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This chapter takes a look at the development and use of indicators and related reporting systems in social impact investing. It introduces the Global Impact Investment Rating System (GIIRS) and the Impact Reporting and Investment Standards (IRIS). It then studies the concept of ‘investment impact’ indicators, which are considered as important bridging and blurring devices. This chapter also identifies the advantages and costs of this system, which include a move away from local self-expression of distinct community initiatives.

This chapter draws upon statistical reports generated from all levels of the statistical system—internal work bulletins, and materials from conferences at the local, provincial, and national levels—to uncover the messiness of actual statistical work and its relationship to planning. It captures not only the centralizing impetus of the expansion but also the varieties of challenges that were encountered in putting into practice the methods that were at the heart of socialist statistics: the periodic reporting system, and the various forms of typical sampling. Statistical work was carried out within the larger unfolding context of increasing complexity, changing (economic) focus, and struggles over administrative devolution. As work became more complicated and demanding, engagement with and discussions about statistical activities grew more sophisticated—methods were expanded, analyses were undertaken, and the results of such analyses were circulated among leaders and bureaucrats. However, a key problem remained—the system that had been set up incentivized the overproduction of reports, and the state had little or no capacity to handle the resultant excess reports.

The “Masters Hypothesis” claims that unprecedented buying pressures from new financial index investors created a massive bubble in agricultural futures prices at various times in recent years. This chapter analyzes the market impact of financial index investment in agricultural futures markets using non-public data from the Large Trader Reporting System (LTRS) maintained by the U.S. Commodity Futures Trading Commission (CFTC). The LTRS data is superior to publicly-available data because commodity index trader (CIT) positions are available on a daily basis, positions are disaggregated by contract maturity, and positions before 2006 can be reliably estimated. The null hypothesis of no impact of aggregate CIT positions on daily returns is rejected in only 3 of the 12 markets. Point estimates of the cumulative impact of a one standard deviation increase in CIT positions on daily returns are negative and very small, averaging only about two basis points. The null hypothesis that CIT positions do not impact daily returns in a data-defined roll period is rejected in 5 of the 12 markets and estimated cumulative impacts are negative in all 12 markets; the opposite of the expected outcome if CIT rolling activity simultaneously pressures nearby prices downward and first deferred prices upward.

The Institute of Medicine (IOM) report To Err Is Human created a media firestorm. Prior to this, interest in patient safety might be described as a cult—a small group of passionate believers—but the IOM report and its aftermath moved patient safety to a prominent place on the broad public agenda, creating pressure for quick action. The federal government announced a safety program at the Agency for Healthcare Research and Quality; President Clinton committed to a “50% reduction in medical errors in five years”; similar events occurred in the United Kingdom. Programmatic efforts in patient safety began to crowd out safety scientists. The litany of fear-provoking incidents of patient harm continued.

This chapter examines the systems of exploitation within the camps and how they strive for maximum efficiency. Camps were required to demonstrate maximum labor utilization (trudovoe ispol'zovanie) of their prison workforce and minimal “lost labor days.” No one could be allowed a free ride on the Gulag's scarce resources. As the Gulag intensified its system of exploitation, it also ceased reporting data on physical labor capability to its party bosses. Camp administrators routinely hid the physical degradation of their workforce by reclassifying and moving sick prisoners. In addition, labor utilization data concealed the scale of the weakened workforce. Camp officials also sought to make disabled inmates invisible in reports and statistics, and to demonstrate the maximum number of bodies being used in production.

This concluding chapter returns to the main themes of the book before ending with a brief overture to developments in 1979. In that year, the statistician Dai Shiguang published two influential articles calling for a complete overhaul of the system of socialist statistics. Dai Shiguang's articles had an electrifying impact in China's statistical world. Within a few years, socialist statistics was cast aside, much like an old cloak, and mathematical statistics was formally reintegrated into the discipline and practice of statistics. An entire way of knowing society came to an end. Facts that the state had both seen and cherished disappeared, along with the periodic reporting systems that supplied them. Other facts, which did not exist before the 1990s, grew to become central.

In chapter 13, Saad B. Omer and Sam F. Halabi examine the evidence, strategies, and challenges surrounding vaccine safety as novel technologies and expanding coverage introduce new factors to consider in maintaining the safety profile of current and new vaccines. While the fundamental principle behind vaccination remains the same, the technologies behind antigenic components have evolved. The chapter provides concrete recommendations on how the regulatory process that assures vaccine safety and efficacy may be adapted to respond to these technologies. The chapter also reviews postlicensure vaccine safety and adverse event reporting systems at both the national and the international level. It assesses weaknesses in the global system for adverse event monitoring, especially in light of advances in immunization coverage programs in low-income countries, and propose potential solutions to address those weaknesses.

This chapter introduces the landscape of data collection and impact measurement among nonprofit and social sector organizations. New technologies for collecting data as well as increasing donor demand for accountability create a culture of impact. However, this push to measure impact has also led to a proliferation of misguided efforts to do so. Many organizations fall into one of three traps when monitoring and evaluating their programs: too few data, too much data, or the wrong data. This chapter introduces the CART principles, which guide organizations to collect data that are credible, actionable, responsible, and transportable. It also outlines the structure of the book and describes the case studies that illustrate the CART principles in action. The chapter explains how the book can be used by both social sector organizations and their funders to develop data systems that support learning, improvement, and impact.