rafmacalaba/datause-encoder-data huggingface dataset:
(26) **&H** = aCX 6TCY / (<sup>3</sup> XY Finally, the fraction of the variance in measured equivalent consumption that is due to measurement error is calculated as:

- (27) _Fraction measurement error in C_ = (&2C - **&H** ) / &C

The methodology to estimate the variance of changes in underlying living standards is analogous: (28) **&AH** = OAC,AX OAC,AY / **GAX,AY** where **6A,B** denotes the covariance between A and B.

The first instrument used is a measure of subjective living standards. In the Russian data, subjective living standards are measured by the question: _Please imagine a 9-step ladder where on the bottom, (thefirst step), stand the poorest people, and on the highest step, (the ninth), stand the rich. On which step are you_ today? Lokshin and Ravallion (1999) show that total household income is a significant predictor of the answer to the subjective welfare question, although its explanatory power is low. In the Polish data, living standards are measured by the question: _How would you rate the general material si uation of your household? Very good, good, average, rather bad or bad._ These answers are converted into a 5 point linear scale. By definition, this instrument should be related to underlying living standards. It seems unlikely that it is correlated with measurement error in equivalent consumption.

The second instrument is equivalent household income. While this instrument is related to underlying living standards, one may worry whether measurement error in equivalent income is independent of measurement error in equivalent consumption. For example, a misspecified equivalence scale would cause a correlation between these error terms. Similarly, households who willingly underreport income (e.g. due to forgetfulness or for fear of taxation), might also underreport consumption. Hence, this second instrument is chosen by lack of a better alternative.

Fortunately, it is possible to deduce how a correlation between measurement error in income and consumption would affect the results. Such a correlation would lead to an upward bias in acy (and the analogous expression for first differences). This would lead us to overestimate the fraction of the variance that can be explained by movements in underlying living standards and to underestimate the part due to measurement error.

14
# 7.4) The Emergence of Today's Homo Economicus

Finally, in Paul Krugman's words<sup>22</sup> , "The theory has become more exciting, more dynamic and much closer to the world view long held by insightful observers who were skeptical of the old conventional wisdom." A backward look at our discipline provides perspective on why development economics has changed so much over half a century.

A Homo Sapiens, the author of these lines is the product of four million years of natural selection. Homo Economicus has evolved at a far more dizzying speed. The Macroeconomicus and Microeconomicus varieties were so different after the war that many thought them to be distinct subspecies. Many differences remain in spite of limited interbreeding. This rapid evolution will surely continue, as the paradigm that guides our work reflects economists' efforts to understand the real world.

Keynes' consumer was a simple beast, not so different from a worm inching its way on a sandy sea bottom, gulping sand into one end of which it spews out a lesser quantity at the other, the 23 difference being the organic matter that it digests . The discussion of investment in the General Theory reflects a subtle Cambridge understanding of capital theory but this leads to little and what readers retained was that investors are driven by animal spirits. John Bull could stand many things but not a 2 percent interest rate, thus the liquidity preference schedule did not intersect the X axis.

In standard microeconomic theory, consumers were alone in a world which transmitted price signals. So little was deduced from the assumption of utility maximization24 that many professors "started from demand". The theory of monopolistic competition examined how businessmen matched marginal cost and marginal revenue, concepts that no one has found a way to quantify from accounting and market data.

Price theory was so far removed from the real world that many came to accept that prices did not matter, a conviction that was buffered by surveys by Oxford Keynesians that purported to show that interest rates does not influence the investment decisions of businessmen, and regressions which showed that the price elasticity of demand for exports is so low that the Marshall Lerner equilibrium condition does not hold. The skepticism about prices accounts for the popularity at that time of Harrod-Domar and input-output models, that dispensed with prices in their description of economic mechanisms.

Micro economists were so fascinated by the implications of market failures stemming from scale economies, externalities and the second best character of equilibria that their students could well think that Adam Smith's invisible hand works so poorly that their chief task, should they become policy makers, would be to correct its working.

> 22 Krugman (1995), p 360 in _Handbook of Economic Innovation and Technical Change,_ ed., Stoneman, Oxford: Blackwell pp 342-36.

> 23 The Phillips machine that is on show in London's Albert and Victoria Museum, that was used in teaching at the time. illustrates the model's simple plumbing.

24 The Giffen paradox; and the Slutzky conditions.

15
participation or the lack of progress in closing other gender gaps. While we cannot capture the underlying norms themselves nor establish causality, the following graphs plot the correlation between normssensitive outcomes and corresponding attitudes and provide some initial evidence of the strength of the linkages between these two sets of variables.

For all the countries with WVS data (Bangladesh, India and Pakistan), Figure 12 depicts a clear negative association between women’s employment rate and the attitude toward precedence of men in obtaining scarce jobs. It is interesting to note that while India and Pakistan have similar slopes, the association in Bangladesh is weaker. This means that even if the share of people supporting the right of men to jobs increases (as it is visible for the more recent cohorts), the share of women working does not decrease by a large amount. Further, despite Bangladesh and Pakistan experiencing large overlaps on the horizontal dimension, such that 70-80 percent of respondents in both countries believe that men have more right to jobs, Bangladesh is placed higher on the vertical axis in terms of the share of women working.

The linkage of educational outcome with the related attitudinal variable is shown in Figure 13. This figure plots the correlation between the share of women having secondary or higher education against the share of respondents who agree that “university is more important for boys than girls” in Bangladesh, India, and Pakistan. We group observations into 5-year birth cohorts with darker shades indicating more recent birth cohorts. In the case of education, the results are mixed. A strong negative association is found only for Bangladesh. In addition, the change in attitudes and education levels is consistent with the passing of time as more recent (darker) cohorts display more progressive views and higher levels of education. The correlation, however, is not present or has the ‘wrong’ sign in the cases of India and Pakistan. In India, recent (darker) cohorts have higher levels of education even if attitudes towards gender roles with respect to tertiary education do not change. In Pakistan, even if attitudes become less progressive, the share of women receiving higher education increases. The persistence of regressive gender attitudes has, seemingly, not been a binding constraint for women’s education outcomes. However, the consequences of static and regressive attitudes may show up in other proximate outcomes to education, like employment.

Financial empowerment can increase the agency of women in the household. Women who contribute financially to household income are more likely to participate in household decision making. The share of women who agree that both spouses should contribute to household income is generally low in all three countries – less than 40 percent, and this share is decreasing with more recent cohorts. As shown in Figure 25
35 Table 5: Further Tests for Employment Rationing 1987-9.

|Elasticity of EGS<br>employment w.r.t.:||1<br>(OLS)|Estimation method<br>2<br>3<br>(IV)<br>(IV)|
|---|---|---|---|
|The number of works<br>in progress (P)|b5|1.094<br>(3.83)|1.243<br>1.136<br>(3.08)<br>(4.85)|
|The average EGS<br>wage rate (W)|b2|-.514<br>(2.30)|-.523<br>-.479<br>(2.32)<br>(2.92)|
|The average ag.|b3|-.011<br>|.102<br>n.a.<br>|
|wage rate (WA)||(.017)|(.144)|

Note: Absolute t-ratios in parentheses, 31 monthly observations, R<sup>2</sup> -.97 for 1,2 and 3. Regressions 1 and 2 included 11 monthly dummy variables and lagged dependent variable. Regression 3 excludes highly insignificant variables (including agricultural wage rate). All regressions passed LK tests for serial correlation, functional form, normality, and heteroscedasticity. See text for further details on the model.
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Chemi**<br>**cal and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Plastic**<br>**Produ**<br>||||||||||||
|**cts**||||||||||||
|**Metal**<br>**Produ**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**cts**||||||||||||
|**Machi**<br>**nery**<br>**and**<br>**Equip**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ment**||||||||||||
|**Energy**|0.00|0.04|0.09|0.13|0.17|0.22|0.26|0.30|0.35|0.39|0.43|
|**Constr**<br>**uction**|0.00|2.93|2.89|2.84|2.80|2.75|2.71|2.66|2.62|2.57|2.53|
|**Trade**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Accom**<br>**modat**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**ion**<br>**and**<br>**Food**<br>**Servic**||||||||||||
|**es**||||||||||||
|**Transp**<br>**ortatio**|0.00|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.06|0.06|
|**n**||||||||||||
|**Bankin**<br>|0.00|0.08|0.08|0.09|0.09|0.10|0.10|0.11|0.12|0.12|0.13|
|**g-**<br>**Financ**||||||||||||
|**ing**||||||||||||
|**Real**<br>**Estate**|0.00|0.24|0.27|0.29|0.31|0.33|0.35|0.37|0.39|0.42|0.44|
|**Public**<br>**Admin**|0.00|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.07|0.06|0.06|
|**istrati**||||||||||||
|**on**||||||||||||
|**Educat**<br>**ion**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Health**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Other**<br>**Servic**<br>**es**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

_Table B. 10. Annual spending associated with households’ consumption under the Counterfactual Scenario (Uniform Economy-Wide Demand Stimulus) relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**|0.22|3.37|2.29|1.42|1.19|1.18|1.18|1.18|0.20|0.19|0.00|
|**ture**||||||||||||
|**Forestr**|0.01|0.18|0.12|0.08|0.06|0.06|0.06|0.06|0.01|0.01|0.00|
|**y**||||||||||||

44
lower graphs show the ASFRs for the reform and non-reform districts, respectively. The bars leaning leftward are the ASFRs for the 1994 census and those leaning rightward are the ASFRs for the 2007 census. The ASFRs are ordered top-to-bottom starting from older to younger age groups. The unit of observation is ASFR per 1000 women in a particular childbearing age group of a 5-year interval.

As can be seen, the ASFRs are fairly normally distributed across age groups, with women aged 25—29 being the most fertile age group but women 10—14 as well as 45—49 being the least fertile age groups. Simple visual observation of the top panel of Figure 2 reveals that the ASFR had decreased for virtually all age groups in reform districts after the implementation of the reform. On the other hand, we do not observe any systematic changes in the ASFRs of the non-reform districts between the 1994 and 2007 census. If anything, the ASFRs have slightly increased for the most fertile age groups between 20 and 29 years of age.

## [Insert Figure 2 about here]

These simple observations imply that the reduction in the TFR of the reform districts is actually driven by a reduction in the ASFRs across virtually all age groups of women of childbearing age. These descriptive figures suggest that it is rather unlikely that the observed reduction in the TFR in the reform districts can be ascribed to other programs, such as the revised family code or family planning, which would target particular age groups rather than all women in their childbearing years.

# 4.3 Regression-based impact estimates on fertility outcomes

The correlations presented in Table 2 show some differences between the reform and non-reform districts for variables potentially affecting fertility outcomes. In this section we present the results of regressions both with and without control variables. Including controls improves on the unconditional mean differences shown above by removing the influence of other observable variables affecting fertility from the estimated impact of the implementation of the reform.

Table 4 reports our main regression results for all (columns (1) and (2)) as well as rural (columns (3) and (4)) and urban (columns (5) and (6) enumeration areas aggregated at the district level. The policy variable of interest is the 1997 Amhara land reform, measured by a binary indicator equal to one if the reform was implemented in at least one of the peasant associations belonging to a particular district. All regressions include district fixed effects. We have 206 observations in the full aggregated data<sup>16</sup> , 198 in the rural aggregated data, and 202 in the urban aggregated data. The inference is based on Huber/White standard errors.

> 16 Note that the adiministrative data has missing information on three districts. Hence six cluster-time observations are missing from the analysis.

14
**_Energy demand models for policy_** **_<u>formulation</u>_**

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Saddler, H., M. Diesendorf, and R. Denniss, 2004, A clean energy future for Australia, Clean Energy Group, Sydney, WWF Australia, (see http://www.bioenergyaustralia.org/reports/Clean_Energy_Future_Report.pdf ).

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Sadeghi, M. and H. M. Hosseini, 2008, Integrated energy planning for transport sector – a case study of Iran with techno-economic approach, Energy Policy, 36(2), pp. 850-66. Saicheua, S., 1987, Input substitution in Thailand’s manufacturing sector: implications for energy policy, Energy Economics, 9(1), pp. 55-63.

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117
Table 1: FDI and Growth: Cross-country Regressions starting in 1970s

|20-year range:|(1)<br>1970-89|(2)<br>1970-89|(3)<br>1970-89|(4)<br>1975-94|(5)<br>1975-94|(6)<br>1975-94|
|---|---|---|---|---|---|---|
|FDI|0.057|0.211|0.559*|-0.176|0.039|0.174|
||(0.213)|(0.163)|(0.303)|(0.227)|(0.218)|(0.490)|
|FDI _×_ Pr. Cr.||1.161*<br>(0.608)|||1.791*<br>(0.908)||
|FDI _×_ School|||1.454*|||1.029|
||||(0.833)|||(1.233)|
|_GDPt−_1|-1.363***|-1.337***|-1.341***|-0.303|-0.309|-0.285|
||(0.382)|(0.364)|(0.364)|(0.504)|(0.485)|(0.481)|
|Pr. Cr.|3.098|0.909|1.436|11.063*|7.212|10.565*|
||(2.139)|(2.067)|(1.981)|(6.341)|(5.778)|(6.128)|
|School|2.319**|2.775***|1.777*|-3.633**|-2.954*|-4.450**|
||(0.914)|(0.865)|(1.009)|(1.619)|(1.575)|(2.170)|
|Inf.|0.105|-0.003|0.029|0.243|0.114|0.186|
||(0.280)|(0.267)|(0.274)|(0.931)|(0.896)|(0.898)|
|Trade|0.539|0.051|0.240|-0.802|-1.549|-1.141|
||(0.430)|(0.445)|(0.440)|(1.074)|(1.306)|(1.240)|
|Govt. Cons.|-0.317|0.233|0.214|2.343|3.103|2.660|
||(0.557)|(0.628)|(0.590)|(2.530)|(2.782)|(2.738)|
|Instit.|0.679**|0.602**|0.674**|0.817|0.665|0.706|
||(0.265)|(0.278)|(0.268)|(0.536)|(0.538)|(0.543)|
|SSA|-0.896|-0.757|-0.892|-0.744|-0.574|-0.821|
||(0.574)|(0.552)|(0.549)|(1.647)|(1.636)|(1.617)|
|B. M. P.|-5.533*|-5.709**|-6.103**|10.894|10.365|10.271|
||(2.991)|(2.844)|(2.938)|(8.049)|(7.816)|(7.691)|
|Constant|10.990**|12.026**|10.974**|-13.586|-11.206|-12.297|
||(4.901)|(4.796)|(4.646)|(14.513)|(13.824)|(13.615)|
|N. Obs.|81|81|81|96|96|96|
|_R_<sup>2</sup>|0.441|0.471|0.468|0.216|0.236|0.225|

**Notes:** This table reports a set of cross-country regressions where the dependent variable is the average growth rate of real annual GDP per capita and the explanatory variables are: net FDI inflows as a percentage of GDP; credit to the private sector by deposit money banks as a percentage of GDP (this variables is scaled by 100); the log of average years of secondary schooling in adult population; the log of initial GDP per capita; the log inflation; the log of export plus import over GDP; the log of government expenditure over GDP; the ICRG investment risk index; the log of the black market premium; and a dummy that takes value one for countries located in Sub-Saharan Africa. Columns (1)-(3) focus on developing and emerging economies for which we have data starting in 1970. Columns (4)-(6) focus on developing and emerging economies for which we have data starting in 1975. Robust standard errors are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

7
covert offshore assets. Inevitably, it is difficult to measure the scale and pattern of this flight capital, but it is undoubtedly large (Hanson, 2003 and references). Omission of offshore holdings will inevitably tarnish any analysis of household financial assets in developing countries.

# **2.  Household financial assets and growth**

Numerous econometric studies have established a causal link between financial depth and GDP growth.<sup>16</sup> The indications are that it is through its influence on the productivity of investment, rather than on its magnitude, that bank intermediation exerts its pro-growth effect, at least in higher income economies. It is the aggregate stock of bank credit to private sector borrowers, rather than the aggregate stock of money, that has been found to be the most robust explanatory variable in these regression analyses. But without mobilized funds for onlending, the banks would not be able to increase the volume of credit. Given the importance of household asset accumulation in easing the challenge for local banks to mobilize funds for onlending, household financial asset holdings do make a potentially significant indirect contribution to growth.<sup>17</sup> It needs to be underlined that this extra link in the chain (from money to credit) is not a very tight one. For example, banks in much of Sub-Saharan Africa hold sizable excess reserves, either finding insufficient bankable lending opportunities to deploy the deposits which they have mobilized, or preferring to invest in high-yielding government securities in cases where domestic financing of government deficits in effect crowds out private lending; in this case additional household deposits are unlikely to be onlent. Furthermore, other countries have experienced the opposite phenomenon, where banks draw on external credit lines to finance more private lending than could be funded out of domestic resources. (Direct offshore borrowing by larger enterprises has also been observed in those cases.) Nevertheless in broad terms (and in an echo of the HoriokaFeldstein savings-investment correlation across countries), banking depth on the deposit

> 16 The finance and growth literature is surveyed in detail by Levine (2005), and summarized more briefly by Honohan (2004a). There remains some doubt as to whether this effect is more important at low or intermediate levels of income – or of financial development (Rioja and Valev, 2004; Aghion et al., 2005) 17 The household dimension often not taken into account, so it is overall financial depth and not the household’s share that is considered in the literature on finance and growth.

12
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26