rafmacalaba/datause-encoder-data huggingface dataset:
# **Striving for Balance in Economics:**

# **Towards a Theory of the Social Determination of Behavior**

Karla Hoff and Joseph E. Stiglitz* <mark>JEL Codes: A12, D00, D01, D02, D03, D50, D64, Z13</mark> Keywords: behavioral economics, culture, priming, gender, role model, psychology, sociology, rationality, altruism, cognition, brain

* Paper prepared for the 2015 meetings of the American Economic Association, Boston, January 3, and forthcoming in _Journal of Economic Behavior & Organization._ Hoff is a Senior Research Economist at the World Bank and was co-director of the _World Development Report 2015_ : _Mind, Society and Behavior_ .  Stiglitz is a University Professor at Columbia University.  We draw heavily upon the _World Development Report 2015_ .  We are indebted to James Walsh for excellent research assistance and to Allison Demeritt, Paul DiMaggio, and Nan Zhou for fruitful discussions and advice.  Financial support from INET and the Roosevelt Institute Project on Inequality, supported by the Ford Foundation, the Bernard and Irene Schwartz Foundation, and the John D. and Catherine T. MacArthur Foundation, is gratefully acknowledged.
21 to converting ruble-denominated trade flows into US dollars,' litis was not the case in 1991 when<sup>around</sup> 98 percent of all transactions were carried out in hard currency<sup>(UN ECE, 1992:85). In consequence, the</sup> data for 1991 provide a statistically less distorted image of the realigmnent<sup>in export patterns.</sup>

# B. The Redirection of T'ormer CMEA Sales to the OECQ

This section addresses the extent of diversion of exports from<sup>the CMEA to the OECD. While more</sup> research Is needed, some preliminary observations concerning the<sup>reorientation of exports can be derived from</sup> examining changes in Poland's exports in 1990 and 1991 (i.e.,<sup>when an almost complete switch to hard</sup> currency settlements in intra-CMEA trade occurred), and in Hungarian<sup>and Polish exports of selected</sup> manufactures to the FSU and the EC.

Although countries experiences varied according to their former involvement<sup>in specialization schemes</sup> within the CMEA, we would argue that the pattern of change in Hungary's<sup>and Poland's tade has been shared</sup> by other former CMEA members. Their econc ies were under broadly<sup>similar administrative economic</sup> systems, they all pursued similar inward-oriente **d** evelopment strategies,<sup>and they all were part of the radial</sup> pattern of industrial specialization organized around the FSU. Further, within<sup>the CMEA trading area, they</sup> all sought to minimize exports of hard currency earners and maximi; z:<sup>ports of goods which could not be</sup> sold in international markets.

According to conventional wisdom, CEE-5 exports of manufactured<sup>goods to the CMEA, especially</sup> to the FSU, consisted of 'soft" goods noncompetitive in world markets because of high production costs and low quality (Marrese and Vanous, 1983). Since in the short term<sup>the quality could not be significantly</sup> improved, the only means available to producers of soft goods to sell<sup>them in international markets was to</sup> offer them at heavily discounted prices. However, there were two obstacles<sup>to this course of action. Frst,</sup> the trade-off between low prices and product quality was limited:<sup>even huge price cuts may not have attracted</sup> customers. Second, by 1991 producers in the CEE-3 performed<sup>in a new environment where financial</sup> performance became linked to sales revenues. So they could no<sup>longer ignore world prices. For these two</sup> reasons, the capacity of SOEs to compete through price discounts<sup>was eroded. Therefore, one would expect</sup> a very limited redirection of exports of manufactures from the<sup>CMEA to the OECD.</sup> This expectation is borne out by Rodrick (1992) who used<sup>the change in the product composition of</sup> industrial exports by area in 1985 and 1990 as a proxy measure<sup>of the extent of the switch in exports by</sup> Poland (and Hungary) following the radical opening of the economy in January 1990.<sup>The composition of</sup> 26 For a coacise discussion of the major problems involved,<sup>see Chapter 2 in Economic Bulletn fio</sup> Europe Vol. 42/90, pp. 29-31.
The differential effect of the ad campaign on recorded malaria incidence across urban and rural settlements aligns with the survey-based evidence. According to the 2019-2021 India Demographic and Health Survey (India IIPS, 2022), solid dwellings represent almost 85% of the total housing units in urban areas, while they are less than 48% in rural areas. Evidence presented above showed significant increases in bed net adoption and decreases of malaria incidence among respondents and households residing in solid houses, with no impacts on households residing in non-solid dwellings. Hence, one may expect that recorded malaria cases are more likely to decrease in localities with a larger proportion of solid dwellers.

Figure 5: Trends in malaria incidence rates (N. of monthly cases/1M people) <!-- Start of picture text --> Urban incidence (cases/1M people): treated vs. control subdistricts<br>Campaign starts<br>0.00<br>-1.42<br>-9.13**<br>Rural incidence (cases/1M people): treated vs. control subdistricts<br>Campaign starts<br>0.00<br>-3.16 -2.31<br>Apr-Jun 2020 Jul-Aug 2020 Sep 2020-May 2021<br>(omitted)<br>10<br>0<br>-10<br>-20<br>10<br>0<br>-10<br>-20<br><!-- End of picture text --> Notes: Each point reports the coefficient on the interaction term Treatment _×_ Period, where Period is a dummy variable equal to 1 at each period on the graph (i.e., Apr-Jun 2020, Jul-Aug 2020 and Sep 2020-May 2021). The omitted period is Jul-Aug 2020. Two separate regressions are estimated for rural and urban incidence rates. Regressions include subdistrict and period fixed-effects, as well as district-level controls interacted with period dummies. Standard errors are clustered at the district level. Bars represent the 95% confidence intervals.* _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01 Annex Table D19 complements our evidence on malaria incidence with an analysis of testing behaviors and positivity rates. It shows non-significant effects on the number of malaria tests conducted for neither settlement category. However, consistent with the reduction in incidence, the table shows a statistically significant reduction in test positivity rates: the share of positive tests out of the total tests conducted decreased by 1.7 p.p. overall, and by 2.2 p.p. in urban localities, while the effect is virtually null in rural areas.

The identifying assumption behind the Diff-in-Diff estimates presented in Table 5 is the parallel trends assumption: in the absence of the campaign, the _change_ in incidence rates in the treatment 26
7 bivariate correlations. They do not establish the effect of, say, parental education on vocabulary recognition, or immunization. Parental education is obviously correlated with wealth, housing quality, access to water, distance to and quality of school, and possibly even with genetic endowments of ability that can be transmitted across generations. There is an important literature that seeks to identify each of these individual effects,<sup>7</sup> and it is clear that the simple patterns we describe here do not do so. What they do is suggest that the collection of these pre-determined circumstance variables (parental education, wealth, location, access to services, etc.), which can not be controlled by the infant or young child, do powerfully shape his or her choice – or opportunity – set.

**Figure 3: Child cognitive skill development by maternal education**

<!-- Start of picture text --> 110<br>100<br>90<br>80<br>70<br>60<br>36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70<br>age in months<br>0-5 years 12 or more years<br>Median TVIP score<br><!-- End of picture text --> Source: Paxson and Schady, 2004.

# _3. Unequal opportunities hold back development – in a number of ways._

The fact that predetermined, morally irrelevant circumstances influence opportunities, and therefore final outcomes, was all that mattered to the second strand of thinking mentioned in the Introduction. A normative consensus – or at least some degree of convergence – was emerging, that judged such inequality of opportunities to be ethically undesirable, from the point of view of social justice. In this section, we argue that it is exactly this sort of inequality – inequality in the predetermined circumstances that shape opportunities – that leads to aggregate inefficiency, in the spirit of the first strand of thinking previously summarized. Consider three telling examples.

The first example is from an agricultural setting in Ghana, where land is allocated by custom, and security of property rights is therefore often linked to the local power structure. Goldstein and Udry (2002) find that individuals are less likely to leave their land fallow (an investment in long-run productivity of the land) if they do not hold a

> 7 See Card (2001) for a survey.
12 lower income developing economies, Sub-Saharan Africa and the LDCs appear to have benefited much less than middle and higher income countries.

**Table 6: Growth and Share of Parts & Component Products in Manufacturing Imports by Income Group and Region**

||Shar<br>M|e of Par<br>anuf. Im|ts & Co<br> ports(|mp in<br>%)|Annual<br>|Growth i<br>Impor<br>|n Parts &<br>ts(%)<br>|Comp<br>|Ann<br>|ual Grow<br>Impo<br>|th in Ma<br>rts(%)<br>|nuf.<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||1980-|1990-|2000-|1980-|1980-|1990-|2000-|1980-|
|CountryGroup|1980|1990|2000|2010|90|00|10|10|90|00|10|10|
|**BY INCOME LEVEL**|||||||||||||
|Developed economies|22.1|23.1|27.4|24.1|10.6|7.9|4.9|7.8|10.1|6.0|6.3|7.4|
|Developing economies|22.9|29.5|38.7|35.6|11.3|13.5|9.9|11.6|8.5|10.5|10.8|10.0|
|High income developing econ.|27.9|31.0|40.4|39.8|13.3|12.3|7.9|11.1|12.0|9.4|8.0|9.8|
|Upper middle income economies|20.3|30.2|39.0|35.9|10.2|16.0|11.2|12.4|6.0|13.1|12.1|10.3|
|Lower middle income economies|19.2|23.6|32.3|26.3|8.9|10.5|12.3|10.6|6.7|7.1|14.6|9.4|
|Low income economies|25.8|23.9|27.6|23.8|5.7|7.1|10.6|7.8|6.5|5.6|12.2|8.1|
|Of which: Least developed co.|25.4|24.1|27.7|23.8|6.2|7.6|11.0|8.3|6.8|6.2|12.7|8.5|
|**Memo:**|||||||||||||
|Developing econ, excl China & India|24.8|28.9|38.7|34.7|10.2|13.4|7.8|10.4|8.5|10.1|9.0|9.2|
|Upper middle income, excl. China|23.7|27.7|38.2|31.7|6.9|16.1|6.7|9.8|5.2|12.4|8.7|8.8|
|Lower middle income,excl. India|20.1|24.0|34.6|28.1|8.2|11.3|10.5|10.0|6.4|7.3|12.8|8.8|
|**BY REGION**|||||||||||||
|World|22.6|24.3|30.5|28.4|11.0|9.7|7.0|9.3|10.3|7.3|7.8|8.4|
|Developing economies|22.9|29.5|38.7|35.6|11.3|13.5|9.9|11.6|8.5|10.5|10.8|10.0|
|Asia|22.9|32.4|42.7|40.7|17.7|13.6|10.3|13.8|13.6|10.5|10.8|11.6|
|Latin America & Caribbean|21.9|20.5|33.2|29.6|1.8|21.8|7.1|9.9|2.5|16.1|8.4|8.8|
|Europe, Middle East & North Africa|24.3|26.9|27.1|20.6|2.4|5.9|11.5|6.5|1.3|5.8|14.5|7.1|
|Sub-Saharan Africa|21.8|22.9|22.9|20.9|6.4|2.3|13.4|7.3|5.9|2.3|14.4|7.4|
|**Memo:**|||||||||||||
|BRICS 5 countries|13.3|28.2|35.6|34.3|18.8|12.3|17.5|16.1|10.2|9.6|17.9|12.5|
|OPEC 12 members|23.0|25.8|24.8|20.0|1.5|5.0|12.8|6.3|0.3|5.4|15.2|6.8|

Note: The classification of parts and components items is based on 75 products at SITC 2 to 5-digit level in revision 2, including textiles & clothing (65+61), machinery & transport equipment (7), metal manufacturing (69), other misc. manufactured goods (8), and manufacturing products are defined as SITC 5+6+7+8-68 in revision 2.

Source: Based on UN COMTRADE Statistics.
## Estimating the E¤ects of Credit Constraints on Productivity of Peruvian Agriculture3

argue that being credit constraint may very well be endogenous. Also, these papers do not use individual e¤ects to control for the heterogeneity of the quality of the farmland so that these papers di¤er from Guirkinger and Boucher (2008) and the present paper.

We note that switching models are not only useful for loan decisions but are also useful for labor supply and household expenditure decisions. For example, Lee (1978) and Adamchik and Bedi (2000) estimate a switching model to analyze wage di¤erences between di¤erent sectors of the economy. We expect our extensions to be useful for such applications as well. The methodology of this paper di¤ers from Wooldridge (1995), who does not consider endogenous switching models. It also di¤ers from Semykinaa and Wooldridge (2010), who use the inverse Mills ratio as an instrumental variable.

This paper is organized as follows. Section 2 introduces the model and states the consistency and asymptotic normality result of our estimator. Section 3 applies the new estimator to data on productivity in Peruvian agriculture and section 4 concludes.

# 2. Model and Theorem

In our application, farmers can be credit constrained or credit unconstrained. Being credit constrained may reduce output of the farm since it may be more di¢ cult to buy the relevant inputs such as fertilizer, machines, as well as to hire farm hands or specialized workers. Thus, being credit constrained may reduce productivity. However, being credit constrained and having low productivity could also be caused by a common unobserved shock such as illness of the farmer. Thus, it is important to account for this sample selection and that is what Maddala and Nelson’s (1974) “switching regression model with endogenous switching”intends to do. In particular, their switching regression model has a selection equation and an outcome equation. Let Wit be equal to one if the farmer i is credit constrained in period t and zero otherwise. If the farmer i is not credit constrained in period t; Wit = 0; then the productivity of the farm is where Xit denotes the regressors, fi<sup>(0)</sup> is an individual speci...c ...xed e¤ect, �� is a time dummy and u<sup>(0)</sup> it<sup>is the error term.The models considered by Maddala and Nelson (1974)</sup>
# **_3.3. Impact of an increase in the price of rice_**

The same approach can be used to simulate the impact of food prices, and we start with a single commodity, namely rice (this commodity is chosen as Ghana is a net importer of rice). For comparability purposes, we also simulate the impact of a 34 percent increase in the price of rice. Imports account for 54.8 percent of the total supply of rice in Ghana, and represent 3.4 percent of aggregate imports. Table 5 summarizes the results. The activities that are most affected by the increase in the price of rice are informal and formal food processing, other services and public administration. The total potential effect on the producer price index would be of 1.46 percent.

Table 6 provides the effects on the cost of living for households. Overall, the increase in cost of living is estimated at a much lower 1.74 percent. There are three main reasons for this lower impact as compared to oil. While rice represents a larger share of total consumption than oil-related imports, the indirect effects of the price increase are substantially lower for rice than for oil imports. In addition, urban households are, again, slightly more affected than rural households.

# **_3.4. Impact of an increase in the price of cereals_**

While rice prices have increase substantially, the price of other food items, and especially other cereals, has also increased. It is therefore useful to assess the overall impact of the increase in cereal prices. The 2005 SAM for Ghana includes four accounts for cereals: Maize, rice, sorghum and millet, and other grains. The prices for the four accounts are each increased by 34 percent. Imports account for 27.4 percent of the total supply of these commodities in Ghana and import propensities vary, reaching 95.7 percent for other grains, as compared to 54.8 percent for rice (because rice is locally produced). Table 7 contains the resulting price change for all the 13
36 _The PNAD data sets_ All of the distributional analysis performed in this paper is based on four data sets (1976, 1981, 1985, 1996) of Brazil's National Household Survey (Pesquisa Nacional por Amostra de _Domicilios: PNAD),_ which is fielded annually by the IBGE. For the latter three years, the survey is nationally and regionally representative, except for the rural areas of the North region (minus the state of Tocantins) which are not surveyed. For 1976, rural areas were surveyed neither in the North nor in the Center-West regions. In this paper, we are concerned only with urban areas, which are defined by state-level legislative decrees. The urban proportions of the population in each year are given in Table 1. The PNAD sample sizes, as well as the proportion of missing income values, are given below in Table A2: Table A2: PNAD Sample Sizes and Missing or Zero Income* Proportions

|Year|Number of<br>households|Number of<br>Individuals|Proportion of<br>individuals with<br>RFPC missing|Proportion of<br>individuals with<br>RFPC = zero|
|---|---|---|---|---|
|1976|84660|385282|0.0052|0.0063|
|1981|110151|477607|0.0073|0.0141|
|1985|127128|520069|0.0073|0.0108|
|1996|91621|329434|0.0291|0.0313|

Note: *: Income is Total Household Income per capita (RFPC).

Each PNAD questionnaire contains a range of questions pertaining both to the household and to individuals within the household. Among the former, are questions about regional location, demographic composition, quality of the dwelling, ownership of durables, etc. The latter include age, gender, race, educational attainment, labor force status, sector of occupation and incomes, both in cash and kind, and from various sources. The main variables used in our analysis are the those related to incomes, education, the demographic structure of the household and labor force participation. Tables A6 - A9 summarize the main items in the questionnaire concerning these variables, and the changes from 1976 to 1996.

Most importantly, the distributions analyzed in this paper (except where explicitly otherwise indicated) have as welfare concept total household income per capita (regionally deflated). It is constructed from summing all income sources for each individual within the household, and across all such individuals, except for lodgers or resident domestic servants. The latter two categories constitute separate households. Total nominal incomes are spatially deflated to compensate for differences in average cost-of-living across different areas in the country, according to the spatial price index given in Table A3 below:
# take-up (panel (d)).

<!-- Start of picture text --> Figure 8. Body Mass Index Gaps, Service Access, and Regularization Take-up<br>(a) Role of School Enrollment (b) Role of Sisb ́en Enrollment<br>-0.32 -0.32<br>-0.35 -0.36<br>Enrolled Not Enrolled SS Access No SS Access<br>(c) Role of Health Insurance Enrollment (d) Role of Regularization Take-up<br>-0.32<br>-0.32<br>-0.37 -0.45<br>Health Access No Health Access Regular Irregular<br>.1 .1<br>0 0<br>-.1 -.1<br>-.2 -.2<br>BMI (std) BMI (std)<br>-.3 -.3<br>-.4 -.4<br>-.5 -.5<br>.1 .1<br>0<br>0<br>-.1<br>-.1<br>-.2<br>-.2<br>BMI (std) BMI (std) -.3<br>-.3<br>-.4<br>-.4 -.5<br>-.5<br><!-- End of picture text --> _Notes:_ The figure illustrates the point estimates for the coefficients _β_ 1, and _β_ 2 as described in each corresponding equation (3). The dots depict the point estimate and the bars depict the confidence intervals at 5% (in between the bar division) and 10% (complete bar) significance level. The omitted category are Colombian children and adolescents. Control variables include children’s sex and age, the maximum level of education completed by the most educated parent and grandparent, and the wealth index is the premigration wealth index for the Venezuelan sample and the current wealth index for the Colombian sample. Standard errors are clustered at the household level.

The results suggest that service and regularization access have a crucial role on the cognitive gap size between Colombian and Venezuelan minors, but that they are not as relevant for the gaps measuring differences on the body mass index (with the exception of the regularization program), which are more related to the nutritional status of children 44
18 functioned as an anchor, the expectacion probably having been that domestic prices would quickly converge to international prices, thereby leading to convergence of nominal wages. The initial slack in the exchange rate in 1990, high (monthly) frequency of wage adjustment and ability to accumulate the unused wage margin under indexation upset such calculations in the second half of 1990, resulting in a wage-price spiral.

Moreover, fiscal imbalance was not an issue in 1990, unlike 1991 and 1992. Under this circumstance, wage indexation (together with that of social money benefits) could create strong inflation inertia and dilute attempts to contain inflation.

# Norm and Actual Real Wages Under Indexation

The starting norm wage bill for the ETP was obtained by taking the Sept. 1989 wage bill and adjusting it by a factor of 0.8 for fourth quarter 1989 inflation. With a factor of 0.3 to be applied to forecast Jan. 1990 inflation of 45%, this alone meant (assuming unchanged employment) a real wage reduction of 32% by Jan. 1990, the first month of the ETP. Table 3 is based on the sample: (a) on average, by Dec. 1990, the norm real wage fell by 50%; (b) the actual real wage exceeded norm wages by 32% in Dec. 1990, but representing nevertheless a substantial decline of 34% relative to Sept. 1989; (c) by March 1991, actual and norm real wages were on average closer to each other, with the Dec. 1990 gap significantly reduced.

Table 3. Actual and Norm Real Wages (Sept. 1989 = 100)

||Decem|ber 1990|Marc|h 1991|
|---|---|---|---|---|
|.____________|Actual|Norm|Actual|Norm|
|Metallurgy|68.0|48.7|55.8|46.0|
|Electromachinery|65.3|51.0|49.7|47.5|
|Chemical|68.8|48.7|50.6|46.7|
|Light|48.5|47.5|41.3|44.4|
|Food|78.9|55.9|64.3|55.9|
|Total sample|65.9|49.8|51.9|47.2|

It is tempting to believe that the extent of real wage reduction portrayed in Table 1 was unanticipated by the designers of the ETP. The scenario probably assumed was one where the fixed exchange rate would quickly lead to a convergence of the price level, hence of nominal and real wages. Assuming real wages should bear some relationship to productivity, it is difficult to believe that a 50% decline in productivity was anticipated between Sept. 1989 and Dec. 1990. However, a self-fulfilling prophecy could result if the reduction in real wages reduces aggregate demand, hence capacity utilization and productivity, as happened in Poland in 1990.

# **PPWW,** Decapitalization and Dividends

PPWW payments exploded in the fourth quarter of 1990. As presaged by Figs. 10-14, there was strong correlation between profitability (metallurgy at the 'high" extreme, light manufacturing at the "low") and incurring the PPWW.
**Figure 2: Proportion of women-managed firms increases with bank credit to GDP ratio**

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Bank credit to GDP ratio (residuals)<br>.1<br>.05<br>0<br>-.05<br>Proportion of women-managed firms (residuals)<br>-.1<br><!-- End of picture text -->

Note: The figure is a partial scatter plot obtained of country-year level variables obtained after controlling for country dummies. The line of fit shown is statistically significant at the 1 percent level.

35