rafmacalaba/datause-encoder-data huggingface dataset:
alternate instruments changes when there are pre existing distortions in the land and credit markets. One of the objectives of our paper is to provide a more realistic assessment of what upgrading projects are likely to achieve if they are not part of a larger set of reforms that relax various distortions that hinder the functioning of the land and housing markets.

In this paper, we develop a dynamic general equilibrium model which includes households, developers, financial institutions and the government to evaluate the effectiveness of alternative instruments. Our findings are based on data from three Brazilian cities, but the approach developed here can be generalized and is relevant for most developing countries where land and housing markets are subject to distortions from excessive zoning and development controls.

We believe that there are three main reasons that the general equilibrium approach we propose here will provide better insights relative to a partial equilibrium assessment of slum improvement programs. First, typical partial equilibrium analysis is based on a households’ marginal benefit and marginal cost without taking supply side constraints into account. Under in situ upgrading, it is usually assumed that households can keep increasing their consumption of one resource so long the marginal benefit exceeds the marginal cost (see for example, Heikkila 2004 who proposes a conceptual framework for application to Brazilian cities). The argument is difficult to defend if there are supply side bottlenecks. Second, marginal benefits of program interventions are calculated from a households’ utility function, while the marginal cost is based on the government’s expenditure / cost function. Fulfilling individual household demand (from their assessment of marginal benefits), solely based on their preferences may not yield a 26
T1 able I-Maanm featuinres of GiRd amid New Systemms' A. Clbnle

||GRdSystemn<br>|Newsystem<br>|
|---|---|---|
|Structure|PAYGDB|PillarII:fundedindividualaccounts<br>(IA's)<br>PillarI:minimumpensionguarantee<br>(MPG)<br>|
|Contributionrate|26%<br>|13%toPillar 1[;<br>I_PGfinancedfromgeneralrevenues<br>|
|Benefits|50%ofbase salary+<br>1%foreveryyear>10<br>uptomaximumof70%<br>basesalary<br>.<br>|Pillar1I:annuity fromIA<br>PillarI:MPG<br>|
|Basesalary|Averageoflast5years<br>(final3yearsindexed)|Notrelevant<br>|
|Pensionableage|Men-65; women-60|Men-age65; women-60<br>|
|Yearsforeligibility|10years|20yearsforMPG;nominimumforIA<br>|
|Pensionifworked<br>feweryears|0<br>_|AnnuityfromIAaccumulation<br>|
|Indexation provisions|Noindexation|PriceindexationofannuityinPillarII<br>AdhocincreasesofMPG<br>|
|Minimumpension|Nominimum<br>|MPG (about27%ofmaleaverage<br>_wage)after 20years<br>|
|Widows|50%ofhusband's<br>pensionorownpension|60%ofhusband'spension(joint<br>annuity)+ownannuity|

# Notes:

1. Contribution rates given for individual<sup>accountsforChileandArgentinainclude</sup> approximately 3% of payroll for survivors and<sup>disabilityinsuranceplusadministrative</sup> costs. For Mexico the contribution rate<sup>doesnotincludesurvivorsand disability</sup> insurance, which are handled separately.

41
Table 2. OLS regressions <u>(dep</u> = log of fertility rate) - All countries

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Constant|1.200|1.037|1.459|1.432|
||(5.51)***|(6.23)***|(5.11)***|(5.92)***|
|Log of fertility at dest|0.343<br>(3.35)***|0.383<br>(4.37)***|0.273<br>(2.40)**|0.291<br>(2.76)***|
|p0:Log of fertility at dest|-0.202||-0.405||
||(0.77)||(1.54)||
|log of p0|-0.025|-0.040|-0.018|-0.053|
||(0.78)|(1.90)*|(0.59)|(2.44)**|
|Selection ratio (sec+tert)|0.001<br>(0.04)||0.001<br>(0.02)||
|Log of remittances|||0.018<br>(1.16)|0.032<br>(2.18)**|
|Urbanization|-0.005<br>(2.62)***|-0.005<br>(2.88)***|-0.004<br>(2.54)**|-0.003<br>(2.24)**|
|GDP per capita|-0.076<br>(2.46)**|-0.079<br>(3.32)***|-0.100<br>(2.80)***|-0.117<br>(3.86)***|
|Adult’s education|-0.256||-0.190||
||(1.70)*||(1.09)||
|East Asia & Paci...c|0.272|0.277|0.308|0.272|
||(2.85)***|(2.99)***|(3.29)***|(2.90)***|
|Sub-Saharan Africa|0.427<br>(4.67)***|0.420<br>(4.72)***|0.537<br>(4.75)***|0.521<br>(5.40)***|
|Latin Am. & Carib|0.350<br>(5.13)***|0.323<br>(5.31)***|0.451<br>(6.49)***|0.486<br>(8.03)***|
|Mena|0.115<br>(1.21)||0.159<br>(1.26)||
|High-income|0.089<br>(0.96)||0.229<br>(2.22)**|0.239<br>(2.70)***|
|Muslims (% of pop)|0.004<br>(4.40)***|0.004<br>(5.59)***|0.003<br>(2.96)***|0.003<br>(4.55)***|
|Catholic (% of pop)|0.001<br>(1.81)*|0.002<br>(2.68)***|0.001<br>(1.43)||
|Observations|175|175|145|145|
|R-squared|0.78|0.77|0.83|0.81|

Robust t statistics in parentheses * signi...cant at 10%; ** signi...cant at 5%; *** signi...cant at 1% 21
disbursements used. In all four specifications, I cannot reject the null hypothesis that the simple correlation between growth and changes in World Bank–financed spending are zero.<sup>6</sup> Although I have argued that changes in World Bank‐financed spending are plausibly uncorrelated with contemporaneous shocks, the estimated slope coefficients in the reduced‐form regressions will not deliver a consistent estimate of the multiplier because they do not account for concurrent changes in other forms of public spending that may occur when World Bank‐financed spending increases. To address this issue, I turn to the first‐stage and second‐stage regressions described above. The first‐stage regressions of changes in total government spending on changes in World Bank‐financed spending are reported in the middle panel of Table 3. Crucially for identification purposes, the first‐stage regressions are all quite precisely estimated, with first‐stage F‐statistics ranging from 10.5 to 13.1. In all four cases, these exceed the Staiger and Stock (1997) rule of thumb of 10, indicating that weak instrument pathologies are unlikely to be a concern in the IV regressions that follow in the bottom panel. It is also noteworthy that the estimated slope coefficients are all less than one, and significantly so in all cases except for the final predicted disbursements measure, indicating that total government spending increases less than one‐for‐one when World Bank‐financed spending increases. The magnitude of this effect is non‐trivial: for example, using disbursements on previously‐ approved projects, the estimated first‐stage slope indicates that when World Bank‐financed spending increases by one dollar, total government spending increases by only 40 cents.

The bottom panel of Table 3 reports the 2SLS estimates of the government spending multiplier. In the first column, I report the OLS estimate of the multiplier to provide a useful benchmark for comparison with the IV results that follow. The OLS estimator delivers a slope of 0.32 that is very strongly significantly different from zero. The remaining columns report the IV estimates of the multiplier. For the two measures of disbursements on previously‐approved projects, and the predicted disbursements measure as well, the IV estimates of the multiplier are smaller than the OLS estimates,

> 6 All of the specifications in this and subsequent tables are estimated pooling country‐year observations and imposing common slopes and intercepts across countries.  An obvious relaxation of this parameter homogeneity assumption is to allow for country fixed effects.  Results obtained in this way are nearly identical to those reported here.  This reflects the fact that in my sample of countries, most of the variation in annual fluctuations in growth, government spending, and World Bank disbursements, occurs within rather than between countries. Unfortunately, however, the available time series for each country are so short (between 20 and 25 annual observations) that allowing both the intercepts and slopes to vary across countries results in extremely imprecise estimates of country‐specific multipliers.

14
Policy Research Working Paper 6650

# **Abstract**

Diversification into high-value cash crops among smallholders has been propagated as a strategy to improve welfare in rural areas. However, the extent to which cash crop production spurs projected gains remains an underresearched question, especially in the context of market imperfections leading to non-separable production and consumption decisions, and price shocks to staple crops that might be displaced on the farm by cash crops. This study is a contribution to the long-standing debate on the links between commercialization and nutrition. It uses nationally-representative household survey data from Malawi, and estimates the effect of household adoption of an export crop, namely tobacco, on child height-for-age z-scores. Given the endogenous nature of household tobacco adoption, the analysis relies on instrumental variable regressions, and isolates the causal effect by comparing impact estimates informed by two unique samples of children that differ in their exposure to an exogenous domestic staple food price shock during the early child development window (from conception through two years of age). The analysis finds that household tobacco production in the year of or the year after child birth, combined with exposure to an exogenous domestic staple food price shock, lowers the child height-for-age z-score by 1.27, implying a 70-percent drop in z-score. The negative effect is, however, not statistically significant among children who were not exposed to the same shock. The results put emphasis on the food insecurity and malnutrition risks materializing at times of high food prices, which might have disproportionately adverse effects on uninsured cash crop producers.

This paper is a product of the Poverty and Inequality Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at tkilic@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 1: List of countries

|Algeria|Djibouti|Mauritania|South Africa|
|---|---|---|---|
|Angola|Ethiopia|Morocco|Sudan|
|Benin|Gabon|Mozambique|Tanzania|
|Botswana|Gambia|Namibia|Togo|
|Burundi|Guinea-Bissau|Niger|Tunisia|
|Cameroon|Kenya|Nigeria|Uganda|
|Chad|Liberia|Rwanda|Zambia|
|Côte d’Ivoire|Madagascar|Senegal|Zimbabwe|
|Congo|Malawi|Sierra Leone||
|Democratic Republic of<br>Congo|Mali|Somalia||

Source: USGS data.

Table 2: Names of mineral commodities and their SITC codes by district, region, and country of production

|Name*|SITC<br>code|Commodity names|Price<br>data|Districts|Regions|Countries of production|
|---|---|---|---|---|---|---|
|SITC_1|120|Helium|Yes|1|1|Algeria|
|SITC_2|264|Garnet (industrial)|Yes|1|1|Kenya|
|SITC_3|273|Stones (crushed,<br>dimension), limestone,<br>gypsum, marble, and<br>silicon|Yes|23|22|Algeria, Angola, Ethiopia, Madagascar,<br>Malawi, Mauritania, Mozambique,<br>Rwanda, South Africa, Sudan, Tunisia,<br>Uganda, and Zambia|
|SITC_4|274|Sulphur|Yes|13|12|Ethiopia, Kenya, Malawi, Namibia,<br>South Africa, Zambia, and Zimbabwe|
|SITC_5|277|Diamond|Yes|27|17|Angola, Botswana, Cameroon, Liberia,<br>Namibia, Sierra Leone, South Africa,<br>Tanzania,and Zimbabwe|
|SITC_6|278|Barite, bentonite, clay,<br>dolomite, fluorspar,<br>graphite, salt,<br>vermiculite, fluorine, and<br>graphite|Yes|42|35|Algeria, Botswana, Chad, Djibouti,<br>Ethiopia, Kenya, Madagascar, Morocco,<br>Mozambique, Namibia, South Africa,<br>Tanzania, Tunisia, Uganda, and<br>Zimbabwe|
|SITC_7|281|Iron ore|Yes|12|10|Sierra Leone, South Africa, Tunisia,<br>Zambia,and Zimbabwe|
|SITC_8|283|Copper|Yes|27|18|Algeria, Democratic Republic of Congo,<br>Mauritania, Morocco, Namibia, Nigeria,<br>Republic of Congo, South Africa,<br>Zambia, and Zimbabwe|
|SITC_9|287|Cobalt, chromium,<br>niobium (columbium),<br>tantalum, titanium,<br>zirconium, and tungsten|Yes|18|17|Botswana, Burundi, Ethiopia, Gambia,<br>Madagascar, Mozambique, Nigeria,<br>Rwanda, Sierra Leone, South Africa,<br>Uganda, and Zimbabwe|
|SITC_10|522|Arsenic trioxide, lithium,<br>manganese, pyrophyllite,<br>soda ash, sodium silicate,<br>and wollastonite|Yes|13|11|Chad, Cote d’Ivoire, Kenya, Morocco,<br>Namibia, South Africa, Zambia, and<br>Zimbabwe|
|SITC_11|523|Phosphate rock, and<br>phosphoric acid|Yes|8|6|Malawi, South Africa, Tanzania, Togo,<br>and Tunisia|

24
**Figure 4: The timing of housing investment: Philippines and China**

<!-- Start of picture text --> The Philippines: Lagging (2 years) China: Contemporaneous<br><!-- End of picture text --> Note: The red lines at +/- .27 represent the confidence interval.

China is undoubtedly one of the most important emerging economies in the world and its urban housing market has been experiencing rapid and unprecedented growth (Chen et al., 2011; EIU 2011). During the past 20 years, China’s urban housing sector soared from 78.64 million square meters in 1997 to 970.30 million square meters in 2011. It took China approximately 15 years to build the equivalent of Europe’s entire housing stock (EIU, 2011). In 2005, housing wealth accounted for 71.34 percent of the total family wealth of Chinese urban households (Qianwei et al, 2013).

Private households are the major source of housing investment in China with more than 80% of homes being privately owned. Mortgage loans are steadily gaining ground in China. According to National Bureau of Statistics, outstanding home mortgage debt grew from below 1 trillion RMB to 3 trillion RMB between 1997 and 2008 but then leapfrogged to 8 trillion between 2008 and 2011. In terms of share in GDP, this jump was from 9 in 2008 to 18 percent in 2011. One of the major reasons is a 580 billion dollar stimulus package in 2008 to weather the global financial crisis and the sharp slowdown of economic growth (Barth et al., 2012).<sup>4</sup> The Philippines, on the other hand, has been experiencing housing shortages over the last several decades. Estimates suggest that the country has a shortage of 484,325 low cost housing units (Manila Bulletin 2012). Rapid increase in land prices has been a major factor in housing shortage and price appreciation (Strassman and Blunt 1993; Ballesteros 2000). The high cost of urban land is due to binding constraints in the supply side of the market, including poor planning and

> 4 See Barth et al( 2012) for a detail discussion on China’s housing market.

13
Table 4: Multidimensional measures and average deprivation shares by category

|**Indicator**|**2008**|**2010**|**2012**|
|---|---|---|---|
|_H_: Multidimensional headcount ratio (%)|34.47|30.38|26.97|
|_A_: Average deprivation share among the poor|0.448|0.432|0.425|
|_M_0:Adjusted headcount ratio|0.154|0.131|0.115|
|**Education**|0.140|0.139|0.139|
|**Childhood and youth**|0.069|0.069|0.063|
|**Labour**|0.109|0.110|0.110|
|**Health**|0.076|0.065|0.065|
|**Standard of living**|0.053|0.049|0.048|

Source: Author’s calculations. Average deprivation shares by category are calculated by calculating the average deprivation shares by dimension as in equation 5, then adding over categories.

Table 5: Rank correlations between deprivation scores across categories

||**Education**|**Childhood and youth**<br>**Labour**|**Health**|**Standard of living**|
|---|---|---|---|---|
|||**2008**|||
|**Education**|1.000|0.209<br>0.201|0.121|0.288|
|**Childhood and youth**|0.209|1.000<br>0.063|0.150|0.240|
|**Labour**|0.201|0.063<br>1.000|0.112|0.100|
|**Health**|0.121|0.150<br>0.112|1.000|0.081|
|**Standard of living**|0.288|0.240<br>0.100<br>**2010**|0.081|1.000|
|**Education**|1.000|0.177<br>0.217|0.061|0.241|
|**Childhood and youth**|0.177|1.000<br>0.024|0.112|0.189|
|**Labour**|0.217|0.024<br>1.000|0.104|0.074|
|**Health**|0.061|0.112<br>0.104|1.000|0.055|
|**Standard of living**|0.241|0.189<br>0.074<br>**2012**|0.055|1.000|
|**Education**|1.000|0.170<br>0.205|0.066|0.248|
|**Childhood and youth**|0.170|1.000<br>0.021|0.108|0.190|
|**Labour**|0.205|0.021<br>1.000|0.095|0.082|
|**Health**|0.066|0.108<br>0.095|1.000|0.040|
|**Standard of living**|0.248|0.190<br>0.082|0.040|1.000|

Source: Author’s calculations.

31
<!-- Start of picture text --> Change in home production, manufacturing, and services across values of<br>4 = 6%, Home<br> = 6%, Manu<br> = 6%, Serv<br> = 5%, Home<br> = 5%, Manu<br>3<br> = 5%, Serv<br> = 4%, Home<br> = 4%, Manu<br> = 4%, Serv<br>2<br> = 3%, Home<br> = 3%, Manu<br> = 3%, Serv<br> = 2%, Home<br>1<br> = 2%, Manu<br> = 2%, Serv<br>0<br>-1<br>2020 2025 2030 2035<br> Year<br> Percentage change in participation<br><!-- End of picture text -->

**Figure 10:** Paths of participation for home production, manufacturing, and services across different values for the growth of world nominal GDP in dollars ( _γ_ ). Blue depicts 6% growth, green 5%, yellow 4%, orange 3%, and red 2%. A solid line depicts home production, a dashed one depicts manufacturing, and a line with circle markers depicts services.

and almost 3% if _γ_ = 2%. In this sense, the model is highly non-linear due to the one-sided nature of the DNWR. More monetary policy accommodation (i.e., a higher _γ_ ) when the shock disappears can help alleviate the unemployment consequences of the shock.

Allowing for migration within U.S. states has a negligible impact on the results discussed so far. Note that when migration across U.S. states is allowed, there is a new elasticity 1/ _κ_ of moving across states that comes into play. RUV calibrate _κ_ to 12 in order to match the evidence found by Autor et al. (2013) on how population across states responds to the China shock. Given that the population response is small, the elasticity of moving across states is calibrated to be low. Additionally, since U.S. states are similarly exposed to the shock we study, the need for reallocation is not large.

25
growth, this may explain the stronger negative results in cross-country regressions relative to the mixed results of panel data studies. In general, with many potential channels affecting the relationship, inconsistent findings may be expected with differences in country coverage, sample period, time horizon, model specification, and econometric method.

# **4.2. Channels from Poverty and Inequality to Growth**

# **4.2.1. Channels by which Inequality Can Boost Growth**

# **_Incentives_**

Inequality provides incentives to work, save, and invest—those who do will receive higher returns than those who do not. Differential returns incentivize good behaviors that promote growth. Milton Friedman (Friedman 1962; Friedman and Friedman 1980) based his opposition to redistributive policies aimed at reducing inequality of outcomes on the grounds of efficiency, arguing that they could distort incentives and induce an inefficient allocation of resources. In a capitalist system, the distribution of income is consistent with the ethical principle, “To each according to what he and the instruments he owns produce.” This implies that in a free market economy, people should be rewarded according to their marginal productivity, resulting in some inequality of outcomes. Friedman emphasized that this inequality of outcomes could be necessary to provide incentives to perform certain types of tasks that could be risky or tedious (Friedman and Friedman 1980). Moreover, compensation schemes that reward relative performance and thus generate inequality can provide incentives for workers to invest in skills and exert strong efforts (Lazear and Rosen 1981).

# **_Savings_**

Different savings rates between rich and poor can affect growth. Kaldor (1957) hypothesized that since the richer save more of their income, higher income inequality can lead to a higher national savings rate, a higher investment rate, and greater accumulation of capital, and consequently higher economic growth. Evidence for the United States (Dynan, Skinner, and Zeldes 2004), for instance, supports the notion that both saving rates and the marginal propensity to save are positively correlated with the level of income, suggesting that higher income inequality can lead to a higher savings rate, consistent with Kaldor’s hypothesis.

# **4.2.2. Channels by Which Inequality and Poverty Can Depress Growth**

# **_Poverty Traps and Human Capital_**

Poverty can undermine growth by hindering the accumulation of human capital through both health and education. Poverty is associated with high rates of malnutrition, especially in developing countries (Cerra et al. 2021, Chapter 14). Stunting (a low height-to-age ratio)—an 28
**<u>Table 3:  ITT panel sample: nutritional outcomes by education level of the mother</u>**

||**Weight for age**<br>**z-score**|**Height for**<br>**age**<br>**z-score**|**Underweight**<br>**(-2SD)**|**Stunting**<br>**(-2SD)**|**Exclusiv.**<br>**BF first**<br>**6m**|**meal: puree/**<br>**boiled**|**Toilet: hole in**<br>**the ground**|
|---|---|---|---|---|---|---|---|
|_Program effects by subgroup:_||**Panel sa**|**mple**||**C**|**ross-sectional sa**|**mple**|
|Unschooled|0.130|0.033|-0.040|-0.010|0.359***|0.377**|0.095|
||_(0.085)_|_(0.117)_|_(0.033)_|_(0.033)_|(0.118)|_(0.170)_|_(0.176)_|
|Primary|0.133**|0.097|-0.035|-0.020|0.179**|0.209**|0.294**|
||_(0.054)_|_(0.061)_|_(0.023)_|_(0.021)_|(0.078)|_(0.093)_|_(0.119)_|
|Secondary and higher|0.242***|0.140|-0.127***|-0.054|0.081|0.279*|0.123|
||_(0.068)_|_(0.086)_|_(0.030)_|_(0.033)_|(0.110)|_(0.152)_|_(0.169)_|
|_Time trends by subgroup:_||||||||
|Unschooled|0.001|-0.108|-0.012|0.042**||||
||_(0.052)_|_(0.069)_|_(0.022)_|_(0.021)_||||
|Primary|0.097***|0.037|-0.047***|0.014||||
||_(0.032)_|_(0.044)_|_(0.014)_|_(0.014)_||||
|Secondary and higher|0.101***|-0.000|-0.035**|-0.006||||
||_(0.039)_|_(0.051)_|_(0.017)_|_(0.019)_||||

Note: Each line report the coefficients from a separate regression run on each different subgroup. In parentheses are standard deviations based on errors clustered at the village level. All reported results are PS weighted estimates on the trimmed sample. Significance level: .01 - ***; .05 - **; .1 - ** 24