rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

Pakistan is the fifth largest country in the world by population and home to over 53 million children under the age of eight (Pakistan Bureau of Statistics, 2017). Twenty million children are out of school (Ersado et al., 2023) and 77% of 10-year-olds are estimated to be unable to read and understand a simple, age-appropriate paragraph (World Bank, processed). Yet, little is known about young children’s school readiness or about the quality of their earliest educational experiences.

This study aims to fill this knowledge gap and documents the skills pre-primary-aged children can demonstrate and the quality of the classrooms they attend in South Punjab, Pakistan. The Punjab province is home to over half of the country’s population but there is widespread variation across the province in human development outcomes. As shown in **Table 1** , data from the most recent Multiple Indicator Cluster Surveys (MICS) study show that enrollment in early childhood education is far lower in South Punjab (27% of children enrolled) than in the rest of the province (36% of children enrolled) (Bureau of Statistics Punjab, Planning & Development Board, Government of the Punjab & UNICEF, 2018). Not surprisingly, the share of children developmentally on track is also markedly different – 52% of 3 and 4 year olds are reported by their parents as developmentally on track in South Punjab compared to 63% of children in the rest of the province (Bureau of Statistics Punjab, Planning & Development Board, Government of the Punjab & UNICEF, 2018).

_Table 1. Share of 3 to 4-year-old Children Enrolled in ECE and Share Reported to be Developmentally on Track_

||**%**<br>Mean|**Enrolled in ECE**<br>95% Confidence Interval|**% De**<br>Mean|**velopmentally on Track**<br>95% Confidence Interval|
|---|---|---|---|---|
|**South Punjab**|**27.32**|**(25.73, 28.92)**|**52.42**|**(50.67, 54.17)**|
|Male|27.85|(25.91, 29.78)|50.21|(47.91, 52.50)|
|Female|26.79|(24.57, 29.00)|54.72|(52.49, 56.96)|
|**Central & North Punjab**|**35.71**|**(34.69, 36.72)**|**62.55**|**(61.54, 63.55)**|
|Male|35.53|(34.18, 36.87)|60.67|(59.29, 62.05)|
|Female|35.90|(34.48, 37.31)|64.54|(63.16, 65.91)|
|**Punjab Overall**|**33.34**|**(32.48, 34.20)**|**59.69**|**(58.81, 60.57)**|
|Male|33.37|(32.36, 34.49)|57.73|(56.54, 58.92)|
|Female|33.30|(32.10, 34.50)|61.75|(60.57, 62.93)|

**_Source: MICS, Punjab 2017-2018_** (Bureau of Statistics Punjab, Planning & Development Board, Government of the Punjab & UNICEF, 2018)

The quality of pre-primary education plays an important role not just in early childhood development but potentially also in primary education (Akyeampong et al., 2023). Research shows that children who have high-quality early childhood education experiences have better cognitive and socio-emotional outcomes and that early school readiness skills are predictive of later schooling success (Duncan et al., 2007; Engle et al., 2011) – though whether this success is short2
increasing resilience to global change.” _Water Resources Research._ 45(7), W00A12, 1-16, doi:10.1029/2007WR006767.

Roodman, David. 2014. “A replication of ‘Counting chickens when they hatch’ ( _Economic Journal_ 2012).” _Public Finance Review._ doi: 10.1177/1091142114537895 Sachs, Jeffrey, John W. McArthur, Guido Schmidt-Traub, Margaret Kruk, Chandrika Bahadur, Michael Faye, and Gordon McCord. 2004. “Ending Africa’s poverty trap.” _Brookings Papers on Economic Activity._ 1: 117-240.

Sanchez, Pedro A. 2010. “Tripling crop yields in Africa.” _Nature Geoscience_ . 3: 299-300.

Stoorvogel, J. J., and E. M. A. Smaling. 1990. _Assessment of Soil Nutrient Depletion in Sub-Saharan_ _Africa: 1983–2000, Volume I: Main Report (Second Edition)_ . Wageningen: Winand Staring Centre for Integrated Land, Soil, and Water Research.

Stoorvogel, J. J., E.M.A. Smaling, and B.H. Janssen. 1993. “Calculating soil nutrient balances in Africa at different scales: I. Supra-national scale.” _Fertilizer Research._ 35: 227-35.

Werker, Eric D., Faisal Z. Ahmed, and Charles Cohen. 2009. “How is foreign aid spent? Evidence from a natural experiment.” _American Economic Journal: Macroeconomics_ 1, 2: 225–44.

Wiig, Henrik, Jens B. Auneb, Solveig Glomsrød, and Vegard Iversen. 2001. “Structural adjustment and soil degradation in Tanzania: A CGE model approach with endogenous soil productivity.” _Agricultural Economics_ 24: 263–87.

35
Table 15: Disaster fatalities and aid from different donor countries - ZINB

||Stor|ms|Flo|ods|Earthq|uakes|
|---|---|---|---|---|---|---|
||Coeff.|ZI Probit|Coeff.|ZI Probit|Coeff.|ZI Probit|
|Ln_(FRENCH AIDj,t-1)_|-0.100|-0.153|0.008|-0.123|1.011<sup>~~a~~</sup>|-0.090|
||(0.423)|(0.535)|(0.248)|(0.219)|(0.814)|(1.787)|
|Ln_(GERMAN AIDj,t-1)_|0.371|-0.342|-0.337***|-0.428***|-1.475***|-1.117|
||(0.229)|(0.787)|(0.117)|(0.164)|(0.354)|(1.140)|
|Ln_(JAPANESE AIDj,t-1)_|0.158<sup>a</sup>|-0.450|-0.284***|-0.150|-0.804***|-0.294|
||(0.185)|(0.403)|(0.080)|(0.108)|(0.245)|(0.438)|
|Ln_(UK AIDj,t-1)_|-0.725|-0.188|-0.682***|-0.593|-0.437|-3.064|
||(0.441)|(0.692)|(0.252)|(0.925)|(0.571)|(1.700)|
|Ln_(USA AIDj,t-1)_|0.500**<sup>a</sup>|0.071|-0.440***|-0.195|-0.042<sup>a</sup>|-1.278|
||(0.228)|(0.433)|(0.107)|(0.158)|(0.294)|(0.698)|

_Notes:_ ZINB-estimates. Dependent variable is _DEATHijt_ . Standard errors (in parentheses) are adjusted for clustering on country-level. The<sup>1st</sup> stage probit regression estimates the probability that nobody in nation and year died from the specific natural disaster. The 2<sup>nd</sup> stage negative-binomial estimates the number of disaster deaths. Covariates from the ZINB baseline specification (Table 11), continent dummies, time trend and constant term included in all 2<sup>nd</sup> stage negative-binomial specifications. ***, **, * indicate significance at the 1, 5 and 10%-level, respectively. aindicates that ZINB model did not converge.

38
# **Appendix I. Database on debt restructurings with Chinese and other creditors**

This appendix section presents a detailed overview on how the data sets on restructurings with private, Paris Club and Chinese creditors were constructed.

**Restructurings with state-owned Chinese creditors:** The starting point for our data compilation is our own work in Horn et al. (2021), which we cross-check, supplement, and update by drawing on a variety of new academic sources and policy reports. We make extensive use of AidData’s newly released Chinese Official Finance Database 2.0 (Custer et al. 2021; Malik et al. 2021), which provides data on Chinese debt restructurings between 2000 and 2017. To update the database until 2021, we draw on academic work by Acker et al. (2020, 2021) on Chinese debt restructurings with African countries, on the case studies presented in Bon and Cheng (2021) and Gardner et al. (2020) as well as on two reports by the Rhodium Group (Kratz et al. 2019, 2020). To verify and complement the information found in this literature, we also consult IMF and World Bank reports.

<u>Rescheduling agreements under the Debt Service Suspension Initiative (DSSI):</u> As part of the G20, China has joined the DSSI. In contrast to Paris Club members, however, it has not disclosed with which debtor countries it has concluded DSSI rescheduling agreements. To identify Chinese rescheduling agreements under the DSSI, we therefore need to rely on information provided by the World Bank and the Paris Club. For 2020, the World Bank’s DSSI website provides detailed information on debt service payments deferred by each bilateral creditor. For 2021, no such data is available yet. To identify Chinese DSSI agreements in this year, we make use of the fact that the DSSI requires comparability of treatment from all bilateral creditors. More specifically, we assume that all eligible countries with debt service obligations toward China Ex-Im Bank or the Ministry of Commerce are granted a debt service suspension by China when they reach an agreement with the Paris Club.

<u>Content of the database: Whenever possible, we not only construct dummy variables for the</u> occurrence of a restructuring but use our sources to collect information on the Chinese creditor entity and on the restructuring terms. In contrast to other data sets, however, we do not provide estimates of the _scope of debt relief_ associated with Chinese debt restructurings. The academic literature has defined debt relief as the reduction in the net present value of the debtor’s outstanding obligations due to the restructuring agreement (see Sturzenegger and Zettelmeyer 2007 or Cruces and Trebesch 2013 for details). Quantifying debt relief or haircuts in this net present value sense requires detailed information on discount rates and on 14
**Table 9: Summary of the Decomposition Results**

||||**Equilibrium**<br>||||**Disequilibrium**<br>||
|---|---|---|---|---|---|---|---|---|
||**Shifts**|**Direction**|**Dete**<br>**Supply**|**rminants**<br>**Demand**|**Shifts**|**Direction**|**Dete**<br>**Supply**|**rminants**<br>**Demand**|
|**Jul.01-Jun.04**<br>**Jul.04-Jun.08**|S<D<br>S<D|Same<br>Opposite|Lend. Cap.<br>T-Bill rate|Industry<br>Alternative Fin.|S<D<br>S<D|Same<br>Same|T-Bill rate<br>T-Bill rate|Industry<br>Alternative Fin.|
|**Jul.08-Jun.09**|S>D|Same|T-Bill rate|Alternative Fin.|S>D|Same|T-Bill rate|Alternative Fin.|
|**Jul.09-Sep.10**|S<D|Same|T-Bill rate|Industry|S<D|Same|T-Bill rate|Industry|
|**Oct.10-Jun.11**|S>D|Same|T-Bill rate.|Industry|S>D|Same|T-Bill rate|Industry|

# **5 Conclusions**

The paper explained the credit contraction to the private sector based on the banking sector’s sources and uses of funds, as well as estimating econometrically a system of supply and demand for private credit. An analysis of the banking sector’s sources and uses of funds reveals a close association of bank credit to the private sector with external capital flows but mostly through the deposit growth rate, which was the main determinant of the sources of funds. The banking sector’s net external liabilities were countercyclical, i.e. they contracted during the capital inflow period and expanded during the capital outflow. In this sense, the sector played a stabilizing role. Only during the last cycle of capital outflow since the Revolution the net foreign liabilities have been pro-cyclical. The banks’ net credit position with the CBE fell during the capital outflow episodes of 2009 and 2011 and increased during the capital inflow episodes, which may be interpreted as an effective countercyclical monetary policy.

The system of simultaneous equations was estimated both assuming continuous market clearing and allowing for transitory price rigidity entailing market disequilibrium. The main results are robust to the market clearing assumption. Up to the global financial crisis, the main elements driving bank credit were from the demand side, though in opposing directions: vigorous industrial activity implied growing demand for bank credit, but available alternative sources of financing implied lower demand for bank credit. After the global financial crisis, the banking sector’s credit to the government played the dominant role from the supply side, while subdued economic activity operated in the same direction from the demand side.

Starting in the last quarter of 2010 capital flows reversed, in tandem with global capital markets, and the events of January 2011 accentuated the outflow. The slowdown in economic activity accounted for between 15 and 20 percent of the predicted total fall in credit, while the expansion of the credit to the government 24
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Trade expansion significantly increases the weight of hydropower in the regional generation portfolio, from 83 to 97 percent. Doing so spurs annual savings of some 4 million tonnes of carbon emissions. The main impact of trade expansion would be to enable a shift away from thermal generation and relatively dispersed small-scale hydropower, toward larger and more cost-effective hydropower resources that would generate some 0.3 TWh of additional power (table 3.5). Other sources of power would also be displaced, decreasing from 17 percent to 3 percent under trade expansion (figure 3.6). The savings in carbon emissions is smaller than that projected in other regional power pools, where trade permits a much larger volume of hydropower to be harnessed.

**Figure 3.6. Power generation mix**

<!-- Start of picture text --> a. Trade expansion b. Trade stagnation<br>3<br>17<br>83<br>97<br>Hydro Other Hydro Other<br><!-- End of picture text --> Source: Derived from Rosnes and Vennemo 2009.

**Table 3.5 . Differences in electricity production and carbon dioxide emissions in trade expansion and stagnation**

||**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|**WAPP**|**SAPP**|**EAPP**|**CAPP**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Produc**|**tion differe**|**nce (TWh)**|||**Em**|**issions sav**|**ings (millio**|**ns of tonne**|**s)**|
|Coal||–41.5|0.7||–40.8||–37.8|0.6||–37.2|
|Diesel|–0.8|–0.3|0.3||–0.8|–0.6|–0.2|0.2||–0.6|
|Gas|–9.2|–5.3|–42.4||–56.8|–4.7|–2.7|–21.5||–28.9|
|HFO|0.2||0.4|–4.9|–4.3|0.1||0.3|–3.6|–3.2|
|Hydro|11.5|47.5|43.4|5.1|107|||||0|
|**Total**|**1.6**|**0.5**|**2.4**|**0.3**|**4.7**|**–5.2**|**–40.7**|**–20.4**|**–3.6**|**–69.9**|

_Source:_ Derived from Rosnes and Vennemo 2009. _Note:_ WAPP = Western African Power Pool; SAPP = Southern African Power Pool; EAPP = East African Power Pool; CAPP = Central Africa Power Pool; TWh = terawatt-hour; HFO = heavy fuel oil 41
||(0.196)|(0.208)|(0.209)|(0.209)|
|---|---|---|---|---|
|Firm Purchased|-1.046**|-1.073*|-1.046*|-1.033*|
|Fixed Assets Y:1|||||
|N:0|||||
||(0.527)|(0.560)|(0.557)|(0.558)|
|Power Outages<br>||-0.173**|-0.153**|-0.156**|
|(days)|||||
|||(0.070)|(0.068)|(0.068)|
|o.iv_h30_ci||(dropped)|(dropped)|(dropped)|
|Crime Losses Y:1||-0.623|-0.670|-0.679|
|N:0|||||
|||(0.666)|(0.668)|(0.668)|
|Firm Inspected Y:1||-0.813|-0.875|-0.879|
|N:0|||||
|||(0.605)|(0.605)|(0.605)|
|How Much Of An||-0.216|-0.254|-0.252|
|Obstacle: Tax Rates|||||
|||(0.230)|(0.229)|(0.229)|
|How Much Of An||0.027|0.041|0.051|
|Obstacle: Labor||(0.300)|(0.300)|(0.300)|
|Regulations?|||||
|Population (logs)|||-37.758***|-37.641***|
||||(10.909)|(10.849)|
|Urbanization (%)|||1.709***|1.723***|
||||(0.423)|(0.427)|
|WBL Index|||16.988*|17.157**|
||||(8.755)|(8.677)|
|Women can work|||2.962|2.967|
|the same night|||(2.814)|(2.807)|
|hours as men Y:1|||||
|N:0|||||
|Women can work|||-0.860|-0.608|
|in the same|||(2.009)|(2.053)|
|industries as men|||||
|Y:1 N:0|||||
|Ratio of Female to|||2.352***|2.486***|
|Male Population|||(0.572)|(0.566)|
|Fertility Rate|||-15.380***|-15.262***|
||||(3.649)|(3.646)|
|Starting a Business|||0.081**|0.081**|
|<br>(DTF)|||||

71
<!-- Start of picture text --> Sono°<br>EN<br>a”<br>°<br>2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> +<br>goeo<br>6<br>o<br>EN<br>a”<br>°<br>2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> +<br>a>eo<br>6<br>o<br>EN<br>a”<br>°<br>2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->
nutritional requirements - a minimum caloric amount - and then complemented with the cost to satisfy non-food essentials (clothing, shelter, private expenditure on education and health, and so on) obtained from households whose total (food) consumption equals the cost of obtaining the nutritional requirements. Poverty lines are typically derived using data from consumption surveys and as such reflect the actual consumption and revealed preferences of households. Poverty lines are thus context-specific, reflecting the consumption needs for achieving the minimum standard in a given society (Ravallion et al. 2008). By implication, each country sets its own national poverty line.

Global poverty measurement does not use national poverty lines, but uses the International Poverty Line (IPL), which itself is anchored to the national poverty lines of the world’s poorest countries. This first international poverty line, based on research by Ravallion, Datt, and van de Walle (1991), was set a PPP$ 1.02 per day per person and became known as the dollar-a-day poverty line.

Since the early 1990s the IPL has been updated with some regularity. The latest update was produced in <mark>defned as the median of the</mark> 2022 following the release of the 2017 PPPs. In this iteration, the IPL was <mark>natonal poverty lines of 28 of the world’s poorest countries a</mark> nd set at PPP$ 2.15, with SDG 1 using it as a benchmark for poverty eradication (Joliffe et al. 2022). With the 2017 PPP update came the recognition that the IPL may be too low to act as an acceptable minimum standard of living for middle-income countries, with subsequent global poverty lines for lower-middle, upper-middle and even high-income countries published thereafter.

In the remainder of this paper, global poverty lines are used to explore self-reliance, with refugees deemed self-reliant when they earn an income greater than the applicable global poverty line. e . ss In the equation above, ii = 1 if yyii > zz , ssee 0 _sii_ is the self-reliance indicator of refugee _i_ , z is the poverty line, and yi is earned i is earned is earned In the equation above, _sii_ is the self-reliance indicator of refugee _i_ , z is the poverty line, and yi is earned i is earned is earned income – defined as the income from wage employment and earnings from self-employment. Earned income does not include gifts (either in-kind or cash) from humanitarian agencies and other sources, nor does it include remittances from non-refugee family members.

The self-reliance indicator can be expressed as the average incidence of self-reliance among the refugee population as follows: Here, _n_ is the number of refugees and _q_ the number of refugees that are self-reliant. Note that for refugees to be self-reliant, their income _yi_ has to exceed the poverty line _z_ so that the bracketed term equals 1 for self-reliant refugees. This is, of course, a complex way of writing down the fraction of self-reliant people, but doing so has the advantage of clarifying how the self-reliance indicator _S_ relates to the Foster–Greer– Thorbecke (FGT) family of poverty metrics (Foster et al. 1984), defined as:
**Table 2. Data summary**

|**Variable**|**Obs**|**Mean **|**Std. Dev.**|**Min**|**Max **|
|---|---|---|---|---|---|
|**Firmcharacteristics**||||||
|Invest|389|0.7609254|0.4270676|0|1|
|Total revenues|334|2.730539|1.63827|1|8|
|Share of energycosts|316|11.43358|10.21259|0.3|70|
|Facilityrented|499|1.817635|0.3865323|1|2|
|Private ownership|491|0.694501|0.4610882|0|1|
|Employment|233|2.613734|1.375957|0|6|
|**Barriers to the adoption of energy **|**efficiency m**|**easures**||||
|Financial barriers|356|6.583883|2.884173|0|14.59367|
|Split Barriers|382|1.025171|1.562384|0|5.50279|
|Knowledge barriers|433|3.667689|3.445591|0|12.38596|
|Technical barriers|420|3.758199|2.553517|0|11.82016|
|Existinglaws and regulation|296|4.832675|2.878283|0|10.71962|
|Internal institutions|401|0.6719663|0.6634264|0|2.093352|
|Energy prices|443|0.9006772|1.039524|0|3|

We present the Pearson correlation coefficients among the various factors in Table 3. Although principle-component analysis controls for correlation within groups of variables, we wanted to better understand correlation between groups of variables. Some of the correlations suggest we should add an interaction term among the factors, which we do below. But before studying the importance of the interaction terms, we focus on our baseline model, which is without the interaction term.

**Table 3. The Pearson correlation coefficient**

|**Column1**|**Financial**<br>**barriers**|<br>**Split**<br>**Barriers**|**Knowledg**<br>**Barriers**|**e**<br>**Technical**<br>**Barriers**|**Existing rules**<br>**and regulation**|**s**<br>**Internal**<br>**institutions**|**Energy**<br>**prices**|
|---|---|---|---|---|---|---|---|
|Financial barriers|1.00|||||||
|Split Barriers|0.30|1.00||||||
|Knowledge||||||||
|Barriers|0.48|0.57|1.00|||||
|Technical Barriers|0.62|0.29|0.59|1.00||||
|Existing rules and<br>regulations|0.46|0.56|0.60|0.54|1.00|||
|Internal institutions|0.46|0.42|0.49|0.50|0.55|1.00||

11
|**Table 2**:**South Asia &**|**China in**<br>_Share of s_<br>|**the Cross Sect**<br>**Serv**<br>_ervices exports in total e_<br><br>|**ion: Sh**<br>**ices Ex**<br>_xports_<br>|**are of S**<br>**ports**<br>**Panel A**<br>_Share of co_<br>|**ervice**<br> <br>_mputer, inf _<br>|**and Exp**<br>_ormation serv_<br>|**orts in Total Goods and**<br>_ices*_<br>|
|---|---|---|---|---|---|---|---|
||**1982**|**1982**<br>**2006**|**2006**|**2000**|**2000**|**2006**|**2006**|
|**Log GDP per capita**|-7.2|-9.3<br>-1.41|-3.01|-2.01**|-1.94*|-3.17*|-3.21*|
||(27.09)|(29.95)<br>(26.37)|(27.1)|(1.06)|(1.08)|(1.90)|(1.89)|
|**Log GDP per capita**<sup>**2**</sup>|0.83|1.1<br>0.59|0.81|0.35**|0.35**|0.53*|0.57*|
||(3.94)|(4.3)<br>(3.82)|(3.9)|(0.18)|(0.18)|(0.30)|(0.30)|
|**India indicator**||0.87|18.3***||7.98***||14.74***|
|||(4.73)|(3.3)||(0.09)||(0.16)|
|**China indicator**||-1.37|8.03||0.38||0.68*|
|||(7.6)|(8.4)||(0.28)||(0.41)|
|**Bangladesh indicator**||-6.3|-17.6***||0.04***||0.10|
|||(4.4)|(2.8)||(0.01)||(0.07)|
|**Pakistan indicator**||-1.1|-9.2***||0.25***||0.34***|
|||(3.2)|(2.5)||(0.04)||(0.09)|
|**Sri Lanka indicator**||-5.1*|-9.7***||-0.0004||0.99***|
|||(2.7)|(2.6)||(0.04)||(0.09)|
|**Control for size**|Yes|Yes<br>Yes|Yes|Yes|Yes|Yes|Yes|
|**Observations**|109|109<br>125|125|137|137|116|116|
||**_hange in sh_**<br> <br>|**_are of services exports_**<br>**1982-2006**<br>||**Panel B**<br>**_Change in s_**<br>|<br>**_hare of comp_**<br>**2000-200**|**_uter services_**<br>**6**<br>||
||**(1)**|**(2)**||**(3)**||**(4)**||
|**Log initial GDP per capita**|1.40|1.27||0.20||0.28**||
||(2.73)|(3.09)||(0.14)||(0.14)||
|**Average annual GDP growth**|-0.69|-0.77||5.73||4.01||
||(0.71)|(0.87)||(3.77)||(0.20)||
|**India indicator**||14.9***||||6.59***||
|||(4.95)||||(0.09)||
|**China indicator**||2.44||||-0.22||
|||(7.48)||||(0.17)||
|**Bangladesh indicator**||-11.61***||||0.11||
|||(4.51)||||(0.08)||
|**Pakistan indicator**||-8.27**||||0.08||
|||(4.11)||||(0.06)||
|**Sri Lanka indicator**||-3.45||||1.00***||
|||(3.70)||||(0.05)||
|**Observations**|93|93||94||94||

_Notes_ : Robust standard errors are reported in parenthesis. * represents significance at 10%, ** represents significance at 5%, *** represents significance at 1%, Country size is measured by area in square kilometers.

Table 2 reports cross-country level and growth regression results for share of service exports in total exports and share of modern impersonal service export (IT and ITenabled service exports) in total exports. In 1982, China and South Asian countries were negative outliers, when we compare the share of their service export in total exports with 14
FIGURE 5. INFANT MORTALITY<sup>TRENDS BY INCOME GROUP</sup> IN COTE D'IVOIRE,<sup>1970-86 (LSMS 1985/1986)</sup> <!-- Start of picture text --> us<br>. High income<br>Jp<br>a _ All income groups<br>Low income<br>o ,<br>so<br>S._<br>Year (1)<br><!-- End of picture text --> PRG: PLOTSM2 NOTE (1): IN 5 - YEAR MOVING<sup>AVERAGE</sup>
# Figure A4. Impulse Responses to a Positive Supply Shock

<!-- Start of picture text --> CPI Inflation Output<br>0<br>0.4<br>-0.01 0.3<br>0.2<br>-0.02<br>0.1<br>-0.03 0<br>5 10 15 20 5 10 15 20<br>Interest Rate Oil Prices<br>0<br>0.06<br>-0.02<br>0.04<br>-0.04 0.02<br>-0.06 0<br>-0.02<br>-0.08<br>5 10 15 20 5 10 15 20<br><!-- End of picture text --> Notes: The impulse responses are based on the economic model introduced in the Appendix A. The impulse responses follow a one-standard deviation of a positive supply shock.

47