rafmacalaba/datause-encoder-data huggingface dataset:
POLICY RESEARCH WORKING PAPER 2333

# Summary findings

Herrera, Perry, and Quintero explain Latin America's growth slowdown in 1998-99. To do so, they use two complementary methodologies.

The first aims at determining how much of the slowdown can be explained by specific external factors: the terms of trade, international interest rates, spreads on external debt, capital flows, and climatological factors (El Nifio). Using quarterly GDP data for the eight largest countries in the region, the authors estimate a dynamic panel showing that 50-60 percent of the slowdown was due to these external factors.

The second approach allows for effects on output by some endogenous variables, such as domestic real interest rates and real exchange rates. Using monthly industrial production data, the authors estimate country-specific generalized vector autoregressions (GVAR) for the largest countries.

They find that during the sample period (1992-98) output volatility is mostly associated with shocks to domestic factors, but the slowdown in the subperiod 1998-99 is explained more than 60 percent by shocks to the external factors.

- This paper a product of the Economic Policy Sector Unit and the Poverty Reduction and Economic<sup>Management Sector</sup> - Unit, Latin America and Caribbean Regional Office is part of a larger effort to understand output fluctuations and growth in the region. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Cristina Palarca, room 18-165, telephone 202-473-5328, fax 202-522-2119, email address cpalarca@worldbank.org. Policy Research Working Papers are also posted on the Web at<sup>www.worldbank.org/research/</sup> workingpapers. The authors may be contacted at gperry@worldbank.org or nquintero@worldbank.org.<sup>May 2000. (62</sup> pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange_<sup>_of ideas ahout_</sup> _development issues. An ohjective of the series is to get the findings out quickly, even if the presentations are less than fully_<sup>_polished. The_</sup> _papers cariy the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its_<sup>_Executive Directors, or the_</sup> _countries they represent._ Produced by the Policy Research Dissemination Center
21 In order to calculate the cost of providing extension in the ATP I program, and also to derive the cost of providing extension to an individual producer in ATPI, we can use estimates made by Estrada-Rizo and Garcia (1997). Table 9 presents these estimates, and Figure 7 derives the cost per extensionist that is associated with ATP I activities.

Table 9: Allocation of extensionist time and fuel cost between ATPb and ATP I clientele

||Percent of time and fuel a|llocated to ATP I activities||
|---|---|---|---|
||Number of ATP I group|s contacted by extensionist||
|1-2|3-5|6-8|9-10|
|20|50|80|90|

Source: Estrada-Rizo and Garcia (1997) Figure 7: Annual cost and time share for technical assistance (ATP-1 and ATPb) as a function of the number of groups per extensionist.

<!-- Start of picture text --> the number of groups per extensionist.<br>90- - 3500<br>tC- e% ifor ATP- I<br>3000<br>7 Annual Cost per Extensionist<br>40 ($US)<br>2500<br>2.so 2.. ~ ~ ~ ~ ~ ~ ~ ~ ~ 2<br>F 40 Iwo<br>130<br>20<br>20 ~~~~ 1--M s w e ~~~~~~~~~5000<br>0 0<br>1-2 groups 3-5 groups 6-8 groups 9-10 groups<br>Groups per Extensionist<br><!-- End of picture text --> Based on the data presented so far, it is possible to estimate the cost of providing ATP I extension services to producers. In 1995-1997 the number of producers per group was approximately 11. Therefore, the cost per producer, as a function of the number of groups per extension-
especially useful when dealing with the skewed and heavy-tailed distributions that are commonplace in welfare analysis ( _e.g._ household consumption, income, and wealth). Some of these techniques are not yet available as stand-alone Stata commands (namely the transformation proposed in Yeo and Johnson, 2000). Robust location and scale estimators are useful when the detection rule itself is sensitive to precisely those extreme values it is designed to flag (Davies and Gather, 1993).<sup>3</sup> `outdetec` <mark>`t` does</mark> _<mark>not</mark>_ <mark>offer an automated way to treat outliers – to replace or drop them – and deliberately so. Any alteration of the raw data is potentially problematic, and must be informed by careful investigation of the nature of extreme values. I</mark> nstead, `outdetect` focuses on sensitivity: it produces an array of statistics using both the ‘raw’ distribution (the data as they are) and its outlier-free counterpart (a distribution where observations that are flagged as outliers have been excluded from all calculations). Borrowing from the analytical framework developed by Hampel (1974) and Hampel, Ronchetti, Rousseeuw and Stahel (1986), it also produces two types of diagnostic plot, the Incremental Trimming Curve (ITC) which plots the value of a statistic of interest against the proportion of extreme values that are trimmed from the sample, and the High-influence Observation Curve (HOC) first suggested by Cowell and Flachaire (2007), which describes the effect of any one (extreme) observation on the estimated value of the statistics. These instruments allow the analyst to assess the influence of extreme values on results, and inform next steps.

Of particular note is the fact that `outdetect` allows for the use of complex survey settings, so that the aforementioned comparisons can be performed using population statistics.

In the last part of the paper we use `outdetect` to investigate the influence of extreme values on key inequality and poverty statistics, computed on the basis of household budget survey data. We use a collection of data sets from the Rural Livelihoods Information System (RuLIS), a joint initiative of the Food and Agriculture Organization (FAO), the World Bank, and the International Fund for Agricultural Development (IFAD). We find that the share of observations flagged as outliers of per capita consumption is 0.8% on average, never exceeding 2.5%; that the presence or inclusion or exclusion of these observations from calculations causes differences of as many as 6 percentage points for the

> 3 Commands `bacon` (Billor, Hadi and Velleman, 2000; Weber, 2010), and `gboxplot` (Bruffaerts, Verardi and Vermandele, 2014; Verardi and Vermandele, 2018) are also available to detect outliers with Stata, though their functionalities and range of application are adjacent, rather than overlapping, with `outdetect` .

3
3 2008 to the tune of U$28.7 and U$64.3 billion respectively (13.1% and 5.7 % of GDP). These aggregate income gains must accrue to households within these countries. Are they correctly captured by empirical analyses of the impacts of rising food prices on household welfare?

As reviewed in Section 2 below, most research on this question seeks to account for the positive effect of rising prices on the income side by measuring not only the food a household consumes, but also the food it produces. This allows researchers to distinguish between net producers and net consumers of food, and to treat price increases as welfare-reducing on the expenditure side, but welfare-increasing on the production or income side.

While this research strategy is appropriate for agricultural sectors dominated by subsistence or family farming, it is likely insufficient in countries with modern agricultural sectors - such as Argentina, Brazil, Uruguay (or indeed the United States) – where most agricultural workers are on wage contracts, rather than family farmers or sharecroppers. Although they work in agriculture, they too are net food buyers. In these predominantly wage-based agricultural sectors, an exclusive reliance on net food purchases for estimating welfare impacts is likely to prove wide of the mark. It would ignore, for example, any effect of higher prices on the wages received by agricultural workers. In a competitive market for farm labor, for example, rising farm-gate prices would raise the marginal revenue product of labor, and hence either wages, employment levels, or both (depending on the elasticity of labor supply). In such a context, ignoring these general equilibrium effects might lead to severely distorted estimates of the distributional consequences of rising food prices.

What is the distribution of the welfare gains and losses arising from higher food prices in such an economy? Can income gains for agricultural workers offset their losses as net food buyers? Are they sufficiently large and widespread to attenuate or reverse increases in poverty? What is the overall effect on inequality? Can government policy, through targeted cash transfers for example, help mitigate some of the negative shocks? When all these effects are taken together, are the poorest hardest hit, as is often claimed?

This paper reports on a first attempt to address these questions for the case of Brazil, where food price inflation in the twelve months to July 2008 reached 18% (while overall inflation was 7%). We combine monthly, regionally-disaggregated data on (consumer-level) food prices with household consumption expenditure and income information to estimate the net effect of the price shock at
41

# IV, Conclusion

After reviewing the analytical problems associated with drawing inferences from cross-country regressions, thi. paper discussed the particular problems associated with interpreting empirical attempts to link macroeconomic policy indicators with growth. By collecting at.4 studying the problems with existing empirical studies of growth, we hope to (1) stimulate researchers to advance the study of long-run growth, and (2) caution readers about the confidence they should place in existing findings. Regionally based pooled cross-section, time-series analyses that allow for more country specific information while maintaining the ability to use standard econometric techniques along with detailed longitudinal case studies that rely more on qualitative analyses may - in conjunction with improved cross-sectional work - augment our understanding of the linkages between policy and long-run growth.
# **Literature**

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Azzarri, C., Carletto, G., Covarrubias, K., A.P., Petracco, de La O. C., Scott, K. and Zezza. A. (2010). “Measure for Measure: Systematic Patterns of Deviation between Measures of Income and Consumption in Developing Countries - Evidence from a New Dataset”. Paper presented at the Fifth International Conference on Agricultural Statistics (Icas-V), 13 - 15 October 2010, Kampala, Uganda.

Azevedo, J.P., and Atamanov, A. (2014). Pathways to the Middle Class in Turkey: How Have Reducing Poverty and Boosting Shared Prosperity Helped? Policy Research Working Paper; No. 6834. World Bank, Washington, DC.

Beegle, K., De Weerdt, J., Friedman, J. and Gibson, J. (2012). “Methods of Household Consumption Measurement through Surveys: Experimental Results from Tanzania.” _Journal of Development Economics_ 98(1): 3–18.

Burbidge, John B., Magee, L. and Robb, L. (1988). “Alternative Transformations to Handle Extreme Values of the Dependent Variable.” _Journal of the American Statistical Association_ 83 (401): 123–27. doi:10.2307/2288929 Chen, S. and Ravallion, M. (2010). The developing world is poorer than we thought, but no less successful in the fight against poverty. Policy research working paper 4703. World Bank, Washington DC.

Coudouel, A., Hentschel, J. and Wodon, Q. (2002). Poverty Measurement and Analysis, in the PRSP Sourcebook, World Bank, Washington D.C.

Datt, G. (1998), “Computational Tools for Poverty Measurement and Analysis,” FCND Discussion Paper No. 50.

Deaton, A. (1997). The analysis of household surveys: a microeconometric approach to development policy. Washington, D.C.: The World Bank.

Deaton, A. and Zaidi, S. (2002). Guidelines for Constructing Consumption Aggregates for Welfare Analysis. World Bank. Washington, DC.

Ferreira, F., Chen, S., Dabalen, A., Dikhanov, Y., Hamadeh, N., Jolliffe, D., Narayan, A., Prydz, E. Revenga, A., Sangraula, P., Serajuddin, U. and Yoshida, N. (2015). A global count of the extreme poor in 2012: data issues, methodology and initial results. Policy Research working paper; no. WPS 7432. Washington, D.C.: World Bank Group.

Haughton, J and Khandker, S. (2009) Handbook on poverty and inequality. World Bank, Washington DC.

22
# **Table A7. Survey Method’s Differential Effects on Employment and Work Status by Prevalence of Informal Employment** **_Male Respondents Only_**

||(1)<br>Employed|(2)<br>Working|
|---|---|---|
|Informal employment in the EA|0.029|-0.010|
||(0.028)|(0.026)|
|Youth|-0.082|-0.058|
||(0.056)|(0.050)|
|LOA|0.016|0.019|
||(0.034)|(0.028)|
|ESR|-0.050|-0.047|
||(0.036)|(0.031)|
|LOA x Youth|-0.025|-0.050|
||(0.073)|(0.067)|
|ESR x Youth|0.103|0.078|
||(0.078)|(0.069)|
|Informal employment in the EA x Youth|-0.100**|-0.060|
||(0.050)|(0.047)|
|LOA x Informal employment in the EA|-0.045|0.011|
||(0.036)|(0.034)|
|ESR x Informal employment in the EA|-0.049|0.007|
||(0.038)|(0.035)|
|LOA x Informal employment in the EA x Youth|0.087|0.060|
||(0.067)|(0.064)|
|ESR x Informal employment in the EA x Youth|0.182**|0.082|
||(0.079)|(0.070)|
|_Treatment Effects_|||
|Effects of LOA on adult men in EAs with low informal employment|0.016|0.019|
|Effects of LOA on adult men in EAs with high informal employment|-0.029|0.030|
|Effects of LOA on young men in EAs with low informal employment|-0.009|-0.031|
|Effects of LOA on young men in EAs with high informal employment|0.033|0.039|
|<br>Effects of ESR on adult men in EAs with low informal employment|-0.050|-0.047|
|Effects of ESR on adult men in EAs with high informal employment|-0.099**|-0.040|
|<br>Effects of ESR on young men in EAs with low informal employment|0.053|0.031|
|Effects of ESR on young men in EAs with high informal employment|0.186*|0.120|
|Observations|1,119|1,119|
|Outcome(Control Group)Mean|0.735|0.832|

_Notes:_ This table presents the treatment heterogeneity of including the LOA module (LOA) or enforcing self-reporting (ESR) by the prevalence of informal employment. This prevalence measure is estimated as the share of all men living in the same EA as the respondent and employed in the informal sector (see the descriptive statistics and definitions of this indicator in Table A4). We present estimated coefficients from a model that includes a triple interaction between the treatment, sex, and the exposure measure. We control for a vector of variables ( ) that includes sex, the prevalence measure, double interactions, and the list of control variables selected using a double-LASSO procedure for each iihss outcome. Under _Treatment Effects_ we report the total effects of the LOA and ESR for each subsample relative to the group XX of individuals with similar exposure in the control group (see Panel B in Figure 2 for a graphic representation of these total effects ). The control mean refers to the mean of the control group for each outcome. Standard errors clustered at the household level are presented in parentheses. *** _p_ <0.01, ** _p_ <0.05, * _p_ <0.1 44
the survey to the next between 1985 and 1998. We then combine this constructed panel with the pre-formed panel provided by the ASI from 1998-2004.

We construct our panel as follows. First we pair firms that appear in consecutive years. We search for exact duplicates in Open and Close values between one year and the next (e.g. we look for a match between the Close value in 1985 and the Open value in 1986) in one of the following six variables: stock of raw materials, fuels, and stores; stock of semi-finished goods; stock of finished goods; inventory; loans; and fixed capital. We identify all exact non-zero matches in Open and Close variables and count the number of digits in each match excluding trailing zeros; we then match every pair of firms that has at least six digits of exact matches.<sup>6</sup> In the case of conflicting firm matches, we take the pair that matches the largest number of digits over the six variables. On average, matched firms have 24 non-trailing-zero digits in common. We apply this technique from 1985-1994 and from 1996-1998.<sup>7</sup> Second, we attempt to link firms over 1995, a year for which firm-level data have not been released, and over other years in which individual firms may not have been sampled. In this step, we consider matches within state, 2-digit industry code, and permanent serial number. Though the ASI provides a permanent serial number for each firm, this number is not unique; however, excluding two periods in which permanent serial numbers appear to have changed, permanent serial numbers are equal across known matches more than 90% of the time. We therefore use the permanent serial number along with state and 2-digit industry codes to bridge gap years. We validate any observations matched in this manner by requiring that they match on at least five of the following six variables: year of initial production, district, ownership, 3-digit industry and growth in labor and fixed capital.<sup>8</sup>

> 6For example, two firms that report 10000 rupees of fixed capital would match 1 digit for this variable. Two firms that report 10003 rupees of fixed capital are credited for 5 digits. However, two firms that report 10002 and 10003 rupees, respectively, receive no credit (though could be matched based on other variables or in subsequent steps).

> 7As discussed above, we do not construct TFP directly from the firm-level data in 1996 and 1997 given the way in which several inputs were reported in those years. However, we are able to perform the matching procedure using the variables listed above, and we impute TFP for these years as discussed below.

> 8The mapping of district codes to geographical regions changes frequently over the period of our survey, so we generate a concordance of district codes over time, using existing concordances as well as the changing codes observed in our known panel matches. We thank Pauline Grosjean and Ben Crost for providing us with their district code concordance, which formed a basis for ours.

11
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repo rate with capital inflows and exchange rate movements.<sup>24</sup> We focus on the period from early 2000, since this is the period from when capital inflows have the potential of being an important influence on monetary policy, and since it also coincides with the period when repo rate started to be used as the policy rate.<sup>25</sup> We compare the policy response during the pre-surge period with that during the surge period as well as during the stops. We estimate simple regressions, in which we control for the impact of GDP growth or inflation (WPI inflation being the relevant inflation series during this period) on monetary outcomes (the policy interest rate or money supply).

Our results indicate two regularities. First, domestic policy priorities take precedence over exchange rate or capital flows in the setting of policy interest rates-- we find the policy rate to be correlated positively with inflation but not ordinarily with capital flows or exchange rate depreciation, Figure 15.

**Figure 15: Policy Repo Rate seems to have tracked Inflation closely, but not exchange rate depreciation or capital flows**

<!-- Start of picture text --> Repo Rate has tracked Inflation Closely..... ......but not Exchange Rate Depreciation or Capital<br>Inflows<br>Repo Rate Exchange rate<br>12<br>20 Capital flows (RHS) 120,000<br>10<br>15 100,000<br>8<br>10 80,000<br>6<br>5 60,000<br>4<br>2 0 40,000<br>0 ‐5 20,000<br>‐10 0<br>Repo Rate WPI inf<br>mln $<br>% , or % change<br>% or % Change<br>2001‐02 2002‐03 2003‐04 2004‐05 2005‐06 2006‐07 2007‐08 2008‐09 2009‐10 2010‐11 2011‐12 2012‐13 2013‐14<br><!-- End of picture text --> Source: RBI Handbook of Statistics on the Indian Economy Second, the policy response to surges and stops consists of reserve management, liquidity management, and capital flow measures. The “domestic orientation” in monetary

> 24Mohan and Kapur caution that in analyzing the growth in reserve money supply, one needs to adjust for changes in the CRR. An increase in CRR, even as it impounds excess liquidity from the banking system, ends up showing a larger expansion in reserve money in the Reserve Bank’s balance sheet.

> 25 During the 1990s bank rate as well as money supply tracked domestic inflation closely.

22
—-Floating ER -<-Fixed ER -- Flexible Price Economy <!-- Start of picture text --> 6%<br>o<br>tadS 5%<br>uo}<br>8 4%<br>E<br>& 3%<br>a<br>3S<br>=)3 2% y os Resource9 revenuese<br>0% fe<br>“7| 70° i 23 4 5 6 7 8 9<br>Quarters after shock<br><!-- End of picture text -->

<!-- Start of picture text --> 0.0%<br>2<br>% -0.1%<br>><br>3<br>2g -0.2%9.29<br>£<br>5 -03%<br>2<br>oO<br>3 -0.4%<br>4<br>“0.5%<br>Quarters after shock<br><!-- End of picture text --> <!-- Start of picture text --> 0.5%<br>=0.4%<br>zz<br>2=| 0.3%<br>g<br>oe<br>E 0.2%<br>RS}<br>Bs<br>< 0.1%<br>0.0%<br>-1 0 1 2 3 4 5 6 7 8 9<br>Quarters after shock<br><!-- End of picture text -->

<!-- Start of picture text --> 0.6%<br>7<br>2 0.5%<br>cay<br>3 0.4%<br>F<br>&S 0.3%<br>§<br>3o 0.2%<br>><br>0.0%<br>-1 0 1 2 3 4 5 6 7 8 9<br>Quarters after shock<br><!-- End of picture text --> <!-- Start of picture text --> 0.6%<br>g ~ 0.5%<br>BE 0 40% a<br>>O :<br>2 0.3% 3<br>anesa g 0.2% a sainaa!<br>& & 0.1% 5<br>& & 0.0% ‘g<br>20.1% :<br>Osx , .<br>o~ 0.2% seeeeee* Annualized<br>(Fixed ER)<br>-0.3%<br>-0.4%<br>Quarters after shock<br><!-- End of picture text --> <!-- Start of picture text --> 0.5%<br>= 0.4%<br>zz<br>2€ 0.3%<br>£<br>oa<br>& 0.2%<br>Re<br>Bs<br>x 0.1%<br>0.0%<br>-1 O 1 2 3 4 5 6 7 8 9<br>Quarters after shock<br><!-- End of picture text -->
<!-- Start of picture text --> OTRI<br>35%<br>30% |<br>25%<br>20%<br><!-- End of picture text -->

<!-- Start of picture text --> 10%<br>-A = _ |<br>All Agriculture Manufactur ing All Agriculture Manufactur ing<br>High income Low and Middle income<br>@ Tariff m= Nom-tariff |<br><!-- End of picture text -->

<!-- Start of picture text --> MAOTRI<br>0.3<br>0.25 |<br>0.15 |<br><!-- End of picture text -->

<!-- Start of picture text --> 0.1 |<br>0 Hi<br>All Agriculture Manufacturing All Agriculture Manufacturing =|<br>High income Low and Middle income<br>Gtariff mNortariff<br><!-- End of picture text -->