# I. Introduction

Economic forecasts matter for government decisions. They are inputs to the formulation of policies that aim to safeguard and advance economies. Accurate forecasts enhance the possibility of timely and targeted interventions. During the COVID-19 pandemic, short-term growth forecasts have been vital for tracking the impact of the pandemic and the likely trajectory of the recovery—which assists authorities in ascertaining the economic costs of the pandemic and charting the way forward. Growth forecasts affect decisions regarding a host of policies related to spending and debt. Research has shown that overly optimistic forecasts can hurt an economy in the long run. They initially lead to short-run increases in output as bullish governments and businesses increase borrowing and spending. But within a few years the debt load spawns economic contractions (Beaudry and Willems, 2022).<sup>2</sup> This idea is not new. The idea that macroeconomic fluctuations can arise due to difficulty in forecasting was raised by Arthur Pigou as long ago as 1927.

Growth forecasts are used by a wide array of stakeholders. They may guide international organizations such as the World Bank and the International Monetary Fund (IMF) in adapting and setting up lending programs. The private sector may use forecasts to tailor investment strategies or reassess country debt sustainability and debt ratings. Surprises in growth forecast releases tend to move financial markets (Campbell and Sharpe, 2009; Clements and Galvão, 2017). Firms with more optimistic views of their future production prospects tend to be highly leveraged (Jochem and Peters, 2017). Misallocation of resources from over-optimism or pessimism at the firm level can hurt society’s economic well-being (Bachmann and Elstner, 2015).

Growth forecasts cannot—and need not—perfectly predict the future. But gross and systematic errors in forecasts may lead policy makers and the private sector astray. Most relevant is the challenge of low data capacity and transparency, a long-standing issue in many countries, particularly developing economies. These data problems impede sound analyses and policy making. This paper focuses on growth forecasts. The paper seeks to unpack the relationship between growth forecast errors and their determinants, with special attention given to the role of data systems.

To study the determinants of growth forecast errors, we rely on time series of growth forecasts published by the World Bank, the IMF, and the private sector between 2010 and 2020. With the benefit of hindsight, we can compute forecast errors by comparing the World Bank and IMF forecasts for a given year with the realized growth rates covering a large number of developing countries. In turn, we estimate regression models where the key explanatory variable is the World Bank’s Statistical Capacity Index (SCI). As far as we know, this paper is the first to assess the empirical relationship between growth forecast errors and data systems.

In sum, this paper presents five empirical contributions to the existing literature on macroeconomic forecasting. First, same-year forecast error—the difference between forecast growth and realized growth—is sizable. Globally, between 2010 and 2020, the average same-year forecast error is 1.3 percentage points for the January World Bank’s forecasts and 1.5 percentage points for the January IMF’s forecasts. Second, the Middle East and North Africa region (MENA) has the largest forecast errors among world regions. Third, on average, improvements in a country’s Statistical Capacity Index (SCI), a measure

> 2 Using the IMF’s WEO data, Beaudry and Willems (2022) found that overestimating annual growth by 1 percentage point over three years reduces real GDP growth three years after that by about 1 percentage point on average.

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