rafmacalaba/datause-encoder-data huggingface dataset:
# Appendix 2: Results with robustness checks

FIGURE A.1 Pass-through: One versus two-quarter sign restrictions

# A. Monetary policy shocks

# B. Global demand shocks

<!-- Start of picture text --> 0.6Ratio Interquartile range Median Ratio1.0 Interquartile range Median<br>0.4 0.5<br>0.2 0.0<br>0.0 -0.5<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>C. Domestic demand shocks D. Global supply shocks<br>Ratio Interquartile range Median Ratio<br>0.0 1.0 Interquartile range Median<br>-0.1 0.5<br>-0.2 0.0<br>-0.3 -0.5<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>E. Domestic supply shocks F. Oil price shocks<br>Ratio Ratio<br>0.3 Interquartile range Median 1.0 Interquartile range Median<br>0.2 0.5<br>0.1 0.0<br>0.0 -0.5<br>-0.1 -1.0<br>All Advanced EMDEs All Advanced EMDEs<br>economies economies<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Two-quarters Two-quarters Two-quarters<br>sign restr. sign restr. Benchmark sign restr.<br>Benchmark Two-quarters Benchmark Two-quarters Two-quarters<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Two-quarters Two-quarters Two-quarters<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Two-quarters Two-quarters Two-quarters<br>sign restr.<br>Two-quarters<br>Two-quarters<br>Benchmark sign restr. Benchmark Benchmark sign restr.<br>Two-quarters Two-quarters<br>Benchmark sign restr. Benchmark sign restr. Benchmark sign restr.<br>Two-quarters Two-quarters<br><!-- End of picture text --> Note: Pass-throughs are defined as the ratio between the one-year cumulative impulse response of consumer price inflation and the one-year cumulative impulse response of the exchange rate change to shocks from country-specific factor-augmented vector autoregression models estimated for 51 economies (29 advanced economies and 22 EMDEs) over 1998-2017. A positive pass-through means that a currency depreciation is associated with higher inflation. Bars show the interquartile range and markers represent the median across countries. In the alternative specification, sign restrictions are applied to the current quarter and next quarter. EMDEs = emerging market and developing economies.

39
**Table 1: Examples of very large corporate farms in developing and transition countries**

|**Company**|**Main country (s)**<br>**of operation**|**Commodities**|**Crop area**|**Comment**|
|---|---|---|---|---|
|**Sime Darby**|Malaysia,<br>Indonesia|Oil palm|600,000 ha|Planned investment of $1+ billion in<br>220,000 ha plantation in Liberia|
|**Cosan**|Brazil|Sugarcane-<br>ethanol|300,000 ha own &<br>300,000 contract growers|Shell Oil joint venture to double<br>production with $12 bn investment|
|**El Tejar**|Argentina, Brazil<br>and Paraguay|Grains, oilseeds|660,000 ha +|Expanding to Colombia|
|**Ivolga**|Russia and<br>Kazakhstan|Grains, oilseeds|1,000,000 ha +||
|**Fibria**|Brazil|Fast growth<br>Eucalyptus|500,000 ha|Merger of Aracruz and JVC|

_Source:_ Review of company websites 24
some of the adaptations that farmers make to adjust to climate. Using cross sectional evidence, the paper models how Latin American farmers have adapted to the range of climates across the continent. Surveys of over 2000 farmers provided detailed information about crops, livestock and irrigation choices. Relying on a three stage integrated model of a farm, the choice of farm type, irrigation, and conditional land value were all calculated.

The results show that the choice of farm type and irrigation are very sensitive to climate. Farmers are more likely to pick crops-only in cooler temperatures whereas they will choose livestock in dryer locations. Farmers are more likely to choose a croplivestock combination in hot locations. Farmers will tend to irrigate in locations that are both cool and dry. Of course, irrigation also requires access to water sources.

Conditional land values are also dependent on climate. Cooler than average temperatures increase land values for all farm types except irrigated crop-livestock farms. Increased precipitation raises land values for all farm types. However, the net revenues of some farm types respond more to cooler and wetter conditions than others. The net revenues of livestock farms are especially sensitive to both temperature and precipitation. The net revenues of dryland crop-only farms are very sensitive to cooler temperatures.

Applying these cross sectional results to future climate scenarios reveals some interesting outcomes. If the future climate scenario is very hot and dry, expected land values will fall by a third by 2100. Dryland crop-only farming will be especially hard hit and the amount of irrigation will fall substantially. Crop-livestock operations will be hurt but less so. If the scenario is hot and dry but not as severe, the impacts will have the same qualitative direction but the magnitude of the effect will be much smaller.

20
to 20 percent, additional inventory holdings made necessary by poor logistics systems cost Latin American economies more than 2 percent of GDP [Guasch and Kogan, 2001, 2006].

Whereas U.S. businesses hold inventories equal to about 15 percent of GDP, inventories in Latin America and other developing regions are often twice that amount [Guasch and Kogan, 2006].

**TABLE 2: LATIN AMERICA RATIOS TO U.S. INVENTORIES (ALL INDUSTRIES)**

||**Raw Materi**<br>|**al Inventory L**<br>**(average o**<br>|**evel Ra**<br>**f all av**<br>|**tios: Ratio**<br>**ailable dat**<br>|**to U.S. Leve**<br>**a for 1990s)**<br>|**l by Indust**<br>|**ry**<br>||
|---|---|---|---|---|---|---|---|---|
||**Chile**|**Venezuela**|**Peru**|**Bolivia**|**Colombia**|**Ecuador**|**Mexico**|**Brazil**|
|**Mean**|2.17|2.82|4.19|4.20|2.22|5.06|1.58|2.98|
|**Minimum**<br>|0.00|0.30|0.10|0.11|0.52|0.86|0.42|0.8|
|**1**<sup>**~~st~~**</sup>**Quartile**|0.36|1.87|1.25|1.39|1.45|2.55|1.06|1.6|
|**Median**<br>|1.28|2.61|2.30|2.90|1.80|3.80|1.36|2.00|
|**3**<sup>**~~rd~~**</sup>**Quartile**|2.66|3.12|3.90|4.49|2.52|5.64|2.06|3.1|
|**Maximum**|68.92|7.21|31.1|34.97|13.59|20.61|3.26|7.1|

||**Final Go**<br>|**ods Inventor**<br>**(average o**<br>|**y Level**<br>**f all av**<br>|**s: Ratio to**<br>**ailable dat**<br>|**U.S. Level b**<br>**a for 1990s)**<br>|**y Industry**<br>|||
|---|---|---|---|---|---|---|---|---|
||**Chile**|**Venezuela**|**Peru**|**Bolivia**|**Colombia**|**Ecuador**|**Mexico**|**Brazil**|
|**Mean**|1.76|1.63|1.65|2.74|1.38|2.57|1.46|1.98|
|**Minimum**<br>|0.01|0.10|0.39|0.11|0.19|0.67|0.35|0.75|
|**1**<sup>**~~st~~**</sup>**Quartile**|0.17|0.87|1.17|1.13|1.05|1.67|0.82|1.1|
|**Median**<br>|0.72|1.60|1.54|2.02|1.28|1.98|1.36|1.60|
|**3**<sup>**~~rd~~**</sup>**Quartile**|1.38|2.14|2.11|3.18|1.63|2.86|2.14|2.00|
|**Maximum**|31.61|5.29|3.87|21.31|5.31|7.94|4.91|5.2|

_Source: Guasch and Kogan, 2001._ Other determinants of logistics costs, like loss in sales due to transport interruptions or average time to clear customs, have a direct impact on productivity in Latin America. Investment climate surveys in Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua confirm that most entrepreneurs consider inadequate infrastructure a serious issue. An analysis of these surveys plus Indonesia, conducted for the publication by Fay and Morrison [2007], supports this finding.

11
- Schweinhart, L. J. (2005). _Lifetime effects: the High/Scope Perry Preschool study through age 40_ . Number 14. High/Scope Foundation.

- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. _Journal of the Royal Statistical Society: Series B (Methodological)_ , 58(1):267–288.

- Valk, J.-H., Rashid, A. T., and Elder, L. (2010). Using mobile phones to improve educational outcomes: An analysis of evidence from Asia. _The International Review of Research in Open and Distributed Learning_ , 11(1):117–140.

- Walker, S. P., Chang, S. M., Powell, C. A., Simonoff, E., and Grantham-McGregor, S. M. (2007a). Early childhood stunting is associated with poor psychological functioning in late adolescence and effects are reduced by psychosocial stimulation. _The Journal of Nutrition_ , 137(11):2464–2469.

- Walker, S. P., Grantham-McGregor, S. M., Powell, C. A., and Chang, S. M. (2000). Effects of growth restriction in early childhood on growth, IQ, and cognition at age 11 to 12 years and the benefits of nutritional supplementation and psychosocial stimulation. _The Journal of Pediatrics_ , 137(1):36–41.

- Walker, S. P., Wachs, T. D., Gardner, J. M., Lozoff, B., Wasserman, G. A., Pollitt, E., Carter, J. A., Group, I. C. D. S., et al. (2007b). Child development: Risk factors for adverse outcomes in developing countries. _The Lancet_ , 369(9556):145–157.

- Walsh, C. S., Power, T., Khatoon, M., Biswas, S. K., Paul, A. K., Sarkar, B. C., and Griffiths, M. (2013). The ‘trainer in your pocket’: Mobile phones within a teacher continuing professional development program in Bangladesh. _Professional Development in Education_ , 39(2):186–200.

- York, B. N., Loeb, S., and Doss, C. (2018). One step at a time: The effects of an early literacy .

- text messaging program for parents of preschoolers. _Journal of Human Resources_

33
<!-- Start of picture text --> 400%<br>350%<br>300%<br>250%<br>200%<br>150%<br>100%<br>50%<br>0%<br>St. Lucia 1988Samoa 1991Samoa 1990Mongolia 1996St. Kitts and Nevis1998Vanuatu 1985Vanuatu 1987Dominica 1979St. Kitts and Nevis1995Nicaragua 1972Antigua and Barbuda1995Dominica 1995Honduras 1998St. Lucia1980Honduras 1974Dominican Republic1979El Salvador 1986Tonga 1982St. Kitts and Nevis1989Belize 2000Samoa 1983Samoa 1983Zimbabwe 1981Nicaragua 1998Haiti 1980Georgia1991Jamaica 1988Guatemala 1976Nepal 1987Lao PDR1993<br>Loss per GDP<br><!-- End of picture text --> Fig. 2: 30 largest monetary disaster losses since 1970 Source: own calculations based on data by EMDAT (CRED, 2009); Munich Re, 2008.

Although only a part of these losses are in fact capital stock losses, it seems evident that losing a substantial portion of produced assets will impact the capital accumulation process, affect produced wealth and as a consequence impair income creation. Barrito (2008) suggests that, if factoring in reported disaster losses as the stock losses, capital accumulation may fall significantly and permanently short of regularly reported increments to capital (see Figure 3). Consequently, capital accumulation in vulnerable countries such as El Salvador, Fiji and St. Lucia may be strongly affected by one or multiple events whereas in large and diversified economies no significant effect may be identified.

Given a lower capital accumulation path, it is straightforward to expect adverse welfare effects, such as changes in consumption and consumption volatility over time. The key question we pursue in this paper is to assess whether natural disaster losses can be considered to affect consumption and whether including them in savings measures may help to improve the predictive power of savings constructs. Our entry point is to adjust for disasters by adding the disaster related depreciation in terms of losses of capital stock to the other regular depreciation of capital.

7
<u>Table 2: Summary statistics of within-country and between-country price differentials.</u>

|||||**|Ln(Pi**|**kt/Pjkt)|**||
|---|---|---|---|---|---|---|
||**Road_km**<br>**(average)**|**Road_quality**<br>**(average)**|**Obs**|**Mean**|**Std. Dev.**|**Max-Min**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|**WITHIN-COUNTRY**|||||||
|BDI-BDI|136.8|77.5%|4'629|0.23|0.20|1.65|
|DJI-DJI|227.6|78.4%|222|0.11|0.09|0.54|
|DRC-DRC|465.6|31.5%|1'853|0.55|0.54|2.75|
|ETH-ETH|520.1|53.5%|470|0.23|0.21|1.26|
|KEN-KEN|421.8|96.8%|153|0.23|0.15|0.68|
|MOZ-MOZ|382.2|98.2%|369|0.18|0.16|1.10|
|MWI-MWI|368.6|90.8%|851|0.17|0.13|0.79|
|RWA-RWA|154.5|87.2%|19'856|0.15|0.14|1.10|
|SDN-SDN|604.2|79.4%|166|0.18|0.14|0.68|
|SOM-SOM|424.2|84.3%|653|0.40|0.38|2.00|
|TZA-TZA|507.9|66.0%|441|0.21|0.19|0.97|
|UGA-UGA|333.1|66.7%|170|0.24|0.17|0.83|
|ZMB-ZMB|541.1|91.2%|349|0.21|0.18|0.84|
|Maize|||10'232|0.27|0.30|2.75|
|Rice|||10'832|0.15|0.17|2.00|
|Sorghum|||9'118|0.18|0.18|1.79|
|**Average values**|**206.2**|**81.5%**|**30'182**|**0.20**|**0.23**|**2.75**|
|**BETWEEN-COUNTRY**|||||||
|DRC-BDI|479.0|65.4%|3'638|0.41|0.39|2.72|
|MWI-MOZ|274.0|87.7%|20'785|0.28|0.21|1.40|
|RWA-BDI|427.6|59.3%|7'695|0.36|0.33|2.16|
|RWA-DRC|577.4|35.7%|49|0.39|0.28|0.95|
|SDN-ETH|510.8|54.7%|153|1.22|0.57|2.11|
|SOM-DJI|657.8|31.9%|17|0.58|0.45|1.24|
|SOM-ETH|334.4|57.1%|705|0.18|0.15|1.11|
|SOM-KEN|589.8|63.3%|266|0.42|0.43|2.24|
|TZA-BDI|606.0|91.8%|81|0.33|0.18|0.81|
|TZA-DRC|600.2|95.5%|299|0.32|0.19|0.91|
|TZA-KEN|500.7|76.1%|1'417|0.34|0.16|1.02|
|TZA-MWI|600.2|95.5%|299|0.32|0.19|0.91|
|TZA-RWA|500.7|76.1%|1'417|0.34|0.16|1.02|
|UGA-DRC|712.4|87.3%|112|0.40|0.27|1.22|
|UGA-KEN|451.1|91.1%|267|0.24|0.16|0.79|
|UGA-RWA|677.9|95.2%|1'609|0.27|0.19|0.89|
|ZMB-DRC|499.2|84.5%|286|0.33|0.23|1.11|
|ZMB-MOZ|603.5|68.0%|51|0.24|0.18|0.71|
|ZMB-MWI|499.2|84.5%|286|0.33|0.23|1.11|
|ZMB-TZA|603.5|68.0%|51|0.24|0.18|0.71|
|Maize|||15'252|0.38|0.35|2.72|
|Rice|||15'448|0.30|0.24|2.12|
|Sorghum|||7'955|0.27|0.28|2.45|
|**Average values**|**370.2**|**78.3%**|**38'655**|**0.33**|**0.30**|**2.72**|

Source: Authors’ calculations Some stylized facts emerge from Table 2. First, East and Central African countries have heterogeneous distributions of within-country relative prices and are different in terms of 12
_Table 4: Descriptive statistics of the sample_

||**All c**<br>**(N=1**|**hildren**<br>**3,820)**|**Gi**<br>**(N=6**|**rls**<br>**,895)**|**B**<br>**(N=6**|**oys**<br>**,925)**|**Ru**<br>**(N=8**|**ral**<br>**,082)**|**Ur**<br>**(N=5**|**ban**<br>**,738)**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|<br>**SD**|**Mean**|<br>**SD**|**Mean**|**SD**|**Mean**|<br>**SD**|
|**Socio-demographics**|||||||||||
|Female|0.51|0.500|-|-|-|-|0.50|0.500|0.53|0.499|
|Age|8.48|4.923|8.46|4.966|8.51|4.878|8.49|4.840|8.47|5.210|
|Household size|6.12|2.109|6.10|2.156|6.13|2.060|6.30|2.042|5.46|2.215|
|Number of women|1.20|0.573|1.21|0.596|1.19|0.549|1.17|0.533|1.30|0.690|
|Number of girls in the household|1.90|1.347|2.40|1.285|1.39|1.209|1.98|1.365|1.63|1.244|
|<br>Number of boys in the household|1.89|1.354|1.36|1.230|2.43|1.262|2.00|1.357|1.48|1.260|
|Years of education|3.55|2.441|3.67|2.473|3.43|2.403|3.32|2.227|4.26|2.899|
|Child lives in urban area|0.22|0.412|0.23|0.420|0.20|0.403|-|-|-|-|
|Quintiles of consumption expenditure:|||||||||||
|Poorest|0.20|0.400|0.21|0.408|0.19|0.392|0.23|0.423|0.08|0.274|
|Poor|0.20|0.400|0.22|0.411|0.19|0.388|0.23|0.418|0.11|0.309|
|Middle|0.20|0.400|0.21|0.404|0.19|0.396|0.21|0.409|0.15|0.360|
|Rich|0.20|0.400|0.18|0.387|0.22|0.412|0.19|0.393|0.23|0.422|
|Richest|0.20|0.400|0.19|0.388|0.21|0.411|0.14|0.344|0.43|0.495|
|**Deprivations**|||||||||||
|Child not attending school|0.06|0.242|0.06|0.245|0.06|0.238|0.06|0.244|0.06|0.234|
|Child has no formal education|0.003|0.059|0.002|0.048|0.005|0.068|0.004|0.060|0.003|0.053|
|Child faced illness in last 2 months|0.01|0.104|0.01|0.107|0.01|0.101|0.01|0.113|0.00|0.059|
|Child is stunted|0.07|0.262|0.07|0.251|0.08|0.273|0.08|0.266|0.07|0.251|
|Unsafe source of drinking water|0.32|0.468|0.32|0.468|0.32|0.467|0.40|0.490|0.04|0.206|
|Unimproved toilet facility|0.56|0.496|0.56|0.497|0.57|0.495|0.65|0.476|0.24|0.428|
|No access to electricity|0.76|0.424|0.75|0.431|0.78|0.418|0.93|0.255|0.16|0.370|
|Four or more people per room|0.42|0.493|0.41|0.492|0.42|0.494|0.46|0.498|0.25|0.435|
|Floor: natural, non-permanent material|0.86|0.344|0.86|0.350|0.87|0.338|0.96|0.200|0.52|0.500|
|No television/ radio/mobile phone/ fixed phone<br>**_Source:_**_Authors’ calculations; Datafrom ESS 2018/19._|0.42|0.493|0.43|0.495|0.41|0.491|0.49|0.500|0.16|0.371|
- Users should use their own knowledge of the Public Financial Management processes in the country to make a preliminary version of the sheet. World Bank economic counterparts will also be useful in confirming responses.

The following definitions and clarifications may be useful when completing these templates.

# **_Unitary versus dual budgets_**

A unitary budget is a straightforward concept; using this system, a country prepares one and only one document characterizing all annual spending by functional and economic categories. Many countries, however, operate under a dual budget system, in which the budget is split in two, usually prepared and managed by separate entities within the government. Dual budget systems generally comprise (i) a recurrent budget (RB) and (ii) a development budget (DB). For many African countries DBs were convenient mechanisms in the first two decades of independence, as government responsibilities gradually expanded beyond the provision of law and order. DBs largely focused on public capital investment projects such as power supply, public housing, roads and bridges, schools and universities, and hospital and clinics, although even these included recurring activities such as malaria eradication and crop research. Outside donors were willing to finance such expansion, and separate budgets facilitated the coordination of aid.

Unlike RBs, DBs covered individual projects, allowing donors to closely monitor the projects being financed and also to identify future projects. Donors therefore preferred the dual budget system. The size of a DB was determined by the availability of aid, at the margin an add-on exercise.

In recent years, the composition of DBs has gradually changed due to the growing inability of domestic budgets to shoulder recurrent costs and by the increasing ―ring-fencing‖ of donor-aided projects. Recurrent expenditures go into the DB because they are aid financed, not because they are capital investments, which blurs the distinction between capital and recurrent expenditure. Nowadays, projects frequently contain three types of expenditures: (i) new investment, (ii) rehabilitation of poorly maintained past investments (often aid financed), and (iii) recurrent funding.<sup>6</sup>

## **Basic budgetary institutions, national level (template 5)**

This includes factors that define and characterize the overall strategic framework of the budget process.

> 6 Excerpts from World Bank (1999: 53).

25
|Incidence of subsidies (% tot exp.)|10.2|7.4|0|
|---|---|---|---|
|Change in government spending following reform (bn. LYD)*|n.a.|-0.66|-1.28|
|Savings to the government following reform (% govt. exp.)*|n.a.|1|2|
|Poverty headcount (%, international poverty line)|8.5|10.5|16.6|
|Poverty headcount (%, national poverty line)|14.4|17.3|24|
|Inequality (%, Gini)|30.2|31|33.2|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations. (*) Estimates exclude savings from reduced waste, smuggling and administrative costs While food subsidies are relatively progressive, a significant share is “wasted” (about 35% of government spending on food subsidies), which would support a move to replace them with cash transfers. This paper’s analysis can provide guidance for the size of cash transfers that would compensate for food subsidy reform. One can look for guidance in the estimates of the per-capita monetary value of subsidies received by the various quintiles of the population (Table 28). For example, under a scenario of full subsidy elimination, maintaining the poverty rate constant at 8.5 percent is feasible if a per capita transfer of LYD 175 per year is allocated to the population in the first quintile. If the objective is rather to compensate the population falling in the first quintile for the totality of their loss, the transfer could be LYD 180 per capita, again granted only to the population in that group. And if the objective is to compensate the average member of the population (a way to address in part the needs of the middle class in a compensation scheme) then cash transfers could amount to, for example, 201 LYD per year per person, which is the average monetary value that a Libyan person derives from food subsidies today.

**Table 28: Per-capita Monetary Value of Food Subsidies (in LYD/capita/year)**

||**Q1 **|**Q2 **|**Q3 **|**Q4 **|**Q5 **|**Total**|
|---|---|---|---|---|---|---|
|Flour|13.0|13.9|14.5|15.2|14.5|14.0|
|Flour-bread|46.1|54.7|56.8|60.3|71.3|55.1|
|Semolina|2.1|2.3|2.1|1.9|1.5|2.1|
|Rice|21.8|24.7|26.2|26.7|27.1|24.7|
|Sugar|15.1|16.7|16.9|17.5|17.0|16.4|
|Tea|4.2|4.8|4.8|4.7|4.5|4.6|
|Macaroni|16.7|17.8|17.9|18.0|18.0|17.5|
|Vegetable Oil|36.5|39.2|40.3|40.5|40.7|39.0|
|Paste tomatoes|13.5|14.6|14.8|14.8|14.8|14.3|
|Milk for children|1.5|2.8|4.1|6.2|8.5|3.8|
|Milk (concentrated)|9.1|10.5|10.4|9.7|9.6|9.8|
|Total|179.7|202.1|208.8|215.5|227.5|201.4|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

The above examples dealt with eliminating all subsidies in one step but, alternatively, another possibility may be to sequence the reform over products and over time. Price liberalization could start with items that are likely to have a small impact on households such as semolina<sup>25</sup> and move onto bigger ticket items

> 25 A caveat to our analysis is that it does not take into consideration the nutritional consequences of food subsidy reform. Such an analysis may be needed before arriving at a view on how small is the impact on households particularly if the reform is not accompanied by cash transfers.

32
**Imported sugar is the natural channel through which a supply side shock would typically be cushioned, but government interventions that restricted imports from COMESA limited the role of imports.** Kenya imports sugar every year to cover the structural deficit between domestic production and consumption. In 2011 imports collapsed due to government blocking imports of COMESA sugar<sup>37</sup> (see the left hand panel of Figure 4), although the COMESA regional shortage and high world prices would likely have led to some decline in any case.  Note that the lockout of COMESA sugar began _before_ the drop in local sugar production – so the source of supply side easing had been cut off before the crisis even began.  Imports of sugar rocketed up in early 2012, in response to reversal by the government, even as local sugar production also recovered. The easing in supply brought retail sugar prices back down, although not quite to their previous levels (something to which we will return later).

**Figure 4: Imports of sugar (left panel), and Comparison of total supply with a naive prediction (right panel)**

_Source: Data is from Trademap and KNBS. Sugar marked as raw is brown and does not require further processing for human consumption. All series have been smoothed using a four-month moving average (current month and three previous)._

**When import response has not been inhibited, falls in domestic production have not had**

**such large price effects.** Contrast the situation in 2011 with the import response to the much smaller drop in production in 2010 (which from looking at Figure 3, still looks somewhat unusual for the season).  The left hand panel of Figure 4<sup>38</sup> shows the very strong import response in 2010, primarily from COMESA countries, easing tightness in local supply over the period and resulting in stable domestic sugar prices.  In 2009 there was only a slight price hike (see Figure 2) corresponding to the seasonal drop in production.  The import response to tight supply in 2009 was smaller than in 2010, but still much quicker than that in 2011 (see left hand panel of Figure 4), which is why the price spike was much smaller.  Note that the raw imports here are primarily of brown sugar, which is sold directly to consumers as opposed to truly raw sugar that has yet to be refined at all.

> 37 The Government of Kenya allowed few imports in 2011 due to a pending lawsuit regarding imports from COMESA (FAS, 2012).

> 38 Including only HS codes 17011190 (raw cane sugar, not jaggery) and 17019990 (refined sugar, not for industrial use).  As explained earlier, raw sugar under this HS code is actually refined or partially refined brown sugar. Amounts under beet sugar are negligible and can be ignored.

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