rafmacalaba/datause-encoder-data huggingface dataset:
-Tsacws-Non-terhve Pay Dlfferen 4-

Munoz, 1986). A subsample of individuals employed in the private and public sector, and having received at least 6 years of schooling has been drawn from this database. This subsample provides a meaningful control group to teachers. The subsample was limited to wage-earners for two reasons. First, earnings of the self-employed reflect returns to capital as well as labor which makes them in principle uncomparable to wage-ettrnings. Second, the inclusion of self-employed in the control group would add a selectivity problem.

Table I presents summary statistics of selected variables. It reveals that there is little difference between the teacher and the non-teacher groups with respect to sex, age, experience, and nationality. Notable differences can be observed, however, for the location of employment; most non-teachers work in Abidjan (602) while only 302 of the teachers are employed in this city. This reflects that schools in Cote d'Ivoire are more evenly distributed across regions than firms and administrations.

The variables representing the education level show, as one would have expected, a significant difference between teachers and non-teachers: teachers are better educated both in terms of number of years of schooling and diplomas received. Twice as many teachers work for the Government than non-teachers. Teachers also receive about 502 more salary (before tax) than other occupations. At the same time, they work 60 hours less per month and enjoy 3 months of vacation per year, which is two more months thar their non-teaching counterparts. Non-teachers, however, receive more in-kind benefits, such as transportation and housing allowances, as well as bonuses and commissions. (2)
# **TABLE 6: Impacts on CBCC Outcomes - 18-Month Follow-Up**

|||**Class**|**room**<br>**D**|**ependent va**|**riable:**|**Number of**|
|---|---|---|---|---|---|---|
|||<br>**Obser**<br>**Ind**|<br>**vation**<br>**ex**|**Total En**<br>**(repo**|**rollment**<br>**rted)**|<br>**STC-trained**<br>**Teachers**|
|**Variables**||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|T2 (careiver trainin)||0.554**|0.555**|10.989|10.892**|1.492***|
|g g||(0.278)|(0.279)|(6.725)|(5.318)|(0.138)|
|T3 (T2 + careiver incentives)||1.214***|1.205***|7.667|6.760|1.612***|
|g||(0.281)|(0.286)|(6.815)|(5.390)|(0.140)|
|T4 (T2 + arentin trainin)||1.006***|0.996***|17.727***|13.418**|1.554***|
|pg g||(0.270)|(0.277)|(6.534)|(5.190)|(0.135)|
|Lagged Dependent Variable (Baseline)|||0.014<br>(0.076)||0.640***<br>(0.071)||
|Mean (standard deviation) of dependent variable for the||0.0|00|63.2|89|0.000|
|controlgroup||(1.0|00)|(25.0|03)|(0.000)|
||T2=T3|0.019**|0.024**|0.623|0.440|0.389|
|F-test for Equality of Parameters - p-value|T2=T4|0.094*|0.112|0.297|0.622|0.645|
||T3=T4|0.446|0.445|0.129|0.205|0.668|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|
|Lagged Dependent Variable?||No|Yes|No|Yes|No|
|Number of observations||189|189|187|187|189|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at the CBCC level using baseline and 18-month data on Classroom Observations, Enrollment and STC training of teachers. Standard errors in parentheses. Classroom Observation Index is obtained by weighting underlying variables with Inverse Covariance Weights (ICW) as in Casey et al. (2012) and described in detail in the Appendix. For each round, indices are stardardized using the mean and standard deviation for the control group in that round. We use all CBCCs for whom the relevant variables are available for baseline and the 18-Month Follow-Up. STC stands for 'Save the Children'.

48
<mark>Desai, N., and M. Krishnaraj (2004). An overview of the status of women in India.</mark> _<mark>Class, Caste, Gender. Ed. Manoranjan Mohanty. New Delhi: Sage Publications</mark>_ <mark>, 296-319.</mark> Desai, R. M., and S. Joshi (2014). ‘Collective Action and Community Development: Evidence from Self-Help Groups in Rural India’. _World Bank Economic Review,_ 28(3): 492-524.

<mark>Deshpande, A. (2000). ‘Does caste still define disparity? A look at inequality in Kerala, India’.</mark> _<mark>The American Economic Review</mark>_ <mark>,</mark> _<mark>90</mark>_ <mark>(2): 322-325.</mark> <mark>Deshpande, A. (2001). ‘Caste at birth? Redefining disparity in India’.</mark> _<mark>Review of Development Economics</mark>_ <mark>,</mark> _<mark>5</mark>_ <mark>(1): 130-144.</mark> Deshpande, A. (2002). ‘Assets versus autonomy? The changing face of the gender-caste overlap in India’. _Feminist Economics_ , 8(2): 19-35.

<mark>Dirks, N. B. (2011).</mark> _<mark>Castes of mind: Colonialism and the making of modern India</mark>_ <mark>. Princeton University Press.</mark> <mark>Dreze, J., and A.K. Sen (2002).</mark> _<mark>India: Development and participation</mark>_ <mark>. Oxford University Press, USA.</mark> Dubey, A., Gangopadhyay, S., and W. Wadhwa (2001). ‘Occupational Structure and Incidence of Poverty in Indian Towns of Different Sizes’. _Review of Development Economics_ 5(1): 49-59.

Dumont, L. (1980). _Homo Hierarchicus: The Caste System and Its Implications_ . Chicago: University of Chicago Press.

Dube, S. (2005). ‘UNTOUCHABLES’. In Lindsay Jones, _Encyclopedia of Religion_ , 14 (2nd ed.), Thomson Gale, 9474–9478.

<mark>Eswaran, M., Ramaswami, B., and W. Wadhwa (2013). Status, caste, and the time allocation of women in rural India.</mark> _<mark>Economic Development and Cultural Change</mark>_ <mark>,</mark> _<mark>61</mark>_ <mark>(2), pp.311-333.</mark> <mark>Galasso, E., and M. Ravallion (2005). ‘Decentralized targeting of an antipoverty program’.</mark> _<mark>Journal of Public economics</mark>_ <mark>,</mark> _<mark>89</mark>_ <mark>(4): 705-727.</mark> <mark>Goldin, C. (1994). ‘The U-shaped female labor force function in economic development and economic history’ (Number. w4707). National Bureau of Economic Research.</mark> <mark>Gupta, D. (2000).</mark> _<mark>Interrogating caste: Understanding hierarchy and difference in Indian society</mark>_ <mark>. Penguin Books India.</mark> Hasan, A. B. R. Rizvi and J. K. Das. (2005). People of India: Uttar Pradesh, Volume 42, Part 2, <mark>Anthropological Survey of India.</mark> Huber, J. D., and P. Suryanarayan (2016). ‘Ethnic Inequality and the Ethnification of Political Parties: Evidence from India’. _World Politics_ , 68(1): 149-188.

Hutton, J. H. (1946). _Caste in India: Its Nature, Function and Origin_ . Bombay: Indian Branch.

Jaffrelot, C. (2003). _India's silent revolution: the rise of the lower castes in North India._ Columbia University Press, 210–211.

21
A13 **Sri** Lanka

**Table A13: Regional disparity** trends in Sri **Lanka**

|Year|Max/Mi<br>(MMR)|Simple<br>coefficient<br>n of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighted<br>gini index<br>(Gu)|Weighted<br>gini index<br>(Gw)|Theil<br>index<br>(T)|
|---|---|---|---|---|---|---|---|
|1990|2.506|0.354|0.376|0.340|0.209|0.205|0.069|
|1991|2.496|0.321|0.358|0.321|0.186|0.196|0.063|
|1992|2.769|0.345|0.390|0.344|0.200|0.214|0.074|
|1993|2.915|0.349|0.399 _|0.351|0.205|0.220|0.078|
|1994|3.121|0.374|0.426|0.375|0.219|0.235|0.090|
|1995|3.362|0.394|0.452|0.397|0.230|0.249|0.101|

**Figure A13: Regional disparity trends** in **Sri Lanka**

<!-- Start of picture text --> 0.500<br>0.450<br>~0.400<br>0.300<br>0.050<br>0.1000<br>1990 1991 1992 1993 1994 1995<br>Year<br>Simple coefficient of vaniation (CVu) XWeighted coefficient of variation (CVw) Relative mean Deviation (Rw)<br>Unweighted gini index (Gu) --+Weighted gini index (Gw) Theil index (T)<br><!-- End of picture text -->

**48**
# **Table A7: Time reference of sales and Interview length**

||**Labor Productivity (logs)**<br>**based on sales of the last**<br>**month**|**Labor Productivity (logs)**<br>**based on sales of the**<br>**usual month**|
|---|---|---|
||**coef/se**|**coef/se**|
|Total duration of water shortages last month (in days)|-0.028***|-0.037***|
||(0.008)|(0.011)|
|Interview length (in minutes)||-0.0003|
|||(0.001)|
|Log of total number of employees in regular month|-0.552***|-0.561***|
||(0.100)|(0.112)|
|Largest owner has secondary education or higher: Y/N|0.300***|0.286**|
||(0.088)|(0.136)|
|Manager experience (years)|0.017**|0.015**|
||(0.007)|(0.007)|
|Largest owner has a job in formal sector or looked for<br>one over last 2 years Y/N|0.045|-0.004|
||(0.136)|(0.134)|
|Largest owner started another business in the last three<br>years Y/N|-0.092|-0.134*|
||(0.074)|(0.078)|
|Firm uses electricity for activity Y/N|0.024|0.071|
||(0.068)|(0.083)|
|Firm uses machinery in its current operations Y/N|0.030|0.058|
||(0.078)|(0.089)|
|Firm uses vehicles in its current operations Y/N|0.399***|0.477***|
||(0.082)|(0.100)|
|Manufacturing firm Y/N|0.118|0.143*|
||(0.074)|(0.086)|
|Produces or sells under contract from another business<br>or person Y/N|0.242**|0.391***|
||(0.120)|(0.147)|
|Firm uses bank account to run business Y/N|0.198*|0.164|
||(0.104)|(0.133)|
|Firm has a loan at this time Y/N|-0.127|0.002|
||(0.130)|(0.074)|
|Firm pays informal payment to remain unregistered Y/N|-0.002|-0.219|
||(0.123)|(0.158)|
|Firm experienced crime last month Y/N|-0.062|0.067|
||(0.207)|(0.231)|
|Firm pays for security Y/N|0.098|-0.110|
||(0.105)|(0.198)|
|Constant|5.858***|6.155***|
||(0.198)|(0.209)|

43
harvest, which will minimize recall bias for crop production estimates. However, even in this case, input application may have occurred several months before and the harvest may have been conducted over several months. Adding to this, survey data are often collected over several months, often twelve or more. These data potentially suffer from recall bias since both farming decisions and data collection occur over the course of several months, leading to long and variable recall periods across households. This paper studies the extent of recall bias in agricultural statistics, using data from three national surveys conducted over a year such that the recall period has sufficient random variation across sample households. This work does not speak to concerns about the overall quality of data from smallholders. However, it does address at least one aspect of data collection – length of recall period – which could compromise data quality, and assesses whether differences in recall periods consistently affect the reported values.

Using various specifications on key input and crop production measurements, we generally find little evidence of recall bias for the average farm household. With regard to harvest estimates and fertilizer usage, we find only isolated incidents of statistically significant results for some specifications including interaction terms. Findings are similar for cash crop sales in Kenya and Rwanda and labor use in Rwanda. The most evidence of recall bias in production data is found for tobacco sales in Malawi, where, interestingly, the direction of the bias is generally positive, contradicting the initial hypothesis that the forgetting of events as time passes would lead to under-reporting and suggesting, instead, other possible sources of bias. We also find statistically significant evidence of recall bias in labor use for maize crops in Kenya and Malawi. These results represent substantial changes in terms of magnitude with regard to the mean, but they are in opposing directions, with over-reporting in Kenya and under-reporting in Malawi as recall durations are increased.

Finally, we find some evidence supporting the salience hypothesis. Hired labor usage requires payments and is not common, suggesting some salience. Yet, we find recall decay in reporting any hiring in Kenya and Malawi. We find less evidence of decay in labor in Rwanda, where it is a comparatively much rarer event than in Kenya and Malawi. Although not very common in any country, hiring labor may be an event of small relevance, especially in relation to the household’s own labor contribution. Fertilizer usage is a single, more expensive event for farm households. In view of the prohibitive costs of fertilizers and the poor welfare of most farmers in our samples, the lack of recall bias in financially taxing events such as fertilizer purchase and 17
**Table 3 Correlates of school enrollment and attendance (marginal effects)**

||**En**<br>|**rolled in sch**|**ool**|**At**<br>|**tended sch**|**ool**|
|---|---|---|---|---|---|---|
||**All**<br>**children**|**Girls**|**Boys**|**All**<br>**children**|**Girls**|**Boys**|
|<br>Wage work – not working|<br>ref.|<br>ref.|<br>ref.|ref.|ref.|ref.|
|Wage work – fewer than 40 hrs|-<br>0.779***<br>(0.025)|-<br>0.760***<br>(0.048)|-<br>0.797***<br>(0.013)|-<br>0.771***<br>(0.025)|-<br>0.754***<br>(0.046)|-<br>0.787***<br>(0.013)|
|Wage work – 40 or more hrs|-<br>0.794***<br>(0.015)|-<br>0.783***<br>(0.023)|-<br>0.806***<br>(0.018)|-<br>0.785***<br>(0.015)|-<br>0.775***<br>(0.023)|-<br>0.797***<br>(0.017)|
|Household chores – not working|ref.|ref.|ref.|ref.|ref.|ref.|
|Household chores – fewer than 40 hrs|-<br>0.090***<br>(0.009)|-<br>0.104***<br>(0.011)|-<br>0.072***<br>(0.013)|-<br>0.092***<br>(0.009)|-<br>0.105***<br>(0.011)|-<br>0.074***<br>(0.013)|
|Household chores – 40 or more hrs|-<br>0.098***|-<br>0.172***|-0.005|-<br>0.106***|-<br>0.190***|0.003|
||(0.033)|(0.047)|(0.045)|(0.034)|(0.047)|(0.045)|
|Urban [yes=1]|0.042***<br>(0.008)|0.036***<br>(0.011)|0.048***<br>(0.012)|0.034***<br>(0.008)|0.028**<br>(0.011)|0.040***<br>(0.012)|
|Age|0.226***<br>(0.007)|0.206***<br>(0.009)|0.248***<br>(0.010)|0.228***<br>(0.007)|0.206***<br>(0.010)|0.252***<br>(0.010)|
|Age squared|-<br>0.011***<br>(0.000)|-<br>0.010***<br>(0.000)|-<br>0.012***<br>(0.000)|-<br>0.011***<br>(0.000)|-<br>0.010***<br>(0.000)|-<br>0.012***<br>(0.000)|
|Female [yes=1]|0.025***<br>(0.007)|||0.026***<br>(0.007)|||
|Any disability [yes=1]|-<br>0.371***<br>(0.041)|-<br>0.322***<br>(0.058)|-<br>0.442***<br>(0.057)|-<br>0.381***<br>(0.040)|-<br>0.312***<br>(0.057)|-<br>0.477***<br>(0.053)|
|Mother alive [yes=1]|0.051**|0.073***|0.023|0.054***|0.082***|0.018|
||(0.020)|(0.028)|(0.028)|(0.020)|(0.029)|(0.028)|
|Father alive [yes=1]|0.058***<br>(0.016)|0.042**<br>(0.021)|0.073***<br>(0.024)|0.056***<br>(0.016)|0.038*<br>(0.021)|0.072***<br>(0.024)|
|Total number of children [>18] in hh|-<br>0.024***|-<br>0.025***|-<br>0.023***|-<br>0.024***|-<br>0.025***|-<br>0.024***|
||(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|(0.003)|
|Total number of adults [18-60] in hh|-0.003|-0.002|-0.003|-0.003|-0.004|-0.003|
||(0.002)|(0.003)|(0.003)|(0.002)|(0.003)|(0.003)|
|Female hh head [yes=1]|-0.017|-0.028*|-0.005|-0.015|-0.028*|-0.002|
||(0.011)|(0.015)|(0.016)|(0.011)|(0.015)|(0.016)|
|Female most educated hh member<br>[yes=1]|0.031***|0.036***|0.026*|0.026***|0.036***|0.015|
||(0.009)|(0.011)|(0.013)|(0.009)|(0.011)|(0.014)|
|HH edu at least secondary [yes=1]|0.002|0.020|-0.017|0.009|0.026*|-0.008|
||(0.010)|(0.013)|(0.015)|(0.010)|(0.013)|(0.015)|
|HH receives remittances [yes=1]|0.047***|0.044***|0.051***|0.050***|0.042***|0.058***|
||(0.011)|(0.014)|(0.016)|(0.011)|(0.014)|(0.016)|
|High income hh [yes=1]|0.046***|0.056***|0.034***|0.049***|0.059***|0.037***|
||(0.007)|(0.010)|(0.010)|(0.007)|(0.010)|(0.010)|

21
- such as childcare duties for example (Blundell _et al._ , 2018). In line with that, we see that families with smaller children – thus also having higher labor supply constraints – respond slightly less to employment growth than families with older children (Panel B of Table 7).

**Education** Last, we split our analysis based on distinct educational attainment levels. As shown in Panel C of Table 7, households headed by individuals with moderate education levels exhibit the most pronounced response to enhancements in local employment. Similarly, for families with heads having lower education, we find an increase in the likelihood of program departure when local employment improves. Conversely, households led by individuals with higher educational qualifications also see an increased probability of program exit; however, the effect is smaller and loses statistical significance. One plausible rationale behind this observation is that households headed by highly educated individuals already see an increased probability of departing from the program, as shown by the results obtained from prior survival analyses. Consequently, improvements in local employment might not be a necessary condition for those families to leave. Furthermore, these households might encounter supplementary constraints, as shown by the results in Panel A in Table 7.

# **6 Conclusion**

CCTs have been a popular tool for poverty alleviation and human capital development. The impact of transfers on education and health outcomes has been the subject of several studies (Kabeer and Waddington, 2015; Mill ́an _et al._ , 2019; Baird _et al._ , 2014). In contrast, graduation patterns of the transfers and the dynamics out of the programs remain vastly understudied. This paper contributes to the literature on cash transfers, CCTs in particular, by providing evidence on employment growth as an opportunity to graduate from social assistance programs. To the best of our knowledge, graduation from these kinds of programs has been highly neglected so far, possibly due to the lack of identified longitudinal data.

We start by analyzing the sociodemographic factors related to the program graduation and show that being male, being better educated, and living in urban areas are associated with a shorter time in BF. To isolate the effect of local employment growth on the probability of leaving BF, we combine several administratively identified datasets and follow families over a period of 84 months. By applying a shift-share IV approach and controlling for family- and time-fixed effects, we find that an increase in local employment has a positive and significant effect on 23
# **Table 4B - Impact of FD Law on Bank Loan Margins to Firms Given State Public Debt**

This table reports OLS estimates of the impact of the FD Law on bank credit margins to private firms in states of varying ex ante public indebtedness. The variable _InterestRatef,b,m_ is the annualized average interest rate of the outstanding loan given to firm _f_ , by bank _b_ in month _m_ . _Collateralf,b,m_ is the average share of loans received by firm _f_ from bank _b_ in month _m_ that are guaranteed. _Maturityf,b,m_ is the average monthly maturity (in logs) of the outstanding loans received by firm _f_ from bank _b_ in month _m_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the firm-bank-month level. Standard errors are reported in parentheses and are doubled clustered at the state and month levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Interest<br>(1)|Ratef,b,m<br>(2)|Collate<br>(3)|ralf,b,m<br>(4)|Matur<br>(5)|ityf,b,m<br>(6)|
|---|---|---|---|---|---|---|
|Postm*DebtStates,16Q1|0.004|0.005|0.016***|0.015***|-0.001|0.003|
||(0.014)|(0.014)|(0.002)|(0.002)|(0.007)|(0.007)|
|Observations|1,252,105|1,252,105|1,252,105|1,252,105|870,454|870,454|
|R-squared|0.859|0.859|0.747|0.749|0.678|0.679|
|Firm-Bank FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Bank-Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector-Month FE|No|Yes|No|Yes|No|Yes|

51
buy _teff_ , wheat and maize, respectively. These are large impacts.<sup>28</sup> The dramatic deterioration in access to staples in a short amount of time reflects one of the key aspects of the war – the blockade of trade between Tigray and the rest of the country. Note that these are net impacts and combine the effects due to unavailability of these staples on the market as well as reduced purchasing power of households due to lost income and increase in the prices of these staples. In fact, as discussed in Section 3.2, most households report that increase in prices, due perhaps supply shortages, and reduction in incomes are the main reasons for their inability to purchase _teff_ and wheat. We do not find statistically significant effect on households’ ability to purchase oil – although the coefficient is relatively large, it is imprecisely estimated. In column 5, we find similar detrimental impacts of the war on broader access to food. In the aftermath of the war, the share of households who report being unable to buy food (either of the staples in columns 1-4) increased by 26 percentage points, approximately a 159 percent increase relative to pre-war national levels.

**Table 4: The Impact of violent conflict on households’ access to food and market**

||(1)<br>Unable to buy<br>teff|(2)<br>Unable to buy<br>wheat|(3)<br>Unable to buy<br>maize|(4)<br>Unable to buy<br>oil|(5)<br>Unable to buy<br>food|
|---|---|---|---|---|---|
|Wartime dummy|-0.027|0.038<sup>**</sup>|0.002|0.060<sup>***</sup>|0.048<sup>**</sup>|
||(0.017)|(0.019)|(0.011)|(0.020)|(0.020)|
|Tigray × Wartime dummy|0.301<sup>***</sup>|0.236<sup>**</sup>|0.047<sup>*</sup>|0.136|0.261<sup>***</sup>|
||(0.095)|(0.100)|(0.028)|(0.101)|(0.095)|
|Household fixed effect|Yes|Yes|Yes|Yes|Yes|
|R-squared|0.006|0.007|0.000|0.005|0.006|
|Mean dep. var (pre-war)|0.082|0.064|0.037|0.103|0.164|
|No. observations|19495|19495|19495|19495|19495|

Notes: the outcome variables in this table come from a series of questions eliciting whether a household was not able to buy each of the above staple foods. All estimations use sampling weights to capture systematic non-response and attrition in phone surveys. Standard errors, clustered at district (woreda) level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

Table 5 reports the impacts of the violent conflict on livelihood activities, mainly overall labor market participation of households and participation in farm, non-farm business and wage employment. The war disrupted households’ overall labor market participation in Tigray, with engagement in any activity down by 10 percentage points after the war broke out. Besides the overall impact on livelihoods, sectoral differences in the resilience of different sectors are evident.

> 28 Compared to the pre-war period for the full sample, these amount to 367, 269 and 127 percent increase in inability to buy teff, wheat and maize, respectively.

27
> - 37 -

appear unnecessary, it is not. Our previous discussion has shown that these countries were affected by the international inflation rate because: (i) the international prices experienced wide fluctuations; and (ii) the external debt was very large.

Second is the consolidation of the non-financial and central bank's quasi-fiscal deficits. This consol,dation enables us to correct for countries' different practices in recording the PS deficit. This will make the PS performance comparison among countries more accurate. It is common for some countries to include all of their current financial expenses in their non-financial PS accounts, while others include them as part eof the central bank. In some countries central banks assume a prominent role in granting low-cost loans to the public and private sectors or in servicing a substan&tial portion of the exte=nal debt or both without necessarily reflecting these expenses in the accounts of the non-financial public sector.

The six countries have been classified into three subgroups according to their PS performance. A first subgroup consisting of Portugal and Morocco were the countries that undertook a remarkable fiscal adjustment; a second group consisting of Algeria and Turkey undertook a limited adjustment; and a third group consisting of Pakistan and Yugoslavia undertook no fiscal adjustment (See Figure S and Table 2). All six countries' real PS deficits showed a deterioration in the early-1980s resulting from the effect of the external shock. In the early-1980E the six countries confronted a sharp increase in the real interest rate as a result of a higher nominal international interest rate and of a fall in the international inflation rate. The differences in PS performance among countries after 1982 are explained by the extent to which these countries undertook