Including limitations in growth of harvested area and the impacts of increased growing season temperatures and potential evapotranspiration would inflate all these aid projections. Two hundred million sub-Saharan Africans were undernourished in 2002, and if the observed 1982–2002 trend continues this total may increase to almost 600 million people by 2030.The ‘business as usual’ projected yields analysis displays three key characteristics: i) yield trends and 2030 yield outcomes vary dramatically by region, ii) in developed nations a continuance of observed yield trends may result in physically unrealistic results, and iii) in Africa, Central America and Southern Asia, a continuance of current trends will result in very low levels of food availability. Given increasing globalization and technological limits, one might imagine that yields would have converged since 1960. This has not been the case; rather the green revolution has followed a course in which the ‘green get greener’, with growth then slowing above 5,000 kg per ha per year. Yields in 1961 have been a very good predictor of yields in 2007 ; countries with lower yields have tended to increase their yields more slowly. This has enhanced differences between nations. In 1961 the highest regional yields were four times the lowest. This ratio rose to nine in 2007. A continuance of these trends will increase this ratio to 11 by 2030. As yield growth lags and per capita harvested areas decline, per capita cereal production will reach dangerous levels in Africa, Central America and Southern Asia. As an alternative to using observed yield trends from Table 3,plastic seedling pots we can specify a target 2030 per capita production and solve directly for the required yield . This approach uses trends in per capita harvested area but then estimates 2030 yields algebraically. Two sets of cereal production targets were used. A minimal threshold of 190 kg ha−1 annum−1 was specified for African nations. The other countries were set to their 2007 values.
This represents a modest ‘agricultural growth’ scenario, in which per capita production for most of the world maintains parity with current levels and Africa experiences modest gains. This exercise suggests two salient results. First, the target yield gains for Africa appear small in magnitude but large in ratio. Looking at Eastern Africa, for example, we see that a transition from yields of 1,482 kg per hectare per year to 3,500 kg per hectare per year would make the region relatively food secure. This is a large percent increase, but not technically impossible– farming efficiencies would have to approach those used in Southern America in 2007 . A second feature of Fig. 9b is that maintaining current per capita cereal production levels in Northern America and Eastern Asia will require substantial increases in yields, much greater than those anticipated by historical trends. As per capita harvested area diminishes rapidly in these regions, very large increases in yields will be required to produce enough cereal to feed the future population at its current level of per capita cereal consumption. For Eastern Asia to maintain a per capita cereal production of 314 kg per person per year, yields will need to increase from their 2007 level of 5,418 kg per hectare per year to a very high yield of 8,600 kg per hectare per year. This may not be technically feasible or environmentally sustainable. Although there is a substantial gap between maximum theoretical yield for the major world crops of maize, rice, and wheat and actual average yields, in recent years the gains in yields have declined. Rice yields in China and Indonesia and wheat yields in India and France, for example, have not increased substantially in the past decade . Increasing yields beyond levels attained in these countries will require either enormous investments in freshwater irrigation systems or a transformation of the agricultural systems to incorporate new technological advances, both of which are highly unlikely in the next few decades. Investing in regions with extremely low agricultural productive capacity, therefore, will be a much less expensive way to improve global agricultural production. Bringing maize yields from 700 to 5,000 kg per hectare is far easier than increasing yields from 7,000 kg per hectare to 8,000 kg per hectare . This observation turns the standard food security paradigm on its head.
With lower population growth and more effective farming practices, by 2030 it could be an agriculturally active Africa that helps alleviate global shortages of cereal production.For many goods, consumers face ex ante uncertainty regarding the quality of the good and rely on imperfect signals to infer quality. Traditionally, expert opinion and social learning have helped consumers resolve these information asymmetries. For an expert’s take, consumers may consult Consumer Reports when buying an automobile or household appliance or they may read reviews by professional critics when selecting a movie or choosing among dining options. Alternatively, consumers may confer with peers who own the automobile or who have eaten at the restaurant. In recent years, however, online sites that cheaply aggregate consumer reviews have recently expanded and have begun supplementing both of the traditional mechanisms. But are these sites playing an important role in determining consumer demand? Despite the theoretical potential of digital word-of-mouth to influence consumer choices, it is difficult to estimate its impact on purchasing decisions. Products that receive positive reviews are ones that appeal to consumers , and these products would likely experience high sales even in the absence of positive reviews. In a recent paper published in the Economic Journal, we leverage a feature of the display system at Yelp. com—a popular site that allows users to leave reviews of local businesses—to estimate the effect of positive Yelp ratings on restaurant customer flows. Yelp reviewers assign businesses ratings from one to five stars in whole star increments. When a user searches Yelp.com, Yelp presents a list of businesses that meet the search criteria or fall within the category of interest. Figure 1 on the next page reproduces an excerpt from a sample search on Yelp.com. Businesses are sorted according to relevance and rating, and for each business the average rating is prominently displayed, rounded to the nearest half star. The number of stars in the average rating is easily visible, particularly because the color of the stars changes at whole star thresholds. We downloaded the entire history of reviews from Yelp.com for each restaurant in San Francisco, CA and recorded the date of the review, the rating assigned , and the reviewer’s unique user identifier.
We then reconstructed the average rating and total number of reviews for each restaurant at every point in time and matched these data with reservation availability data from a large online reservation website. As Figure 1 demonstrates, Yelp aggregates all reviews for a given business and displays the average rating prominently. However, when Yelp computes the average rating, they round off to the nearest half-star. Two restaurants that have similar average ratings can thus appear very differently on Yelp. For example, a restaurant with an average rating of 3.24 displays a 3-star average rating, while a restaurant with an average rating of 3.26 displays a 3.5-star average rating. In actuality, the true underlying quality of these two restaurants is similar on average, allowing us to identify the effect of the Yelp rating on customer demand while controlling for unobserved quality. If Yelp reviews have significant impacts on consumer demand,container size for raspberries then we should observe a sharp increase in reservations at each major rounding threshold . In the paper we use a technique known as regression discontinuity to estimate the effect of Yelp. Here, we present several figures that graphically summarize the results from the regression discontinuity estimator. Figure 2 plots mean 7:00 p.m. reservation availability by Yelp rating. Panel A focuses on the window where restaurants have either 3 or 3.5 stars, and Panel B focuses on the window where restaurants have either 3.5 or 4 stars. There are clear jumps in the mean availability at both the 3.5 and 4 star thresholds. Moving from 3 to 3.5 stars—which occurs when a restaurant’s rating crosses 3.25 stars—reduces the likelihood of availability from about 85% to about 60%. Moving from 3.5 to 4 stars—which occurs when a restaurant’s rating crosses 3.75 stars—reduces the likelihood of availability further to below 40%. Interestingly, for the most part, it appears that a step function is a good approximation to the overall relationship between Yelp ratings and restaurant availability. That is, restaurant availability appears to respond primarily to the displayed rating, and not the underlying average review score . Overall, we find that a half star increase in Yelp ratings decreases reservation availability by 19 percentage points during peak dining hours. If Yelp is providing information about new restaurants, that information should be most valuable among restaurants that are unfamiliar to patrons. We divide restaurants into familiar/ unfamiliar groupings along two dimensions. First, restaurants with fewer than 500 reviews are likely to be less frequented and less well known than those with more than 500 reviews. For restaurants with fewer than 500 reviews, an extra half-star on Yelp reduces reservation availability by 20 to 30 percentage points depending on the reservation time. In contrast, for restaurants with more than 500 reviews, for whom there is likely less hidden information about quality, there is no discontinuous change at any threshold associated with additional Yelp stars.
A second test for whether the Yelp effect is due to solving information problems groups restaurants according to whether there are external sources of quality information. Here, we note that quality information is easily available for restaurants which have a Michelin star or those which appear in the San Francisco Chronicle’s annual Top 100 Restaurants listing. In contrast, crowd sourced information may be more important for restaurants excluded from these prestigious rankings. We again find that an extra half-star on Yelp reduces reservation availability by 20 to 30 percentage points at all three times for restaurants without external recognition but that the Yelp ranking does not similarly advantage restaurants which have been externally accredited. These results support the hypothesis that Yelp is most valuable when there is less external information about restaurants, though other differences between the two groups of restaurants may also play some role. The high return to positive Yelp ratings naturally creates an incentive for restaurants to manipulate their own ratings by leaving false reviews. Manipulation is feasible in this context because Yelp is crowd-sourced—any restaurateur can, in principle, leave himself a 5-star review. Furthermore, the significant increases in business at Yelp thresholds create a strong incentive for restaurants to attempt to manipulate their ratings to fall above a threshold. Is it possible that the increases in demand that we observe in Figure 2 at Yelp rounding thresholds are the result of specific restaurants strategically manipulating their ratings so that they fall right above the rounding thresholds? If so, this would invalidate our research design, because restaurants above the rounding threshold would not be directly comparable to restaurants below the rounding threshold. However, if specific restaurants manipulate their reviews to fall right above the thresholds, then some of restaurants above the thresholds have “true” Yelp ratings that are lower than their observed Yelp ratings. To generate a significant drop in reservation availability at the threshold, these restaurants must sell out virtually all the time, despite the fact that they receive low ratings from true Yelp reviewers. It seems ex ante surprising that a restaurant that receives poor reviews would be extremely crowded, though it is theoretically possible. Using a short theoretical model, we show that although restaurants face incentives to manipulate Yelp ratings, it does not make sense for them to try to stay right above the Yelp rounding threshold. The intuition is simple: given that a random stream of reviews will change each restaurant’s average rating over any time period, a restaurant which is just above a threshold has a very similar likelihood of just missing that threshold after new reviews come in as a restaurant which is just below the threshold. Both restaurants therefore face similar incentives to try and push their Yelp scores into safer territory.