metadata: authors: 0: author: Olper,Alessandro
1: author: Swinnen,Johan F. M.
count: World
docna: 0: docna: Mass media and public policy :  global
            evidence from agricultural policies
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Taxation & Subsidies,Economic Adjustment and Lending,Social Analysis,Quality of Life & Leisure,National Governance,Government Policies,Public Sector Economics,Food & Nutrition Policy,International Trade and Trade Rules,Youth and Governance,Food Security,Public Finance Decentralization and
            Poverty Reduction
teratopic: Culture and Development,Public Sector Development,Macroeconomics and Economic Growth,Social Protections and Labor,Health, Nutrition and Population
abstracts: cdata!: Mass media play a crucial role in
            information distribution and in the political market and
            public policy making. Theory predicts that information
            provided by the mass media reflects the media's
            incentives to provide news to different groups in society
            and affects these groups' influence in policy making.
            The paper use data on agricultural policy from 69 countries
            spanning a wide range of development stages and media
            markets to test these predictions. The empirical results are
            consistent with theoretical hypotheses that public support
            for agriculture is affected by the mass media. In
            particular, an increase in media (television) diffusion is
            associated with policies that benefit the majority to a
            greater extent and is correlated with a reduction in
            agriculture taxation in poor countries and a reduction in
            the subsidization of agriculture in rich countries, ceteris
            paribus. The empirical results are consistent with the
            hypothesis that increased competition in commercial media
            reduces transfers to special interest groups and contributes
            to more efficient public policies.
repnme: repnme: Mass media and public policy :  global
            evidence from agricultural policies
display_title: Mass media and public policy :
            global evidence from agricultural policies
listing_relative_url: /research/2013/02/17292999/mass-media-public-policy-global-evidence-agricultural-policies
topicv3: Agriculture and Food Security m1326205 435,Political Economy of Reform m1326263 836,Agricultural Policies m1326884 170,Trade m1326924 698,Taxation m1327615 1121
publishtoextweb_dt: 2013-02-01T00:00:00Z
historic_topic: Culture and Development,Public Sector Development,Macroeconomics and Economic Growth,Social Protections and Labor,Health, Nutrition and Population
no_of_pages: 48
author_keys: 0: author_key: 958669
1: author_key: 479937
model_extractions: WPS6362 Policy Research Working Paper 6362 Mass Media and Public Policy Global Evidence from Agricultural Policies _Alessandro Olper Johan Swinnen_ The World Bank Development Economics Vice Presidency Partnerships, Capacity Building Unit February 2013
Policy Research Working Paper 6362

# **Abstract**

Mass media play a crucial role in information distribution and in the political market and public policy making. Theory predicts that information provided by the mass media reflects the media’s incentives to provide news to different groups in society and affects these groups’ influence in policy making. The paper use data on agricultural policy from 69 countries spanning a wide range of development stages and media markets to test these predictions. The empirical results are consistent with theoretical hypotheses that public support for agriculture is affected by the mass media. In particular, an increase in media (television) diffusion is associated with policies that benefit the majority to a greater extent and is correlated with a reduction in agriculture taxation in poor countries and a reduction in the subsidization of agriculture in rich countries, ceteris paribus. The empirical results are consistent with the hypothesis that increased competition in commercial media reduces transfers to special interest groups and contributes to more efficient public policies.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at alessandro.olper@unimi.it and Jo.Swinnen@econ.kuleuven.be.

_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
# Mass Media and Public Policy:

# Global Evidence from Agricultural Policies

_Alessandro Olper and Johan Swinnen_<sup>_1_</sup>

## **JEL classification:** D72, D83, Q18

**Keywords:** Mass Media; Information; Agricultural Protection; Political Economy

**Sector Board:** Public Sector Governance (PSM)

1 Alessandro Olper is an associate professor at the University of Milano, Italy, and a researcher at Centre for Institution and Economic Performance; his email address is <u>alessandro.olper@unimi.it. Jo Swinnen is a professor of economics and director of Centre</u> for Institution and Economic Performance at the University of Leuven, Belgium, and visiting professor at Stanford University, United States. His email address is <u>Jo.Swinnen@econ.kuleuven.be.</u> This work was supported by the KU Leuven Research Council (Methusalem project). The authors would like to thank the participants in several conferences and meetings, the editor, and three anonymous referees for the _World Bank Economic Review_ for useful comments and suggestions.
There is a rapidly growing body of literature on the economics of the mass media. This literature has led to a series of important new hypotheses and insights in an area that has been long neglected by economists.<sup>2</sup> An important strand of this literature concerns the role of mass media in political markets and its effect on public policy making. Most of this literature on the relationship between the mass media and public policy is theoretical. A few empirical studies have attempted to assess the effect of media on policy outcomes. Some key findings from this literature suggest that access to mass media empowers people politically and, as such, increases the benefits they receive from government programs (Strömberg and Snyder 2008). This influence has been found for different types of government programs and different countries, such as unemployment relief in the United States (Strömberg 2004b), public food provision and disaster relief in India (Besley and Burgess 2001, 2002), and educational spending in Uganda and Madagascar (Reinikka and Svensson 2005; Francken et al. 2009). All of these studies measure the effect within a single country, which has the benefit of holding many other factors fixed but has the potential disadvantage of limited variation in policy and media.

Our paper seeks to contribute to this empirical literature by analyzing the impact of mass media on policy making for a specific type of policy across a wide variety of countries and years. We use a new dataset from the World Bank that includes measures of agricultural subsidization and taxation for a much wider set of countries and a longer period of time than has been previously available (Anderson and Valenzuela 2008). We use these data as the dependent variable.

2 See McCluskey and Swinnen (2010) and Pratt and Strömberg (2011) for reviews.

2
Agricultural policy (subsidization or taxation) is an excellent policy instrument to study the impact of media on policy choice across a wide variation of countries for both empirical and theoretical reasons. Empirically, agricultural policy is an important policy for governments in both rich and poor countries. In poor countries where agriculture is a very important share of the economy and food is a major consumption item, the importance of agriculture as a public policy issue is obvious. However, in rich countries, agricultural policy remains disproportionately important compared to the relatively small share of agriculture in terms of economic output. For example in the European Union, the Common Agricultural Policy continues to absorb 40 percent of the entire European Union budget. Another symptom of the continued importance of agricultural policy for rich countries is the impasse in the current WTO negotiations, where disagreements over agricultural policies threaten to undermine the entire WTO agreement.

Another empirical factor is the substantial ad hoc and case study evidence that mass media can play an important role in influencing agricultural policy. Several studies have highlighted the important role of mass media in influencing voters and government policy on key recent agricultural and food policies, such as the use of genetically modified organisms (Curtiss et al. 2006; Marks, et al. 2003; Vigani and Olper 2012), policy reactions to food safety crises (Swinnen et al. 2005; Verbeke et al. 2000), and trade disputes (Kuzyk and McCluskey 2006; Swinnen and Francken 2006).

Agricultural policy is also an interesting case from a theoretical perspective. The literature on the political economy of agricultural policy identifies group size (the number of farmers versus the number of food consumers in the economy) as an important causal factor. Group size is argued to play an important role because it affects collective action 3
costs (based on Olson 1965) and affects the per capita costs and benefits of agricultural policy, which, in turn, affects political outcomes in the presence of voter information costs (based on Downs 1957) or when political activities are proportional to the size of potential policy costs and benefits (Swinnen 1994). Recent papers in the media economics literature claim that the mass media can play an important role in public policy by altering these political economy mechanisms (Stromberg 2001, 2004a; Kuzyk and Mc Cluskey, 2006). Oberholzer-Gee and Waldfogel (2005) argue that the link between group size and political mobilization depends on the structure of media markets. In a series of influential papers, Strömberg (2001, 2004a) has shown that competition among mass media outlets leads to the provision of more news/information to large groups such as taxpayers and dispersed consumer interests, altering the trade-off in political competition and thus influencing public policy. He refers to this outcome as “mass media-competition-induced political bias.” The purpose of our paper is to evaluate whether the mass media have an impact on the political economy of agricultural policies by exploiting taxation and subsidization data from 69 countries that were observed from 1960 to 2004. The paper also contributes to an emerging body of literature analyzing whether the diffusion of free and independent media are key ingredients in more efficient public policies. Besley and Burgess (2001, 2002) use a political agency model to demonstrate that a more informed and politically active electorate increases the incentives for a government to be responsive. Prat and Strömberg (2005) find for Sweden that people who begin watching commercial television (TV) news programs increase their political knowledge and participation. Overall, this and other evidence (such as Besley and Burgess 2001, 2002; Francken et al. 2009, 2011; 4
Reinikka and Svensson 2005; Strömberg and Snyder 2008) support the notion that the mass media reduce the power of special interest lobbies relative to unorganized interests.

The paper also contributes to the literature on the political economy of agricultural policies. There is extensive literature, both theoretical and empirical, on the determinants of agricultural policy making (see de Gorter and Swinnen 2002 and Swinnen 2010 for surveys), but no previous study has examined the role of the media in this process. Our paper is the first to do so.

Our analysis, which exploits both cross-country and time-series variation in the data, indicates that the mass media may have a substantive impact on public agricultural policy. In the developing world, agricultural taxation is reduced by the presence of mass media outlets, whereas agricultural support is reduced in developed countries. Our results thus suggest that competition in the media market reduces policy distortions in agricultural markets.

# **_I. CONCEPTUAL FRAMEWORK_**

In this section, we first present a conceptual framework based on Strömberg’s (2004a) theory of mass media and political competition. Then, we discuss the main implications of the model in light of the worldwide characteristics and regularities of agricultural policies, and we identify testable hypotheses regarding the effect of mass media competition on agricultural policy outcomes.

# **_The basic theory_**

In Strömberg’s (2004a) model, mass media outlets affect public policy because they provide the channel through which politicians convey campaign promises to the 5
electorate. Political parties make binding announcements regarding the amount of public money they plan to spend on various government programs. The two parties propose public spending with the objective of maximizing their vote shares within a given budget constraint.

Commercial media<sup>3</sup> are the only channel through which the parties’ platforms are announced to the voters. Media firms allocate a certain quantity of information on political platforms and proposed spending levels with the objective of maximizing their audiences (number of readers/viewers), all of which are voters. Voters purchase media products (e.g., newspapers) and adjust their expectations of how much the parties will spend on the basis of information provided by the media. They then vote for one of the parties. The party that wins the election implements the promised spending plan.

Voters value news related to political platforms in the media because it allows them to maximize the benefits they receive from government programs. It is assumed that readers (voters) use the news they receive from the media to decide on private action, which affects the value they realize from a government program. More precise news about future policies makes it more likely that readers will take the correct private actions. For example, early news about changes in agricultural subsidies help farmers to produce the correct crops to realize the full value of these subsidies.

3 In his theoretical derivations, Stromberg (2004a) refers to the media as “newspapers” but explains that for the purposes of the main points of his analysis, the cost and revenue structures of TV and radio are similar in the relevant aspects.

6
Voters purchase the media product that provides them with the most information on the government programs they value, conditional on other (exogenous) characteristics, such as ideological preferences.

The media maximize expected profits. They have two types of costs: “first copy costs,” or the cost of producing one unit of news space, and “reproduction and distribution costs,” or the average cost of reproducing and delivering the media product to a certain audience. This cost function is consistent with the notion that there are roughly constant long-run marginal costs in distributing TV and radio news (or in printing and delivering newspapers). The revenue of the media includes the price they can charge for their product plus the price per reader/viewer paid by advertisers. This structure of the media industry implies that different groups receive different media coverage. News items of interest to small groups and groups with limited attractiveness for advertisers receive less coverage.

This bias in the mass media’s news coverage translates into policy bias. As media coverage of different issues changes, the efficiency with which politicians can reach different groups with campaign promises also changes. If a party promises a group that receives very little news coverage that it will raise spending, only a small fraction of the voters who would benefit become aware of the promise. Therefore, a spending promise to this group will not win many votes for the party. Consequently, this group of voters will not attract many favorable policies. Instead, promising to raise spending for groups that attract substantial media coverage (for example, because they are large groups or because the groups are more valuable to advertisers) will lead to a stronger voter response and thus to policies that are more favorable to these groups.

7
# **_Implications for agricultural policy_**

What does this theory imply for the impact of mass media on agricultural policy?<sup>4</sup> The most important stylized fact about agricultural policy is the so-called development paradox, the policy switch from the taxation to the subsidization of agriculture associated with economic development (Anderson and Hayami, 1986; Anderson, 2009). The classic interpretation of this pattern is that when a country becomes richer, farm groups, compared to consumer and taxpayer groups, become more effective in collective action situations as the number of farmers declines and development reduces communication and transportation costs. Both factors reduce organizational costs and free rider problems in collective action situations (Olson, 1965). Moreover, because the per capita cost experienced by the rest of society falls with fewer farmers, the opposition of taxpayers and consumers to (agricultural) subsidies decreases as the number of farmers decreases as a result of economic development (Becker 1983; Swinnen 1994; Anderson 1995).

The model developed here suggests that the relationship between agricultural policy and economic development will be affected by the introduction of media competition in the political market. Voter preferences and government policies will be affected by how the media industry provides information to citizens.

4 Stromberg’s (2004a) basic model has a fixed budget constraint. This assumption is later relaxed (see section 3.2 and footnote 16 of his paper) to allow for government programs, such as taxes and subsidies, which influence the budget itself. Endogenizing the government budget in this extended model does not affect the main results (or our hypotheses).

8
One key prediction of the model is that, ceteris paribus, government transfers such as agricultural protection should be biased toward large groups as an effect of media competition. Because the agricultural group (the number of farmers) is relatively large in poor countries and relatively small in richer ones, an important implication of the model is that, ceteris paribus, the effect of media competition on agricultural policy should differ in poor versus rich countries. More specifically, we expect that the impact on agricultural protection induced by mass media competition should be positive in poor countries and negative in rich countries. Thus, we can formulate the following empirical prediction.

Hypothesis 1 (Group size effect): _Mass media competition-induced political bias will reduce agricultural protection in rich (developed) countries but will increase agricultural protection in poor (developing) countries, ceteris paribus_ .

Another prediction of the model is that, ceteris paribus, government transfers will be biased toward groups that are more attractive to advertisers. Stromberg (2004a) refers to the case of the show _Gunsmoke_ , which was cancelled despite its high ratings because its audience was perceived as “too old and too rural” to be of much interest to advertisers.

The implication for agricultural protection is not obvious because “being attractive to advertisers” may apply to many things, including age (young people are more easily influenced by advertisements than older people), income (richer people have more money to spend), and so forth. The latter argument would imply that mass media 9
competition would induce government transfers to be biased toward _relatively_ richer groups who have more income to spend and are therefore more attractive to advertisers. Nevertheless, the implication for agricultural protection is not trivial. The relationship between economic development and the rural-urban income gap is the subject of some debate. Recent papers have argued that this relationship is nonlinear, with a relatively small gap in very poor countries (as incomes in both urban and rural areas are very low), urban incomes rising relative to rural incomes when countries grow, and the income gap narrowing again at high income levels (see Hayami 2007; McMillan and Rodrik, 2011). This nonlinear relationship would also follow if the spread of TV were uneven between urban and rural areas, reflecting the differences in income (demand for TVs). There would be a relatively small gap in very poor countries (as TV distribution in both urban and rural areas is very low) and a rise in urban TV distribution relative to rural TV distribution when countries grow, with the gap in TV distribution narrowing again at high income levels. In either case, this channel would add a nonlinear relationship between average incomes and media effects, with a prourban media bias effect that would be strongest at medium income levels.

However, a problem with using observed (sectoral) income to document this nonlinear relationship between the rural-urban income gap and overall income is that the observed sectoral income levels are obviously affected by the policies themselves. With occasionally very large subsidies or taxes, these policy transfers clearly affect the relative income measures. With transfers going from rural to urban areas in poor countries (and vice versa in rich countries), we would expect the pretransfer rural-urban income ratio to be higher (lower) than that observed in poor (rich) countries. In medium income 10
countries, the transfers are relatively lower; thus, the observed income ratio is closer to the pretransfer ratio.

This situation would imply that the expected “advertiser-value effect” of media competition on agricultural protection should be as follows: small in (very) poor countries, because pretransfer rural and urban incomes are similar, and low and negative in rich and emerging countries, where pretransfer urban incomes are much higher than rural incomes. Thus, we can formulate the following empirical prediction (conditional on an observed nonlinear relationship between the rural-urban income ratio and economic development).

Hypothesis 2 (Advertiser value effect): _Mass media competition-induced political bias will reduce agricultural protection in rich (developed) and emerging countries and will have little effect in poor (developing) countries, ceteris paribus_ .

In combination, these two effects imply that the total media effect will increase agricultural protection (or reduce agricultural taxation) in poor countries owing to the group size effect, reduce agricultural protection in emerging countries owing to the advertiser value effect, and more strongly reduce agricultural protection in rich countries owing to the reinforcement of group size and advertiser value effects.

The next sections present the data and empirical strategy used to test the hypotheses .

11
# **_II. DATA AND EMPIRICAL SPECIFICATION_**

We test our predictions on annual observations from a sample of approximately 70 developing and developed countries from all continents. Overall, we employ a panel of more than 2,000 observations, but the panel structure is unbalanced. Specifically, for a few developed countries, the starting year is approximately 1960; for the majority of the sample, the starting year is approximately 1970; and for transition countries, it is approximately 1992. The last year of observations is 2004. Table S.1 (in the supplemental appendix, available at http://wber.oxfordjournals.org/) reports the full list of countries with data and the 1970 and 2002 values of key policy and media variables.

# **_Dependent variables_**

Our dependent variables are measures of agricultural protection. As an indicator of agricultural taxation and subsidization, we use the _relative rate of assistance_ ( _RRA_ ) to agriculture, taken from the Agricultural Distortions Database from the World Bank (see Anderson and Valenzuela 2008 for details). The _RRA_ index is calculated as the ratio between the agricultural and nonagricultural nominal rates of assistance: where _NRAag_ is the nominal rate of assistance to agriculture and _NRAnonag_ is the nominal assistance to nonagricultural sectors. The _NRAag_ measures total transfers to agriculture as a percentage of the undistorted unit value. It is positive when agriculture is subsidized, negative when it is taxed, and zero when net transfers are zero. The _NRAag_ at the agricultural level is obtained as the weighted average of assistance at the product level using the undistorted value of production as a weight. It includes a wide range of policies, such as the assistance provided by all tariff and nontariff trade measures applied to 12
agricultural products and any domestic price-distorting measures. In addition, the price equivalent of any direct interventions on inputs is included.

One advantage of using _RRA_ (instead of _NRA_ ) as the dependent variable is that, especially in developing countries, an important indirect source of agricultural taxation is trade protection for the manufacturing sector as a component of import-substitution policies. Thus, _RRA_ is a more useful indicator in an international comparison of the extent to which a country’s policy regime has an anti- or proagricultural bias. However, as a robustness check and to assess whether changes in agricultural policy or industrial protection are important elements in the media-protection relationship, we ran a series of additional regressions using the _NRA_ as the dependent variable.

# **_Mass media variables_**

To test our hypotheses, we use the penetration of TV sets as an indicator. More specifically, our variable is the natural logarithm of TV sets per 100 inhabitants, based on data from the Arthur S. Banks Cross National Time-Series Data Archive, supplemented by UNESCO Statistics on TV and data from the International Telecommunication Union (2010).<sup>5</sup> 5 Both the Arthur Banks and the International Telecommunication Union data are based on data originally collected by UNESCO. These data are collected annually from 1970 onward. From 1960 to 1970, the data were collected every five years. Thus, for that period, we only use data for 1960, 1965, and 1970 without any interpolation. A potential limitation of these data lies in the fact that some countries have a licensing scheme whereby TV sets (or radios) must be registered. Because households may have more than one TV receiver or may not register, the number of licensed receivers may understate the true number of TVs and 13
The rationale for using this proxy is that, although the share of informed voters is not observed, we can observe the share of media users. Because both move together, it is sufficient to examine the levels and changes in the share of media users to test the effect of media bias (see Strömberg 2004b). Moreover, in our specific context, another justification for the use of these indicators is derived from Strömberg’s (2004a, p. 266) argument that “the emergence of broadcast media increased the proportion of rural and low-education media consumers as it became less expensive to distribute radio waves than newspapers to remote areas, and as these groups preferred audible and visual entertainment to reading. As politicians could reach rural and low-education voters more efficiently, the model predicts an expansion in programs that benefits these voters.”

# **_Income and group size variables_**

The hypotheses advanced in section II imply that the relationship between the _media_ variable and _RRA_ ( _NRA_ ) is conditional on the level of development (income), partially due to group size effects. As an indicator of development (income), we use real per capita GDP in purchasing power parity ( _gdppc_ ) taken from the Penn World Tables.<sup>6</sup> The most direct indicator of (relative) group size is the share of agricultural employment, _emps_ , based on FAO data. However, as is well known, both are strongly correlated because the agricultural employment share decreases with economic development. Therefore, _gdppc_ is itself an indicator of relative group size.

radios. Our identification strategy exploits the within-country variation in the data (see section 5). As long as the number of licenses and of TVs/radios display similar growth paths, this limitation of the data should not pose a major problem.

> 6 Specifically, we use the variable _rgdpch_ from the Penn World Tables, version 6.3.

14
In our basic specifications, we use _gdppc_ as the primary indicator for the conditional effect of media on agricultural protection because the employment share data are of poor quality, which precludes a consistent comparison across countries and over time, especially for developing countries (see Timmer and de Vries 2007 for a discussion). One reason for the poor quality is the differences in national definitions of “agricultural labor.” Another reason is that the yearly “observations” in the FAO labor statistics are linear interpolations between census data collected once every decade. We understand that there are also problems with the measurement of _gdppc_ because national accounts data are noisy over short time horizons (see Deaton 2005), but we believe the data problems are less important for _gdppc_ than for _emps_ . However, we perform a series of robustness checks using _emps_ as indicator.

Both variables (the level of development and employment share) are also included as control variables because both have been identified as major determinants of agricultural protection outside the media effect.

# **_Other control variables_**

In addition to the variables discussed above, in the empirical specifications, we include controls that are likely to affect the level of agricultural protection, as suggested by previous studies. Standard control variables in studies on the political economy of agricultural policies are indicators of comparative advantage, trade status, terms of trade effects, and political institutions (see Olper 2007; Swinnen 2010; Olper and Raimondi 2012). To control for comparative advantage, we include agricultural land per capita, _landpc_ , and the agricultural export share, _exps_ , measured as net exports over production. These two variables are based on data from FAO and the World Bank’s Agricultural 15
Distortions Database. Because of the possibility that governments set agricultural protection to exploit terms of trade effects, we also control for country size using the log of population, _logpop_ . We proxy for political institutions by adding the Polity2 index of democracy taken from the Polity IV database (Marshall and Jaeggers 2007).<sup>7</sup> In addition to these standard control variables, we use a series of covariates to check the robustness of our findings. They include two different indicators of (aggregated) openness: the ratio of trade to GDP from the Penn World Tables and the Sachs-Warner index of openness as defined by Wacziarg and Welch (2008).<sup>8</sup> We use government consumption to GDP from the Penn World Tables as a proxy for government size. Finally, because economic crises may trigger policy reforms, we add two lags of a crisis variable measured with a dummy equal to one for every year that the real GDP per capita growth rate is negative (zero otherwise). Table 1 presents summary statistics for the variables described above.

7 The Polity2 index assigns a value ranging from −10 (autocracy) to +10 (democracy) to each country and year, with higher values associated with stronger democracies.

8 Wacziarg and Welch (2008) updated the Sachs and Warner index and exploited its time dimension. The index is equal to one when a country is considered open and zero otherwise. Thus, it captures reforms in the overall trade policy.

16
# **_III. DESCRIPTIVE STATISTICS AND ANALYSIS_**

Figures 1 and 2 present trends in the agricultural policy indicators ( _RRA_ and _NRA_ ) and the media variable (number of TVs per 100 inhabitants) for the period from 1970 to 2004 for selected countries. Table S.1 in the online appendix presents the figures for 1970 and 2002 for all countries in the dataset. The figures reveal that there is substantive variation in all indicators both over time and between countries.

Figure 1 illustrates the stylized fact that _RRA_ and _NRA_ are higher in rich countries (Canada and Germany) than in poor countries (India and Kenya). Support for farmers was particularly high in Germany, with an _RRA_ between 50 percent and 100 percent for much of the period. The level of taxation faced by farmers was particularly severe in India before 1980, with _RRA_ figures of approximately 50 percent. Figure 1 also shows that the variation in _NRA_ and _RRA_ between countries has declined over time, with agricultural taxation declining ( _RRA_ increasing) in poor countries and agricultural subsidies for agriculture falling ( _RRA_ decreasing) in rich countries. On average, the _RRA_ for the full sample increased from approximately 0 percent in the 1970s to approximately 20 percent in the 1980s, subsequently declining to approximately 10 percent in 2000 (the _NRA_ values differ, but the patterns are similar).

Figure 2 illustrates the differences in TV penetration across countries and over time. The number of TVs increased everywhere over time, but, not surprisingly, the numbers are much higher in rich countries than in poor ones. The number of TVs increased from approximately 35 per 100 inhabitants in 1970 to approximately 60 in Germany and 75 in Canada by 2000. In the poor countries, the number of TVs was nearly 17
zero until the mid-1980s. It remains very low in Kenya. The increase is somewhat more rapid in India, reaching approximately 10 per 100 inhabitants in 2000.

A comparison of figures 1 and 2 suggests a different relationship between agricultural policies and the spread of mass media in rich and poor countries: for poor countries (such as Kenya), the _RRA_ increased over the 1970–2004 period, and mass media also grew. In contrast, for rich countries (such as Germany), the _RRA_ declined, whereas mass media continued to grow.

To further analyze this relationship between mass media and agricultural policies, table 2 reports simple pair-wise correlations between the media variable and the agricultural protection indicators for different levels of development (organized as percentiles of per capita GDP). The pair-wise correlations are consistent with the suggestions from figures 1 and 2. The correlation coefficient is approximately +0.30 for percentiles below the median (<50 percent). For per capita GDP values above the 75th percentile, the correlation becomes negative, and increasingly so, reaching a value below −0.60 at the 95th percentile. These correlations are consistent with hypotheses 1 and 2: there is a different relationship between agricultural policies and the spread of mass media in rich and poor countries. For poor countries, the spread of mass media is associated with an increase in _RRA_ ( _NRA_ ), whereas in rich countries, the growth of mass media is associated with a decline in _RRA_ ( _NRA_ ).

Of course, these are merely descriptive statistics. For a more formal analysis, we now turn to an econometric analysis.

18
# **_IV. ECONOMETRIC STRATEGY AND IDENTIFICATION ISSUES_**

Our hypotheses suggest that in countries with low _gdppc_ , the media variables, and _RRA_ should be positively related, and when _gdppc_ is high, there should be an inverse relationship between these variables. A priori, we do not know at what level of _gdppc_ the relationship changes sign. By using a general specification, we can derive the _gdppc_ value at the turning point, if any, from the estimated coefficients: where _RRAit_ measures the relative rate of assistance in country _i_ and year _t_ , _mit-1_ refers to the one-year lagged media variable, and _xit-1_ is a vector of additional controls. Taking the partial derivative of _RRA_ with respect to the media variable, we obtain Given our hypotheses, we expect that α 1 > 0 and α 2 < 0, such that α 1 + α 2 _gdppc_ is positive (negative) as _gdppc_ is higher (lower) than _gdppc_ *, with _gdppc_ * = α 1/– α 2 the level of development at which our media-protection relationship changes sign. We refer to this as the “turning point.” Note that a key requirement for the predictions to hold is that _gdppc_ * should lie within the range of _gdppc_ values in the dataset.

Regarding identification, our main concern is omitted variable bias. If the media variables are correlated with unobserved determinants of the protection level, our estimates will be inconsistent. Note that, a priori, the direction of the bias is not predictable. Therefore, our basic specification always includes a set of country ( η _i_ ) and year fixed effects ( θ _t_ ): 19
(2) _RRAit_ = α 1 _mit_ − 1 + α 2 ( _mit_ − 1 × _gdppcit_ − 1) + α 3 _gdppcit_ − 1 + β _xit_ − 1 +η _i_ + θ _t_ + ε _it_ .

However, this approach does not allow us to properly isolate the causal effect of increased mass media consumption for two main reasons. First, country fixed effects do not control for unobservable, country-specific, time-varying factors correlated with both the media and agricultural protection. Second, potential measurement errors in the _gdppc_ indicator may introduce further endogeneity problems (Deaton 2005). Indeed, the correlation between _gdppc_ and the media variables may bias the estimated media coefficients (and the interaction term between the media and _gdppc_ ), a problem exacerbated by the fixed effects transformation (Wooldridge 2002). Thus, to make our identification assumption more credible, we adopt two additional strategies.

First, given our specific concern regarding (omitted) time-varying factors correlated with both media variables and protection, in addition to the time dummies, we include continent-year interaction effects to control for changes over time that affect countries within a region similarly.<sup>9</sup> In addition, as discussed in the data section, beyond the traditional determinants of agricultural protection that have been found relevant in previous studies, we include several other covariates, such as indicators of trade openness, trade policy reforms, government consumption, and economic crises, to increase the similarity between the countries.

9 These interaction effects capture any regional differences in the agricultural protection dynamic. We also tested a second specification in which we included continentspecific polynomial terms over time, and the results were qualitatively and quantitatively similar.

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Second, to directly address measurement error problems and other forms of endogeneity, we also employ dynamic panel methods. Specifically, we use the system generalized methods of moment (SYS-GMM), developed by Arellano and Bover (1995) and Blundell and Bond (1998). By estimating a system of equations in first differences and levels and employing instruments, this approach should allow for consistent estimations even in the presence of measurement errors and other forms of endogeneity (see Bond et al. 2001). Our SYS-GMM dynamic panel model has the following specification: where ∆ denotes first differences, that is, ∆ _yit = yit – yit-1_ , _RRAit-1_ is the lagged dependent variable and υ _it_ is a disturbance term. In estimating the system of equations (3a)–(3b), the (endogenous) lagged dependent variable is instrumented by its _t –_ 2, _t_ – 3, and longer lags, using the lagged levels for the first-differences equation (3a) and the lagged differences for the level equation (3b). Similarly, to address endogeneity in other explanatory variables (such as the media variable and its interaction with _gdppc_ ), they can be instrumented by their respective _t_ – 2, _t_ – 3, and longer lags. The validity of a particular assumption can then be tested using standard generalized methods of moment tests of overidentifying restrictions. In summary, the SYS-GMM specification should allow for greater flexibility, improved control for omitted time-varying factors through 21
the lagged dependent variable and, finally, greater consistency even in the presence of endogenous regressors. However, it is important to stress that this estimator does not resolve endogeneity problems due to omitted variables with persistent effects, such as when the TV trend is correlated with the _RRA_ ( _NRA_ ) trend, as a result of an omitted (possibly time-invariant) variable.

# **_V. REGRESSION RESULTS_**

This section presents the results of our econometric analyses. We present first the results of the static model and afterward the dynamic panel results. We evaluate the robustness of the results by testing whether the results are sensitive to country and time coverage and to the use of different indicators for key variables.

# **_Static model_**

Table 3 reports the static fixed effects results of different specifications based on equation (2), with columns (1)–(4) using _RRA_ and (5)–(6) using _NRA_ as the dependent variable. In every regression, the standard errors are corrected for heteroskedasticity and autocorrelation of unknown form and are clustered within countries.

The results in column 1 show that the simple fixed effects specification, without controls apart from _gdppc_ , yields statistically significant coefficients ( _p_ -value < 0.01) for both the linear effect and the interaction effect of TV penetration with _gdppc_ . The positive sign for the linear term and the negative sign for the interaction effect are consistent with hypotheses 1 and 2. The penetration of TV is associated with a higher _RRA_ at low levels of development but with a lower _RRA_ at higher levels. In regression 22
(1), the turning point for the relationship is a per capita GDP level of USD 6,013. This number is virtually identical to the sample median value, which is equal to USD 5,949.

Columns (2) and (3) report regressions that control for the standard agricultural protection covariates (agricultural employment share, comparative advantage, country size, and the quality of democracy) and additional variables, such as trade openness, government consumption to GDP, and crises indicators.

In column (4), we add a set of continent-year interaction effects to control for differences in regional protection dynamics. The different specifications yield consistent results both in terms of coefficients and the significance of the media variables.

Columns (5) and (6) report the results of regressions analogous to columns (3) and (4) but using the nominal rate of assistance ( _NRA_ ) as the dependent variable instead of _RRA_ . The estimated media coefficients are very similar, and the results are thus robust to using different indicators of agricultural support.

To put the estimates into perspective, we illustrate the size of the media effects using the Philippines and Taiwan as examples. These two countries have average per capita GDP values in the period covered by the analysis of USD 1,854 and USD 9,987, respectively, which are significantly lower and higher than the critical turning point of the estimated relationship. Using the estimated coefficients of the full model (column (4)), a 10 percent increase in the share of households with TVs would be associated with a 4.8 percent increase in agricultural protection in the Philippines, but the same increase would 23
reduce the Taiwan’ agricultural protection by 6.4 percent.<sup>10</sup> This finding suggests that if there is a causal affect, its magnitude could be substantial.

In a working paper version of this article (Olper and Swinnen 2012), we presented similar regressions using radio penetration as media variable. When using radio penetration as an indicator, the patterns of the results are similar to those obtained using TV, but the significance levels of the media variables are lower and less robust. More specifically, including additional controls, the radio penetration linear term is positively and significantly correlated with protection, but the interaction term with per capita GDP, while still negative, is no longer statistically significant. A possible interpretation of these results is that radio is a more important news source in poor countries, whereas TV matters most in emerging and rich countries.

# **_Dynamic panel model_**

As discussed in section IV, the results obtained from the (static) fixed effects model may still suffer from endogeneity bias, particularly as a result of measurement errors. To account for this problem, table 4 reports the results of dynamic panel estimates that control for persistency in agricultural protection. To avoid problems resulting from the use of an excessive number of instruments in the SYS-GMM estimator, the specification only controls for the standard covariates (as in column (2) of table 3).<sup>11</sup>

> 10 Both elasticities are evaluated at the mean value of the _RRA_ distribution, equal to 12.7 percent.

11 Note that this does not substantially affect our results because the autoregressive term largely absorbs these omitted terms (see Roodman, 2009).

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Columns (1) and (2) report the results using ordinary least squares (OLS) and least squares with dummy variables. The OLS and least squares with dummy variables results serve as benchmarks for the evaluation of the SYS-GMM specification and should be upward and downward biased compared to the SYS-GMM, respectively. In the SYSGMM estimates, the media variables and _per-capita_ GDP are treated as endogenous variables and instrumented with their _t_ − 2 and higher lagged values. The SYS-GMM results for _RRA_ and _NRA_ are presented in columns (3) and (4). As expected, the magnitude of the coefficient on lagged _RRA_ is above the estimated least squares with dummy variables value and below the estimated OLS value (and similar for _NRA_ ). Moreover, neither the basic Hansen test of overidentifying restrictions nor the Differencein-Hansen test, related to the additional instruments used by the level equation, detects any problem with instrumental validity. These observations all suggest that our instruments are valid and informative and the SYS-GMM estimator is consistent.

The estimated coefficients of TV penetration presented in columns (3) and (4) are significant and consistent with hypotheses 1 and 2 in the SYS-GMM regressions. These estimated coefficients measure short-term correlations. To compare them with the static results, one should use the long-run correlations, which can be obtained by dividing the estimated coefficients by one minus the autoregressive coefficient. We obtain values equal to 17.46 for the linear term and − 0.0046 for the interaction with income level and similar values for the _NRA_ specification. Thus, the magnitudes of the estimated (longrun) media correlations in the dynamic SYS-GMM model are (in absolute value) approximately two times higher than those of the static model (see columns (3) and (5) of table 3). This result is consistent with the presence of attenuation bias due to 25
measurement errors in the variables. This problem is exacerbated in the fixed effect specification but is efficiently accounted for in the generalized methods of moment approach (see Wooldridge, 2002, p. 313).<sup>12</sup>

# **_Further robustness tests_**

We performed a series of additional robustness tests to further check our results, testing whether the results are sensitive to country and time coverage and to the use of different indicators for the group size effect and development.

Columns (1)–(4) of table 5 report sensitivity analyses for the media-protection relationship with different ranges of countries and periods. One problem with our results may be that both the structural adjustment programs of the 1980s and the beginning of the GATT Uruguay Round in the mid-1980s caused an effect that interfered with our media-protection relationship: a reduction in agricultural taxation in developing countries and a reduction in agricultural protection in developed countries. Columns (1) and (2) examine this possibility by running the model using only observations before and after 1985, respectively. Although the magnitude of the estimated relationship is different in both periods (approximately three times larger in magnitude after 1985),<sup>13</sup> the relationship is also estimated with high precision for years before 1985, suggesting that the abovementioned confounding effects do not drive the results.

> 12 Note that when comparing the SYS-GMM and the static fixed effects results, at least in terms of the magnitude of the media effect, these may also differ because the two models differ not only in terms of the estimator used but also in terms of the covariates included.

13 One likely reason for this difference is that the expansion of TVs in developing and emerging countries did not begin before the early 1980s.

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Another possibility is that our nonlinear media-protection relationship is driven by some group of sensitive observations related to a particular group of poor or rich countries. To check this possibility, in columns (3) and (4), we excluded from the regressions observations for countries with a _gdppc_ lower than USD 1,000 and higher than USD 25,000, respectively.<sup>14</sup> The results remain consistent and significant, although the sizes of the coefficients change somewhat. Dropping observations for the poorest countries increases the magnitude of the estimated relationship; dropping observations for the richest countries reduces the estimated effects compared to the benchmark SYSGMM regression in Column (3) of table 4. These results are consistent with hypothesis 2 (advertiser value effect), suggesting that farmers in the poorest developing countries may be too poor to attract advertisers and media coverage. The findings may also be consistent with an unbalanced increase in media diffusion between rural and urban areas. If rural areas have some lag in media penetration compared to urban areas, the media protection relationship will be weaker in the poorest countries.

In column (5), we simultaneously exclude observations for the three richest (the United States, Norway, and Switzerland) and the three poorest countries (Mozambique, Ethiopia, and Zimbabwe) from the regression. Once again, the results are very robust.

Columns (6) and (7) of table 5 present an additional robustness check using the agricultural employment share ( _emps_ ) to interact with the media effect. For both _RRA_ and

> 14 Note that the results are fairly robust to the use of other _gdppc_ thresholds. For example, by excluding observations with _gdppc_ values below USD 5,000 or higher than USD 20,000 and thus working with approximately half of the sample, the mediaprotection relationship is still statistically significant.

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_NRA_ , all key variables have signs consistent with our hypotheses. Although the regression results with _emps_ are less stable than those with _gdppc_ (likely as a result of severe measurement errors in the _emps_ variable), the key conclusions are robust to a change in the structural variable.<sup>15</sup> Finally, as discussed in the conceptual framework section, there may be additional nonlinearity in the relationship between media coverage and policies resulting from a nonlinear relationship between economic growth and the rural-urban income gap and the relative urban-rural gap in TV distribution, which would affect the value of both groups to advertisers and thus the channel through which the mass media affect agricultural protection. To test for this, we added an additional interaction effect between TV and the square of _gdppc_ to the model specifications. In some specifications, this additional media 15 There are well-known problems with the agricultural employment data, which are generally linear interpolations between a few observations (often one per decade) and suffer from serious measurement errors (see Timmer and de Vries, 2007). The model specification tests reported at the bottom of the table indicate a well-specified SYS-GMM model for the _RRA_ regression but not for the _NRA_ regression, where the Hansen test rejects the null of the validity of the additional overidentifying restrictions. However, the autocorrelation tests indicate that the model is well specified. Considering the strong measurement errors in the employment data and that Hansen tests have weak power, such results are unsurprising.

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interaction term is significant and negative, consistent with the hypothesis. However, the results are less robust than those without the additional nonlinear term. These results are presented in Olper and Swinnen (2012).

# **_VI. CONCLUSIONS_**

This paper provides evidence of the relationship between mass media competition and agricultural protection for a large group of countries. Strömberg’s (2004a) theory predicts that information provided by the mass media, reflecting the media’s incentives to provide news to different groups in society, affects government policy making and who benefits from government policies. The theory predicts that mass media competition will induce a policy bias toward large groups and groups that are more valuable to advertisers; these groups are more informed because the mass media target them.

We apply this theory to agricultural policy. This results in the hypotheses that ( _a_ ) given the changing role of the agricultural sector due to economic development, the impact of mass media competition on agricultural policy will differ between poor and rich countries, ceteris paribus, and ( _b_ ) this effect is contrary to the so-called development paradox of agricultural policies. Thus, the traditional change in agricultural policy from taxation to subsidization that is associated with economic development will be smoothed in the presence of mass media competition. We hypothesize that this is due to a combination of the group size effect, with larger groups being more attractive to the media, and the advertiser value effect, with richer groups being more attractive audiences for the media.

We use data on agricultural policy from 69 countries spanning a wide range of development stages and media markets to test these predictions. Our empirical results are 29
consistent with the theoretical hypotheses. We find a significant and robust correlation between public support for agriculture and TV penetration, which is conditional on the structure of the economy. In particular, an increase in media penetration is correlated with policies that benefit the majority to a greater extent; it is correlated with a reduction in agricultural taxation in poor countries and a reduction in agricultural subsidies in rich countries, ceteris paribus.

These results are robust to the use of different indicators of agricultural policies, different media variables and different control variables and estimation techniques.

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