rafmacalaba/datause-encoder-data huggingface dataset:
# **Using Behavioral Science to Increase Women’s Participation in Natural Resource Management in Mexico**

_Authors:_ Graciela Reyes-Retana<sup>1, 2, 3</sup> , Gonzalo Pons<sup>1</sup> , Katharina Siegmann<sup>1</sup> , Zeina Afif<sup>1</sup> , Margarita GomezGarcia<sup>4</sup> , Pablo Soto-Mota<sup>5</sup> , Carmen Elena Castaneda Farill<sup>6</sup> 1The World Bank, 2 Cornell University, 3 Mexican Fund For the Conservation of Nature, 4 Blavatnik School of Government, Oxford University,<sup>5</sup> Norwegian School of Economics,<sup>6</sup> Center for Research and Teaching in Economics (CIDE) JEL Classification: <mark>D91, J16, Q23, Q56</mark> <mark>Keywords: Behavior Change Communication, Natural Resource Management, Outreach Strategies, Rural Gender Inclusion, Randomized Controlled Trial, Reducing Emissions from Deforestation and Degradation.</mark>
# **B Variable**

Table B1: Rider audits: demographic survey

|**Variable**|**Defnition**|
|---|---|
|Age|Median age in years of the rider’s age category when demographic<br>survey was responded|
|Employed|= 1 if rider had part-time or full-time job when responded to<br>demographic survey|
|High self-reported socio-economic status|= 1 if rider reported being a member of classes A or B|
|Low education (middle school or less)|= 1 if highest degree obtained by the rider at the time the demo-<br>graphic survey was responded is middle school or lower|
|Number of Supervia rides in typical a week|Number of times rider would normally ride the Supervia in a<br>typical week during which they are not taking any app rides|
|Single|= 1 if rider was not married when responded to demographic<br>survey|
|Years of schooling|Number of years equivalent to rider’s highest level of education<br>when demographic survey was responded|
|Young (18-25 years-old)|= 1 if rider was between 18 and 25 year-old when responded to<br>demographic survey|

Table B2: Rider audits: rides

|**Variable**|**Defnition**|
|---|---|
||All rides|
|Afraid of harassment|= 1 if rider reports being afraid that a stranger might touch them<br>intentionally during the ride in an inappropriate way|
|Against trafc|= 1 if rider is going in direction to the Central Station at the<br>evening or away from it in the morning|
|High crowding|= 1 if rider report of share of passengers standing in the car they<br>boarded is above the median|
|Moved within space|= 1 rider moved inside a car during the ride, but did not switch<br>from public to reserved space or vice-versa|
|Time - AM|= 1 if ride started during the morning|
|Time - PM|= 1 if ride started during the evening|
|Wait time|Time rider spent waiting for the train in the platform (minutes)|
|Time task opened|Local time at which the ride started|
16

||T<br>AY(j,i,t) = g+**co)**<br>88 three-digit in|able**5**<br>**+**s(i,t) +m(t)**+**<br>dustries, 1616|uj,i,t)<br>obs.|||
|---|---|---|---|---|---|
|Public Enterprises||||||
||Sum<br>of<br>|Percentage<br>|of|F-stat|p-value|
||Squares<br>|explained SS||||
|Total SS|25.28|||||
|(Model SS)|11.48|||||
|(R-square)|0.45|||||
|Mean|0.2|||||
|Total SS (corrected)|25.08|||||
|Explain SS (MSS-Mean)|11.28|||||
|Expl. SS/TSS(corr.)|0.45|||||
|c(j)|1.9|0.17||0.64|0.99|
|m(t) + s(i.t)|9.38|0.83||2.41|0.00|
|m(t) orthogonal|1.28|0.11||6.26|0.00|
|s(i,t) orthogonal|6.99|0.62||1.90|0.00|
|u0j,i,t)|13.8|||||
|Private Enterprises||||||
||Sum<br>of|Percentage|of|F-stat.|P-value|
||Squares|explained SS||||
|Total SS|57.6|||||
|(Model SS)|28.59|||||
|(R-square)|0.50|||||
|Mean|1.87|||||
|Total SS (corrected)|5.73|||||
|Explain SS (MSS-Mean)|26.72|||||
|Expl. SS/TSS(corr.)|0.48|||||
|c(j)|4.03|0.15||0.64|0.99|
|m(t) +s(i.t)|22.69|0.85||2.76|0.00|
|m(i,t) orthogonal|3.38|0.13||7.83|0.00|
|s(i,t) orthogonal|16.05|0.60||2.06|0.00|
|u(j,i,t)|29.01|||||
|<br>Ho: the same model (vari|ance & coefficient)|applies to both|public|and private|enterprises|
||F-statistic|p-value||||
|Chow test|1.2|8.47||||
|(Covariance analysis)||||||
|Variance Ratio test|1.8|0.00||||
||Chi-stat|||||
|Generalized Chow test|136.76|8.13||||

Note: Quarterly data. Outliers exceeding mean +/- 3 standard deviations have been removed.
Table 3. Main features of global recessions

|||Globa|l recess|ions||Non-|Global|All|
|---|---|---|---|---|---|---|---|---|
||1975|1982|1991|2009|Average|recessions|downturns|years|
|**Activity**|||||||||
|Output|1.1|0.4|1.3|-1.8|0.3|3.9|2.3|3.7|
|Output per capita|-0.7|-1.3|-0.3|-2.9|-1.3|2.2|0.8|2.0|
|Industrial production|-7.4|-2.2|-0.1|-8.9|-4.6|4.0|0.3|3.5|
|Trade|-1.4|-1.8|3.2|-10.4|-2.6|6.3|2.2|5.8|
|Unemployment rate|1.6|0.3|0.4|0.8|0.8|0.0|0.2|0.1|
|Oil consumption|-0.8|-2.7|0.2|-1.0|-1.1|2.5|0.9|2.3|
|Investment|0.7|-1.1|-1.0|-5.0|-1.6|4.7|2.0|4.4|
|Consumption|2.6|1.6|1.9|-0.1|1.5|3.7|2.7|3.6|
|Output (PPP)|1.8|0.6|1.5|-0.5|0.8|4.2|2.7|4.0|
|Output per capita (PPP)|-0.1|-1.1|-0.1|-1.7|-0.8|2.5|1.2|2.3|
|**Financial markets**|||||||||
|Capital flows|-1.6|-2.3|-3.2|-4.5|-2.9|0.5|-3.8|0.2|
|Credit|0.2|3.2|2.2|3.4|2.2|5.4|3.8|5.2|
|Equity prices|-4.8|-10.9|-1.7|-13.5|-7.7|6.2|-2.9|5.3|
|House prices|-4.3|-3.1|-0.2|-2.5|-2.5|2.2|1.5|1.8|
|Financial conditions|...|-0.2|-0.1|0.3|0.0|0.1|0.2|0.1|
|Inflation|-2.8|-2.5|0.2|-3.6|-2.2|0.2|-0.2|0.0|
|**Interest rates, confidenc**|**e, and**|**uncertai**|**nty**||||||
|Nominal interest rate|-2.5|-1.6|-1.0|-1.9|-1.8|0.1|-0.4|0.0|
|Real interest rate|0.9|1.1|-0.4|0.7|0.6|-0.1|0.1|0.0|
|Business confidence|-1.3|-0.6|-0.5|-0.8|-0.8|0.1|-0.7|0.0|
|Policy uncertainty|...|...|4.2|14.0|9.1|3.4|31.3|3.8|
|**Policies**|||||||||
|Government expenditure|9.2|2.3|3.4|8.7|5.9|4.7|4.8|4.8|
|Policy rate|-2.3|-1.0|-1.2|-1.9|-1.6|0.1|-0.6|0.0|

Note: All variables show percent changes, except in capital flows, unemployment rates, inflation, nominal and real interest rates, and policy rate, in which percentage-point changes of these variables are reported. “Non-recessions” refers to averages during 1950-2019, which are shown as “All years,” excluding years of global recessions. “Global downturns” shows averages during the four global downturns (1958, 1998, 2001, and 2012). “...” indicates that data are either unavailable or aggregates are not computed due to small sample size. For details, see Appendix 2.

51
discounted future pesos at a rate as high as 15% would save 6 million pesos if it recovered nothing from its residual entity. 8 From a fiscal perspective, the choice is clear, even when a province manages the residual entity as poorly as is possible. In practice, however, provinces may be able to recover a significant portion of the residual entities' assets, and may negotiate their way out of some liabilities. In those cases, the choice to privatize should be even clearer.

Table **1:** Estimates of the Costs of Re-Capitalizing a Typical **Public Provincial Bank** Ratio of Net Worth to Assets in Year 1: .115 (Arg. Requirement) Net Worth in Year 1: 25,000,000

|Los<br>Rat<br>Dis|s Rate in Ratio of N<br>e of Growth in No<br>count Rate is 10% f|et Worth to Lia<br>minal Liabilitie<br>or Present Valu|bilities: .090 (S<br>s: .113 (Sample M<br>e Calculations*|ample Mean, Pr<br>ean, 1996)*<br>|ov)*||
|---|---|---|---|---|---|---|
|Year<br>(1)|Net Worth<br>millions<br>(2)|Total<br>Assets<br>millions<br>(3)|Total<br>Liabilities<br>millions<br>(4)|Implied<br>Asset<br>Growth<br>During<br>Cycle<br>(5)|Nominal<br>Re-Cap.<br>Payment<br>Inmillions<br>(6)|PV<br>Re-Cap.<br>Payment<br>millions<br>(7)|
|1|25|217|192|.020|0|0|
|3|-11.9|226|238|.020|42.9|29.3|
|6|-14.8|280|295|.020|_53.1_|27.2|
|9|-18.3|347|366|.020|65.8|25.3|
|12|-22.6|430|453|.020|81.5|23.6|
|96|-9092.0|172747|181839|.020|32731.0|3.2|
|99|-11262.8|213994|225257|.020|35881.6|2.6|
|**Total**|||||**205515**|**383**|

Simulated re-capitalization costs are large not only in comparison with the residual entity, but also with respect to other fiscal variables. In 1996, the average expenditures for the provinces that had privatized their banks was 720 million pesos; their average deficit was 21 million (Table 3). The 383 million pesos, therefore, represent over half of yearly government Inflation in 1995 was relatively low, so it seems implausible that nominal assets and liabilities could have increased so much as to permit a residual entity larger than the old public provincial bank.

> **8** Qualitatively similar results for simulations that assume ditferent scenarios for loss rates in net worth appear in Appendix 1.

7
# _1. INTRODUCTION_

Historically, developing countries have adopted various industrial policies that regulate entry and output capacity of firms with the goal of addressing inadequacies across industries and regions. Regulations imposing artificial constraints on firm entry and capacity can distort efficiency by misallocating resources. In this paper, we focus on the effects of removing such regulations on investment. Specifically, we analyze the impact of deregulatory reforms in India during the 1990s, which eliminated compulsory industrial licensing (known as “license raj”), on the manufacturing firms’ investment decisions. To the best of our knowledge, this is the first paper to study the impact of deregulation on firm-level investment in the context of a developing economy.

The dismantling of the license system in India provides a natural environment to study the effects of deregulation on firms’ investment decisions. Under this system, a license was required to establish a plant, introduce a new product or expand capacity. Through allotments of input and import licenses, the government also controlled the flow of inputs and imported machinery.<sup>1</sup> Following the balance of payments crisis in 1991, the IMF agreed to provide financial assistance to India conditional upon economic reforms. These unanticipated reforms included delicensing, which eliminated the entry and capacity restrictions across industries.<sup>2</sup> We utilize this arguably exogenous shock (see Aghion et al., 2008) in order to identify the effects of deregulation on firm-level investment. To do so, we use data on Indian manufacturing firms for the 1989-2000 period and we estimate an investment equation to uncover a significant positive effect of delicensing on investment. We also show that the increase in investment was predominantly driven by smaller firms, and that firms in states with better credit conditions benefitted more from the deregulation.

Our paper contributes to the growing literature on deregulation and investment. Using industrylevel data for 21 OECD countries, Alesina et al. (2005) show that regulatory reforms are associated with an

> 1 The allotment of input and import licenses were based on the output limit specified in the licenses. See Chamarbagwala and Sharma (2011) for additional details on the industrial licensing system in India.

> 2 While a major portion of delicensing was initiated following the 1991 crisis, some industries were delicensed in 1985 under Rajiv Gandhi, who unexpectedly became the Prime Minister upon the assassination of Prime Minister Indira Gandhi. Aghion et al. (2008) argue that both episodes of delicensing were exogenous shocks.

2
# **_Political bargaining through extra-budgetary transactions?_**

The analysis undertaken here is based on budgetary transactions of state governments for which detailed data is compiled by government agencies on a regular basis. There is concern among policy circles that political factors may in fact have a more powerful influence on extra-budgetary transactions that are not subjected to legislative scrutiny. One channel of particular concern is the debt of state-owned public sector enterprises (PSEs), particularly in the power sector, that is guaranteed by the state government and owed either to a nationalized banking sector or to other central government agencies. It is therefore possible that nonaffiliated states are able to extract greater resources from the central government through the borrowings of their public enterprises that are, in effect, never paid back.

We do not have state level data available on public sector enterprises to check this alternate story of political bargaining between the center and the states. However, one variable we do have data for that would perhaps approximate the borrowing of PSEs is that of loans and advances by the state government which are reported to consist largely of loans to publicly owned enterprises. We find that the effect of political affiliation on such loans and advances by the state government is of similar sign and magnitude as the effect on overall fiscal deficit. Net loans by affiliated state governments is about 9 percent higher than that by non-affiliated state governments, as reported in Table 6. This evidence, and the fact that we do not find any effect of political affiliation on the market debt for which data is available through state budgets, and that we do indeed find substantial political effects on other budgetary transactions (thereby suggesting that legislative scrutiny does not prevent political manipulations from taking place), leads us to speculate that any marginal effect of political affiliation on the finances of state owned enterprises would be similar to the effects described here.

**Table 6. Effect of political affiliation on loans and advances by the state government**

|(t-statistic inparenthesis)|||
|---|---|---|
|_Variable_|_(1)_|_(2)_|
|Political affiliation|5.55|36.47|
|(=1 if center and state govt. belong to same political party)|(0.89)|(2.27)<br>–49.20|
|Affiliation * Seats||(–2.86)|
|||–14.64|
|(1- Affiliation) * Seats||(–1.16)|
||–0.01|–0.01|
|Real state income per capita|(1.58)|(–1.73)|
||–0.001|–0.001|
|Total population|(–1.40)|(–1.34)|
||N=360|N=360|
||R–sq=0.61|R–sq=0.62|

_Note:_ State fixed effects and year effects included; OLS regressions with robust standard errors; dependent variable is net loans and advances made by the state government.

23
nearly all specifications, the SYS‐GMM estimate of the effect of inequality on growth is negative and statistically significant.

The second and third panels of Table 6 report the distribution of results across the 2�<sup>�</sup> �50 possible instrument sets for the differenced and levels equations separately. I first summarize the distribution of the estimated growth effect of inequality from both equations, as well as the distribution of the t‐statistics. Consistent with the findings from the previous section, the estimated growth effects of inequality are overwhelmingly negative and with t‐statistics greater than two in absolute value in the levels equation. In contrast, the estimates tend to be positive and not significantly different from zero in the differenced equation. This pattern is very consistent with the benchmark specification in OBT, and also in the other papers considered here.

The key questions of interest for this paper concern the strength of the internal instruments and the implications for inferences about the growth effect of inequality. As in the previous section, I focus on the levels equation, since this is where the evidence for a negative growth effect of inequality appears to be strongest. Consider first tests of underidentification. The null hypothesis of underidentification is rejected for just over half of the 50 models. However, there is a stark difference between models with a small number of instruments (i.e. those in which the instrument set is collapsed) and with a large number of instruments (i.e. in which the instruments enter “GMM‐style” with a separate instrument for each time period). The null of underidentification is rejected for 24 out of 25 models in the former category, but only for four out of 25 models in the latter category. This is a first indication that problems of weak instruments in this setting appear to be much more pronounced when the number of instruments is large.

I turn next to tests of weak instruments. In only four of the 50 models considered here does the Stock Yogo test reject the null of weak instruments at the least demanding critical value of 30 percent maximal 2SLS bias relative to OLS. Similarly, the Sanderson and Windmeijer test for weak instruments based on maximal size distortion for the coefficient on lagged inequality rejects the null of weak instruments for only 11 out of the 50 possible models for the levels equation. These weak instrument diagnostics suggest that the problem of weak instruments is pervasive in most of the alternative instrument sets considered here. Consistent with the findings on underidentification, it is also worth noting that the problem of weak instruments seems closely related to the number of instruments. The relatively few specifications in which the weak instrument diagnostic tests suggest that instrument strength is not a problem all feature a small number of internal instruments. This is because these 20
# **Stasical A**

|Table I|Balance of Payments: Current Account Suirplus.<br>**_3E_**||
|---|---|---|
|Table II|Balance of Payments: Current Account<br>Surpluses and Deficits.<br>**_35_**||
|Table III|Gross Domestic Investment, Gross Domestic<br>Saving and Resource Balance.<br>_36_||
|Table IV|International Banking and Capital Market Flows. 37||
|Table V|External Public Debt and Debt Services Ratios.<br>_39_||
|Table VI|Gross Domestic Product.<br>_39_||
|Table VII|India: External Debt.<br>_40_||
|Table VIII|India: Balance of Payments: 1987-88 to 1990-91.<br>41||
|Table IX|Bank-wise and Country-wise Break-up of Overseas<br>Branches of Indian Banks. 42||
|Table X|Country-wise Distribution of Indian Banks Overseas.|_43_|
experiences varied from case to case, but that, on the whole, transitional unemployment was quite small. In a survey of more than fifty studies of the adjustment costs of trade liberalization in the manufacturing sector—mostly industrialized economies—Matusz and Tarr (1999) argue that the adjustment costs associated with transitional unemployment are not high and that unemployment durations generally quite short. Indeed, in some cases employment appears to increase more or less instantly—as, for example, Harrison and Revenga (1998) report for Costa Rica, Peru and Uruguay. In their (non-random) sample, developing countries tended to display increasing employment after trade reform, while former centrally-planned countries in transition to a market economy showed the opposite. The attribution problem is huge for the latter countries, however, for so much else was going on. Most studies of trade and employment refer to manufacturing employment, with little indication of whether their results generalize to agriculture or services, or indeed anywhere outside the formal sector. This is a major shortcoming, at least as much conceptual as practical.

Particularly in poor economies it makes no sense to equate meaningful work with formal employment. Most employment is informal, even in manufacturing, and even formal jobs offer little by way of effective social protection or improved safety provisions. Firms and/or workers may consciously prefer informality (Maloney 2004), especially if doing so has tax or regulatory advantages, including remaining below the sights of corrupt officialdom. There is a concern that trade liberalization is associated with great informality. This is disputed—see below—but even where it is true one needs to go a great deal further before one can conclude that liberalization has reduced the overall welfare emanating from work.

A further mystery is whether those laid off following trade liberalization are disproportionately poor. In developed countries, Kletzer (2004) suggests ‘yes,’ but for developing countries we are far from sure. Enterprise surveys report the responses of firms to trade liberalization, but typically give little information on the characteristics of their employees, while household surveys, which do provide this information, cannot easily be matched to enterprises. The latter do, however, generally suggest that, in many low income countries, very few of the poorest are employees in the formal manufacturing sector.

8
disconnect between perceptions and actions? Concern about climate change does not necessarily mean understanding of its drivers and dynamics, nor of the responses needed. Polls show that the public admits to remaining confused over climate change’s causes and solutions.<sup>6</sup> This “green gap” in public attitudes partly stems from how climate science is communicated and how our minds’ (mis)understand climate dynamics.<sup>7</sup> Standard information-deficit models assume that if people “know,” they act differently.<sup>8</sup> Public opinion is today exposed to considerable amounts of information on the causes, dynamics and effects of climate change. This information clearly increased concern, but did not necessarily lead to action.<sup>9</sup> Information can lead to misleading feelings of “empowerment” (you can change the course of nature; the fate of the world depends on switching the light and using less air conditioning) which then turns into ambivalent powerlessness when paired with more “realistic” messages. Conveying a sense of urgency by stressing the unprecedented nature and scale of the problems implies a lack of available models to learn from, encouraging paralysis.<sup>10</sup> Similarly, playing up the multistakeholder nature of adaptation is a reminder that the solution rests with no single actor, resulting in a generalized feeling of limited agency, helplessness, and disempowerment<sup>11</sup> . This might explain why, in developed countries where information on climate change is more available, citizens are less optimistic about a possible solution.

To lead to action, awareness needs to be grounded in information from clear and trustworthy sources. Scientific debate evolves through testing and crosschecking of theories and findings. This can lead news coverage to veer from one extreme to another, resulting in additional confusion for the public, who may perceive this not as scientific progress but as a proliferation of contradictory opinions. Media framing can be confusing or even misleading.12 The journalist’s need to present “balanced” stories has given disproportionate coverage to climate science contrarians lacking comparable scientific expertise and standing.13 The message can also contribute to enabling or disabling the possibility of action. The media shy away from the scientific community’s careful wording to express uncertainty in search of punchier stories. The reader then faces messages lacking scientific caution and containing strong appeals that might be then refuted by other similarly strongly worded statements, hampering the perceived reliability of the information source. In addition to confusing the public (and policymakers) about import, causes, and potential solutions, different types of framing can antagonize individuals and induce a sense of guilt and vilification, as when the

> 6 Wimberly 2008; Accenture 2009.

> 7 Norgaard 2006; Jacques, Dunlap, and Freeman 2008.

> 8 Bulkeley 2000.

> 9 Kellstedt, Zahran, and Vedlitz 2008.

> 10 Immerwahr 1999.

> 11Krosnick and others 2006.

> 12 Boykoff and Mansfield 2008.

> 13 Oreskes 2004; Krosnick 2008.

3