rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 7. Income Redistribution by Country Group, 1980s–2010s** _(difference in Gini points before and after taxes and transfers)_ As national incomes rise, countries engage in more redistribution.

<!-- Start of picture text --> 20<br>AEs EMs LIDCs<br>15<br>10<br>5<br>0<br>1980s 1990s 2000s 2010s<br>transfers<br>Difference in Gini points<br>before and after taxes and<br><!-- End of picture text --> _Sources_ : Authors’ calculations based on data from Standardized World Income Inequality Database (SWIID). _Note_ : AEs = advanced economies; EMs = emerging market economies; LIDCs = low-income developing countries. Country groups are defined according to WEO Methodology. For details see https://www.imf.org/external/pubs/ft/weo/faq.htm#q4b2.

# **3.2.3.Factors and Markets**

# **_Employment of factors_**

In the short and medium term, factors of production such as labor and capital are not necessarily fully employed. Recessions resulting from a variety of shocks, including financial distress and pandemics, can reduce long-term output (Cerra, Fatás, and Saxena 2020) and generate large spikes in unemployment and inequality and declines in capacity utilization (Heathcote, Perri, and Violante 2020). Unemployment creates income losses in the short term, especially for those in lower-income groups such as people with lower educational attainment, ethnic minorities, and women (Hoynes, Miller, and Schaller 2012). Unemployment often results in scarring effects on incomes over the longer term. As shown by von Wachter, Song, and Manchester (2009), 15 to 20 years after a layoff, earnings can be depressed by as much as 20 percent, as workers’ skill set becomes outdated and they lose skills that are specific to the jobs lost in a specific industry. As described in Okun’s law (discussed in Cerra et al. 2021, Chapter 3), unemployment varies inversely with cyclical growth (Ball, Leigh, and Loungani 2017). Higher growth generates employment, which improves inclusion. In general, economic volatility is associated with both lower growth and higher inequality (Cerra et al. 2021, Chapter 11).

Another reason for unemployed or underemployed factors could be poverty traps that entail the inability of low-income individuals to pay any fixed costs of education, move to a booming region, or obtain collateral to obtain credit. Such individuals can be excluded from more remunerative productive activities or remain unable to meet a threshold of productivity. Those 15
Measuring True Sales and Underreporting with Matched Firm-Level Survey and Tax office Data where i 1 i S denote the detection probabilities for sales, raw materials, and labor costs respectively and is the subjective expectation of tax office efficiency. For given tax office efficiency ( ), the detection probabilities increase with the relative gap between the true values and the reported values to tax office. We further assume that once a firm is chosen for tax auditing, any tax evasion will be detected and the true sales/raw materials/labor costs of the firm will be fully revealed.<sup>18</sup> Therefore, a firm maximizes the expected self-retained profit over the choices of reported sales, raw materials and labor costs, taking true sales/raw materials/labor costs as given. A firm’s expected retained profit can be written as: t r t t t t t maxS<sup>ESv</sup> v S 1 S v v P SS<sup>t</sup> S tS t v v `(8)` where denotes firm’s retained profit, and the corporate profit tax rate and payroll tax rate respectively<sup>19</sup> ; and P is the penalty multiplier. Plugging in the three audit probabilities into equation (8), we obtain three first-order conditions with respect to S<sup>ttt</sup> : 18 We can also assume that only part of the underreporting will be detected but this does not change the main implications of the model.

19 In 2003, Mongolia there were two profit tax rate levels for registered firms: 15% if the taxable income is below 100 million MNT and 40% above. We calculated the gross taxable incomes using tax office data and no firms have taxable incomes above the threshold of 100 million MNT. The calculated taxable incomes could be even lower if there are other tax deductible costs not included in the calculation. Hence assuming a single profit tax rate is reasonable. Also the payroll tax rate is flat –formal firms in Mongolia are obliged to pay 19% social security tax on wages or a slightly higher rate (or 20 and 21% in some industries with high injury risks).

17
to the tax administrative and survey data. The general patterns across the different data sources are compared to see whether they suggest a similar trajectory in terms of the impact of COVID-19 on formal firms in Zambia. The tax administrative data is adjusted for inflation, drawing on monthly inflation rates released by the central bank of Zambia, so that comparisons over time can be made in real terms. The survey data is weighted to ensure it is representative of formal firms in Zambia that share the key characteristics that the original ES sampling frame is based on (e.g., having more than five employees, operating in specific sectors, located in urban areas). The survey weights also factor in the small amount of attrition between the baseline and follow-up surveys; however, this has negligible impact because there was a very high response rate across all three rounds. In addition, both data sources are winsorized at 2.5 and 97.5 percent to ensure that the average trends are not entirely driven by outliers.

The next part of the descriptive analysis is to solely use the tax administrative data for the subset of firms that share similar characteristics to the firms that participate in the survey. The first step is to only focus on firms that file each month (i.e. they are ‘perfectly compliant’). This is necessary as the overall trends in the tax administrative data are partly influenced by which firms choose to file in a given month and there is seasonality in when firms choose to file. A drawback from restricting the focus to these firms is that they are likely to be quite distinct from other formal firms. For example, they may be quite profitable and/or large so each month they always exceed the threshold for filing a tax return, whereas other firms may have sales that fluctuate around the threshold for filing. Another difference between the ‘perfectly compliant’ firms and other formal firms is that they may have a lower risk tolerance and/or greater exposure to the tax authority, which means they are less willing to avoid paying taxes. These characteristics could also be associated with other aspects of business operations (e.g. investment in R&D).

The second step in aligning the two data sources involves solely focusing on firms for which there are five or more employees and dis-aggregating the analysis by firm size. Specifically, we look at the trends over time across both data sources for ‘perfectly compliant’ firms with 14
to obtain a license or permit from any authority. Hence, the operational entry costs are fairly low. They operate informally, with few barriers to entry, allowing them to enter and exit the market flexibly throughout the year. They travel around the village on a bicycle, announcing prices and searching for households that have maize to sell. Typically, once the buyers have procured a sufficient quantity of maize, they return with a large vehicle such as a lorry to collect the purchased maize<sup>6</sup> . Many traders operate with little capital (from own savings), with no storage capacity or significant transport assets. This further facilitates entry and exit to the market.

The level of buyer activity in the maize market varies throughout the year. Buyer entry peaks during the harvest season when maize is abundantly available, and when many maize growers are ready to sell due to the erosion of their savings from the previous season. Non local traders from neighboring villages, regions and even from Dar es Salaam travel out to maize growing villages to collect maize grain. In contrast, the lean season sees a sharp reduction in buyer entry in the villages. Nonresident buyers tend to use regional markets and local buyers as agents during seasons of scarcity<sup>7</sup> . This means that the market share of local buyers in primary marketing is higher in the lean season, whereas non local buyers capture a significant market share in the harvest season. Despite the seasonal decline in the number of traders, overall demand for maize is high throughout the year. Buyers remain active in the procurement and trading of maize by revolving their working capital fairly quickly and by taking advantage of seasonal variations in prices so that trading activity tends to peak just before planting and during the harvest (World Bank, 2007).

# **3.0 Analytical framework**

Although the role of itinerant traders is undoubtedly critical given the absence of other marketing outlets, empirical evidence, such as Fafchamps & Hill‟s (2004) analysis of farm-gate buying in Uganda‟s coffee market, shows that farm-gate buying is generally less remunerative for producers than selling at the marketplace. We anticipate a similar outcome for maize producers in Tanzania. We consider whether the prices received by growers who sell at the farm-gate fall as

> 6 Maize is sold on the spot, ungraded and on a cash basis. In some instances the payment is deferred and the buyer pays the maize grower after he or she has sold the maize they collected. This mostly occurs when the buyer is well known to the seller.

> 7 Fieldwork interviews.

4
|P060221|2002|2002|5/9/2002|2 Closed|Brazil|
|---|---|---|---|---|---|
|P060221|2002|2003|4/4/2003|6 Closed|Brazil|
|P060221|2002|2004|5/26/2004|11 Closed|Brazil|
|P060221|2002|2005|4/7/2005|13 Closed|Brazil|
|P060221|2002|2006|12/12/2005|14 Closed|Brazil|
|P060221|2002|2006|4/15/2006|15 Closed|Brazil|
|P060221|2002|2007|12/4/2006|16 Closed|Brazil|
|P060221|2002|2007|1/18/2007|17 Closed|Brazil|
|P060221|2002|2007|4/13/2007|18 Closed|Brazil|
|P060221|2002|2008|11/21/2007|19 Closed|Brazil|
|P060221|2002|2008|1/22/2008|20 Closed|Brazil|
|P060221|2002|2009|9/19/2008|21 Closed|Brazil|
|P060221|2002|2009|4/9/2009|22 Closed|Brazil|
|P060221|2002|2010|11/10/2009|23 Closed|Brazil|
|P060221|2002|2010|4/9/2010|24 Closed|Brazil|
|P060221|2002|2011|10/20/2010|25 Closed|Brazil|
|P060258|1999|1999||1 Closed|Indonesia|
|P060270|1999|1999||0 Closed|China|
|P060270|1999|2000|6/16/2000|2 Closed|China|
|P060270|1999|2001|6/22/2001|4 Closed|China|
|P060270|1999|2002|5/30/2002|7 Closed|China|
|P060270|1999|2003|6/26/2003|10 Closed|China|
|P060270|1999|2004|6/9/2004|12 Closed|China|
|P060270|1999|2005|3/13/2005|14 Closed|China|
|P060329|2001|2001|6/13/2001|1 Closed|Gambia,The|
|P060329|2001|2002|5/9/2002|4 Closed|Gambia,The|
|P060329|2001|2003|4/23/2003|7 Closed|Gambia,The|
|P060329|2001|2004|5/28/2004|10 Closed|Gambia,The|
|P060329|2001|2005|5/27/2005|12 Closed|Gambia,The|
|P060329|2001|2006|7/30/2005|13 Closed|Gambia,The|
|P060329|2001|2006|11/16/2005|14 Closed|Gambia,The|
|P060329|2001|2006|5/16/2006|15 Closed|Gambia,The|
|P060329|2001|2007|11/14/2006|16 Closed|Gambia,The|
|P060330|2000|2000|6/26/2000|1 Closed|Papua New Guinea|
|P060330|2000|2001|6/27/2001|3 Closed|Papua New Guinea|
|P060330|2000|2002|6/27/2002|5 Closed|Papua New Guinea|
|P060330|2000|2003|6/26/2003|7 Closed|Papua New Guinea|
|P060330|2000|2004|6/25/2004|10 Closed|Papua New Guinea|
|P060330|2000|2005|6/1/2005|12 Closed|Papua New Guinea|
|P060330|2000|2006|7/24/2005|13 Closed|Papua New Guinea|
|P060330|2000|2007|10/11/2006|14 Closed|Papua New Guinea|
|P060392|1999|1999||1 Closed|Bolivia|
|P060392|1999|2000|6/26/2000|3 Closed|Bolivia|
|P060392|1999|2001|6/26/2001|5 Closed|Bolivia|
|P060392|1999|2002|6/10/2002|7 Closed|Bolivia|
|P060392|1999|2003|6/11/2003|10 Closed|Bolivia|
|P060392|1999|2004|6/8/2004|13 Closed|Bolivia|
Boys’ enrollment in public schools Unadjusted difference between treatment and comparison districts <!-- Start of picture text --> co<br>ioe) TM<br>@<br><2)<br>fio<br>Ox<br>oO won ec pene I I I I I ee<br>S50<br>Te)Oo ceeeccetesccccecnceneensneneceet<br>i (cn) ee<br>2003 2004 2005 2006 2007 2008<br>Year<br>--------- Lower bound see Comparison District<br>oe Treaiment District <-------- Upper Bound<br><!-- End of picture text --> Source: Author's calculations using EMIS School Cansus Data
10 data set contains information on 2148 loans to 55 developing countries that were made betw.en 1968 and June 1981. Since much has been written on the general nature of these loans they will not be further discussed here. However, the Appendix Table A.1 contains information on some characteristics of our data.

Some information on the loars are among the elements of the _ vector in equation (4). These include maturity of the loan, a dummy variable that indicates whether the loan is public, and finally a dummy variable that indicates if the loan is syndicated. A possible problem could arise from the inclusion of maturity to the extent that banks determine spread and maturity simultaneously. However, based upon practices in the Eurocurrency market and the previous literature, loan maturity is assumed to be determined prior to the spread determination (see Feder and Just (1977), Edwards (1984)).

Some borrowers' characteristics are also included in the _ vector. These include information on general economic conditions of the borrower and are presumably important in measuring the riskiness of the borrower. We employed variables that are similar to the ones employed in the previous studies of credit terms and these variables are: total debt-to-GNP ratio, debt service-to-exports ratio, reserves-to-GNP ratio, imports to GNP ratio, GNP growth, lagged value of investment to GNP ratio, rate of devaluation and rate of inflation.9 The total debt, and debt service variables are obtained from the World Bank's World Debt Tables. The remaining variables are obtained from IMF's International Financial Statistics.

The expected signs of these indicators have been discussed extensively in the literature ( for reviews see McDonough (1982), Eaton and Taylor (1986) and Edwards (1984)), hence we will only briefly comment on this issue. It has been suggested that total debt to GNP ratio will have a
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Harrower, Sarah and John Hoddinott, 2004. ―Consumption Smoothing and Vulnerability in the Zone Lacustre, Mali,‖ Food Consumption and Nutrition Division Discussion Paper no. 175, International Food Policy Research Institute, Washington, DC.

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29
Table 1: Unconditional convergence 1970-2010, PPP and and market-exchange rate measured productivity

|||_De_<br>10-year log c|_pendent varia_<br>hange in labor|_ble:_<br> productivity||
|---|---|---|---|---|---|
||1970-80s<br>(1)|1980-90s<br>(2)|1990-2000s<br>(3)|2000-2010s<br>(4)|Panel<br>(5)|
|**_All economies_**||||||
|Initial productivity (MER)|0.028<sup>_∗_</sup>|0.034<sup>_∗∗_</sup>|_−_0.013|_−_0.052<sup>_∗∗∗_</sup>|_−_0.003|
|Initial productivity (PPP)|0.022|0.078**|-0.028|-0.066***|-0.005|
|_Convergence rate (MER, annual)_|_-0.28%_<sup>_∗_</sup>|_-0.33%_<sup>_∗∗_</sup>|_0.13%_|_0.53%_<sup>_∗∗∗_</sup>|_0.03%_|
|_Convergence rate (PPP, annual)_|_-0.22%**_|_-0.75%_|_0.28%_|_0.68%***_|_0.05%_|
|**_EMDEs_**||||||
|Initial productivity (MER)|0.002|-0.030|-0.032|-0.045**|-0.026**|
|Initial productivity (PPP)|-0.026|-0.003|-0.067|-0.019|-0.030|
|_Convergence rate (MER, annual)_|_-0.02%_|_0.30%_|_0.33%_|_0.46%_<sup>_∗∗_</sup>|_0.27%_<sup>_∗∗_</sup>|
|_Convergence rate (PPP, annual)_|_0.26%_|_0.03%_|_0.69%_|_0.19%_|_0.30%_|
|**_Advanced economies_**||||||
|Initial productivity (MER)|-0.191***|-0.185***|-0.125***|-0.079*|-0.101***|
|Initial productivity (PPP)|-0.310***|-0.420***|-0.237**|0.092|-0.294***|
|_Convergence rate (MER, annual)_|_2.12%_<sup>_∗∗∗_</sup>|_2.04%_<sup>_∗∗∗_</sup>|_1.34%_<sup>_∗∗∗_</sup>|_0.82%_<sup>_∗_</sup>|_1.74%_<sup>_∗∗∗_</sup>|
|Convergence rate (PPP, annual)|_3.71%_<sup>_∗∗∗_</sup>|_5.45%_<sup>_∗∗∗_</sup>|_2.71%_<sup>_∗∗∗_</sup>|_-0.88%_|_3.48%_<sup>_∗∗∗_</sup>|
|Observations (All)|98|98|98|98|392|
|Observations (EMDE)|69|69|69|69|276|
|Observations (AE)|29|29|29|29|116|
|_Note:_|||_∗_p_<_|0.1; _∗∗_p_<_0.05;|_∗∗∗_p_<_0.01|

Note: Decade dummies are used in the panel specification but country fixed effects are not. Initial productivity is the average of log productivity over the 10-years in the preceding decade, measured in US dollars at 2010 prices and exchange rates. Productivity growth is calculated as the change in average log-productivity between the two decades (10-year average growth). Productivity is assumed to grow at its average rate between 2010-8 for the final year of the decade, 2019.

27
21. Demand fell across all sectors, but the decline was larger for goods than for services. Retail sales collapsed as uncertain consumers postponed durable goods purchases and their holidays. The automotive industry observed a sharp fall in demand, which led to large-scale restructuring, with considerable job losses (Figure 11). Chrysler and General Motors filed for bankruptcy (in May and June 2009, respectively) and the U.S. Treasury rescued the companies by becoming equity shareholders, as part of the $787 billion fiscal stimulus package put in place by the U.S. government.

<!-- Start of picture text --> Figure 11: U.S. Monthly Retail Sales<br>390 80<br>380 75<br>370 70<br>360 65<br>350 60<br>340 55<br>330 50<br>2006-01 2007-01 2008-01 2009-01 2010-01 2011-01<br>Monthly retail sales, total Motor Vehicle and Part Dealers (right )axis)<br>Source: Federal Reserve.<br>US$, millions US$, millions<br><!-- End of picture text --> 22. Capacity utilization rates fell sharply during the crisis. Starting in September 2008, the production and capacity utilization levels for manufacturing declined abruptly (Figure 12). As demand for durable goods collapsed, capacity utilization rates in manufacturing plummeted. The automobile sector was hit particularly hard, as its capacity utilization rates reached historic low levels, below 40 percent. The restructuring of most automobile companies was inevitable given the low levels of capacity utilization and the bankruptcy filing of many of them.

16
# 2.4 Feedback mechanisms in development programs

The intervention we study is akin to a ‘citizen report card’ experiment. This concept was first developed by civil society organizations in Bangalore, India, in 1993 in response to concerns about the quality of public services. Citizen report cards provide citizen feedback on various programs, and are meant to serve as a social accountability mechanism that identifies poor performance and stimulate improvement (World Bank, 2001). They are part of an approach to making services work for the poor that emphasizes “client power.” This approach has gained prominence in the last decade; the World Development Report 2004 “Making Services Work for Poor People” emphasizes the need to increase poor citizens’ voice and participation.<sup>10</sup> However, as Duflo and Banerjee (2006) document, despite widespread enthusiasm, few programs that increase beneficiary control have been rigorously evaluated, and the evidence so far is mixed. In addition, feedback mechanisms appear to yield varying degrees of impact on quality and quantity of service provision, as well as on usage. To some extent, this could be attributable to differences in implementation protocols, which are typically not formally tested. For instance, as Duflo and Banerjee (2006) discuss, mediated vs. mechanized protocols could mean the difference between success and failure for this class of interventions.<sup>11</sup> While, ultimately, all citizen report cards and feedback mechanisms prompt users to give feedback on services, there is no consensus on the best way to elicit that feedback.

Most citizen report card interventions in development programs target the delivery of health and education services. The few rigorous evaluations that have been done are also specific to those sectors. Bjorkman and Svennson (2009) show that community monitoring of

> 10 World Bank Development Report (2004).

> 11 As cited in Banerjee and Duflo (2006), see examples of mediated feedback process in Kremer and Chen (2001) and mechanized process in Duflo et al. (2011).

8