rafmacalaba/datause-encoder-data huggingface dataset:
- **2.3 Welfare impact of high sugar prices: Effects on household income and poverty incidence**

**How much would a decline in Kenyan sugar prices, driven by a move towards a more competitive market, affect consumer welfare?** To have a closer look at this question we will examine the effects on consumers of a sugar price decrease of 20 percent, rounding up the scenario discussed in section 2.1 – removal of licensing, a reduction in the import tariff to 25 percent together with cutting some of the costs of imports. The decreases that would be possible in sugar prices through policy reform exceed these, but this kind of decrease represents a realistic goal in the shorter term (where sugar would be treated in the same way as other finished goods in the EAC at a tariff of 25 percent).  We will show that even a 20 percent decrease in sugar prices has surprisingly large effects on incomes and the incidence of poverty in Kenya.

**Figure 7: Mean annual household expenditure on sugar** **_per adult equivalent_ (left panel), Mean annual household expenditure** **_share_ on sugar (right panel), both by income decile**

_Source: KIHBS (2005/06), author calculations._

**Sugar is an important good for Kenyan households, and particularly the poor.** Households spend more on sugar as they become richer, but the _proportion_ of their income spent on sugar falls progressively. The left hand panel of Figure 7 shows household annual per adult equivalent<sup>46</sup> expenditure on sugar by income decile. We can see quite clearly that higher income households consume more sugar per adult equivalent than lower income households, although this trend slows towards the richest end of the distribution.  The right hand side of Figure 7 shows that the household expenditure _share_ (the percentage of their total consumption expenditure) on sugar _declines_ strongly with income.  So as people become wealthier, they spend only a small portion of their additional income on sugar – something that makes intuitive sense.

> 46 An adult equivalent is a measure of household size that is adjusted for children.  The idea is essentially that children do not require as much as adults and so a household of an adult and two children would have a lower per adult equivalent than three (which would be the adult equivalent of a household of three adults).

14
performance.<sup>10</sup> We also include exchange rate regime categorization and an index for controls on capital account, both of which come from data with the IMF Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER).

# **Table 6: Factors Associated with Exchange Rate Depreciation, April-August 2013 (including other macro variables)**

|**Dependent Variable: P**|**ercent ch**|**ange in No**|**minal E**|**xchange R**|**ate betw**<br>|**een April-**<br>|**August, **|**2013**|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Increase in Current Account Deficit,|||||||||
|2010-12 over 2007–09|0.20**|0.21**|0.20*|0.19*|0.20**|0.16|0.13|0.22**|
||[2.19]|[2.05]|[1.98]|[1.95]|[2.07]|[1.55]|[1.13]|[2.31]|
|Avg. Annual % Change<br>in RER, 2010-2012|-0.35**|-.39***|-.42**|-.49***|-.38***|-.38***|-.29**|-.37***|
||[2.30]|[2.84]|[2.66]|[3.37]|[2.79]|[2.96]|[2.27]|[2.76]|
|Size (External Financing, 2010–12,|||||||||
|Log)|1.2***|1.3***|1.2***|1.1**|1.2***|1.1***|.96**|1.2***|
||[3.07]|[3.28]|[3.13]|[2.71]|[3.08]|[3.20]|[2.31]|[3.10]|
|Reserves/M2 Ratio, 2012|-1.17|-0.36|0.10|-0.64|-0.58|-1.92|-3.61|-1.22|
||[0.41]|[0.13]|[0.03]|[0.23]|[0.21]|[0.60]|[1.20]|[0.42]|
|Real GDP Growth , 2012|0.08<br>[0.30]||||||||
|General Public Debt, 2012||0.02<br>[0.82]|||||||
|Fiscal Deficit 2012, % of GDP|||0.13<br>[0.67]||||||
|Inflation, 2012||||0.10**|||||
|||||[2.10]|||||
|Capital Control Index, 2012|||||0.01||||
||||||[0.49]||||
|Increase in Capital Controls Index,|||||||||
|2010–12||||||0.19*|||
|||||||[1.82]|||
|Exchange Rate Regime, 2012|||||||.61***<br>[2.72]||
|World Governance Indicator, 2012||||||||0.22<br>[0.22]|
|Observations|43|42|43|43|43|43|42|43|
|R-squared|0.49|0.51|0.50|0.52|0.49|0.54|.58|0.49|
|Adj. R-squared|0.43|0.44|0.43|0.46|0.43|0.48|.52|0.43|

Note: An increase in real exchange rate (RER) is depreciation; we take average annual percent change in RER during 2010, 2011 and 2012. Current account deficit (CAD) is current deficit as percent of GDP; we take average annual increase in CAD during 2010–12 over 2007–09. Robust t statistics are in parentheses. *** indicates the coefficients are significant at 1 percent level, ** indicates significance at 5 percent, and * significance at 10 percent level.

We also calculate changes in controls on capital flows between 2010 and 2012. For the business environment or institutional quality, we include “Doing Business” ranking of the 10 We also calculated change in inflation rate in 2010–2012 over 2007–2009, but it is not significant in the regressions. For GDP growth we also consider values in 2013 Q1, or growth forecast for 2013.

12
**Figure 10 – People living in rural East Asia Pacific by income level and population size**

<!-- Start of picture text --> Papua New Guinea<br>Cambodia<br>Timor-Leste<br>Vietnam<br>Myanmar Lao PDR Thailand<br>60<br>Agrarian Threshold (60%)<br>PhilippinesIndonesia China<br>Mongolia<br>Malaysia<br>Korea, Rep.<br>New Zealand<br>Australia<br>Japan<br>0<br>1000 2000 4000 8000 16000 32000<br>GDP 2010 per capita (2005 $)<br>Share of total population living in rural areas, 2010<br><!-- End of picture text -->

**Source: World Bank (2014a). Note: Data are for 2010.**

With regard to labor market regulations, appropriately designed and implemented policies will help Vietnam’s people better grasp economic opportunities in a risky world. The minimum wage, in particular, will be better able to fulfill this role if it is used as an instrument to correct market failures in distributing gains in productivity between employers and employees, rather than as an instrument to fight poverty or guarantee a certain level of living standard. Other policy and regulatory instruments are much more effective – possibly in combination with the minimum wage – for assuring that people who work full time are able to live above (or rather substantially above) the poverty line. In contrast, the global evidence on the effectiveness of minimum wages in reducing poverty and inequality is checkered at best. At the same time, moderate minimum wages have been effective in correcting labor market failures caused for instance by monopsonistic competition or information asymmetry. In the short run, Vietnam’s private sector would benefit if the country moderated the growth of minimum wage adjustments. In the medium term, stakeholders should refocus their attention from monthly to hourly minimum wages. This will help shift the attention from a desire to use minimum wage

- 23 -
Foreign Investment across the Belt and Road: Patterns, Determinants and Effects<sup>1</sup> Maggie Xiaoyang Chen<sup>2</sup> and Chuanhao Lin<sup>3</sup> JEL: F2, F1 Key words: foreign investment, the Belt and Road Initiative, transportation cost, time, growth

> 1 We are grateful to Chunlin Zhang, Michele Ruta, Caroline Freund, Daniel Reyes, Jose Ramon Perea, Ana Fernandez, and participants of the World Bank Belt and Road Initiative Study Author Workshop for helpful comments and suggestions and Haixiao Wu for excellent research assistance. <mark>Funding from the China World Bank Partnership Facility is also acknowledged.</mark>

> 2 George Washington University. Email: <u>xchen@gwu.edu.</u>

> 3 George Washington University. Email: <u><mark>linch@email.gwu.edu.</mark></u>
that would provide credible evidence against increasing indebtedness of the left-out households due to MFI penetration.

To provide some suggestive evidence on the changes in loan sizes and market shares over time, we utilize the panel dataset. The number of households who reported borrowing from informal sources in either of the two survey years is small (189 in 2000 and 134 in 2007). Thus the sample size is not adequate for a formal econometric analysis of the loan size variations across households that borrow from informal sources. A closer look at the data reveals some obvious coding mistakes for the loan size data, leading to very large outliers in the amount of loans. For instance, the largest borrower in 2007 borrowed some 1.05 million taka, but it is a household with only 0.14 hectare of land, less than primary education for its head and with only one worker who is self-employed in agriculture. To avoid undue influence of dubious outliers, we restrict our analysis to loan amounts of Taka 50, 000 or less, thus dropping of about 4.4 percent of the sample. The proportions of households which had not been MFI members in both years (“never member”) in both full and restricted samples are similar to each other. Note that focusing on the restricted sample may also be desirable because this is indeed the main clientele of MFI lenders. We also performed some robustness checks by restricting our sample to loan amount of Taka 100,000 or less. Overall results reported here remain unaffected. Loan outstanding numbers for both years are deflated using the consumer price index with base year 2005.

Figure 1 plots the average sizes of loans from different sources for both years. The loan size for each category in each year is estimated from data on households which reported positive borrowing. The average loan sizes are substantially higher for MFI loans compared with informal loans in both years. While average loan sizes have increased for both MFIs and informal sources, it declined in the case of bank loans. Even with somewhat larger increase, average size of informal loan is still lower than that of MFI loans in 2007 (Tk. 8,073 vs. Tk. 8,681).

Is the increase in average size of informal loan sufficient to more than offset the decline in the propensity to borrow from informal sources between 2000 and 2007? To answer this question, we report in the upper panel of Table 6 the average loan sizes when households with no loans are also included in the sample. Average loan size in this case thus incorporate any change in the borrowing rate from each source. For the full sample, the average size of informal loan in 2007 is 38 percent lower than that in 2000. Most dramatic decline in loan sizes happened for the households that became member of MFI after 2000 (“new members”, 22 percent of sample). These households were not member of MFI in 2000, and borrowed about Taka 1245 from informal sources in that year. After becoming member, their borrowing from informal sources declined to Taka 251 in 2007. Even for households which were not members of MFIs in either of the years (“never members”, 52 percent of the sample), the average size of informal 25
Table 1. Summary Macroeconomic Indicators, 1980-89

||1980|1984|1989|
|---|---|---|---|
|Real GNP growth rate (percent)|-1.1|6.0|1.7|
|Resource gap, minus-surplus (in<br>percent of GNP)|5.5|2.8|-0.9|
|Average annual change in wholesale<br>|90.3|50.3|69.6|
|price index (percent)||||
|<br>Public sector borrowing requirement,|10.3|6.5|5.6 1/|
|PSBR, (in percent of GNP)||||
|<br>Annual growth in broad money<br>(percent)|66.3|58.0|73.3|
|In percent of GNP||||
|<br>Exports (fob)|5.0|14.7|14.6|
|Imports (fob)|12.9|20.6|19.9|
|Trade balance|-7.9|-5.9|-5.2|
|Current account balance|-5.8|-2.9|1.2|
|Non-interest current account balance|-3.9|0.3|4.8|
|In billions of US Dollars||||
|Debt outstanding and disbursed|16.3|20.7|41.0|
|<br>Debt service|1.0|4.0|7.2|
|Debt over GNP (percent)|27.9|41.2|51.0|
|Debt over exports (percent)|443.9|210.2|216.4|
|Debt service ratio 2/ (percent)|27.8|41.1|37.8|

Source Statistical Appendix, Tables 1-2 1/ Likely to be revised upward to around 6.0 percent when final results of SEEs become available.

- 2/ Debt service over exports of goods and services plus workers'

remittances.

The shift to tradables meant that the resource gap was cut in half. The public sector borrowing requirement (PSBR) was also cut significantly. So was inflation, although less success was achieved in reducing the growth in money supply. The most conspicuous progress was in terms of exports, whose share of GNP nearly tripled to 14.7 percent. Sluggish domestic demand and booming demand in oil-exporting Middle Eastern countries were the main reasons in addition to the change in relative prices. Exports to OECD countries also increased rapidly.

16. The rapid increase in exports permitted both a sizable increase in imports and a major reduction in the trade and current account deficits. By 1984 a small non-interest current account surplus had even emerged. In the 8
<!-- Start of picture text --> 6007 ---<br>500 === Production ‘H<br>w 400 '<br>< t<br>e<br>Lt '<br>xoQ Hni<br>® 300<br>Ke)Cc f<br>200<br>100<br>1980 1990 2000 “2010<br><!-- End of picture text --> ~~ee~~
# Table 6: The value of market access from Belt and Road projects when built in complement

|||||||Model|Specificati|on|With|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||Labor|Labor|θ=2,|θ=8.1,||Oxford||factor|||
||Base-|share|share|lower|agri-|θ=15.7,|Econ.|National|realloc-|||
|US$ Millions|line|β=0|β=0.8|bound|culture|mining|GDP|Population|ation|Min.|Max.|
|Panel A) Annual benefits ---ΔV||||||||||||
|China-Indochina Peninsula Economic Corridor|273|167|297|125|384|547|442|832|<br>292|125|<br>832|
|New Eurasian Land Bridge|34|21|37|16|47|65|56|105|<br>37|16|<br>105|
|China-Central Asia-West Asia Economic Corridor|26|15|28|12|35|47|38|78|<br>28|12|<br>78|
|Bangladesh-China-India-Myanmar Economic|28|16|30|14|36|44|46|89|<br>31|14|<br>89|
|China-Mongolia-Russia Economic Corridor|24|14|26|11|33|47|42|65|<br>26|11|<br>65|
|China-Pakistan Economic Corridor|18|10|19|9|23|28|22|59|<br>20|9|<br>59|
|All Projects Built in Complement|417|251|455|194|579|806|667|1,290|<br>452|194|<br>1,290|
|Panel B) Present value of benefits with 6% disc|ount rate, 0|% growth -|--ΔV/0.0|6||||||||
|China-Indochina Peninsula Economic Corridor|4,548|2,776|4,957|2,088|6,401|9,121|7,371|13,867|<br>4,874|2,088|<br>832|
|New Eurasian Land Bridge|568|347|619|268|781|1,075|930|1,750|<br>616|268|<br>105|
|China-Central Asia-West Asia Economic Corridor|428|250|469|206|579|790|634|1,300|<br>466|206|<br>78|
|Bangladesh-China-India-Myanmar Economic|460|260|506|225|607|741|762|1,483|<br>510|225|<br>89|
|China-Mongolia-Russia Economic Corridor|392|241|427|179|553|789|693|1,083|<br>428|179|<br>65|
|China-Pakistan Economic Corridor|293|161|323|145|385|470|368|983|<br>328|145|<br>59|
|All Projects Built in Complement|6,951|4,180|7,591|3,240|9,654|13,433|11,114|21,500|<br>7,531|3,240|<br>1,290|
|Panel C) Present value of benefits with 6% disc|ount rate, 5|.5% growth|--- (ΔV*|1.055)/(0.|06-0.055)|||||||
|China-Indochina Peninsula Economic Corridor|57,582|35,150|62,761|26,435|81,038|115,474|93,312|175,552|<br>61,708|26,435|<br>832|
|New Eurasian Land Bridge|7,195|4,397|7,841|3,399|9,889|13,610|11,773|22,155|<br>7,794|3,399|<br>105|
|China-Central Asia-West Asia Economic Corridor|5,414|3,164|5,934|2,614|7,332|9,996|8,029|16,458|<br>5,897|2,614|<br>78|
|Bangladesh-China-India-Myanmar Economic|5,821|3,292|6,404|2,849|7,680|9,378|9,649|18,779|<br>6,455|2,849|<br>89|
|China-Mongolia-Russia Economic Corridor|4,962|3,054|5,402|2,270|6,998|9,990|8,775|13,715|<br>5,416|2,270|<br>65|
|China-Pakistan Economic Corridor|3,706|2,037|4,091|1,835|4,869|5,950|4,657|12,449|<br>4,159|1,835|<br>59|
|All Projects Built in Complement|87,999|52,924|96,096|41,024|122,218|170,056|140,699|272,190|<br>95,345|41,024|<br>1,290|

47
2. Step 2 : Compute the share of each state _i_ in the national population of the children of schoolgoing age. This paper used the national population of 12 to 14 years old, as computed by CONAPO.

Pop. Share of State _i_ = (Population of 12-14 Age in State _i_ ) / (National Pop. of 12-14 Age) 3. Step 3 : Multiply the total available funds for transfers to states with the respective population shares computed in Step 2 to arrive at each state’s share of federal transfers. This paper used the Total Federal transfers for Secondary Education provided by SEP.

# FORMULA 2  Productivity adjusted allocation

The impact of Federal education transfers in increasing enrollments differs among states. It can be shown that under this situation, if the Government is aiming at maximizing enrollment, it should adjust the per student formula by factors reflecting the elasticity of enrollment with respect to the Federal subsidy of one state relative to that of another. The uniform per-student allocation formula is a special case that is obtained if the elasticities are uniform for all states. LopezAcevedo (1999a) shows that differences in enrollment elasticity among states can be significant at lower secondary education (see table 1 in the annex ).<sup>1</sup> It also shows that given the current pattern of coverage, states which lag behind in enrollment rate and income tend to have higher elasticities. This implies that under this formula those lagging states would be given relatively more Federal transfers per child. In other words, the states with the lowest enrollment rates are likely to show the largest gains for each additional peso of federal subsidy and thus should be favored, if higher overall enrollments and assistance to poorer states are the goals. Efficiency and equity objectives coincide. Thus Formula 2 adds an adjustment to the Formula 1 allocation based on the number of potential students in a state.

1. It can be shown that enrollment is maximized if a state is allocated an amount that is proportional to its enrollment elasticity with respect to federal education spending.  That is, a state _i_ with an enrollment elasticity that is twice that of state _j_ should be given twice as much money. Enrollment elasticity here refers to the percentage change in enrollment per percentage increase in Federal education spending per child.  The amount of transfers to be allocated to a state _i_ for a given total budget, following the above concept, can be computed using the following steps.

1. Step 1.  Get estimates of enrollment elasticity for each state  i  (ALPHAi).

> 1 ✩ Based on Probits of Enrollment of Secondary School Aged Children using ENIGH 1994.  The elasticity used is the highest in the range of elasticties across the Mexican states - states with elasticity of a similar magnitude included Guerrero, Hidalgo, Nayarit and San Luis Potosí. Note that these are point elasticities, evaluated at the mean of federal transfers and enrollment probability for each state, but in the estimation of additional funding, the elasticity is assumed to be constant over the 10% and 20% intervals used as the two scenarios.

4
was six times as high (22.3 percent). The recent crash was not associated with either pos‐ itive or negative equity shocks.

Third, in contrast to the other crashes, the appreciation of the U.S. dollar was as‐ sociated with greater volatility during the crashes of 1991/92 and 2014/15. A 0.5 percent appreciation in the exchange rate is associated with a 39.6 percent increase in variance during the 2014/15 crash and a 12.1 percent increase during the first Gulf War crash.

For robustness, we re‐estimate the parameters for each crash period using three sample windows—the full sample, the relevant sub‐sample (either the pre‐boom period before 2004 or the boom period starting in 2004), and the 250‐day window that ends with the end of each crash (Table 3). The results indicate that the mean volatility (estimated using the GARCH specifications) for each crash period is similar for sample windows of different sizes. Similar to the previous estimates, the conditional variance for the latest crash period is estimated to be between 4.3 percent and 6.1 percent while estimates of the conditional variance of the earlier crashes are about four times as high (ranging from 15.2 to 24.9 percent). Finally, we estimated the model for the post‐2000 period using the Brent benchmark and the MSCI world equity index; the results were substantively similar for the relevant periods.

# **IV. Conclusion**

It has often been argued that we are now in an era of higher “commodity price volatility” (Dobbs et al 2011; McNally and Levi 2011). In this context, it is tempting to assert that the 2008/09 and 2014/16 crashes reflect structural changes in commodity markets that have engendered greater oil price volatility. However, this note documented that oil price vol‐ atility during the 1985‐2003 period was nearly identical to that of the 2004‐2015 period (Table 1).

We show that there are two key differences between the 2008/09 and 2014/15 oil <!-- Start of picture text --> 5 —<br><!-- End of picture text --> _— 5 —_
**Table 1. Several low-income countries are achieving the MDGs**

|**Selected Millennium**<br>**Development Goal**|**Low-income countries that h**|**ave achieved the goal**|**Low-income countries that ar**|**e on track to achieve the goal**|
|---|---|---|---|---|
|<br>**_Poverty_**|· Cambodia<br>· Kenya<br>· Mauritania||· Central African Republic<br>· Ethiopia<br>· Ghana||
|**_Universal primary_**<br>**_education_**|· Myanmar<br>· Tajikistan||None||
||· Tanzania||||
|**_Gender parity in primary_**<br>|· Bangladesh|· Mauritania|· Benin<br>·|Sierra Leone|
|**_education_**|· Gambia, The|· Myanmar|· Burkina Faso<br>·|Solomon Islands|
||· Ghana|· Rwanda|· Burundi<br>·|Togo|
||· Haiti|· Tanzania|· Cambodia||
||· Kenya|· Uganda|· Comoros||
||· Kyrgyz Republic|· Zambia|· Ethiopia||
||· Madagascar|· Zimbabwe|· Guinea||
||· Malawi||· Nepal||
|**_Gender parity in_**|· Bangladesh||· Gambia, The<br>·|Nepal|
|**_secondary education_**|· Kyrgyz Republic||· Malawi<br>·|Rwanda|
||· Myanmar||· Mauritania||
|**_Under-five mortality rate_**|None||· Bangladesh<br>·|Madagascar|
||||· Eritrea<br>·<br>· Lao PDR|Nepal|
|**_Access to safe drinking_**|· Afghanistan|· Malawi|· Benin||
|**_water_**|· Burkina Faso|· Nepal|· Cambodia||
||· Comoros||· Guinea||
||· Gambia, The<br>· Ghana<br>· Korea, Dem. Rep.||· Uganda||
||· Kyrgyz Republic||||
|**_Acces to sanitation_**|· Lao PDR<br>· Myanmar||· Rwanda||
|’|<br>· Tajikistan||||

_Source:_ Authors’ calculation based on World Development Indicators database (as of March 2011). _Note:_ List of low-income countries is based on fiscal year 2011 World Bank classification; see table A1.13 in World Bank (2011a).

**Table 2. Lagging countries are surprisingly close to getting on target**

||**Average di**<br>**All ff tt ti**|**stance to getting on target (gaps, %)**<br>**Countries that are**|
|---|---|---|
||**o arge counres**|**close to the target**<br>**far from the target**|
|**MDG 1.a Extreme poverty**|39(96)|17<br>67|
|**MDG 1.c Hunger**|19(60)|9<br>35|
|**MDG 2.a Primary education completion**|20(96)|9<br>40|
|**MDG 3.a Gender parity in primary education**|7(22)|4<br>14|
|**MDG 3.a Gender parity in secondary education**|16(52)|8<br>29|
|**MDG 4.a Child mortality under five**|23(59)|8<br>38|
|**MDG 5.a Maternal mortality**|32(80)|11<br>51|
|**MDG 7.c Access to safe drinking water**|25(76)|14<br>41|
|**MDG 7.c Access to sanitation**|27(50)|16<br>34|
|_Source:_Authors’ calculations based on data from<br>**Simple average**|the World Development In<br>23|dicators database<br>11<br>39|

_Source:_ Authors’ calculations based on data from the World Development Indicators database. _Note:_ A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap). Figures in parentheses indicate the range of variation (Maximum value – Minimum value) of countries off target, by MDG. Averages and numbers of countries cover only those with data--and that may vary by MDG.

8