rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Final specification of country-specific VARX*( _p, q, r_ ) models

||||3 comm<br>Oil, A|odity model<br>gri, & Metal||Sin|gle c|ommodit<br>Oil only|y model|
|---|---|---|---|---|---|---|---|---|---|
||_p_|_q_|_r_|AIC|selected|_p_|_q_|_r_|AIC|
|arg|3|1|1|707.4|O-M|3|1|1|706.4|
|austlia|3|1|1|2887.9|O-A-M|3|1|1|2878.3|
|bra|3|2|1|903.5|M|3|2|1|902.5|
|can|3|2|1|3209.2|O-M|3|2|1|3204.9|
|china|3|1|1|1850.3|A|3|1|1|1839.7|
|chl|3|2|1|1577.8|O-A|3|1|1|1564.6|
|euro|2|1|1|3344.0|O-M|2|1|1|3335.2|
|india|3|1|1|1905.4|M|3|1|1|1894.4|
|indns|3|1|1|1295.4|A|3|1|1|1285.3|
|japan|3|1|1|2945.9|A-M|3|1|1|2938.4|
|kor|3|2|1|2558.9|A|3|2|1|2553.1|
|mal|3|1|1|2043.7|O-A-M|3|1|1|2037.3|
|mex|3|2|1|1366.2|O-A|3|2|1|1364.1|
|nor|2|2|1|2743.8|O-A-M|2|1|1|2726.9|
|nzld|3|2|1|2662.9|M|3|1|1|2657.5|
|per|3|2|1|859.9|O-M|3|2|1|858.8|
|phlp|3|1|1|1659.9|O-A|3|1|1|1659.4|
|safrc|2|1|1|2650.3|O-A|2|1|1|2639.9|
|sarbia|2|1|1|1443.1|O-A-M|2|1|1|1441.8|
|sing|2|1|1|2100.5|O-A|2|1|1|2089.9|
|swe|3|1|1|2826.8|O-A-M|3|1|1|2810.3|
|switz|3|2|1|3120.8|O-A|3|2|1|3112.2|
|thai|2|1|1|1735.0|O-A|2|1|1|1729.3|
|turk|3|2|1|1216.8|A|3|2|1|1214.6|
|uk|2|1|1|3128.2|O-M|2|2|1|3120.7|
|usa|3|1|na|3230.6|O-A-M|3|1|na|2817.1|

Note: Please refer to Table A1 in the Appendix for a glossary of acronyms. The specification used is Equation (4), where _p_ =lag length of domestic variables (maximum lag is three), _q_ =lag length of foreign variables (maximum lag is two), and _r_ =lag length of global variables(maximum lag is one). AIC = log _L_ - (number of parameters). Each model includes at most one automatically detected intercept-shift dummy, at the significance level of 5 percent. A column labelled “selected” reports the set of commodity prices included in the final version of VARX* models, where O, A, and M means the oil, agriculture, and metal prices, respectively. For the US, the commodity prices are treated as its endogenous variables, thus the value of _r_ is not available (na).

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Delhi, as shown in Appendix Figure C1.<sup>8</sup> Appendix Table D5 provides some summary statistics of the survey sample. Compared to non-users, bus users (both new and continuous) are predominantly aged between 31 and 40, more often homemakers, more likely to reside in households with a monthly income exceeding |20,000, and less inclined to travel for work. Given these differences, we constructed a comparable sample of nonusers, new users, and continuous users using propensity score matching. We match women on the following variables: age, occupation,<sup>9</sup> total average monthly household income and expenditure on travel, ownership of private vehicles, and driving knowledge.

Our study sample of the Delhi primary survey is the matched sample ( _n_ = 1, 290) consisting of 184 non-users, 182 new users, and 924 continuous users. Compared to the treated sample in the CPHS data, the matched sample is characterized by higher levels of education and annual household income. Approximately 68 % of the bus users in the matched sample hold a graduate degree, and 85 % of them report an annual household income of over |240,000.

**Perceptions of Buses** In Table A1, we compare perceptions of buses between non-users and users (panel A), as well as between new users and continuous users (panel B) in the post-scheme period. We examine the differences in their perceptions of five aspects: 1) affordability and availability of bus transit; 2) safety regarding accidents, crashes, threats, and thefts; 3) connectivity; 4) bus frequency, waiting time, travel duration, and unnecessary stops; 5) accessibility to bus stops. In panel A, we can see that non-users consistently give lower ratings across all five perspectives; they find bus travel less satisfactory across all five dimensions compared to users. They are particularly concerned about safety issues. The average rating concerning safety among non-users is 1.63, indicating a level of satisfaction that falls between highly unsatisfactory and unsatisfactory. In panel B, we find that compared to continuous users, and new users tend to give a lower rating to safety (0.13 points lower) but higher ratings to affordability and the availability of free commutes. Specifically, new users, on average, rate affordability of buses 0.19 points higher than continuous users, and this difference is statistically significant at a 1% level. These results suggest that when it comes to transportation, women prioritize not only safety concerns but also _affordability_ . Reducing costs in public transportation thus has the potential to encourage women to choose buses as a viable option for job search and

> 8The generation points are all major locations within the city where trips originate or are attracted to. These include major work areas, shopping districts, and schools.

> 9The occupation variable consists of the following categories: service, business, informal worker, daily wager, homemaker, and student.

7
many of the regressions. Stanbic’s relatively heavy reliance on government securities is therefore already controlled for, and thus the results from the profitability, costs, and credit growth regressions for Stanbic-UCB cannot be ascribed to this factor. A general decline in the attractiveness of government securities as an investment alternative (as suggested by the figures for 2003 and 2004 in Figure 6) and the natural tendency to shift away from these securities in the wake of a privatization make it likely that Stanbic’s government securities share will decline in the future.

Will all sectors benefit equally from increased lending? Figure 7 suggests not. In the preprivatization period, Stanbic lent about 60 percent of its portfolio to the manufacturing sector. Merging its operations with UCB reduced that figure to 40 percent in 2002, but the decline continued to about 20 percent in 2004. Lending to manufacturing by the composite bank is at its lowest level since 1996. These figures indicate a true shift in strategy for Stanbic, away from its traditional base, manufacturing, and toward other types of lending.

# **Share of Lending Devoted to Manufacturing**

<!-- Start of picture text --> M 0.7<br>A<br>0.6 Stanbic<br>N<br>U 0.5<br>F UCB<br>0.4<br>S<br>0.3 UCB+Stanbic<br>H<br>(Virtual)<br>A 0.2<br>Sector<br>R<br>0.1<br>E<br>0<br>1996 1998 2000 2002 2004<br><!-- End of picture text -->

**Figure 7**

The bank-level analyses lead to at least three conclusions. First, portfolio quality improved just prior to the UCB-Stanbic privatization, and Stanbic has maintained that level. Of course, all new loans are good loans, and thus one would expect the share of NPLs to rise over time, though not to UCB’s levels in the late 1990s. Second, cost 21
_<mark>Economic Surveys</mark>_ <mark>,</mark> _<mark>36</mark>_ <mark>(1), 214-234.</mark> **<mark>[</mark> 4]** <mark>Choquette-Levy, N., Wildemeersch, M., Oppenheimer, M., & Levin, S. A. (2021). Risk transfer policies and climate-induced immobility among smallholder farmers.</mark> _<mark>Nature Climate Change</mark>_ <mark>,</mark> _<mark>11</mark>_ <mark>(12), 10461054.</mark> **<mark>[</mark> 1]** <mark>Clement, V., Rigaud, K. K., de Sherbinin, A., Jones, B., Adamo, S., Schewe, J., ... & Shabahat, E. (2021).</mark> _<mark>Groundswell Part 2: Acting on Internal Climate Migration</mark>_ <mark>. World Bank.</mark> **<mark>[</mark> 1]** <mark>Clingingsmith, D., Khwaja, A. I., Kremer, M. (2009). Estimating the Impact of The Hajj: Religion and Tolerance in Islam's Global Gathering,</mark> _<mark>T</mark> he Quarterly Journal of Economics_ <mark>, Volume 124, Issue 3, August 2009, Pages 1133–1170.</mark> **<mark>[</mark> 2]** <mark>Cottier, F., & Salehyan, I. (2021). Climate variability and irregular migration to the European Union.</mark> _<mark>Global Environmental Change</mark>_ <mark>,</mark> _<mark>69</mark>_ <mark>, 102275.</mark> **<mark>[</mark> 1]** <mark>Cui, X., & Feng, S. (2020). Climate Change and Migration.</mark> _<mark>Handbook of Labor, Human Resources and Population Economics</mark>_ <mark>, 1-15.</mark> **<mark>[</mark> 1]** <mark>Dallmann, I., & Millock, K. (2017). Climate variability and inter-state migration in india.</mark> _<mark>CESifo Economic Studies</mark>_ <mark>,</mark> _<mark>63</mark>_ <mark>(4), 560-594.</mark> **<mark>[</mark> 1]** <mark>Davis, K. F., Bhattachan, A., D’Odorico, P., & Suweis, S. (2018). A universal model for predicting human migration under climate change: examining future sea level rise in Bangladesh.</mark> _<mark>Environmental Research Letters</mark>_ <mark>,</mark> _<mark>13</mark>_ <mark>(6), 064030.</mark> **<mark>[</mark> 1]** <mark>De Brauw, A., & Carletto, G. (2012). Improving the measurement and policy relevance of migration information in multi-topic household surveys.</mark> _<mark>Living Standards Measurement Study Working Paper</mark>_ <mark>,</mark> _<mark>14</mark>_ <mark>, 3358986-1199367264546.</mark> **<mark>[</mark> 2]** <mark>De Brauw, A., Mueller, V., & Lee, H. L. (2014). The role of rural–urban migration in the structural transformation of Sub-Saharan Africa.</mark> _<mark>World Development</mark>_ <mark>,</mark> _<mark>63</mark>_ <mark>, 33-42.</mark> **<mark>[</mark> 2]** <mark>Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climateeconomy literature.</mark> _<mark>Journal of Economic Literature</mark>_ <mark>,</mark> _<mark>52</mark>_ <mark>(3), 740-98.</mark> **[3]** <mark>D'Errico, M., Letta, M., Montalbano, P., Pietrelli, R. (2019). Resilience Thresholds to Temperature</mark> 41
# **1 INTRODUCTION AND EXECUTIVE SUMMARY**

**Before the current financial crisis hit progressively high, middle, and low-income countries, the world economy was experiencing a remarkable decrease in macroeconomic volatility.** Since the 1970s output growth volatility had steadily declined in almost every region of the world (Figure 1). In its 2007 World Economic Outlook the IMF reported that volatility had fallen progressively since its 1970s peak, from 3.8 to 2.7 percent of GDP.<sup>2</sup> Furthermore, the frequency of major recessions had also diminished: crisis volatility, which captures the frequency and magnitude of major recessions, declined from an average of more than 4 percent of total volatility at the end of the 1970s, to less than 1 percent at the beginning of the new century. The decrease in macroeconomic volatility was even more significant for non-OECD East Asian countries, were volatility dropped from an average of 7.85 percent in the beginning of the 1970s, to an average of 2.35 percent in 2006. This process began at a very strong pace in the 1970s, slowed down in the 1980s and 1990s (a spike can be even observed during the 1997/98 financial crisis), but gained momentum again at the beginning of the new century driving volatility below pre-crisis levels.

**This is good news, as output growth volatility negatively affects in the short term poor households adopting detrimental coping strategies, and, in the medium term, economic growth.** Output growth volatility reduces people’s welfare through its effect on the labor market, earnings, and consumption. Households, in particular poor ones, have limited capacity to insure against shocks. Their earnings and consumption patterns follow therefore closely overall macroeconomic trends, increasing in good times and decreasing in bad ones, and the very limited ability of these poor households to insure against shocks makes them often adopt coping strategies that can have long-term harmful consequences, in particular on future generations. Moreover, macroeconomic volatility has a negative and robust impact on GDP growth. Our estimates indicate that if the standard deviation of the growth rate of per capita GDP in East Asia would decrease from 1.4 percent (the volatility in the period 19962000) to 0.9 percent (the average level observed in OECD countries over the same period), the growth rate would increase by 0.48 percentage points. Reducing volatility therefore improves both household welfare and economic performance.

> 2 World Economic Outlook (2007), _Globalization and Inequality_ , Chapter V: “The Changing Dynamics of the Global Business Cycle,” www.imf.org

2
consolidation and to fight inflation, however, the effects of these reforms are not expected to be seen in the short-term.

Since 2019, Sudan has been experiencing soaring rates of inflation, which were partially driven by the higher energy and staple food prices (Figure 1). More recently, the Russia-Ukraine crisis that started in March 2022, has further exacerbated the country’s social and economic problems, through Sudan’s higher import costs of wheat,<sup>4</sup> given the high dependency of the country on imported wheat and food and the country’s trade connections to Russia and Ukraine.<sup>5</sup> In particular, Sudan is expected to face significant negative food security implications as food prices reach historic peaks due to the Russian invasion of Ukraine. All of the economic and social problems that have negatively impacted Sudan are expected to have increased the number of poor in the country, which represented about 61.1% of the population based on the latest available household survey in 2014/2015.

_Figure 1 – Evolution of inflation rate (%), 2019 - 2022_ <!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Inflation rate (%)<br>Percent<br>January-19 March-19 May-19 July-19 September-19 November-19 January-20 March-20 May-20 July-20 September-20 November-20 January-21 March-21 May-21 July-21 September-21 November-21 January-22<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on data obtained from the Central Bureau of Statistics (CBS) of Sudan, and World Bank Currently, there are efforts in Sudan to rollout a new Social Safety Net Program, which seeks to assist the most food insecure households in the most affected states and localities, by providing unconditional cash and food transfers to beneficiary families in 11 states of Sudan. The Sudan Emergency Safety Nets Project

> 4 Sudan Monthly Update, March 2022, World Bank, Macroeconomics, Trade and Investment (MTI) Global Practice

> 5 Sudan imports 50 percent of its wheat from Russia and 4 percent from Ukraine.

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OZIER - REPLICATION REDUX – p. 31 of 41 (particularly since most of these efforts are successful given code availability, as in Figure 3), but the discovery of a problem in re‐running an analysis _is_ new information. The problem is not limited to editors’ survey responses: Gertler, as chair of 3ie’s board of directors, saw many of the 3ie replications unfold firsthand. He and his coauthors report that of 27 replication studies 3ie commissioned, 20 upheld the original results, while 7 reported being “unable to fully replicate the results in the original article.” Galiani, Gertler, and Romero note: “The only replication published in a peer‐reviewed journal claimed to refute the results of the original paper,” citing the present case of Davey, et al. (2015).

Before making any recommendation, however, Galiani, Gertler, and Romero have led by example, following in a long tradition. Replication efforts that focus on a single paper face a problematic set of incentives surrounding what makes an interesting publication. Replication efforts that group together a large number of papers have more options, particularly in relation to how that work is distilled in short summaries and media reports: the fraction of a category of papers that proves to be replicable, by some standard, is of interest to readers, no matter whether it is high or low. This kind of multi‐paper exercise can also be combined with other types of analysis. Galiani, Gertler, and Romero (2018) examined hundreds of papers, combined their analysis with a survey of journal editors, and published their analysis in _Nature_ . Camerer, et al. (2016), for example, examined 18 prominent lab‐ experimental economics papers for their results’ reproducibility using a new set of experimental participants, and combined it with predictions of replicability. Eleven (61 percent) of 18 studies’ results were replicated successfully; the paper describing the effort was published in _Science_ . Camerer, et al. (2018), examined 21 experiments across the social sciences, found that 13 (62 percent) replicated in a general sense, but discovered that the replicated effects were generally smaller than the originals; this analysis was published in _Nature Human Behaviour._ Humphreys (2015) observes that slight changes in levels of statistical significance in a single re‐ visited study should not be able to radically shift the academic or policy community’s beliefs (or the
According to the National Statistical Office, there are at least five factors that explain the level of economic growth achieved between 1996 and 2001. These include: (1) a good performance of the export sector, particularly coffee, cocoa and cotton; (2) investments associated with the privatization program; (3) the expansion of the timber industry; (4) increased salaries in the public sector<sup>8</sup> ; and (5) job creation and multiplier effects associated with the construction of the Chad-Cameroon pipeline. The National Statistical Office also explains that the poor performance of the economy between 2001 and 2007 is due mainly to the fact that growth occurred in low productivity sectors such as the urban informal sector and traditional agriculture.

The data presented in table 2.1 also reveal a significant amount of inequality in the distribution of per adult equivalent expenditure. The share of the richest decile is equal to almost 12 times that of the poorest decile in 1996, about 11 times in 2001 and 9.3 times in 2007. Furthermore we note that, for all three years, the share of expenditure of every decile up to the sixth is strictly less than its population share (10 percent). For the seventh decile, the share of expenditure is about 8 percent in 1996, and a little over 10 percent in 2001 and 2007. Table 2.3 shows that the Gini measure of overall inequality has hovered around 40 percent in 1996 and 2001 and declined slightly to about 39 percent in 2007.

# **2.2. Changes in Poverty over Time**

Figure 2.1 presents a picture summarizing the evolution of aggregate poverty from 1996 to 2007 based on TIP curves associated with poverty measures which are members of the Foster-Greer-Thorbecke (FGT) family. The acronym TIP stands for the _three I’s of poverty_ because the curve provides a graphical summary of _incidence_ , _intensity_ and _inequality_ dimensions of aggregate poverty based on the distribution of poverty gaps (Jenkins and Lambert 1997)<sup>9</sup> . These dimensions are shown as follows: (1) the length of the non-horizontal section of the curve reveals poverty _incidence_ ; (2) the _intensity_ aspect of poverty is represented by the height of the curve; and (3) the degree of

> 8 No indication is provided as to whether this salary increase reflected gains in productivity.

> 9 This curve is constructed in four steps: (1) rank individuals from poorest to richest on the basis of the welfare indicator **y** ; (2) compute the relative poverty gap of individual **i** as **gi=max{(1-yi/z), 0}** where **z** is the poverty line; (3) form the cumulative sum of the relative poverty gaps divided by population size; and (4) plot the resulting cumulative sum of poverty gaps as a function of the cumulative population share.

7
studies focused on the relevance of the opportunity cost for money particularly in the short run in addition to the redeployment of classical modeling technologies in the short and long runs using partial adjustment models and auto-regressive distributed lag models.

In his paper, Ball (2012) challenged the conventional wisdom about the instability of the short-run money demand by examining quarterly data for the US from 1959 through 1993. Following the work of Hoffman and Rasche (1991) and Stock and Watson (1993), Ball interpreted long-run money demand as a co-integrating relationship among real narrow money balances M1 interest rates and output. To explain short-run deviations from the long-run relationship, Ball used Goldfeld’s partial-adjustment model. The key innovation of his paper is the choice of the interest rate in the money demand function. Instead of using a short-term market rate, such as the treasury bill rate or the commercial paper rate, Ball used the average return on ‘‘near-monies.’’<sup>9</sup> His findings showed that the long-run money demand is stable regardless of whether the interest rate is measured by the return on near monies or a money-market rate (the T-bill rate). However, the deviations of money holdings from their long-run levels are smaller with the return on near monies. In addition, Goldfeld’s partial-adjustment model yielded reasonable parameter estimates using the return on near monies, and it is not rejected in favor of a less structured error-correction model. Most importantly, he found that the deviations of money holdings from the predictions of the model are small. Thus, there is little evidence of shifts in the money demand function, even in the short run.

Hossain (2012) also challenged the conventional wisdom of the instability of money demand function in developed countries since the mid-1980s. He investigated the presence of an economically meaningful, stable narrow money demand relationship using annual data for Australia for the period 1970–2009. He employed an Auto-regressive Distributed-lag (ARDL) cointegration approach. The results suggested the presence of a long-run equilibrium relationship between real narrow money balances, real income, a representative domestic interest rate (e.g., the yield on Australian government short-term bonds) and the nominal effective exchange rate of the Australian dollar. The statistical tests suggested no significant instability in the narrow money demand relationship despite financial deregulation and innovation in Australia since the early

> 9 Near-monies is defined as savings accounts and money market mutual funds that are close substitutes for M1.

11