0.92|-2.17|1.91|
|lpats_pop|127|-11.79|3.14|-17.98|-4.66|
|lmort|76|4.70|1.22|2.15|7.99|
|lip|116|0.83|0.62|-2.30|1.46|
|eurfrac|172|0.20|0.36|0|1.00|
|oilprueba2|183|0.20|0.40|0|1|
|cen_latabs|173|25.69|17.12|0.42|67.47|
|landlock|191|0.20|0.40|0|1|
|ssa|201|0.25|0.43|0|1|
|lcts|150|2.92|0.75|0.83|4.44|
|malfal94|149|0.29|0.41|0|1|

# **Description**

lypc Log (Income per capita in 2000, 1985 U.S.$ PPP) Source: Calderón, Fajnzylber, and Loayza (2005) rule Rule of Law index. Source: Kaufmann, Kraay, and Zoido-Lobatón (2002) lpats_pop Log (patents granted in the U.S. and Europe/ total population). Sources: USPTO, EPO, and World Bank. lmort Log (settler mortality). Source: Acemoglu, Johnson, and Robinson (2001) lip Log (average IP index 1960-1995). Source: Park and Ginarte (2001) Fraction of population speaking one of the major languages of Western Europe: English, French, German, eurfrac Portuguese, or Spanish. Source: Hall and Jones(1999). oilprueba2 Dummy variable taking value of 1 if country is net exporter of oil. cen_latabs Absolute value for country centroid latitude. landlock Dummy variable taking value of 1 lacks of access to the sea. ssa Dummy variable taking value of 1 if country is located in Sub-Saharan Africa. lcts Log (Constructed Trade Share). Source: Frankel and Romer (1999). malfal94 Malaria Index, year 1994. Source: Gallup and Sachs (1998).

31
**3**

> The second method for examining causality uses panel data and exploits the cross-country and time-series dimensions of the data. We assemble a panel dataset, with data averaged over each of the seven _5-year_ intervals composing the period **1960-1995.** We use two Generalized Method-ofMoments (GMM) dynamic panel estimators that correct inherent problems with the purely crosssectional estimator. Specifically, these estimators address the econometric problems induced **by** country-specific effects, endogeneity, and the routine use of lagged dependent variables in growth regressions [Arellano and Bond **1991;** Holtz-Eakin, Newey, and Rosen **1990].** In the first GMM panel estimator, the strategy for addressing possible omitted variable bias created **by** country-specific effects is to difference the regression equation. Thus, we first take differences to eliminate country-specific effects and thereby remove omitted variable bias. Next, we instrument the right-hand-side variables (the differenced values of the original regressors) using lagged values of the original regressors (measured in levels) as instruments. This last step removes the inconsistency arising from simultaneity bias, including biases induced **by** the differenced lagged dependent variable. This _dfference_ dynamic-panel estimator

> has increasingly been used in studies of growth. [Caselli, Esquivel, and LeFort **1996;** Easterly, Loayza, and Montiel **1997].**

The second **GMM** dynamic panel estimator goes beyond the _dfference_ dynamic-panel estimator. The problem with the _difference_ estimator is that it generally suffers from weak instruments, which yields large biases in finite simples and poor precision even asymptotically [Alonso-Borrego and Arellano **1996;** Bond, Hoeffler, and Temple **1977].** Specifically, lagged values of the levels of the original regressors frequently make weak instruments for the differenced values of the regressors used in the dynamic-panel equation. This occurs in the current setting if lagged values of financial development do not forecast _changes_ in financial development. To mitigate this problem, we use a
# **References**

- <mark>Alicke, M. D., Klotz, M. L., Breitenbecher, D. L., Yurak, T. J., & Vredenburg, D. S. (1995). Personal contact, individuation, and the better‐than‐average effect.</mark> _<mark>Journal of Personality and Social Psychology</mark>_ <mark>,</mark> _<mark>68</mark>_ <mark>(5), 804.</mark>

- <mark>Alicke, M. D., & Govorun, O. (2005). The better‐than‐average effect.</mark> _<mark>The Self in Social Judgment</mark>_ <mark>,</mark> _<mark>1</mark>_ <mark>, 85‐106.</mark>

- <mark>Banerjee, A., & Duflo, E. (2006). Addressing absence.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(1), 117‐132.</mark>

- <mark>Barrera‐Osorio, F., Galbert, D., Gaspard, P., Habyarimana, J. P., & Sabarwal, S. (2016). Impact of public‐private partnerships on private school performance: Evidence from a randomized controlled trial in Uganda.</mark>

- <mark>Bold, T., Filmer, D., Martin, G., Molina, E., Rockmore, C., Stacy, B., Svensson, J. & Wane, W. (2017). What Do Teachers Know and Do? Does It Matter?.</mark>

- <mark>Cannell, J. J. (1988). Nationally normed elementary achievement testing in America's public schools: How all 50 states are above the national average.</mark> _<mark>Educational Measurement: Issues and Practice</mark>_ <mark>,</mark> _<mark>7</mark>_ <mark>(2), 5‐9.</mark>

- <mark>Chaudhury, N., Hammer, J., Kremer, M., Muralidharan, K., & Rogers, F. H. (2006). Missing in action: teacher and health worker absence in developing countries.</mark> _<mark>The Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>20</mark>_ <mark>(1), 91‐116.</mark>

- <mark>Chetty, R., Friedman, J. N., & Rockoff, J. E. (2014). Measuring the impacts of teachers II: Teacher value‐added and student outcomes in adulthood.</mark> _<mark>The American Economic Review</mark>_ <mark>,</mark> _<mark>104</mark>_ <mark>(9), 2633‐2679.</mark>

- <mark>Cooper, A. C., Woo, C. Y., & Dunkelberg, W. C. (1988). Entrepreneurs' perceived chances for success.</mark> _<mark>Journal of Business Venturing</mark>_ <mark>,</mark> _<mark>3</mark>_ <mark>(2), 97‐108.</mark>

- <mark>DellaVigna, S. (2009). Psychology and economics: Evidence from the field.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>47</mark>_ <mark>(2), 315‐372.</mark>

<mark>Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self‐assessment implications for health, education, and the workplace.</mark> _<mark>Psychological Science in the Public Interest</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(3), 69‐106.</mark> <mark>Ehrlinger, J., Johnson, K., Banner, M., Dunning, D., & Kruger, J. (2008). Why the unskilled are unaware: Further explorations of (absent) self‐insight among the incompetent.</mark> _<mark>Organizational behavior and human decision processes</mark>_ <mark>,</mark> _<mark>105</mark>_ <mark>(1), 98‐121.</mark>

- <mark>Ehrlinger, J., & Dweck, C. S. (2011). If I don’t see it, it must not exist: How preferential attention allocation contributes to overconfidence.</mark> _<mark>Florida State University</mark>_ <mark>.</mark>

13
32 amply demonstrated impact of maternal education on non-wage outcomes like child mortality and fertility where there is no signaling involved (Hobcraft, 1993). It is hard to explain the effect of maternal education on child mortality if schooling has no impact on knowledge or cognitive skills (Glewwe, 1995). Even in areas where a _priori_ one might expect the quality of schooling to be very low the health benefits of education are present. In data from sixteen SSA countries women with primary education have 24 percent lower child mortality than women with no education and among women with a secondary education mortality is 50 percent lower46.

The most plausible way to interpret the wage-schooling regressions is that schooling expands cognitive skills and that these increased cognitive skills account for higher wages.

1) **T.c a negative macro** finding jwst **failiire to** control for quality<sup>9</sup> Even though on average the effect of schooling on wages is the result of increased cognitive skills, it is still the case that quality of schooling, in the limited sense of the increase in cognitive skills created by an additional year of schooling, must vary enormously across countries. Perhaps the problem is not pure measurement error m measuring years of schooling (which, given the arguments above, it is not) but that years of schooling do not measure accumulation of productive human capital.

First, the general expectation is that the exclusion of quality of education measures will bias the estimate of the returns to the quantity of schooling _upwards,_ due to what Schultz (1988) refers to as the "general underlying positive covariance between quantity and quality of schooling." Behnnan and Birdsall (1983), in perhaps the most famous paper emphasizing the impact of failure to measure quality on estimates of the returns to schooling, show that without controls for quality the wage impact of years of schooling is seriously overestimated by a multiple of 2.

I can simulate the impact of ignoring quality on my estimates if I assume that the "true" education 4 These figures are complied from recent Demographic and Health Surveys (DHS) which record child morality **(0 qj)** by level of maternal education.
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2403 Can The World Cut Poverty in Half?<br>How Policy Reform and Effective<br>Aid Can Meet International<br>Developnment Goals|Paul Collier<br>David Dollar|July 2000|E. Khine<br>37471|
|WPS2404 The Distfibution of Mexicos Public<br>Spendincg onE'ducation|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2405 Marginal Willingness to Pay for<br>Education ancd the Determinants of<br>Enrollment in \Mexico|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2406 How Mexico s Financiai Crisis<br>Affected Income Distribution|Gladys Lopez-Acevedo<br>Angel Salinas|July 2000|M. Geller<br>85155|
|WPS2407 Utility Pr vatization and the Needs ot<br>the Poor in Latin America: Have We<br>Learned Enough to Get It Right?|Antonio Estache<br>Andr6s Gomez-Lobo<br>Danny Leipziger|August 2000|G. Chenet-Smith<br>36370|
|WPS2408 What Makes Banks Special? A Study<br>of Banking, Finance, and Economic<br>Developmernt|Biagio Bossone|August 2000|E. Mekhova<br>85984|
|WPS2409 How Did the World's Poorest Fare<br>in the 1990s'0|Shaohua Chen<br>Martin Ravallion|August 2000|P. Sader<br>MC3-556|
|WPS2410 Is Functional Literacy a Prerequisite<br>for Entering the Labor Market?<br>An Analysis of Determinants of Adult<br>Literacy and Earnings in Ghana|Niels-Hugo Blunch<br>Dorte Verner|August 2000|H. Vargas<br>37871|
|WPS2411 Natural Openness and Good<br>Government|Shang-Jin Wei|August 2000|H. Sladovich<br>37698|
|WPS2412 Urbaniz:ation withiout Growth:<br>A Not-So-Urcommon Phenomenon|Marianne Fay<br>Charlotte Opal|August 2000|A. Abuzeid<br>33348|
|WPS2413 Foreign Direct Investment in Services<br>and the Dormestic Market for Expertise|James Markusen<br>Thomas F. Rutherford<br>David Tarr|August 2000|L. Tabada<br>36896|
|WPS2414 Pension Reform ana Capital Market<br>Development: "Feasibility" and<br>"Impact" Preconditions|Dimitri Vittas|August 2000|A. Yaptenco<br>31823|
|WPS2415 Infrastructure Restructuririg and<br>Regulaetion: Bijilding a Base for<br>Sustainable Growth|Ian Alexander<br>Antonio Estache|August 2000|M. Salehi<br>37157|
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Table 5: Psychosocial impact of days since last worked

||(1)<br>Wellbeing|(2)<br>Stress|(3)<br>Sociability|
|---|---|---|---|
|Days Since Work|-0.098|-0.064|0.042|
||(0.013)|(0.012)|(0.019)|
|Shrp. q-val: Days Since Work|0.001|0.001|0.009|
|Observations|3148|3165|3165|

**Notes:** This table reports the impact of an additional day since a respondent last worked on the three margins of psychosocial well-being which were collected in the weekly surveys. Each column shows the OLS estimates with individual, camp and enumerator fixed effects, a weekly time trend, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). The sample includes only those in the employment treatment arm. Standard errors are clustered at the block level.

Table 6: Employment impacts on purposefulness

||(1)<br>Value to family|(2)<br>Value to community|(3)<br>Value to community|
|---|---|---|---|
|Work|0.112|0.072|0.006|
||(0.072)|(0.090)|(0.081)|
|Cash|-0.063|-0.086|-0.067|
||(0.079)|(0.110)|(0.100)|
|Shrp. q-val: Work|0.543|0.752|1.000|
|Test Work=Cash|0.006|0.068|0.345|
|Shrp. q-val Work = Cash|0.019|0.073|0.131|
|Observations|726|726|726|

**Notes:** This table reports the treatment effect of the employment and cash arms on one’s ranking of oneself relative to the most valuable person in their family (Column 1) or community (Column 2), where the latter is conditional on family ranking in order to isolate community beyond family. Rankings range from 1 to 10, with 10 being the most valuable individual in the family/community and 1 being the least. Outcomes are standardized. Each column shows the OLS estimates of Equation (1) including dummies for each treatment arm and controlling for the baseline measure of the dependent variable (ANCOVA), camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014). Each row presents the coefficients of the specified treatment relative to the control group (which received a small weekly cash payment). Standard errors are clustered at the block level.

38
<!-- Start of picture text --> Model Utility = 10 Counterfactual Utility = 10.1216, Reallocation = 0.36708<br>0 ftee yg nn eee eee eee eee gee eee eee eens 0 cette tt tne eee nee eee ee ee eee<br>gs 2 :.: :‘: :.: :.: 42=:.: :.: :.: :‘:<br>A::::A::::<br>2-4°: : : : ° : : : :<br>£ i cc: : Boece: cece eeed: 2-4£ ek : cca: \ccc ccc ee eeeet:<br>| ny=D OR > \) — Actual i Se<br>=——= Modeled : : : === Avg. Efficiency : : :<br>10 12 14 16 18 10 12 14 16 18<br>In(population) In(population)<br>Counterfactual Utility = 10.0191, Reallocation = 0.19917 Counterfactual Utility = 10.0887, Reallocation = 0.44004<br>=::::s::::<br>a: a<br>A::::A::::<br>°<br>a-4 es : : : peter e eee eee : a-4° eet eee eee : bee eee eee eee dee : eee eee Ngee : eee ee ee :<br>=:£<br>°——= Actual re \ vO: ° =——= Actual a oe ne<br>=== Avg. Amenities}: : : == Avg. Exc. Frictions : :<br>10 12 14 16 18 10 12 14 16 18<br>In(population) In(population)<br><!-- End of picture text -->
aside savings) and how much confidence they have that disputes with financial institutions will be resolved quickly and efficiently. The surveys should be broken down by age, gender, geographic area, socio-economic levels and years of formal schooling. Updated surveys can later determine if the financial education program has been effective and what additional consumer protection measures may be needed.

**Future work should include further analysis and financing of programs.** Global surveys of consumer protection and financial literacy would be helpful as would diagnostic reviews of countries in other regions, such as Latin America and Asia. Detailed analysis of specific issues would also be useful. Consideration should also be given to ways in which international financial institutions could finance development—and execution—of national programs of consumer protection and financial literacy.

# **Key Recommendations**

## **Institutional Structures**

- It would be best if consumers could go to a single agency to obtain help related to financial services.

- If a consumer protection agency is responsible for financial services, a specialized department for financial services should be created with adequate resources and expertise in financial sector issues.

- If a consumer protection agency is responsible for consumer protection in basic financial services such as consumer credits, the agency should become responsible for all retail financial services.

- Creating a financial consumer protection agency could also be an alternative.

- Financial consumer protection authorities should participate in international networks such as FinCoNet.

## **Consumer Disclosure**

- Simple and standard consumer disclosure would help consumers with complex financial products—and allow them to compare offers by different providers.

- Key Facts Statements should be prepared in order to give consumers a simple summary of the important terms and conditions of the financial contract. For each generic product class, it is best to use a standardized format written in plain language in a page or two.

- The Key Facts Statements should be developed by the professional associations, which should recommend their use by member financial institutions.

- Consumers have almost no ability to amend a financial institution’s contract. For high-volume core retail financial products, the use of standard provisions in retail financial contracts (developed by the professional associations) is desirable.

## **Business Practices**

- All financial institutions dealing with the public should be registered with a financial supervisory agency.

3