rafmacalaba/datause-encoder-data huggingface dataset:
<u>(84; 1.311) (55; 0.993) (84; 0.052) (53; 0.091)</u>

* The table reports the average predicted and observed transaction costs in Malawi kwacha per kilogram of maize (constant 2000 prices) in levels and shares (expressed as a percentage of sales prices, all summarized by supplying district). The predicted levels (the first column) are within sample predicted per unit transaction costs from estimated transaction cost equations, and the observed levels (the second column) are averages based on survey data on transaction costs from Fafchamps et al. (2005). Likewise, shares are based on predicted transaction costs and on trader survey data. Numbers of observations and standard deviation are given in parentheses (.) below the mean. The t-test reports the (absolute) t-statistic of the difference in average values.

Predictions of per unit transaction costs by rural region or urban center vary from 1.3 to 1.8, with a standard deviation between 1 and 1.3. In this exercise, all prices are Malawi kwacha per kilogram in constant 2000 prices. The overall average of predicted unit transaction costs is 1.6 (median: 1.3). Survey observations have per unit transaction costs by rural region or urban center varying from 1.5 to 2.3. The overall average of unit transaction costs from the trader survey is 1.7 (median: 1.4). Predicted shares of transaction costs in terms of selling price by rural region or urban center vary from 6 to 22 percent, with an overall average of 9 percent (median 6 percent). Survey observations of transaction costs shares by rural region or urban center vary from 20 to 21 percent, with an overall average of 21 percent (median 20 percent). The equality test confirms equality for the unit transaction costs and rejects equality for shares (both with the exception of the rural north, possibly because of the number of observations).

We attribute the rejection of equality for shares to the difference in the type of data used for this exercise. Survey data on purchase and sale prices reported by traders are (and should be) well below market prices. The available evidence indicates that market prices, on average, are 9 to 38 percent above the sales prices reported by traders. In addition, the trader data refer to a single completed trade transaction. Such documented transactions are likely to bias transaction costs upward. The observed differences between purchase and sales prices in the survey data also contain a component of trader profit that is not well captured in the estimations. Conversely, monthly aggregate district market prices are averages computed over one month and a range of transactions among a range of traders and other market participants. The process of aggregation 22
drought and flood at the regional level positively correlates with drought and flood reconstructions based on MERRA-v2. This makes it unlikely that households “systematically” mis-reported the occurrence of these shocks, thus as well as the others.

# **III. Factor Analysis**

A nice detailed discussion of Factor Analysis is provided in Rencher and Christensen (2012) Chap. 13. A brief review and implementation is provided in STATA 13 Manuals and references therein.<sup>23</sup> Interested readers may consult STATA’s documentation. All technical presentations are omitted here for brevity.

> 23 See <u>http://www.stata.com/manuals13/mvfactor.pdf.</u>

44
publicly. Thus the higher public share in Latin America is actually _counteracting_ the effects of the higher levels of income inequality and average income.

**Table 7. Decomposition of differences in under-five mortality inequality between Latin America and Sub-Saharan Africa**

||_SSA_|_LAC_|_Contributiontodifference_|
|---|---|---|---|
|CIU5MR(actual)|0.089|0.172|0.083|
|Ginicoefficient|0.459|0.550|0.002|
|GNPpercapita atPPP|1352.105|3946.250|0.053|
|Public healthspendingaspercenttotal|46.616|48.992|-0.002|
|Predicted|0.106|0.159|0.054|
|Residual|-0.017|0.013||

The final column in Table 6 allows us to quantify the impacts of the various variables in terms of their contribution to explaining the gaps in health inequality between the two regions. This column is derived by applying the coefficient of each independent variable to the regional difference in the means of that variable. For example, the difference in the Gini coefficients is equal to 0.555-0.459 = 0.091. Applying the coefficient on the Gini index of-0.028 from Table 6, gives the figure of 0.002 in Table 7. The results in Table 7 suggest only a tiny part of the explanation of the higher health inequalities in Latin America are due to the higher levels of income inequality there. This small effect is offset entirely by the counterbalancing effect of the higher share of public financing in that continent. The higher (explained) health inequality in Latin America is almost totally attributable to its higher levels of GNP per capita.<sup>7</sup>

# **4. Corroboration and Explanation**

The results in Tables 6 and 7 provide only a little comfort to policymakers. While the coefficient signs suggest that reducing income inequality and raising the share of health expenditure financed publicly might reduce health inequalities, neither effect is at all strong and neither is statistically significant. What the results do suggest, however, is something very _sobering-economic growth tends to lead to increases in health inequalities, not reductions._ Countries that are successful in raising their per capita incomes will typically pay a price in terms of higher health inequalities. We are likely to see low and persistently low levels of health inequality in poor countries that do not grow.

Two questions immediately arise. First, is this finding on the effect of rising income consistent with the evidence to date on _changes_ in socioeconomic inequalities in health? In other words, have health inequalities widened during periods of economic growth? Second, what is the reason for the apparent tendency of health inequalities to increase with rises in average income?

> 7 There is, of course, an additional unexplained component, due to the fact that according to the model Latin America has more health inequality than expected and Sub-Saharan Africa somewhat less.

_23_
field experiment in Uganda." _(No. w31570). National Bureau of Economic Research._

- Bandiera, Oriana, Niklas Buehren, Robin Burgess, Markus Goldstein, Selim Gulesci, Imran Rasul, and Munshi Sulaiman. 2020. "Women’s Empowerment in Action: Evidence from a Randomized Control Trial in Africa." _American Economic Journal: Applied Economics_ 12 (1):210-259.

- Banerjee, Abhijit, and Sandra Sequeira. 2023. "Learning by searching: Spatial mismatches and imperfect information in Southern labor markets." _Journal of Development Economics_ 164.

- Barham, Tania , Karen  Macours, and John A. Maluccio. 2018. "Are Conditional Cash Transfers Fulfilling Their Promise? Schooling, Learning, and Earnings After 10 Years." _CEPR Discussion Paper_ DP11937.

- Barham, Tania, Karen Macours, and John A. Maluccio. 2024. "Experimental Evidence from a Conditional Cash Transfer Program: Schooling, Learning, Fertility, and Labor Market Outcomes after 10 Years." _Journal of the European Economic Association_ :jvae005. doi: 10.1093/jeea/jvae005.

- Barrera-Osorio, F., L. L. Linden, and J. E. Saavedra. 2019. "Medium- and LongTerm Educational Consequences of Alternative Conditional Cash Transfer Designs: Experimental Evidence from Colombia." _Am Econ J Appl Econ_ 11 (3):54-91.

- Barrera-Osorio, Felipe, Andreas de Barros, and Deon Filmer. 2024. "Long-term impacts of primary school scholarships: Evidence from Cambodia." _Journal of Policy Analysis and Management_ 43 (1):10-38. doi: <u>https://doi.org/10.1002/pam.22533.</u>

- Bassi, Vittorio, and Aisha Nansamba. 2022. "Screening and Signalling NonCognitive Skills: Experimental Evidence from Uganda." _The Economic Journal_ 132 (642):471-511.

- Bastagli, Francesca , Jessica  Hagen-Zanker, Luke  Harman, Valentina  Barca, Georgina   Sturge, Tanja  Schmidt, and Luca  Pellerano. 2016. "Cash transfers: what does the evidence say? A rigorous review of programme impact and of the role of design and implementation features." _ODI Policy Brief_ .

- Beam, Emily A. 2016. "Do job fairs matter? Experimental evidence on the impact of job-fair attendance." _Journal of Development Economics_ 120:32-40. doi: <u>https://doi.org/10.1016/j.jdeveco.2015.11.004.</u>

- Beaman, Lori, Sylvan Herskowitz, Niall Keleher, and Jeremy Magruder. 2021. "Stay in the Game: A Randomized Controlled Trial of a Sports and Life Skills Program for Vulnerable Youth in Liberia." _Economic Development and Cultural Change_ 70 (1):129-158.

- Behrman, Jere, Susan Parker, and Petra Todd. 2008. 219Medium-Term Impacts of the Oportunidades Conditional Cash-Transfer Program on Rural Youth in

31
|HUNGARY:|TAB<br>SELECTED|LE**1**<br>)ECONOMI|C INDICATO|RS||
|---|---|---|---|---|---|
||i989|1990|1991|1992<br>Prel.|1993<br>Proj.|
|_r;_<br>''<br>n7rowth<br>(an_val_change. %)|0.4|-3.3|-10.2|-4.5|-2.0|
|o/w**indutiai**prduction|-2.0|-7.7|.16.7|-10.0|**2.0**|
|hxpotuONFS (volume indices)|1.2|-2.7|-8.7|**4.7**|-10.0|
|**Imporu**GNFS (volume indices)|1.8|-4.6|-7.7|-4.1|**2.0**|
|Cu..t<br>Account<br>(million dolls)|.1438|**123**|**267**|314|-1;00 -|
|**_(%_**of GDP)|-4.9|fl.4|**0.b**|0.9|4.5|
|Direec Forcign Investmnent<br>**(million**dollan)|187|**311**|1459|1415|1300|
|Consolida<br>statebudget||||||
|**(%**of CDP)|-1.3|-0.1|_-4.5_|-7.6|-7.0|
|CPI<br>**(annual**<br>**Mte**of change,**%)**|17.0|28.9|_35.0_|23.0|2-24|
|PPI (avenge)<br>**(annual**mte of change, %)|**15.4**|22.0|32.6|11.5|10-12|
|NomiMua<br>devaluation<br>(ate of**c'ange, %)**|22.0|**5.0**|22.0|_5.5_||

The impact of external shocks and domestic reform on aggregate economic activity has been severe: measured GDP has contractec. 'y a cumulative 18 percent between 1989 and 1992, reflecting the collapse of CMEA trade, the restrictive impact of stabilization po!icies on domestic demand and the falling output in the SOE (State-Owned Enterprise)<sup>sector.</sup> Production in both industry and agriculture declined by 30-35%, while the rate of consumer price inflation accelerated and peaked at _35%_ in 1991, declining thereafter to 23% in 1992.' 3 See Solimano (1993a) and (1993b) for a comparative analysis of output behavior during the post-socialist transitions.

_5_
figures, in 2003 international issuance in euro surpassed that in dollars, and by 2005 the euro accounted for about 60 percent of international bond issuance, compared to 30 percent for the dollar.

However, the issuance of euro has been primarily by private sector entities in the advanced countries rather than developing country sovereign or non-sovereign issuers. And developing country issues are concentrated among European countries, especially those that are recent or prospective EU members. Table 1 gives a breakdown of amounts issued in euros by developing countries in 2003-05.

While the amount issued by Eastern Europe borrowers is significant, the issuance by countries in other regions is very modest. The exact definition of international bonds also makes a difference for the ranking of the two currencies, and the ECB takes a more conservative definition, in effect treating the euro area as a single country, so that international euro issues from countries within the euro area are excluded. This definition suggests about equal shares for the dollar and the euro by 2003 (see Table 2). Whatever their relative importance, it remains that the euro very quickly rivalled the dollar in bond issuance. But if we compare the issuance by developing countries in Table 1 with Table 2, we see that euro issuance by developing countries outside Eastern Europe over the 2 3⁄4 years from January 2003-September 2005 (the equivalent of $25 billion, or roughly $10 bn. annually) is only one-twenty-fifth of the total net issuance of eurodenominated bonds (about $250 bn. annually over 2003-2005).

The relative importance of the two currencies has since changed dramatically, however, and there was a remarkable expansion in issuance of dollar securities in 2005Q3-2006Q2, far exceeding the issuance of euro-denominated debt, which stayed roughly constant relative to levels seen in 2003Q3-2005Q2. This expansion in dollar denominated debt, linked to leveraged buyouts in developed countries, has continued more recently. Time will tell if this trend is a durable one.

In other dimensions, the international role of the euro has developed less rapidly (Galati and Tsatsaronis, 2003). In foreign exchange markets, the euro’s role is roughly the same as the deutsche mark’s in the past. The dollar is a much more important vehicle currency, appearing as one of the currencies in a foreign exchange market trade more than twice as often as the euro. The dollar was one of the two currencies in forex transactions in 96 percent of all currency trades in 2004, and the euro only 44 percent.<sup>2</sup> As for exports and imports of goods and services, the euro’s role in invoicing and settlement has seen notable increases in the past two years for euro area countries’ trade outside the euro area, as well as for the trade of EU new member states<sup>3</sup> . Thus, the

> 2 See ECB (2005), p. 29.

> 3 See ECB (2005), chapter 4.

4
# **APPENDIX A: UGANDA**<sup>**8**</sup>

Introduced in the early 20th century, cotton was Uganda’s most important cash crop for several decades. Output reached 60,000 tons of lint during the early 1930s and was sustained at this level for almost four decades, with an annual value of about US$300 million in today’s terms. The success of cotton in Uganda pre‐1970 partly reflects the fact that this was a forced‐labor crop. Output fell drastically as the result of the political and economic turmoil of the 1970s; in just four years it dropped from 78,000 tons of lint to 14,000 tons (1972‐76), and reached a record low of 2,000 tons in 1987.

Uganda’s policy reform program, begun in the late 1980s, addressed both macro and sectoral issues, including in agriculture. Reforms introduced in the cotton sector in 1993, coupled with the high cotton prices of the mid‐1990s, evoked a considerable supply response, with cotton production reaching 20,000 tons in two years. A well‐functioning research program was put in place. Grow‐ ers began receiving prompt payments and entrepreneurial activity increased enormously, following the entry of many private entities at all levels of primary processing, marketing, and trade.

Despite these achievements, however, cotton output in 2005‐09 averaged only about 25,000 tons of lint. In a recent review of Uganda’s cotton sector post‐ reform, Baffes (2009) conjectures that the fundamental issue is low profitability. This partly reflects low productivity—especially in areas with productive soils, cotton has been displaced by higher priced food crops—and partly the fact that cotton is a new crop for many of its producers, who have limited knowledge of its growing requirements.

The history of cotton by‐products in Uganda mirrors that of cotton. Before the reforms of the early 1990s, all cotton seed processing facilities were owned and operated by the cooperative unions or the Cotton Board. After the reforms, all cotton seed processing facilities were privatized, as were the ginning opera‐ tions. Currently, Uganda has five privately‐owned cotton seeds processing com‐ panies, of which four are independent and one is associated with a ginning oper‐ ation. It has 50 ginneries each handling very small volumes—which explains why the crushing facilities are independent entities. The ginning overcapacity reflects the large size of the sector during the 1950s and 1960s.

Demand has determined the location of the cotton seeds processing indus‐ try. While Uganda’s cotton production is spread around the country, all five cot‐ tonseed processing companies are located in or near Kampala, the key oil con‐

> **8** Most of the information in this Appendix is based on Vasilaky (2009).

**_Page 28_**
But with the Thatcher-Reagan revolutions in the late 1970s, the Golden Age receded, and capitalism reverted to the form it had in the late 19<sup>th</sup> century.<sup>11</sup> Capital was already being slowly rebuilt before; but from the late 1970s, with reduced taxes on profits and income (a point which Piketty extensively documents), and the quasi elimination of taxes on inheritance, the rebuilding accelerated and β began its steady climb, reaching in the early 21<sup>st</sup> century values from around a century ago. The growth rate of advanced capitalist economies declined because the convergence came to an end, and both the functional and personal distribution deteriorated; the first moving against labor, the second against everybody but the top 1%.

Why is this a different interpretation of economic history from many others and how does it relate to the _r_ > _g_ inequality? Piketty’s view of the Golden Age is that it was a very special and unrepeatable phenomenon in the history of capitalism. Due to the process of convergence, Europe’s capitalist economies and Japan grew faster than they would have if they were at the technological frontier. Increasing population growth rate also drove _g_ ever higher (note that _g_ is the sum of population growth and the growth of per capita income). On the other hand, institutional factors, including high taxation and the threat of Communism (which Piketty does not mention) kept _r_ low, and thus uniquely in the history of capitalism reversed the inequality _r_ > _g_ (Chapter 10). All positive development during the Golden Age—and this is no exaggeration—flowed from the reversal of that inequality.

Now, the reinterpretation of the 20<sup>th</sup> century economic history of capitalism is obvious if we compare Piketty’s argument with those present in some influential recent books by top economists and economic historians like David Landes’ _The Wealth and Poverty of Nations_ (1999), Angus Deaton’s _The Great Escape_ (2013), Gregory Clark’s _A Farewell to Alms_ (2011), and Daron Acemoglu and James Robinson’s _Why Nations Fail_ (2013). For these authors, the entire period after the Industrial Revolution is seen as a final “enfranchisement” of man from the “brutish and short” Malthusian existence. A wellpublicized graph by Gregory Clark, based on Angus Maddison’s data, illustrates it best: after thousands of years of stagnation, the world ‘s output, starting from the Industrial Revolution, is following an exponential curve to which there is no apparent end. The elixir of economic growth once discovered

> 11 The Thatcher-Reagan revolution was driven, Piketty writes (Chapter 2, p. 164), by the factually correct idea that the US (and to a lesser extent UK) preeminence was being eroded. But this fact was wrongly interpreted as being due to the bloated welfare state rather than to the general catch-up of the war-ravaged capitalist economies of Europe. In other words, the Thatcher-Reagan revolution changed capitalism but failed to raise the rate of growth which was its ostensible motivation in the first place.

8
This demographic shift in DFS usage was furthered by government programs to transfer cash to support struggling households during the pandemic. For example, those dependent on India’s Direct Benefit Transfer (DBT)-based income support programs were compelled to use DFS to access their entitlements (Saroy et al., 2022). More generally, Gentilini et al. (2020) report that nearly 17% of the world’s population was covered by at least one government COVID-19-related digital cash transfer scheme in 2020-2021. Because governments needed to deposit these transfers into a formal account, increased use of digital payments was also associated with the presence of formal financial institutions. In the Indian context, for example, proximity to brick-and-mortar banking establishments was significantly linked to digital payment adoption during the pandemic (Saroy, 2022). And the Findex 2021 data reveal that 39 percent of adults in developing economies opened their first financial account for the purpose of receiving a direct government payment (such as a wage or benefit payment) or a wage payment from a private-sector employer, and that most of those accounts were with brick-and-mortar financial institutions (Demirguc-Kunt et al., 2022). Finally, there is also evidence that the pandemic-induced increase in use of digital payments persisted after the initial waves of COVID-19 had subsided (Jonker et al., 2022; Ardizzi et al., 2020).

Despite the proliferation of studies on the role of digital payments during COVID-19, what has been lacking is systematic global data on drivers of DFS usage and, until the release of the 2021 Global Findex data, on DFS usage itself during the pandemic, and accompanying empirical analysis to measure the relative impacts of those drivers.<sup>7</sup> This paper is an attempt to begin filling this research gap. We first establish the wide variation across countries during the COVID-19 period in the increase in digital payments and usage of digital financial services. Our

> 7 Sahay et al. (2020) note that, “to the best of our knowledge, there are no comprehensive global studies on fintech and financial inclusion, reflecting in part the limited availability of cross-country data.”

6