rafmacalaba/datause-encoder-data huggingface dataset:
69 9

-

# Table A.13: **RWeeeson** Coefficients for **Esthath hcoin IuAiplers**

## District Data

||AGW|AR|AP|AY|R|Wage<br>Multi-<br>plier a/|Income<br>Multl-<br>pller e|
|---|---|---|---|---|---|---|---|
|Rural Areas<br>||||||||
|(1) OLSI<br>|-.324<br>(7.8)<br>|.027<br>(10.9)<br>|1.087**.5**<br>(1.9)|.0444<br>(8.2)|.89|.356|.292|
|(2) OLS-PHs'<br>|-.0182<br>(.9)<br>|.014<br>(3.5)<br>|2.70***4**<br>(.4)<br>|.0213<br>(3.6)<br>|.99|.306|.251|
|(3) 2SLS-PHYd<br>Rural Towns<br>|.0839<br>(.4)<br>|.0051<br>(.4)<br>|3.88***4**<br>**(.3)**<br>|.018<br>(.6)|.72|.302|.248|
|(1) OLS<br> <br>|.0059<br>(.2)<br>|-. 0033<br>(1.9)<br>|1.21**.4**<br>(3.1)<br>|.0124<br>(3.3)|.46|.155|.254|
|(2)<br>OLS-PH<br> <br>|-.0414<br>(1.4)<br>|-.0005<br>(.2)<br>|4.13**4**<br>(.7)<br>|.0212<br>(4.1)<br>|.09<br>|.198|.325|
|(3)<br>2SLS-PH<br>|-.0011<br>(.02)|.0002<br>(.03)|-7.60<br>(.9)|.0240<br>(1.4)|.04|.237|.388|
|Rural Areas**Pkhs**<br>Rural TownsL<br>||||||||
|(1) OLS<br> <br>|-.318<br>(6.5)<br>|.0238<br>(8.1)<br>|2.30 .8<br>(3.5)<br>|.0568<br>(3.9)<br>|.89<br>|.510<br>|.545|
|(2)<br>OLS-PH<br> <br>|-.121<br>(2.7)<br>|.020<br>(3.7)<br>|6.01,4<br>(.7N<br>)<br>|.0485|.26|.504|.576|
|(3)<br>2SLS-PH|-.173<br>|.0258<br>|-1.68.5<br>|.0707<br>|.01|.538|.635|
||(1.1)|(2.0)|(1.2)|(2.4)||||

## Notes:

AGW = total agricultural wage earnings in each district (used as a proxy for total agricullural ,ncome; AR = AGW*roa **d** ensity. AP - AGW*population density. AY = AGW^daily agricultural wage rate (used as an alternative to agricultural income per agricultural popu a c- Left side RNFY = Rural nonfarm wage eamings estimated as rural nonfarm labor*daily agricultural wage rate. There were 83 observations.

The t-ratios are listed in parenthes under regression parameters.

- a/ The wage multiplier is calculated " aRNFY/BAGW evaluated at the sample means of all relevant variables (gven in Table Al10)

- b/ Ordinary les squares estimate.

- C/ Ordinary least squares with thc Pris-Houftker adjustmen for heteoekedasticity.

- d/ Two-stage _les_ squars with th Prui-Houtaker adjustment for heteroekedasticity

- e/ In convetlng **forr.** wage to **inomr multIplrs,** we make two adjustmen. Firt, we multiply all wage multipliers by 0.82, the ratio of wage to totl **incoma in agricultun** relative to nonfim activity (Swa/Swn). Second, because rural town **wage** rat" **_we_ doule** those **inrl** ar, we multiply the rura town wage multiplier by two See note 13 for detais.

- I/ Multipliers calculated as sum of rural a plus rural towns.
Galasso et al. (2001) hypothesize that the treatment effect of the vouchers may have been an ”empowerment effect” in that workers who received vouchers seem to have been more comfortable in approaching private employers. They might also have been perceived by employers to be different from regular _Trabajar_ workers. One possible reason for the low employer take-up rate is that taking up the voucher requires formalizing the employment arrangement, which could imply additional costs for the firm (such as severance payments to fire the worker).

The Galasso et al. (2001) study reports both intent-to-treat estimates and local average treatment effect (LATE) estimates of program impacts. The intent-to-treat estimates give the effect of the program offer to participants (irrespective of whether participants took advantage of the program). The LATE estimates use the randomized group assignment as an instrument for program participation status. The LATE estimate represents the average program effect for the group induced to participate in the voucher program as a result of being assigned to the treatment group (known in the literature as the group of ”compliers”).<sup>7</sup> The outcome variables are the changes in earnings and employment between the last follow-up wave (May, 2000) and a baseline survey in December 1998. A limitation of the analysis is that there was some attrition in the experimental samples, with only 77.5% of those interviewed at baseline staying until the fourth round. Galasso et al. (2001) find that private sector employment improved among voucher recipients, with an employment rate of 14% for voucher recipients and 9% for the control group. However, the program had no impact on incomes measured 18 months after the program. Nevertheless, the program is deemed cost-effective, because it yielded employment impacts at very low cost, because of the low take-up of the subsidy by employers.

_Trabajar II program in Argentina_ : In response to the macroeconomic crisis in the mid-1990s, the Government of Argentina introduced in May 1997 the _Trabajar II_ program, which provided short-term work opportunities at relatively low wages and targeted unemployed workers from poor families. Under the program, local governmental and nongovernmental organizations submitted proposals for socially useful projects, such as projects to repair local infrastructure. The proposals had to be viable with respect to a set of criteria and were given priority according to how well they targeted poor areas, what benefits they were likely to bring to the community,

> 7 For discussion and development of LATE estimation methods, see Imbens and Angrist (1994) and Heckman and Vytlacil (2005).

13
characteristics of the child’s mother are included. To avoid _ad hoc_ functional form assumptions, age and education variables as well as household size are broken into categories each of which is entered as a dummy variable. OLS is used to estimate each model, with standard errors clustered at the PSU. (The <u>Addendum gives the actual regressions.)</u> As discussed above, in the event that household wealth is simply a poor indicator of nutritional outcomes, we also use household per capita consumption. For the relevant subset of countries, we estimate the regressions using household consumption per person (with, as noted in Section 4, some slight variations in the variables included in ��� and ����). The results were similar; details are found in the <u>Addendum.</u> Tables 12 and 13 present the results for Models 2 and 3 for underweight women and undernourished children respectively. The tables give the proportion of undernourished individuals who fall into the poorest 20% and 40% of the distribution of the predicted values based on wealth and (unlike prior tables) the additional covariates. We find that, on average, 32% of underweight women are found in the poorest 20% based on the predicted values from Model 2 (Table 12), as compared to 28% using only the household wealth index (Table 8). Focusing instead on the poorest 40%, the proportion rises to 56% using Model 2, as compared to 51% using wealth alone. Adding the individual variables (Model 3) we now find that (on average) 37% of underweight women are found in the poorest 20% in terms of the predicted values, rising to 61% for the poorest 40%. Similar improvements are evident for both stunting and wasting in children (comparing Tables 13 and 8).

However, it is clear that these augmented regressions still do a poor job at identifying undernourished individuals within households. While the predictive power is improved, it is not enough to change our conclusion that targeting based on the available household poverty data misses a large share of undernourished women and children.

# **7. Conclusions**

There are multiple constraints on effective policy interventions in practice. Here we have focused on a key informational constraint, and asked whether household poverty might provide a reliable guide for policy efforts trying to reach deprived individuals, as indicated by anthropometric measures of undernutrition, recognizing that poverty is an individual characteristic. We do not claim that information is the only constraint. Even if undernourished women and children are almost solely found 21
public understanding of highly complex issues such as global warming may be neither feasible nor necessary for effective policy making.

Paradoxically, as evidence for climate change pours in and scientific consensus increases, interest in the issue throughout many Western nations declined during the 1990s and into the early 2000s (e.g. Hellevik, 2002, Immerwahr, 1999, Saad 2002). For example, Gallup polls for the United States show that the percentage of people who, “personally worry a great deal about global warming” dropped from 35% in 1989 to 28% in 2001, while the percentage who worry “not at all” rose from 12 to 17% during the same time period (Saad 2002). Even more dramatically, biannual national level survey research in Norway finds a significant and steady downward trend in public interest and concern about global warming, with the percentage of respondents who replied that they were “very much worried” declining steadily from 40% in 1989 to less than 10% in 2001 (Hellevik 2002, Barstad and Hellevik 2004). See also work by Kellsted et al. 2008 discussed below.

# **_Is There a Link between Information and Action?_**

On the one hand, if the public lacks complete information, neither concern nor action is likely. For example, Bostrom, et al. 1994 argue that this general public misunderstanding of climate science is part of the widespread failure of response: “When asked what could be done, respondents tended to focus on general pollution control rather than reductions of carbon dioxide emissions or energy consumption.” On the other hand, the research discussed above reports somewhat conflicting patterns regarding information as a pre-requisite for action.

For example, in their survey of 1,218 Americans, Bord, O'Connor and Fischer 2000 did find that the key determinant of stated behavioral intentions to address global warming is a correct understanding of the causes of global warming. They write that “Knowing what causes climate change, and what does not, is the most powerful predictor of both stated intentions to take voluntary actions and to vote on hypothetical referenda to enact new government policies to reduce greenhouse gas emissions.” Similarly, the authors found that those who did not know the causes of climate change were less likely to say they would take action or support for government policies. Thus they conclude that, “General pro-environmental beliefs and perceptions that global warming poses serious threats to society also help to explain behavioral 13
implies even slower poverty reduction, indicating a larger gap with the 2009/10 HNLSS poverty estimate. Full information on the backcasted series using different pass-through rates can be found in Table _11_ .<sup>22</sup> Figure 4 Testing sensitivity of backcasted series to different pass-through rates

# a) Poverty rate, % (US$1.90)

- b) Number of poor, millions (US$1.90)

<!-- Start of picture text --> 60.0 95.0 89.4<br>55.0 56.4 90.0<br>85.0<br>50.0 78.5<br>46.3 80.0<br>45.0 71.4<br>75.0<br>40.0 39.1 70.0<br>42.2<br>35.0 65.0<br>65.2<br>30.0 60.0<br>Backcast, pass-through=0.42 Backcast, pass-through=0.42<br>Backcast, pass-through=0.75 Backcast, pass-through=0.75<br>Backcast, pass-through=0.87 Backcast, pass-through=0.87<br>Backcast, pass-through=1 Backcast, pass-through=1<br>PovcalNet PovcalNet<br><!-- End of picture text -->

# c) Poverty rate, % (US$3.20)

# d) Number of poor, millions (US$3.20)

<!-- Start of picture text --> 85.0 150.0<br>79.5<br>142.7<br>80.0 140.0<br>76.5<br>75.0 71.0 130.0<br>125.9<br>70.0 73.5<br>120.0<br>118.0<br>65.0<br>110.0 113.4<br>60.0<br>100.0<br>Backcast, pass-through=0.42<br>Backcast, pass-through=0.42<br>Backcast, pass-through=0.75<br>Backcast, pass-through=0.75<br>Backcast, pass-through=0.87<br>Backcast, pass-through=0.87<br>Backcast, pass-through=1<br>Backcast, pass-through=1<br>PovcalNet<br>PovcalNet<br><!-- End of picture text --> Note: the figure shows backcasted series using different values of the pass-through rate used to account for the difference between growth in national accounts and in household consumption as measured in household surveys. Household consumption data from the 2018/19 NLSS is matched to sectoral GDP growth rates (MFM-Tool World Bank) based on 22 It should be noted that the purpose of this exercise is to check whether different assumptions of the pass-through rate can help explain the difference between the backcasted estimate and the 2009/10 HNLSS estimate, rather than trying to estimate a specific value of the pass-through rate for Nigeria.

17
the case of wasting and underweight, but not in stunting. However, the dominance test is significant only by multiple comparison approach (mca) rule. No significant dominance was found using the more strict, intersection union principle (iup) rule . Overall, the results presented here are in line with the findings from absolute and relative inequality analyses presented in earlier sections; the poor did not benefit equally from the improvements in child nutritional status. On the other hand, in Figure 2b, the concentration curves overlapped and in some cases crossed the line of equality.

<!-- Start of picture text --> Figure 2a. Concentration curves of selected child undernutrition indicators<br>(2000-2014)<br>Stunting Wasting Underweight<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>cumulative proportion: children under 5 cumulative proportion: children under 5 cumulative proportion: children under 5<br>E arlier Survey L atest Survey E arlier Survey L atest Survey E arlier Survey Latest Survey<br>L ine of Equality Line of Equality L ine of Equality<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>cumulative proportion: stunting cumulative proportion: wasting<br>cumulative proportion: underweight<br>0 0 0<br><!-- End of picture text --> <!-- Start of picture text --> Note: Computed from DHS 2000 (Earlier Survey) and mini-DHS 2014 (Latest Survey). Values in the horizontal axis are ranked by wealth<br><!-- End of picture text --> 27
33 investors that the Fujimori government was indeed committed to sustained reform (World Bank 1995a). The government's strong commitment to privatization was evident through this period. The government's plan included more than 200 SOEs. By the end of 1994 over US$3 billion in revenues had been raised from privatization, over US$7 billion by April 1997 (see statistical appendix table 5).

For all these reasons, private participation in SEDAPAL was politically desirable at that time, at least to the market-oriented technocrats who were responsible for designing the concession transaction. The concession was a logical component of a sweeping privatization program designed to restore business confidence and remove bottlenecks to modernization. Additional funds would allow SEDAPAL to improve service, which would especially benefit the lower and middle income consumers who lived in communities that suffered the most frequent interruptions, and who could least afford to build private cisterns for water storage. Private operation would also remove a fiscal drain; government transfers to SEDAPAL in 1991 were US$12.2 million (LMG Consultants 1993). However, political desirability was less strong in the top ranks of the administration, for reasons we describe below.

The reform was also politically feasible, in the sense that the government would be able to overcome opposition and implement the concession. The _autogolpe_ had left Fujimori in complete control of the apparatus of the state with the backing of the military and most of the public. The main political opponents of SEDAPAL's privatization, the workers, were not veto players. High unemployment and the growth of the service and informal sectors had reduced union membership from one third of the workforce in the 1970's to only 12 percent by the early 1990's. As we saw in Table 5 unions had low
25

# Table 1. Syndicate Bank and the Commercial Banking System. Selected Years 1960-74 (percentage sharel

|Category|1960|1965|1970|1974|
|---|---|---|---|---|
|Deposits|0.9|1.8|2.6|4.0|
|Loans and<br>Advances|1.0|1.7|2.6|3.9|
|Branches|2.9|3.3|4.1|3.8|
|Borrowing<br>Accounts|-|15.0|-|12.0|

Source: N. K. Thingalaya, Innovations in Banking: The Syndicate Experience, Domestic Finance Studies No. 46 (Washington D. C.: World Bank, Development Economics Department, 1978).
# 1. INTRODUCTION

In this paper we describe a new model of the Latin American region, built for the purpose of studying North-South macroeconomic interactions. We then use it to discuss the macroeconomic transmission of global shocks to Latin America.

Section 2 of the paper presents the model and discusses its simulation properties, both empirically and by means of a simple analytical representation.

In section 3 we examine the effects of stylized global shocks on Latin America, in order to investigate the nature of the process by which such shocks are transmitted to the region from the OECD. In each case, we first briefly discuss the effects of these shocks on the world economy, as simulated by the global econometric model (GEM) developed at London Business School and the National Institute of Economic and Social Research. We then consider the implications suggested for Latin America when our regional model is connected up to GEM. The stylized shocks considered are a US monetary contraction and a US fiscal expansion.

In section 4, we consider a historical experiment. We investigate how the 1980s might have differed for Latin America if fiscal and monetary policies in the OECD had been different. In particular we ask how out-turns might have changed if the global monetary contraction of the early 1980s had been avoided, or if fiscal policies had been better coordinated across the OECD. The results shed light on the question of whether coordination failures amongst the OECD countries exacerbated the adjustment problems of Latin America in the 1980s.

The concluding Section 5 draws attention to limitations of the study, in the context of policy reactions in Latin America in the face of shocks emanating from OECD countries.

# 2. **THE LATIN AMERICAN MODEL**

This section describes the properties of our macro-econometric model of Latin America. The full specification of the model is given in Srinivasan and Vines (1990). The model is an aggregate one which covers aU the principal Latin American countries with the exception of the oil producer, Venezuela. The model has been econometrically estimated using aggregate data for the region constructed from individual country data supplied by the World Bank. Its broad theoretical structure is similar to the models of the Newly Industrializing Economies, Other Asian Economies, and African Economies described in Srinivasan and Vines (1990, 1991a,b) and Allen (1991).

The model consists of roughly forty equations. It includes a fully specified incomeexpenditure process, a supply side, and a foreign trade sector, together with a full set of balance
12 excluding all households for which housing expenditures were missing from the data (being 18 percent of the original SUSENAS).16 All estimated models include household demographic composition variables and dummy variables for season and region (urban/rural; Jakarta/other; East Java/other). The income variable is household consumption expenditure per person (including imputed incomes from own-production). For owneroccupiers the SUSENAS imputes a rental equivalent. Rice prices are the unit values implicit in the expenditures and quantities given in the primary data tapes.<sup>17</sup> For further discussion of these variables and an assessment of the data quality see van de Walle (1988a, b).

Table 1 gives mean prices by sector and other summary data, including the housing quantity indices implicit in the actual expenditures and the hedonic indices. It is of interest to note that the implicit quantity index for housing consumption is generally higher in urban areas, suggesting that the positive effect on housing demand of higher urban incomes has generally outweighed the negative effect of the higher urban housing prices. But notice that this is less pronounced for Jakarta; the considerably higher housing rents in that city have clearly led to lower average consumption levels relative to other urban areas, though still exceeding rural levels.

# IV. Estimated Demand Parameters

Table 2 gives the main parameter estimates obtained for both demand models discussed above and their standard errors. (For brevity the parameters on household demographic and other characteristics are omitted but are available on request.)
23 Table 5. Determinants of the Interest Rate for Indonesia

|Variable|Parameter Estimate|T-Statistic|
|---|---|---|
|I. Indonesia (1978-87) Quart|erly Using Monetary Base|(MB)|
|DEP Variable-<br>rIDN|||
|CONST|0.639|1.9|
|rISp|0.654|2.1|
|GDP/MB|0.255|2.0|
|7r|0.458|1.2|
|7IDN.1|0.130|0.6|
|||R-Square-<br>0.395<br>Durbin-Watson<br>-<br>2.0|

II. Indonesia (1978-87) Quarterly Using Monetary Base (MB)

|DEP Variable: rIDN||
|---|---|
|CONST|-0.472<br>1.3|
|fSGP+e|-0.078<br>0.8|
|GDP/MB|0.210<br>1.5|
|ir|0.544<br>1.3|
|nIDN-1|0.253<br>1.2|
||R-Square-<br>0.308<br>Durbin-Watson-<br>2.0|

Note: e is the change of the exchange rate between the home country's currency and the Singapore Pound.

Source: IFS.
productivity growth than would be predicted by global growth convergence trends. This is not broadly the case for African countries in any time period or sector, with within-sector productivity growth across sectors showing no pattern of countries either consistently outperforming or underperforming predicted levels of convergence.

The next section investigates trends in changes in economic structure for African countries in recent decades to understand whether their growth patterns have benefitted from convergent pressures in manufacturing in past decades, or in services in more recent decades, or neither. Appendix B contains further comparisons of African sectoral convergence to other countries for which the data are available.

# **Structural Change in African Economies**

Using the same GGDC data, we adopt the framework of McMillan and Rodrik (2012) to show the pace, intensity, and consistency of structural change in African countries over four decades—dating back to the 1970s. These analyses plot, for a given time period, the sector-level change in aggregate labor share on the horizontal axis with the within-country, relative productivity level of the sector on the vertical axis. These graphics show visually the pattern of structural change for each decade for the countries for which data are available. We are thus able to assess and characterize historical structural change on three dimensions: pace (given by the relative length of the fitted line), intensity (given by the slope of the fitted line), and consistency (comparing pace and intensity across decades).

These analyses identify two broad growth patterns among African countries for which there are data:

# **<u>Pattern 1</u>**

# **<u>Pattern 2</u>**

- Early transition of labor out of agriculture

- Pace of structural change slows in the 1990s/2000s

- Wholesale & retail trade is not the main labor absorbing sector in the 2000s

<u>Countries: Botswana, Nigeria, South Africa,</u> Mauritius, the Arab Republic of Egypt

- Late transition of labor out of agriculture, if at all

- Pace of structural change doesn’t slow markedly in 1990s/2000s

- Wholesale & retail trade is the main sector absorbing labor in the 2000s

<u>Countries: Zambia, Tanzania, Ethiopia, Senegal,</u> Malawi, Ghana, Kenya As an example, Figure 5 shows the historical patterns of structural change in a country following each pattern: Botswana (pattern 1) and Ethiopia (pattern 2).

# **<u>Pattern I—Early Reformers</u>**

9