metadata: authors: 0: author: Arezki,Rabah
1: author: Djankov,Simeon
2: author: Nguyen,Ha Minh
3: author: Yotzov,Ivan Victorov
count: World
docna: 0: docna: Reform Chatter and Democracy
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Trade and Multilateral Issues,Macroeconomic Management,Commodity Risk Management,Government Policies,Quality of Life & Leisure,Social Analysis,Youth and Governance,National Governance
teratopic: International Economics and Trade,Macroeconomics and Economic Growth,Agriculture,Public Sector Development,Governance,Social Development
abstracts: cdata!: This paper explores the dynamics of
            media chatter about economic reforms using text analysis
            from about a billion newspaper articles in 28 languages. The
            paper shows that the intensity of reform chatter increases
            during economic downturns. This increase is more significant
            in democracies. Using instrumental variable techniques, the
            analysis finds the relationship to be causal. The paper also
            documents that reform chatter is followed by actual reforms,
            suggesting that democracies benefit from a
            "self-correcting" mechanism stemming from changing
            popular attitudes toward reform.
repnme: repnme: Reform Chatter and Democracy
display_title: Reform Chatter and Democracy
listing_relative_url: /research/2020/07/32239446/reform-chatter-democracy
topicv3: Political Economy of Reform m1326263 836,Structural Policy and Reform m1326450 1130,Economic Shocks and Trade m1326672 1141,Trade m1326924 698,Commodity and Resource Prices m1503849 3034
publishtoextweb_dt: 2020-07-13T00:00:00Z
historic_topic: International Economics and Trade,Macroeconomics and Economic Growth,Agriculture,Public Sector Development,Governance,Social Development
origu: Office of the Chief Economist (MNACE)
no_of_pages: 38
author_keys: 0: author_key: 1596791
1: author_key: 124574
2: author_key: 1057980
3: author_key: 1670614
model_extractions: Policy Research Working Paper 9319

# Reform Chatter and Democracy

_Rabah Arezki Simeon Djankov Ha Nguyen Ivan Yotzov_ Middle East and North Africa Region Office of the Chief Economist July 2020
Policy Research Working Paper 9319

# **Abstract**

This paper explores the dynamics of media chatter about economic reforms using text analysis from about a billion newspaper articles in 28 languages. The paper shows that the intensity of reform chatter increases during economic downturns. This increase is more significant in democracies.

Using instrumental variable techniques, the analysis finds the relationship to be causal. The paper also documents that reform chatter is followed by actual reforms, suggesting that democracies benefit from a “self-correcting” mechanism stemming from changing popular attitudes toward reform.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rarezki@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Reform Chatter and Democracy**

Rabah Arezki, Simeon Djankov, Ha Nguyen and Ivan Yotzov

**Keywords:** Reform, Chatter, Media, Business Cycle, Democracy. **JEL Codes** P1; P4; D7; E3.

Rabah Arezki is the Chief Economist for the Middle East and North Africa at the World Bank and a Senior Fellow at Harvard’s Kennedy School of Government. Simeon Djankov is Director for Policy with the Financial Markets Group at the London School of Economics and Senior Fellow at the Peterson Institute for International Economics. Ha Nguyen is a Senior Economist in the Chief Economist Office of the Middle East and North Africa at the World Bank. Ivan Yotzov is PhD candidate at the University of Warwick. We thank Olivier Blanchard, Nick Bloom, Jeremy Evans, Jeffrey Frankel, Andrei Freund, Nick Lore-Edwards, Tarek Masoud, and Carlos Vegh for comments and suggestions. The findings, interpretations, and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work.
# **1. Introduction**

_<mark>“Democracy is the worst form of government, except for all the others.”</mark>_ <mark>W</mark> inston Churchill The debate over the relative benefits of democracies over autocracies has become heated. Democracies are often criticized for being less responsive. The above-mentioned quote epitomizes the apparent trade-off between legitimacy and efficiency in democracies. But is it really so? In the present paper we provide evidence against that trade-off. Indeed, we uncover one important channel through which democracies display a built-in mechanism for self-correction that is “reform chatter”. We find that reform chatter peaks during economic downturns, followed by improved popular attitudes in turn leading to actual reforms.

The political debate over reforms had traditionally revolved around their timing and consequences. The pioneering work on the political economy of reforms steered the debate toward the determinant of reforms and issues of political feasibility (Alesina and Drazen 1991). Yet little is known about attitudes toward reform in the public square. Arguably, understanding public chatter illuminates the presence of support for public policy.

This paperuses text analysis from about a billion newspaper articles to explore the dynamics of reform chatter. Our measure of chatter intensity uses Dow Jones FACTIVA, a global repository of newspaper articles. The approach is akin to Baker et al. (2016) who use the same data to construct an index of economic policy uncertainty. We refine the search method in several directions, beyond the search of keywords like “economic reform” in press articles. In particular, we distinguish between international and local media sources; and across media in 28 different languages. Also, we use machine learning techniques such as topic modeling to ascertain the informational content of reform chatter (Blei et al. 2003).

We compute a measure of attitudes around reform to get a sense of whether or not reform is put forth with a positive lens. This paper contributes to the large and growing economic literature using text analysis and machine learning techniques to analyze data (see Gentzkow et al, 2019 for a review). The existing literature on sentiment focuses mostly on the United States and on the predictive power of sentiment on economic activity. An example of use of sentiment analysis in finance is Tetlock (2007) who considers the sentiment of the Wall Street Journal’s “Abreast of the Market” column as a predictor of market fluctuations in the United States. A more recent paper is Shapiro et al. (2019), who show that sentiment shocks have a positive effect on consumption, output, and real interest rates.

We show that the intensity of reform chatter increases during economic downturns. This increase is more significant in democracies. We find that the relationship between democracy and reform chatter is causal, using instrumental variable techniques. We finally document that reform chatter is followed by actual reforms, suggesting that democracies benefit from a “self-correcting” mechanism stemming from media chatter.

Our paper is also related to the literature on media and economics. Besley and Burgess (2002) show that having a more informed and politically active electorate strengthens incentives for 2
governments to be responsive. Djankov et al. (2003) find that government ownership in media undermines political and economic freedom. Besley and Prat (2006) develop a model of democratic politics in which media capture is endogenous. The model offers insights into the features of the media market that determine the ability of the government to exercise such capture and hence to influence political outcomes. Our paper is the first to use media data to account for the intensity of and attitudes in reform chatter.

Lastly, our paper is linked to the literature on economic reforms and the political business cycle. Fernandez and Rodrik (1991) provide a theory to explain the “status quo bias” against efficiency enhancing reforms. In their model, individual-specific uncertainty about distributional gains creates a bias against reform. Early work on the political business cycle dating back to Nordhaus (1975) shows that macroeconomic cycles could be induced by electoral cycles. Brender and Drazen (2005) provide evidence that political budget cycles are stronger in weaker democracies (citizens in more established democracies do not get fooled by promises made by politicians). Our paper presents evidence on the importance of polity in mediating reform chatter over the business cycle including during crises.

The remainder of the paper is organized as follows. Section 2 presents the data. Section 3 lays out the empirical strategy and main results around the evolution of the intensity of reform chatter. Section 4 presents several extensions around sentiment analysis and the nexus between reform chatter and actual reforms. Section 5 concludes.

# **2. Data**

This section describes the main sources of data used in this paper. The discussion focuses first and foremost on reform chatter given the novel nature of the data, while other, more established, sources are discussed briefly.

# **2.1 Reform chatter**

We make use of text in the form of newspaper articles as our main data source. The data are extracted from Dow Jones FACTIVA via the Dow Jones Data, News and Analytics Platform. Dow Jones FACTIVA is a global repository of newspaper articles covering over one billion articles published in 28 different languages. The full list of languages is listed in Appendix Table A1. Beyond the date of publication and the text, Dow Jones provides several classifiers to navigate searches. These classifiers include language codes, subject codes, country/region codes, industry codes, and publisher information.

The search in each language is conducted so that at least one of the words relating to the economy and at least one relating to reforms appear. For example, in English, the precise query is: _[(“economy” OR “economic”) AND (“reform” OR “reforms”)]_ . This search is performed for each country and each language, over the period from January 1, 1980 to December 31, 2018. The search yields 9.3 million (unique) reform articles. In addition, we obtain the number of total articles 3
|Region|Reform Articles|Reform Articles|Total Articles|Total Articles|
|---|---|---|---|---|
||All Languages|English|All Languages|English|
|East Asia & Pacific<br>|3,500,314<br>|1,175,823<br>|352,563,913<br>|138,617,246<br>|
|Europe & Central Asia|4,122,797|1,410,412|738,535,949|222,592 653.|
|Latin America & Caribbean|763,613|204,056|57,263,777|15,266,344|
|Middle East & North Africa|479,158|334,911|55,675,457|27,709,664|
|North America|1,909,878|1,476,013|412,543,976|366,091,216|
|South Asia|394,358|350,026|61,500,403|56,818,065|
|Sub-Saharan Africa<br>|318,308<br>|244,649<br>|24,359,146<br>|16,992,789<br>|
|Total|11,488,426|5,195,890|=1,702,442,621|844,087,977|
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To capture the cyclical component of economic activity, we use annual growth in GDP at constant prices. The data are from the IMF’s World Economic Outlook, April 2018 update (IMF 2018). The data are available for 193 nations over the period 1980-2018. The identification strategy consists of instrumenting economic growth with commodity terms of trade shocks. Data on commodity prices are obtained from the IMF’s database for a basket of 45 commodities (see Gruss and Kebhaj, 2019).

To measure the level of democracy, we use the Polity IV database from the Center of Systemic Peace (Marshall et al., 2018). We use the following three measures: political competition, constraints to the executive and a composite measure of democracy. The first measure (POLCOMP) combines measures of the regulation of participation and the competitiveness of participation in the political arena. This variable takes values between 1 (suppressed) and 10 (institutionalized electoral) political competition. The second measure (XCONST) captures the extent to which there are executive constraints in a polity. The measure refers to the presence or absence of formal checks on the power of the executive branch. It takes values between 1 (unlimited authority) and 7 (executive parity or subordination). The third measure (POLITY2) captures the level of democracy. The composite index combines data on the (i) competitiveness and openness of executive recruitment, (ii) constraints on executive power, and (iii) political competition. This variable takes values between -10 (strongly autocratic) and +10 (strongly democratic).

We consider a country as having: i) strong (weak) political competition if the country’s average political competition index is greater or equal to (strictly lower than) 7; ii) strong (weak) executive constraints if its average executive constraint index is greater or equal to (strictly lower than) 5; and iii) a democratic (autocratic) political system, if the average Polity2 index is greater or equal to (strictly lower than) 0. For example, the United States is coded as having a high level of political competition, with an average score of 9.8 over the sample period 1980 to 2018. We find that 77 countries have strong political competition and 88 have weak competition; 79 countries have strong executive constraints and 86 have weak ones; there are 111 democratic countries and 55 autocratic ones.

In section 3 we use alternative cutoff points for the various measures. We also use an alternative measure of democracy from Freedom House.

Data on education attainments are from Barro and Lee’s (2013) data set, obtained from the 2018 Update July 4. Structural reforms are from Ostry et al. (2009). The data set captures reforms along 6 dimensions: (i) domestic finance; (ii) capital account (iii) current account transactions; (iv) trade (v) agricultural regulation (vi) network reforms. Each of these indicators is normalized to take values between 0 and 1, where higher values denote more liberalization. In our analyses, the variables are organized into three groups using principal component analysis, where the first principal component is used in the analysis. Indicators (i),(v),(vi) are grouped as 'domestic' indices; indicators (ii),(iii),(iv) are termed 'international' indices. Finally, an aggregate index is used. Note that reforms are defined as changes in the indices.

We also use Doing Business data from the World Bank (2020) to capture regulatory reforms. Doing Business benchmarks aspects of business regulation based on work on the regulation of 7
Aln(C In(ComPI ) ) A ComPI Aln(C In(ComPI ) A ComPI
where export commodity price shock is calculated as follows: ∆ln�R e e � = ∑ � (4) JJ denotes the international price of commodity RRλλII eett ii,tt jj=1 ωωjj × ∆l n�λλjj,tt _j_ in year _t_ . The weight, , is time-invariant and country-commodity specific. It is calculated as the average of a country’s gross exports of jj,tt jj commodity λλ _j_ over GDP over the sample period. The export commodity price shock index captures ωω the long-run exposure of a country to its commodity exports and the changes in international commodity prices. As most countries are price takers in any given commodity, the variation stemming from terms of trade is plausibly exogenous, and thus satisfies standard exclusion restrictions.

Columns (1) to (4) of Table 2 present the main results, using an ordinary least squares (OLS) specification. The coefficient on GDP growth is negative, indicating reform chatter tends to increase during downturns. Columns (5) to (8) present the two-stage least square (2SLS) regressions where GDP growth is instrumented by commodity terms of trade shocks. Column (5) shows that GDP growth does not systematically affect reform chatter. However, the results in columns (7) and (8) support the earlier findings. Economic downturns boost reform chatter significantly in more democratic countries, as well as in countries with strong executive constraints or strong political competition. The coefficient associated with the interaction between GDP growth and democracy implies that in response to a 1% decrease in GDP growth reform chatter in democracies goes up by 0.116 percentage points more than it does in autocracies. The coefficients associated with GDP growth are not significant for columns (5) to (8), implying that fluctuations in economic activity have no effect on reform chatter in autocracies.

The fact that reform chatter increases in democracies during economic downturns suggests that these countries have more public discussion about reforms when faced with a difficult situation. Political competition and other attributes of democracies provide the space for self-correction of government policy. Instead, in weaker democracies, the fact that reform chatter does not respond to economic activity suggests that there is little space to allow for corrections of policy.

The instruments used in the 2SLS are adequate: Appendix Table A2 presents the first stage regressions. Both lagged and contemporaneous commodity shocks are associated with higher GDP growth, with a stronger effect coming from the lagged commodity terms of trade shocks. Since the main regressions have two endogenous variables, the strength of the instruments is summarized by the multivariate F-statistic, as proposed by Cragg and Donald (1993). The statistics for each specification are reported in Table 2, in the row above Observations. The values exceed 10 in all specifications, implying that weak identification is not an issue in the IV framework. Appendix Table A3 shows the reduced-form regressions, where reform chatter is regressed against lagged and contemporaneous commodity terms of trade shocks and their interactions with democracy.

9
|Dependent variable:|o|Bi|@)<br>Reform|a |<br> Mentions|oOo<br>- All Lan|©<br>guages|(7)|Sy)|
|---|---|---|---|---|---|---|---|---|
|Estimation:||OLS (Full|Sample)|||25|L5||
|GDP Growth|-0.015°*" <br>(0.005)|= -10.002<br>(0.002}|-0.011°<br>=<br>(0.006}|-0.016"*<br>(0.008)<br>||0.063<br>(0.042)|0.037<br>(0.025)|0.037<br>(0.027)|0.040<br>(0.029)|
|GDP Growth X Democracy||<br>-0.045***<br>(0.015)||||-0.116°**<br>(0.037)|||
|GDP Growth X Strong Executive Constraints|||-0).064°°*<br>(0.017)||||-0_124°**<br>(0.043)||
|GDP Growth X Strong Political Competition||||-0).046°"<br>(0.016)||||-0_123***<br>(0.040)|
|YearFE|rs|é|rs|rs|rs|v|v|vw|
|<br>ISOS|vo|re|wo|ra|ra|ra|re|ve|
|C-D F Statistic<br>‘Observations<br>NumberofClusters|5,603<br>182|4,822<br>160|4,694<br>160|4.694<br>160|19.056,<br>5,603<br>182|11.462<br>4 822<br>160|13.293<br>4,004<br>160|13.018<br>4.604<br>160|

Notes: This table presents the main results wing OLS and the instrumental variable strategy. The dependent vanable im all columns ts the economic reforms article count in all languages normalized by the total number of articles. GDP growth is instrumented using contemporaneous and lagged changes in the groee export commodity price index, as described in the text. Standard errors are clustered at the country level and reported in parentheses, stars indicate *** po 0.01, ** p< 0.08, * p< 01.
country’s initial polity measures. Specifically, we compare a country’s Polity2 in 1990 with 0, executive constraints in 1990 with 5 and political competition in 1990 with 7. If the country’s initial polity measures are larger than the above-mentioned cutoffs, the countries are considered to have better polity. In Appendix Table A10, we use 5-year rolling average Polity. At year _t_ , a country is defined to have better polity if the country’s backward 5-year window Polity average (i.e. from year _t-1_ to _t-5_ ) is larger than the above-mentioned cutoffs. This is to address the potential evolution of countries’ Polity, such as the democratization of many Eastern European and Latin American countries in the 1990s. Finally, we also use a Democracy measure from Freedom House. Our results show that the finding that economic downturns cause reform chatter to increase more in democracies holds.

Our results hold when we consider the higher income and lower income countries separately. Appendix Table A11 presents the results for high and upper-middle income countries, while Appendix Table A12 presents the results for low and lower-middle income countries. Within each income group, reform chatter responds more to downturns in countries with better polity. The results are stronger and more statistically significant for the low and lower-middle income countries, suggesting the overall results are driven primarily by poorer countries.

The results remain robust when crisis years are removed (Appendix Table A13). This robustness check confirms that our results are not driven by crisis years or extreme growth shocks. We also show that the results are not driven by the degree of press freedom. Appendix Table A14 shows that when Press Freedom is included, the interactions of executive constraints and political competition with GDP growth remain highly significant. However, the interaction of the aggregate index Polity2 (Democracy) becomes insignificant. We can conclude that our main results are not affected solely by press freedom but rather by some elements of democracy.

Our main results are not driven by human capital either. Appendix Table A15 shows that while high human capital does make reform chatter increase more in bad times, human capital does not crowd out the effects stemming from a higher level of democracy.

An important caveat is that negative economic shocks are likely to affect media coverage of all economic topics, not specifically economic reforms. It may even be that, as a share of _economic news_ , the number of articles discussing reform decreases in response to economic downturns. To address this concern, we focus on the corpus of articles in Factiva categorized as “Economic News”. Table A16 presents the IV results using this alternative dependent variable. The coefficients on all the interactions are negative and highly significant, suggesting that reform chatter increases even within the sub-sample of economic news articles. In contrast, reform chatter decreases in less democratic countries in response to negative economic shocks.

11
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Notes: This figure present the distribution of the maim sentiment index measure. This measure captures the average sentiment in every country-year across all sources and languages. It is constructed using the polyglot project lexicons of positive and negative words.
<!-- Start of picture text --> aa 4 ° oo oo;<br>af id , by \. AX VEL —<br>z o = te i nf : od 7 ‘<br>BE ly og * a Ww<br><V\‘<br>Ooo¥ 4 ‘<br>V .<br>. a Pay, . : Sm =<br>* oe oo ae<br>Co<br>1980 1990 2000 2010 2020<br>year<br>—s— East Asia & Pacific == -=- Europe & Central Asia<br>-cER---- Latin America & Caribbean — 4—-- Middle East & North Africa<br>—-~—— North America -— -+- - South Asia<br>° Sub-Saharan Africa<br><!-- End of picture text -->
||7|@|@|@®||e|©|@|©)|
|---|---|---|---|---|---|---|---|---|
|Dependent variable:||||Sentime|nt Index||||
|Estimation:||OL|S|||2|5L5||
|GDP Growth|O.0lae*<br>(0.004)|O.007*<br>(0.003)|0.014"<br>(0.006)|0.012%" |<br>(0.005))|0.025<br>(0.018)|0.023<br>(0.015)|0.028<br>(0.018)|0.030<br>(0.019)|
|GDP Growth X Democracy||0.018***<br>(0.005)||||-0.040"*<br>(0.019)|||
|GDP Growth X Strong Executive Constraints|||0.010<br>(0.007)||||-0.066°**<br>(0.021)||
|GDP Growth X% Strong Political Competition||||0.014**<br>(0.007)||||-0.055***<br>(0.020)|
|CountryFE|v|v|w|v|v|ri|v|v|
|<br>Year FE|we|rs|‘A|‘a|vw|rs|vw|rs|
|C_D<br>F<br>Statistic<br>Observations<br>|3,602<br>|4,822<br>|4.694<br>|4,694<br>|19.067<br>5,602<br>|11.462<br>4,822<br>|13.293<br>4.694<br>|13.018<br>4.694<br>|
|NumberofClusters|182|160|160|160|182|160|160|160|

Notes: Standard errors are clustered at the country level and reported in parentheses, stars indicate *** p < O01, ** p< 0.05, * p< 01.
News" by Dow Jones to limit the potential noise stemming from the large pool of articles used initially (Appendix Table A17). The results remain robust and highly significant in this subsample, further supporting the results on the dynamics of reform attitudes presented in this section.

Next, we explore whether reform chatter is associated with reforms. Actual reforms are difficult to measure. Data on structural reforms for a large sample of countries are drawn from Ostry et al. (2009) and the World Bank’s Doing Business project. The results presented in this subsection are mere associations. However, the strong relationship between reform chatter and actual reform suggests that reform chatter is not “cheap talk”.

The main specification for testing the relationship between reform chatter and actual reforms is: R R R R R R R ii,tt SSaaRRcca ccRRa ss ii tt ii,tt ii,tt ii ii,tt R R = ααR + ββ captures the changes in the principal component of domestic, international + λλR RRha aaR + γγR RRha aaR × λλR RRaaRRa cc + εε and aggregate indices. and are country and time fixed effects. The main coefficients of ii,tt interest are SSaaRRcca ccRRa and , which highlight the potential heterogeneity in the impact of reform chatter on ss ii tt actual reforms in the data. αα ββ λλ γγ Using data on structural reform (Ostry et al. 2009), Appendix Table A20 Panel A shows that reform chatter at time _t_ is associated significantly with more domestic reforms (Column 2) but neither international reforms (Column 3) nor composite reforms (Column 1). However, Table A20 Panel B show that for more democratic countries, reform mentions are significantly associated with the composite index of actual reforms (see Columns 1-3). Columns 4-6 in Panel B show that the results are not significant for international reforms. Columns 8-9 in Panel show that domestic reforms are associated with reform chatter in democracies, though the result in Column 7 is not significant when using the democracy index.

The attitudes towards reform presented in Table A21 are associated with actual reform. In Panel B, columns 1-3 reveal that there is no heterogeneity alongside polity. These results suggest that reform attitudes are associated with actual reforms—when considering intensity of reform chatter the association with actual reform holds more in democracies.

15
||(1)|(2)|(4)|(4)|
|---|---|---|---|---|
|Dependent variable:||1 Any D|B Reform||
|Reform Articles:||All S|ources||
|Reform Index,|0.030*<br>(0.016)|O.046**<br>(0.021)|0.050°"<br>(0.021)|0.054**<br>(0.022)|
|Reform Index; X Strong Dempcracy||-0,003<br>(0.034)|||
|Reform Index, X Strong Executive Constraints|||-0.015<br>(0.036)||
|Reform Index; X Strong Political Competition||||-0.026<br>(0.036)|
|Country FE<br>|il<br>|a<br>|v<br>|v<br>|
|Year FE|v|a|ri|"i|
|R<br>a<br>Mean of Dependent Variable<br>Observations<br>;<br>NumberofCountries|0.267<br>0.579<br>2.807<br>188|0.249<br>0.623<br>2271<br>162|0.240<br>0.632<br>2,167<br>162|0.240<br>0.632<br>2.167<br>162|

|Dependent variable:<br>Reform Articles:|(1)|(2)<br>1 Any D<br>All So|(3)<br>B Reform<br>urces|(4)|
|---|---|---|---|---|
|Reform Index,_;|0.034"<br>(0.017)|0.080"<br>(0.036)|0.052<br>(0.035)|0.059°<br>(0.032)|
|Reform Index,_,; X Strong Democracy||-0).057<br>(0.044)|||
|Reform Index;_; X Strong Executive Constraints|||-0.027<br>(0.047)||
|Reform Index;, X Strong Political Competition<br>||||-0.047<br>(0.046)<br>|
|Country FE|vf|i|a|v|
|Year FE|f|i|i|‘i|
|R<br>Mean of Dependent Variable<br>Observations<br>;<br>NumberofCountries|0.268<br>0.579<br>2807<br>188|0.250<br>(0.623<br>2.3991<br>162|0.240<br>0.632<br>2167<br>162|0.240<br>0.632<br>2,167<br>162|
||Oo|@|@|@|
|---|---|---|---|---|
|Dependent variable:<br>Estimation:||1 Any D<br>O|B Reform<br>LS||
|Sentiment Index;|O.066***<br>(0.025)|0.129%"<br>(0.055)|0.129"*<br>(0.044)|0.126%**<br>(0.045)|
|Sentiment Index, X Democracy||-0.004<br>(0.066)|||
|Sentiment Index, X Strong Executive Constraints|||-0.015<br>(0.065)||
|Sentiment Index; X Strong Political Competition||||-0).007<br>(0.063)|
|Country FE<br>|il<br>|wf<br>|wf<br>|a<br>|
|Year FE|il|wf|wf|a|
|R<br>Mean of Dependent Variable<br>Observations<br>;<br>NumberofCountries|0.269<br>0.579<br>2,807<br>188|0.256<br>0.623<br>2,221<br>162|0.246<br>0).632<br>2,167<br>162|0.246<br>0.632<br>2,167<br>162|