rafmacalaba/datause-encoder-data huggingface dataset:
regression analysis could be extended by incorporating different structure lag so as to refine the basic specification. In addition, the link between the percentage change in employment and the cumulative change in employment and the health outcomes could be further explored.

**Table 4: Contemporaneous "Fixed Effects" Panel Regression: Dependent variable=Death Rate**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Drop in SOE employment|0.023**|0.022**|0.014|0.016*|
||(0.01)|(0.01)|(0.009)|(0.009)|
|Urbanization Rate||-5.465***|-3.359***|-2.961***|
|||(0.985)|(1.045)|(1.033)|
|GDP per capita|||-0.045***|-0.053***|
||||(0.009)|(0.010)|
|Industrial Production as a share of GDP||||4.058***|
|||||(1.254)|
|Observations|279|279|279|279|
|Number of provinces|31|31|31|31|
|R-squared|0.02|0.13|0.2|0.23|

Note: Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Number of observations drop to 279 because the key independent variable is defined as change in SOE employment. SOE employment change is denominated in 100 thousand units and GDP per capita is denominated in thousand Chinese Yuan.

# **_V. Policy Menu for Health in Cities_**

The findings presented in the preceding section suggest with some qualifications, first, that income growth can positively influence the health of populations usually measured by longevity. Second, microeconomic research points firmly to large private gains from improvements in health. In our view, so long as these results are fairly robust, it is desirable to trace the steps leading from rising incomes to better health in urban settings without investing further effort in determining the direction of causality between income and health. We maintain that it is only with the growth of GDP—and possibly increased education—that countries are in a position to effectively tackle existing and emerging health problems (see also Sala-i-Martin 2005). Whether they do so is a separate matter but the nature of the problems is such that at low levels of income, it is far harder to mobilize the needed resources and the skills for this purpose. A very small 25
-49-

Five, the composition of the diet for the extremely-poor is<sup>different, as</sup> well as the price and income elasticities of demand for food. At<sup>very low</sup> income levels, households consume a diet composed of cheap calories.<sup>Some</sup> evidence also shows that in the range of extreme-poverty<sup>Engel's Law (as</sup> income ir,creases the proportion spent on food falls) is violated<sup>85</sup> . There is, in addition, "... compelling evidence that the poor are more responsive - to - income, own-prices, and cross-prices than the rich" (Behrman and<sup>Deolalikar,</sup> 1988, p. 677). Studies also show that a distinction is required between<sup>the</sup> income (or expenditure) elasticity of demand for food and the income<sup>(or</sup> expenditure) elasticity of demand for nutrients (of which<sup>more below).</sup> Six, for extremely-poor households nutritional status appears to have a direct impact on productivity, both for adults and for children.<sup>For adults</sup> studies by Strauss (1986) for Sierra Leone and Deolalikar (1988) for<sup>India</sup> find that agricultural labor productivity increases with calorie 86 availability 6. For children it appears that school performance<sup>also improves</sup> with nutrition: anthropometric indicators like height for age (which<sup>reflects</sup> the cumulative outcome of nutrition) appear to positively influence both the 87 probability of being in school as well as relative performance Finally, the importance of intrahousehold inequality is higher. While this inequality is probably not unique to the extremely-poor, it is 85Lustig (1984, pp. 443-4 and table 14.4) presents evidence based on the 1977 Income-Expenditure Survey that shows significant differences in the diet composition of Mexicans when classified by income groups; she also runs<sup>log-</sup> linear regressions of food expenditures on total expenditures<sup>for poor</sup> households in 18 regions and finds that the associated<sup>elasticity exceeds</sup> unity in 13 out of the 18 regions (op. cit., table 14.8). Lipton<sup>(1988a)</sup> quotes evidence of the failure of Engel's law for Northeast<sup>Brazil; see also</sup> Streeten (1989b).

86Note that the causality between nutrition and productivity is not obvious: if higher labor productivity increases income then nutrition<sup>may also</sup> increase (given greater food consumption); conversely, more nutrition<sup>may</sup> increase labor productivity which then increases income.<sup>The Strauss and</sup> Deolalikar studies correct for this endogeneity (see Behrman<sup>and Deolalikar,</sup> 1988, pp. 683-86).

87These results are found in studies of children in China and Nepal; Behrman and Deolalikar (1988, pp. 688-89) point out, however, the possibility of self-selection bias: school performance is observed only for those who did go to school (and did not drop out).
The financial amplification mechanism can work through distinct channels, which can potentially affect both the demand and supply of liquidity in the economy. When credit constraints arise from high collateral requirements (Kiyotaki and Moore, 1997), a drop in the value of collateral during recessions limits borrowers’ability to access funds resulting in a contraction in the amount of credit supplied. At the same time, a tightening of agents’borrowing capacity can lead consumers to increase precautionary savings, while reducing consumption and ultimately credit demanded (Guerrieri and Lorenzoni, 2011). Recent empirical literature has also investigated the link between lending standards, informational asymmetries and access to credit during the business cycle. For example, Dell’Ariccia and Marquez (2006) present a framework showing that when informational asymmetries in loan markets become less severe, as may occur during the expansionary phase of the cycle, lending standards become less stringent. This leads to higher credit growth and to increased financial systemic vulnerability.

Credit demand and supply can also be affected by excessive leverage in the economy. A high debt overhang can make firms and households reluctant to resume borrowing in the aftermath of recessions, keeping credit demand low. Similarly, excessive indebtedness among financial intermediaries, including banks can prevent their ability to secure liquidity in the inter-bank market, thus affecting the liquidity supplied to the economy (IMF, 2013).

Chada et al. (2010) study how liquidity effects affect monetary conditions in a simple endowment economy model, providing a comprehensive strategy to analyze the demand and supply drivers of liquidity levels in the economy.

Our work also contributes to the recent empirical literature on credit-less recoveries, examining the lending-growth nexus during the business cycle. The first authors to document these episodes were Calvo et al. (2006). In their seminal work on post-collapse crisis recoveries in emerging countries they name these episodes Phoenix Miracles, as output is found to “rise from its ashes” without any recovery in the stock of credit. The recovery in output is explained by a process of financial engineering whereby firms discontinue long-term investment projects in order to restore liquidity to finance activity amidst financial constraints.

More recent studies document significant cross-country heterogeneity in the distribution of postcrisis performance. Using the same sample of crisis episodes of Calvo et al. (2006), Huntley (2008) re-examines the evidence on post-crisis performance using GDP per-capita data. The path of postcrisis recoveries is characterized by a bi-modal distribution leading to two different types of recoveries: slow and fast recoveries. Sixty percent of all recovery episodes are found to take place in five years, accompanied by strong investment levels, credit and consumption; in the remaining cases, output 4
Policy Research Working Paper 6908

# **Abstract**

This paper reports on a randomized field experiment that uses price incentives to address economic and gender inequality in land tenure formalization. During the 1990s and 2000s, nearly two dozen African countries proposed de jure land reforms extending access to formal, freehold land tenure to millions of poor households. Many of these reforms stalled. Titled land remains the de facto preserve of wealthy households and, within households, men. Beginning in 2010, the study tested whether price instruments alone can generate greater inclusion by offering formal titles to residents of a low-income, unplanned settlement in Dar es Salaam at a range of subsidized prices, as well as additional price incentives to include women as owners or co-owners of household land. Estimated price elasticities of demand confirm that prices—rather than other implementation failures or features of the titling regime—are a key obstacle to broader inclusion in the land registry, and that some degree of pro-poor price discrimination is justified even from a narrow budgetary perspective. In terms of gender inequality, the study finds that even small price incentives for female co-titling achieve almost complete gender parity in land ownership with no reduction in demand.

This paper is a product of the Agriculture and Rural Development 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 authors may be contacted at dali1@worldbank.org, matthew.collin@economics.ox.ac.uk, kdeininger@worldbank.org, stefan.dercon@economics.ox.ac.uk, jsandefur@cgdev.org and az332@georgetown.edu.

_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
distribution.<sup>4</sup> We denote by E the number of calories provided by the reference food basket . The ratio between E and the minimum energy requirement _ER_ , identified (qq) based on a normative criterion, provides the coefficient ⁄E needed to rescale qq (qq) the basket to return a basket yielding exactly the normative energy requirement _ER._ λλ= E (qq) 00 qq qq =<sup>qq</sup> 00 qq Finally, to value , a price vector is needed. Consistently with the choice underlying λλ quantities, we take the median values of the unit values for households belonging to first 00 qq decile of per capita expenditure as a reference group. Denoting by the vector of prices of the food items included in the bundle for period _t_ , the food poverty line for period _t_ pp<sup>tt</sup> is given by: 00 qq F 00 Similarly, the food poverty line at _t+_ 1 prices can be computed as: FF<sup>tt</sup> = pp<sup>tt</sup> ∙qq Similarly, the food poverty line at _t+_ F 00 FF<sup>tt+1</sup> = pp<sup>tt+1</sup> ∙qq The ratio between the food poverty lines provides an estimate of the true-cost-of-living index between _t+_ 1 and _t_ , as a Laspeyres-type price index based on the period _t_ subsistence/reference basket: The identification of the food basket can be based on period _t+_ 1 data instead of period _t_ . That would imply repeating the steps above designing the basket with reference to the consumption pattern of period _t+_ 1, creating a Paasche-type price index.

In our application _t_ = 2017 and _t+_ 1 = 2022. Table 1 compares the food poverty lines based on the 2017 and 2022 food baskets, respectively. If we assume that the energy requirement correctly identifies a minimum and unique standard of living over time, we clearly see that the poverty lines based on the 2022 food basket are _not_ utility consistent: the 2017 basket allows to achieve the minimum energy requirement at a lower cost both in 2017 and 2022. This is why we decided to base our implicit food price index on the 2017 food basket.

> 4 The exact composition of the reference food bundles is detailed in the Amendola et al. (2023).

6
**4**

# Alleviating Transitory Food Crisis in Africa: International Altruism and Trade

by Victor Lavy*

# Table of Contents

|I.|Transitory Food Insecurity: The Stylized Facts|3|
|---|---|---|
|II.|The Empirical Framework|6|
|III.|Intc iational Altruism: The Extent and Speed<br>ofthe Response|10|
|IV.|Economic and Political Determinants of Generosity|18|
|V.|Conclusions|23|
|Refer|ences|24|

***** This paper was written under the auspices of the African Food Security Unit of the World Bank, as a background paper to the Food _aid in Sub-Saharan Africa_ study. I would like to thank Shlomo Reutlinger for his assistance and useful insight and comments on the paper. I would also like to thank the World Bank Research Committee for financial support.

Victor Lavy, currently at the Department for Population and Human Resources of the World Bank, is on leave from the Department of Economics at the Hebrew Univeristy of Jerusalem.
**<u>Table 3: Centers started charging fees</u>**

||Project|centers|Non-project centers|
|---|---|---|---|
||2010<br>Midline|2013<br>Endline|2013<br>Endline|
|**Percent of centers charging**||||
|No fee|52|30|19|
|Mandatory fee|42|61|67|
|Voluntary fee|6|4|1|
|Both mandatory and voluntary fee|-|5|13|
|**Mandatory fee: amount charged if**<br>**charging**||||
|Less than 10,000 IDR|76|46|24|
|10,000-25,000 IDR|23|48|64|
|25,000-50,000 IDR|1|3|9|
|More than50,000IDR|-|3|3|
|Note: All numbers are percent of centers<br>midline and endline but information is fr<br>approximately10,000IDR in 2010.|surveyed. No<br>om the same v|t necessaril<br>illages ove|y the same centers in<br>r time. 1 USD =|

30
# **Tables**

**Table 1. Ex-ante equivalent variation for a consumer from perfect price stabilization at mean price for various parameters** (relative risk aversion, ρ, commodity budget share, γ, income elasticity, η, price elasticity, α, coefficient of variation of price, σP; welfare changes are calculated using equation (1) and expressed as percentage of income)

|η|α<br>ρ:|0|||2|||4||
|---|---|---|---|---|---|---|---|---|---|
||γ: 0.01|0.15|0.3<br>|0.01|0.15|0.3<br>|0.01|0.15|0.3|
|_Mediu_|_m fluctuations (_σP= 20%_)_|||||||||
|0|-0.1<br>-0.002|-0.030|-0.060|-0.002|0.060|0.300|-0.001|0.150|0.660|
|0.25|-0.1<br>-0.002|-0.041|-0.105|-0.002|0.049|0.255|-0.001|0.139|0.615|
|0.25|-0.4<br>-0.008|-0.131|-0.285|-0.008|-0.041|0.075|-0.007|0.049|0.435|
|0.5|-0.4<br>-0.008|-0.143|-0.330|-0.008|-0.053|0.030|-0.007|0.038|0.390|
|0.5|-0.7<br>-0.014|-0.233|-0.510|-0.014|-0.143|-0.150|-0.013|-0.053|0.210|
|_Large_|_fluctuations (_σP= 30%_)_|||||||||
|0|-0.1<br>-0.005|-0.068|-0.135|-0.004|0.135|0.675|-0.003|0.338|1.485|
|0.25|-0.1<br>-0.005|-0.093|-0.236|-0.004|0.110|0.574|-0.003|0.312|1.384|
|0.25|-0.4<br>-0.018|-0.295|-0.641|-0.017|-0.093|0.169|-0.016|0.110|0.979|
|0.5|-0.4<br>-0.018|-0.321|-0.743|-0.017|-0.118|0.067|-0.016|0.084|0.878|
|0.5|-0.7<br>-0.032|-0.523|-1.148|-0.031|-0.321|-0.338|-0.030|-0.118|0.473|

46
to the vector [ _λ π χ θ_ ] of estimated coefficients that correspond to the different observable and unobservable transmission channels.

Since (1) is a dynamic model with fixed effects, these estimates may be biased for finite _T_ (Nickell 1981). Since Given the time coverage of the dataset is relatively long (54 quarters), we suspect that the inconsistency of estimates should not pose a major problem (since the bias is of _O_ � _T_ <u>1</u> �). Nevertheless, our coefficients are estimated using bias-corrected Least Squares Dummy Variables (LSDV) (Bruno 2005), under the strictest condition for bias approximation (up to _O_ � _NT_ <u>1</u><sup>2</sup> �), with bootstrapped standard errors.<sup>6</sup> In our simulations where we examine possible tapering scenarios (Section 7), we discipline our conditioning variables by implementing a vector autoregressive (VAR) specification comprising the various transmission channels. We thus modify our baseline specification 1 to where the matrix **TC** _it_ = [ **L** _it_ **PB** _it_ **C** _it_ **X** _it_ **GFI** _it_ ] is composed of the (global) transmission channels, along with controls ( **X** _it_ ) and aggregate developing-country gross inflows ( **GFI** _it_ ), **_β_** _k_ is a vector of coefficients, and **_ε_** _it_ is a vector of disturbances.<sup>7</sup> Since this application is designed to capture the effect of the movements in primarily global variables, _i_ in this case applies either to the developing or high-income aggregate.

To estimate (2), we impose additional assumptions. Our (structural) identification strategy imposes a Cholesky decomposition of the covariance matrix, with an ordering such that the first variable cannot respond to contemporaneous shocks (within the same quarter) from any other variables, the second one responds to contemporaneous shocks affecting only the first variable but no others, and so on.<sup>8</sup> Since the system given by (2) produces independent estimates of aggregate gross inflows, we use these as a useful cross-check for our scenario projections which are based on (1).

# **4.2 Data sources and definitions of key variables**

Our analysis draws primarily on balance of payments data from the International Monetary Fund’s International Financial Statistics (IFS) for gross portfolio and FDI inflows. We supplement these two flows with bank lending data from the Bank of International Settlements’ Locational Banking

> 6The correction is initialized by the Anderson & Hsiao (1982) consistent estimator for _β_ 0, and the bootstrapped asymptotic variance-covariance matrix is constructed with 100 replications.

> 7We rule against a panel VAR specification, which would allow for country-specific controls, because the channel variables we include in **TC** are not country specific; introducing country-level controls would also further reduce the degrees of freedom in an already small sample.

> 8The specificities of our VAR identification assumptions are discussed in Subection 7.1.

9
TABLE A10: Impact of text messages on individual intermediary outcomes: nutrition and stimulation

||**ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Mean**<br>**Control**<br>(4)|**Obs**<br>(5)|
|---|---|---|---|---|---|
|**_Nutrition_**||||||
|**Nutrition index**<br>|0,06|( 0,04)|0,12|0,00|2.500|
|**_Beverages and soups_**||||||
|Soy Milk|0,06|( 0,06)|0,33|0,37|2.501|
|Juice|0,16|( 0,11)|0,15|3,72|2.501|
|Soup|0,00|( 0,06)|0,96|0,71|2.501|
|**_Vegetables_**||||||
|Salad|-0,13|( 0,09)|0,17|0,34|2.501|
|Potatoes|0,10|( 0,07)|0,18|1,08|2.501|
|Tomatoes|0,06|( 0,12)<br>|0,58|2,97|2.501|
|Onion|0,22|( 0,13)|0,10|1,98|2.501|
|Other vegetables|-0,02|( 0,09)|0,85|1,69|2.501|
|**_Staples_**||||||
|Rice|0,03|( 0,08)|0,73|5,85|2.500|
|Beans|-0,06|( 0,09)|0,49|6,09|2.501|
|Bread|-0,02|( 0,12)|0,85|4,12|2.501|
|Tortilla|0,11|( 0,10)|0,28|5,82|2.499|
|Cookies|0,13|( 0,09)|0,16|1,28|2.501|
|**_Proteins_**||||||
|**Protein index**|0,06|( 0,04)|0,11|0,00|2.501|
|Milk|-0,04|( 0,11)|0,74|1,76|2.501|
|Eggs|0,30|( 0,09)|0,00|1,72|2.501|
|Cheese|-0,00|( 0,10)|0,98|2,17|2.500|
|Meat|-0,00|( 0,06)|0,98|1,22|2.501|
|Breast milk|0,22|( 0,25)|0,38|3,15|802|
|**_Stimulation_**||||||
|**Stimulation index**|0,16|( 0,04)|0,00|0,00|2.501|
|Buy toys for the child|0,03|<br>( 0,01)|0,01|0,86|2.501|
|Tell tales to the child|0,04|( 0,02)|0,02|0,79|2.501|
|Read books to child|0,04|( 0,02)|0,05|0,27|2.501|
|Have pen and paper|0,02|( 0,02)|0,12|0,84|2.501|

Each row corresponds to a separate estimation (equation 1) and shows the ITT estimates on aggregate indices or individual questions measuring investments in nutrition or stimulation as reported by the caregiver. Questions on food items measure how many days in the last 7 days the child was given the specific item. The nutrition index was calculated by summing the days over all food items. The protein index was calculated by summing the days over all protein items. The individual items about stimulation are answers to yes/no questions, and the index is obtained by summing over yes answers. The nutrition, protein, and stimulation indices are standardized using the mean and standard deviation for control households. All the regressions 58 include controls for the stratification variables, child age and gender, enumerator fixed effects, a dummy variable for a set of households surveyed in 2016, as well as village fixed effects. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01.
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