rafmacalaba/datause-encoder-data huggingface dataset:
Economics and Policy in Africa, University of Pretoria.

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22
**Figure 3: Do You Understand the Definition of the Fiscal Rule in Your Country?** Number of respondents

<!-- Start of picture text --> Skipped question,<br>31<br>Yes, it's a<br>No, I don't quantitative rule<br>understand, 20 that limits the<br>deficit, debt . . .,<br>85<br>Yes, all of the<br>above, 40<br>Yes, it's a<br>medium-term Yes, it's an annual<br>fiscal target, 8 budget target, 13<br><!-- End of picture text --> Source: Online survey conducted by the World Bank in late 2018. Some of the possible answers are abbreviated in the Figure.

# **Several other noteworthy results emerged from the survey:**

- Respondents who thought fiscal rules were not being followed were most likely to blame the public-sector wage bill and other current spending. Other cited factors included tax exemptions and low tax collection and the circumvention of the rules, by statistical tricks or cuts in maintenance and public investment.

- But most respondents (around two-thirds) thought fiscal rules contributed positively to budgeting, accounting, and fiscal performance in their countries. Of those who did not believe this, some pointed to a lack of transparency (32 percent), and some said governments did not respect the rules (20 percent).

- Most of the respondents said that the rules were determined to satisfy the requirements of international creditors. They pointed to a lack of political will, a lack of knowledge, and corruption, poor management, and lack of supervision over implementation as issues.

- Most respondents (85 percent) believed that the reports on fiscal rules produced by fiscal watchdogs were credible. Some 42 percent said that the establishment of a fiscal council or an expert independent body would encourage prudent fiscal policies. But many also implied that stronger transparency and oversight of the enforcement of fiscal rules— especially in election years—was needed. Some thought that compulsory sanctions for noncompliance with fiscal rules should be put in place.

18
|**posit**<br>2009, where the dependent variable is a<br> month if the client deposited less than<br>l. The variable `Group Liability Loan`<br>ual liability loan. `Loan Amount` refers to<br>MFI branches in the sample. `Number of`<br>`ge in Months` is the diference between<br>e includes clients who received both an<br>, are given in parentheses beneath each<br>(4)<br>-0.187**<br>(0.084)<br>0.000<br>(0.000)<br>0.093<br>(0.131)<br>0.016*<br>(0.010)<br>0.657***<br>(0.125)<br>Yes<br>Yes<br>0.513<br>2929<br>0.205|
|---|
|**ulsory De**<br> to March <br>a particular<br>month leve<br> an individ<br>one of the <br>an. `Loan A`<br> The sampl<br>vidual level<br>(3)<br>-0.238***<br>(0.038)<br>0.000<br>(0.000)<br>0.000<br>(0.036)<br>-0.002<br>(0.018)<br>0.000<br>(0.003)<br>0.382***<br>(0.046)<br>No<br>No<br>0.055<br>2929<br>0.205|
|**ssed Comp**<br>anuary 2008 <br>lue of 1 for <br>e individual-<br>oan, 0 if it is <br> dummy for <br>he current lo<br>sed, plus 1. <br> at the indi<br>(2)<br>-0.238***<br>(0.037)<br>0.000<br>(0.000)<br>0.001<br>(0.036)<br>0.381***<br>(0.044)<br>No<br>No<br>0.055<br>2929<br>0.205|
|**riable: Mi**<br> data from J<br>es on the va<br>ons are at th<br>up liability l<br>`ampura` is a <br>ved before t<br> was disbur<br>rs, clustered<br>(1)<br>-0.234***<br>(0.035)<br>0.391***<br>(0.031)<br>No<br>No<br>0.054<br>2929<br>0.205<br>|
|**Table 6: Dependent Va**<br>This table reports results from OLS regressions with panel <br>dummy for missing a compulsory deposit. This dummy tak<br>the required amount of Rs. 100, and 0 otherwise. Observati<br>`Dummy` is a dummy variable equal to 1 if the client had a gro<br>the amount the client received for that particular loan. `Behr`<br>`Previous loans` is the number of loans that the client recei<br>the month of the observation and the month when the loan<br>individual and a group liability loan. Robust standard erro<br>point estimate.<br>Group Liability Loan Dummy<br>Loan Amount<br>Behrampura<br>Number of Previous Loans<br>Loan Age in Months<br>Constant<br>Control for Calendar Month<br>Individual FEs<br>R-squared<br>N<br>Mean of Dep Var<br>* _p <_0_._10, ** _p <_0_._05, *** _p <_0_._01|

27
<!-- Start of picture text --> a<br>o conooono oo olhw@ @; 80<br>=<br>o ° o 60 :£<br>e<br>Hoo on © pono gg 40 6<br>g” Gg Coon a<br>2<br>[5 ooo SLE Een ag oO a<br>el et eee Qo Ss<br>===su=) H<br>oe = =6coom ao Solio<br>ao Oo a 0<br>0 10 20 30 40 50 60 70 80<br><!-- End of picture text --> Collected Tariff Rate (%) Source: Department of Customs, Nepal Note 1: Number of total observations is 256971 Note 2: 58421 observations where statutory rate > collected rate Note 3: 36 observations where statutory rate < collected rate
the extent of specialization in vertically differentiated goods, will determine a country’s rate of economic growth. Finding convergence between countries within a good (i.e. countries which produce at a quality level that is further away from the frontier quality will see faster improvements over time in the quality of their output), he argues that countries producing goods with little differentiation (for instance, commodities), will grow more slowly than those producing goods with a higher degree of differentiation.<sup>3</sup> With these issues in mind, this paper studies empirically the dynamic evolution of the quality of exports across countries using highly disaggregated data on their trade with the United States over the time period 1990-2001. Our main findings are as follows. To begin, we find evidence of unit value divergence. That is, non-OECD countries, on average, experience lower growth rates in the unit values of their exports compared to OECD countries. Since OECD exports are characterized by higher unit values to begin with, there is divergence in quality taking place over time. Furthermore, this occurs despite a pattern of conditional convergence in quality over time, i.e., ceteris paribus, within product categories, varieties with lower initial quality levels experience faster growth in quality. The data suggest that the divergence in quality between OECD and non OECD countries is partially driven by the product mix (i.e., the fact that products exported by the OECD experience intrinsically higher growth rates), consistent with the argument of Hausmann, Hwang and Rodrik (2007) that what you export does matter for growth. However, it is also driven by the higher average quality growth rate in OECD countries, controlling for product mix, suggesting that other country-specific factors impeding convergence are at work. Finally, we see that there is very little technological “leap-frogging” by countries. The quality of new exports, on average,

> 3Furthermore, he argues that this is an important explanation for the differences in economic growth rates across developing countries; countries that were producing in sectors where the technological frontier was not far ahead grew slowly while others that were in goods where the frontier was much further ahead grew faster because of the greater ”impact from convergence”.

3
9

**Control variables** . We also include a set of control variables that are commonly used in poverty equations: overall income per capita ( _GDP per capita_ ) to control for economic development, a measure of human capital level ( _Education_ ); a variable of financial deepening ( _Private credit/GDP_ ); growth of the consumer price index ( _Inflation_ ) to control for the macroeconomic environment; and an indicator of institutional quality ( _Law and order_ ) drawn from the International Country Risk Guide (ICRG) database which measures the strength and impartiality of the legal system, and the popular observance of the law.

Data sources are provided in Appendix 3.

# **C- Methodology**

The poverty regression presented above poses several challenges for estimation. The first is the presence of unobserved period- and country-specific effects. The second challenge is that most explanatory variables could be jointly endogenous with poverty, so we need to control for the biases resulting from simultaneous or reverse causation. For this purpose, we estimate the coefficients of our model by using the System Generalized Method-ofMoment (GMM) estimator developed by Blundell and Bond (1998). The estimator combines two sets of equations. The first set includes first-differenced equations where the right-hand-side variables are instrumented by the levels of the series lagged one period or more. The second set consists of the equations in levels with the right-hand side variables being instrumented by lagged first or higher-order differences.

The consistency of the GMM estimators depends on whether lagged values of the explanatory variables are valid instruments in the poverty regression. We examine this issue by considering the Hansen test of over-identifying restrictions. The no rejection of the null hypothesis implies that instrumental variables are not correlated with the residual and are satisfying the orthogonality conditions required. A serial correlation test is also carried out and demonstrates that the errors exhibit no second-order serial correlation.

# **D- Results**

A look at the plots in Appendix 4 which compare the trade-poverty relationship in the top country group and the bottom country group in terms of financial development, education and governance,<sup>3</sup> suggests that the effect of trade openness on poverty would depend on a variety of country characteristics. Indeed, for each conditional variable considered, the

> 3 Countries are classified in the top (bottom) group if they belong to the top one-fourth (bottom threefourths) of a rank distribution given by each conditional variable (financial development, education level and quality of institutions).
27 (Hausmann and Hidalgo 2012). Such changes will make the manufacturing space accessible to more countries, with the concomitant reduction of manufacturing jobs in the advanced countries. Manufacturing will therefore provide more long-term economic benefits to African economies than other activities: it still generates economies of scale, sparks industrial and technological upgrading, fosters innovation, and has big multiplier effects.

# **_The Hidden Treasures of a Multi-polar World_**

Human history is certainly not a linear or a teleological process but one can conjecture with some level of confidence that today’s low-income countries could still benefit enormously from the new job opportunities made possible by the economic progress observed in many large emerging economies over the past three decades. The world economy has changed dramatically in the period 1980-2010. Emerging and developing economies now represent about half of global GDP (measured in PPP terms), as shown in Figure 4.

**Figure 4. GDP based on PPP, share of world (Percent of World)**

<!-- Start of picture text --> 80<br>Projected<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Advanced economies Emerging market and developing economies<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> Source: IMF Data Mapper, October 2012 The upcoming economic “graduation” of large emerging countries such as China, Brazil or India form low-wage, labor intensive industries opens up enormous growth and job-creation opportunities (an estimated 119 million manufacturing sector jobs will have to be relocated to low-income countries as estimated by Lin (2011) to low-income countries—provided that they can organize themselves to attract these jobs with more competitive wages and lower transaction
_20_ **_How_** _Fast_ **_Has Chbwae Inidut Growe?_** This last result seems quite improbable,<sup>and once again calls attention to the possibility of</sup> upward bias in available measures of real<sup>output growth fnr the collective sector.</sup> A final point about e'nergy data. There<sup>is a variety of material suggesting that</sup> overestimates of output growth may not be confined to<sup>TVE enterprises and the collective sector.</sup> Some examples: Energy consumption per 10,000 yjan of output<sup>in Beijing's electric power industry dropped</sup> from 4.62 to 4.07 tons of standard coal<sup>during 1986/87, indicating a rise of 13.6 percent in</sup> energy productivity, even though coal consumption<sup>per kldowatt-hour</sup> of power produced did not change [Beijing 1988, pp. 291, 372].

Nationally, the electric power industry<sup>reports a 7.5 percent drop in unit energy</sup> requirements during 1980/85 (Table 11-B)<sup>even though coal consumption per kwh for large</sup> power plants (6000 kw and above), which<sup>produce a large share of total power output, declined</sup> by only 3.6 percent during the same period (Ene gy<sup>1986, p. xxx].</sup> The chemical industry reports thai energy productivity<sup>increased by 12.6 percent in 1984/85</sup> (Table 11-B). During the same perio:'. !owever,<sup>8 of 16 unit energy coeffici:nts for major</sup> plants actually increased! The remainiig<sup>eight coefficients declined by an average of</sup><sup>_5.8_</sup> percent. Only 1 of 16 coefficients for major<sup>plants declined by more than the reported</sup> industry-wide average (Energy 1986, p. 87].

The building materials industry reported a<sup>20 percent rise in energy productivity during</sup> 1980/85, but unit energy requirements at major<sup>plants decline by a maximum of 6.3 percent</sup> [Energy 1986, p. 88] Reported energy productivity in coal production<sup>improved in every year but 1984/85 (Table</sup> l 1-B), showing a cumulative gain of nearly<sup>five percent. Yet data for major enterprises show</sup> a rising treiid for unit energy requirements;<sup>power consumption per ton of raw coal rises in</sup> every year [Energy 1986, pp. 86, 493-500]

# V. CONCLUSIONS

This survey reveals considerable evidence<sup>pointing to the existence of upward bias in</sup> measures of China's real industrial output<sup>during the past decade. The issue is not whether such</sup> bias exists, but whether or not its presence<sup>substantially alters our perception of the rate and</sup> pattern of Chinese industrial growth.

To clarify this issue requ res an investigation<sup>of the possible extent of upward bias. This in turn</sup> will require an analysis of possible links<sup>between upward bias, which itself is difficult to</sup> observe, and other economic patterns that<sup>may be more readily measurable.</sup>
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- <mark>Kramarski, B., & Zeichner, O. (2001). Using technology to enhance mathematical reasoning: Effects of feedback and self-regulation learning.</mark> _<mark>Educational Media International</mark>_ <mark>,</mark> _<mark>38</mark>_ <mark>(2-3), 77-82.</mark>

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- <mark>Lemov, D. (2015).</mark> _<mark>Teach like a champion 2.0: 62 techniques that put students on the path to college</mark>_ <mark>. John Wiley & Sons.</mark>

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53
It is interesting that government employees took a hit to their (generally comfortable) standard of living in 2008 and 2010, but not in 2009. This is consistent with the idea that most public servants have relatively stable incomes, which makes them vulnerable to changes in the prices of the goods and services that they purchase. In contrast, those working in the informal sector (“basic sales and services”) did very well in 2008 and 2010, but less so in 2009.

**Table 5.  Measuring the Impact of the 2008-09 Recession on Real Expenditure per capita by marital status, education, occupation, and sector of work**

||Real|Change in|real expenditure|per capita c|ompared to||
|---|---|---|---|---|---|---|
||expenditure||2007:|Q1||Number of|
||per capita|2007|2008<br>_baht/month_|2009|2010|households|
|Widowed|4,054|-145|-123|-4|14|38,396|
||||_0.90_|_0.28_|_0.87_||
|Separated|4,735|-96|-299|13|-167|16,943|
||||_0.25_|_0.11_|_0.36_||
|Attends school|3,746|-66|15|**134**|169|83,638|
||||_0.25_|_0.03_|_0.53_||
|Attends school, aged 15-19|4,177|-97|**165**|189|266|24,910|
||||_0.02_|_0.81_|_0.43_||
|Primary education only|3,362|-62|-82|**101**|30|125,009|
||||_0.40_|_0.00_|_0.10_||
|Lower secondary education only|4,181|-176|-23|24|27|41,688|
||||_0.20_|_0.58_|_0.97_||
|Upper secondary education only|5,199|21|106|271|164|39,395|
||||_0.40_|_0.13_|_0.32_||
|Upper vocational education|6,423|-307|**289**|-125|**0**|14,799|
||||_0.02_|_0.07_|_0.01_||
|Higher education (to bachelor)|9,090|-42|102|182|420|32,066|
||||_0.54_|_0.71_|_0.27_||
|Post-graduate higher education|13,786|194|274|559|112|4,879|
||||_0.90_|_0.78_|_0.66_||
|Ill/disabled|3,102|129|295|161|311|7,616|
||||_0.28_|_0.46_|_0.41_||
|Government employee|7,264|-105|**-933**|**67**|**-565**|29,727|
||||_0.00_|_0.00_|_0.02_||
|Other employee|4,975|-40|-152|**207**|**-46**|68,079|
||||_0.30_|_0.00_|_0.03_||
|Self-employed|3,399|-79|-157|**63**|-5|96,242|
||||_0.10_|_0.00_|_0.25_||
|Work in: shop sales|5,348|-156|16|-18|185|25,885|
||||_0.41_|_0.83_|_0.20_||
|Work in: food and market stalls|4,604|-63|7|140|102|11,313|
||||_0.74_|_0.42_|_0.82_||
|Work as: manager|6,138|122|55|227|249|22,323|
||||_0.85_|_0.39_|_0.91_||
|Work as: farmer|3,018|-43|-83|76|78|29,398|
||||_0.58_|_0.08_|_0.98_||
|Work in: subsistence agriculture|2,336|-190|-355|**-68**|-201|11,769|
||||_0.09_|_0.02_|_0.28_||
|Work in: basic sales and services|4,628|116|**635**|**162**|**744**|16,899|
||||_0.01_|_0.02_|_0.00_||
|Work as: professional|9,881|-135|-693|207|-194|14,725|
||||_0.24_|_0.07_|_0.43_||

Losers and Winners in Recession: Thailand 2008-09 20
<!-- Start of picture text --> Share of Chinese urban households,' % Share of total urban disposable income,' %<br>0 0.1 0.1 Segments by annual income 13<br>025oS 01eree 57 |O5=9.4 |04.15.6_ |33 ——______————_© Global affluent 055__2.0>== heeee 6.9 |<br>12.6 . .<br>05 26 a2 : (>200,000 renminbi) 18 36 al<br>© Mass affluent ae<br>(100,001-200,000 renminbi) 216|<br>99.3 MM go g 49.7 © Upper middle class 95.2<br>173 (40,001—100,000 renminbi) 78.4<br>Hi Lower middle class 505M ooo<br>(25,001—40,000 renminbi)<br>HB Poor (<25,000 renminbi) 10.2 fay 99<br>1985 1995 2005 20152 20252 1985 1995 2005 20152 20252<br>66 109 191 280 373 509 1,625 5,132 12,544 26,059<br>100%, millions of urban households 100%, billion renminbi<br><!-- End of picture text -->