rafmacalaba/datause-encoder-data huggingface dataset:
reduces growth by 0.2 percentage points. but in their favorite instrumental variables regression (regression (4) in table 6) this effect increases to 0.5 percentage points, similar to previous studies.

Using a panel of 122 countries for the three decades from 1960 to 1990. Barro (1995) analyzes the impact of inflation on growth running the standard regressions and using instruments. The instruments used are lagged inflation, and, alternatively, prior colonial status, which is found to be highly correlated with inflation, because, for example, former French colonies in Africa have been in the CFA franc zone. Indices of central bank independence are found not to be good instruments. He finds also that a reduction of 10 percentage points in the rate of inflation would increase the rate of growth by 0.2 to 0.3 percentage points, while it would increase the investment rate by 0.4 to 0.6 percentage. The coefficients on inflation in the investment equations are only significant in the regressions that instrument inflation. Assuming the usual estimate of the effects of investment on growth (0.1), it can be concluded that a ten percentage points reduction in inflation increase growth by 0.2 or 0.3 out of which roughly 0.05 are due to the investment channel.

Another aspect of the relationship between inflation and growth is the possibility of nonlinearities. Levine and Zervos (1993) confirm Levine and Renelt (1992) findings that inflation is not a robust determinant of long-run growth. The former paper analyzes the possibilities of nonlinearities by distinguishing between high and low inflation and find that results are strongly influenced by some outliers. Fischer (1993) also separates inflation in low (less than 15 percent), medium (15 to 40) and high (above 40), and finds that the coefficient is declining when going from low to high inflation. This is not surprising, since increasing inflation from 10 to 20 percent should be more damaging for growth than going from 180 to 190 percent. This suggests that inflation should enter nonlinearly in the regression, such as the log of inflation or other transformation that reduces the impact of high inflation rates. Indeed, De Gregorio (1993) finds that the coefficient is more stable across different inflationary regimes when inflation is introduced in log form.

The issue about non linearities of the relationship between inflation and growth has been carefully explored in Sarel (1995). He estimates a regression such as (7), with inflation in a log form. Sarel argues that omitting this break could induce an underestimation of the effects of inflation on growth. The results show that there is 22
_CHAPTER 4. AREA LEVEL MODELS_ 22 based on the FH model with arcsin transformation. Corral and Cojocaru (2019) estimated poverty indicators in Moldova by districts and Seitz (2019) estimated poverty at the district level in Central Asian countries. The FH model has been extended in many different ways. For the case where the sampling errors _ed_ in (4.2) are correlated, Isaki, Huang and Tsay (1991), an later Isaki, Tsay and Fuller (2000), extended the FH model and used it to estimate census undercounts by different poststrata. Multivariate versions used to estimate several dependent area indicators were proposed by Fay (1987), and were utilized by Datta, Fay and Ghosh (1991) and Datta et al. (1996) to improve the direct estimates of median income for four-person families. A bivariate FH model with a _t_ -distribution was considered by Bell and Huang (2006) to account for outliers in the poverty ratios for school-aged (5-17) children for US states in 2002. Later, Benabent and Morales (2016) used a bivariate FH model to estimate poverty proportions and gaps at the province level for the years 2005 and 2006.

A subarea-level model, used to produce estimates at two different nested aggregation levels, was introduced by Fuller and Goyeneche (1998), and a similar model was studied by Torabi and Rao (2014). Unmatched sampling and linking models, where a one-to-one transformation of the target indicator _g_ ( _δd_ ) is taken as response variable in the linking model (4.1) but retaining the sampling model (4.2), were originally studied by You and Rao (2002b). A FH model with measurement error in the covariates was introduced by Ybarra and Lohr (2008). This model was used by Marchetti et al. (2015) using big data covariates to estimate poverty rates and mean income in the ten provinces of the Tuscany region in Italy.

The FH model has been extended to “borrow strength” over time, as well as across areas, by Rao and Yu (1992; 1994). They added AR(1) time effects nested within the area effects in the FH model. You, Rao and Gambino (2003) applied the Rao-Yu model to estimate monthly un-
|**HS**<br>**code**||||**Year**|||
|---|---|---|---|---|---|---|
|**2 digits**|**_Type of products_**|**2001**|**2002**|**2003**|**2004**|**2005**|
|||279<br>|281<br>|277<br>|281<br>|287<br>|
|39|_PLASTICS_|10.8|11.1|11.2|10.9|10.7|
|||_(3.8)_|_(5.8)_|_(6.3)_|_(4.4)_|_(4.7)_|
|||113<br>|113<br>|113<br>|113<br>|117<br>|
|40|_RUBBER_|8.3|10.0|9.5|9.0|8.9|
|||_(5.5)_|_(16.2)_|_(12.1)_|_(9.4)_|_(9.3)_|
|41|_RAW HIDES AND SKINS_|79<br>5.0|79<br>4.7|79<br>4.8|79<br>4.9|79<br>4.8|
|||_(0.1)_|_(1.0)_|_(0.6)_|_(0.5)_|_(0.6)_|
|42|_ARTICLES OF LEATHER_|39<br>36.2|39<br>34.5|39<br>36.4|39<br>32.4|39<br>27.9|
|||_(80.2)_|_(42.4)_|_(27.8)_|_(19.9)_|_(15.1)_|
|||111|111|111|111|111|
|43|_FURSKINS_|9.5|9.3|9.4|9.4|9.4|
|||_(4.1)_|_(3.8)_|_(4.2)_|_(4.2)_|_(4.3)_|
|||173<br>|173<br>|170<br>|170<br>|170<br>|
|44||14.6|14.4|14.3|14.4|14.4|
||_WOOD_|_(2.9)_|_(3.1)_|_(3.0)_|_(2.9)_|_(2.8)_|
|||13|13|13|13|13|
|45|_CORK_|5.0|5.0|5.0|5.0|5.0|
|||_(0.0)_|_(0.1)_|_(0.0)_|_(0.0)_|_(0.0)_|
|46|_PLAITING MATERIALS_|12<br>19.3|12<br>19.6|12<br>19.6|12<br>19.7|12<br>19.8|
|||_(1.9)_|_(1.0)_|_(1.0)_|_(0.5)_|_(0.3)_|
|||23|23|23|23|25|
|47|_PULP OF WOOD_|13.6|13.7|13.4|13.2|14.0|
|||_(3.6)_|_(3.6)_|_(4.3)_|_(4.8)_|_(3.5)_|
|||212<br>|214<br>|213<br>|213<br>|213<br>|
|48|_PAPER & PAPERBOARD_|12.8|12.5|12.5|12.4|12.6|
|||_(4.2)_|_(4.5)_|_(4.3)_|_(4.4)_|_(4.3)_|
|||27<br>|27<br>|27<br>|27<br>|27<br>|
|49||5.6|5.2|5.1|5.1|5.0|
||_PAPER & PAPERBOARD_|_(7.3)_|_(7.0)_|_(7.0)_|_(7.0)_|_(6.9)_|
|||26<br>|26<br>|26<br>|26<br>|26<br>|
|50||4.6|4.6|5.0|5.0|5.0|
||_SILK_|_(1.2)_|_(1.2)_|_(0.0)_|_(0.0)_|_(0.0)_|
|||82|82|80|80|80|
|51|_WOOL ANIMAL HAIR_|12.9|12.4|13.2|13.4|13.4|
||_,_|_(4.0)_|_(5.1)_|_(4.4)_|_(4.3)_|_(4.1)_|

50
as one additional piece of information for determining the magnitude for such adjustment.

From a practical point of view, a reduction in the Fiscal Deficit, if recommended for whatever reason, will have a positive impact on the Trade Surplus (or reduce the deficit). In estimating the precise magnitude of the change in the Trade Balance we can only suggest the use of the regression technique we have discussed in reference to the Colombian data. Given the estimation of the required adjustment in the Trade Balance, we can then estimate the precise adjustment required in the real exchange rate using the less controversial regression technique described previously. We expect the causality relation to go as follows: reduced fiscal deficit generates improved Trade Balance that in turn requires a real devaluation.

# (e) PUBLIC DEBT, REAL INTEREST AND PRIVATE CREDIT

In an economy open to external credit, the public debt does not crowd out private credit as it can be readily obtained abroad at the prevailing real rate of interest. Countries in need of structural adjustment, however, do not normally face unrestricted access to external credit and getting it becomes the long run objective of the adjustment process. In the transition, internal public debt becomes a determining factor of the real cost of credit. We recommend that internal public debt do not exceed a level proportional to the size of the productive activities of the public sector in the economy (e.g. do not include transfers in the measure of the size of the Public Sector). Such target may be difficult to implement initially as public debt is likely to exceed this ratio.In that case, the least that should be required is that this ratio be observed on all incremental credit granted by the institutionalized financial system (mainly Banks and Financieras).

Short run increases in Public Debt are helpful for instrumenting short run falls in inflation, as liquidity is restricted. In the medium run, however, the real interest service of such debt adds to the fiscal deficit and contributes to even higher inflation. In addition, the real interest rate is likely to have risen and this contributes negatively to growth and resource mobilization. In consequence, we discourage the use of policies based on increasing internal public debt that go beyond a level that requires a real interest rate in excess with that compatible with the adjustment program. In general, most countries trying to implement structural adjustment programs have 64
Table A.10: Average effect of policies across income categories, by the second-stage lag

|**Income Category / Policy Lag**|**3**|**5**|**7**|
|---|---|---|---|
|Low-income economies|0.55|0.19|0.24|
|Lower-middle-income economies|-2.67|1.14|8.09|
|Upper-middle-income economies|-0.98|-2.19|-0.70|

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving average._ Table A.11. Share of significant second-stage coefficients as a percentage of total eligible first-stage regressions by regions (top) and income levels (bottom); replacing shares of “Renewable energy consumption” and “Renewable electricity output” with shares of “Modern Energy Generation over Total Generation” and “Modern Energy Capacity over Total Installed Capacity” to exclude large hydropower

||**Group**|**Total eligible**<br>**first-stage**<br>**regressions**|**Share with significant**<br>**second-stage**<br>**coefficients**|**Share with positive**<br>**and significant**<br>**second-stage**<br>**coefficients**|
|---|---|---|---|---|
||SSA|227|18%|13%|
||EAP|93|12%|0%|
|on|ECA|40|53%|35%|
|egi|LAC|96|6%|6%|
|R|MENA|49|24%|22%|
||SAS|32|38%|31%|
||Total|537|19%|13%|
||High|36|0%|0|
|e|Upper Middle|142|17%|11%|
|com|Lower Middle|252|25%|16%|
|In|Low|107|15%|14%|
||Total|537|19%|13%|

_Notes: Regression specification: RISE index, UNGA affinity IV with five years moving average._ 63
# **Table** **<u>3. North-South Trade</u>**

|Specification|||||Poiss|on maximum lik|elihood|
|---|---|---|---|---|---|---|---|
|Dependent variable<br>|||B<br>|ilateral im|ports of the N<br>|orthern country<br>|from the Southern partner<br>|
|Trade flows||All|products|||Manufacturing|All products<br>|
|Years|||1990|-2006|||1990-2006 (every 3 years)<br>1990-2006 (with 1-year lag)<sup>#</sup>|
|Sample of importers||All|||EU||All|
|Model|(1)|(2)|(3)|(4)|(5)|(6)|(7)<br>(8)|
|Ln GDP exporter|0.71<sup>~~a~~</sup><br>(0.08)|||||||
|Ln GDP importer|0.51<sup>a</sup><br>(0.09)|||||||
|Ln distance|-0.61<sup>a</sup><br>(0.10)|||||||
|Common border|1.37<sup>a</sup><br>(0.33)|||||||
|Common language|0.42<sup>a</sup><br>(0.12)|||||||
|Past colonial links|0.10<br>(0.19)|||||||
|North-South EIA|0.36<sup>a</sup>|-0.06||||||
||(0.12)|(0.07)||||||
|N-S EIA with standards|||0.06|||||
|harmonization|||(0.08)|||||
|N-S EIA with stds harmonization||||-0.20<sup>b</sup>|-0.19<sup>b</sup>|-0.22<sup>b</sup>|-0.31<sup>b</sup><br>-0.25<sup>b</sup>|
|& promotion of regional stds||||(0.08)|(0.09)|(0.09)|(0.14)<br>(0.09)|
|N-S EIA with stds harmonization||||0.52<sup>a</sup>|0.36<sup>a</sup>|0.55<sup>a</sup>|0.70<sup>a</sup><br>0.57<sup>a</sup>|
|& promotion of international stds||||(0.15)|(0.13)|(0.14)|(0.15)<br>(0.14)|
|Observations|49,522|52,448|1,731|1,731|1,209|1,731|683<br>1,702|
|Log pseudolikelihood|-13,618.21|-10396.75|-889.30|-889.23|-643.31|-839.91|-366.30<br>-887.09|
|FE exporter|Yes|||||||
|FE importer|Yes|||||||
|FE year|Yes|||||||
|FE exporter × year||Yes|Yes|Yes|Yes|Yes|Yes<br>Yes|
|FE importer × year||Yes|Yes|Yes|Yes|Yes|Yes<br>Yes|
|FE dyad||Yes|Yes|Yes|Yes|Yes|Yes<br>Yes|

_Source:_ Authors’ analysis based on data described in the text.

30
represented in Table 1, but more generally across the world. The only weak exception is New Zealand between 1995 and 2014.

While it is difficult to provide an unequivocal explanation for individual nations, there are two factors to be kept in mind in the case of New Zealand. First, the wage share in New Zealand was quite high in 1985, above 50%, and then it went into a trough; Table 1 picks up the data during a low period, and so the increase in 2014 picks up a base effect. Second, New Zealand does stand out as a nation with a remarkable balance of policies that made its firms nimble and efficient and, at the same time, shores up labor markets with well‐designed, progressive laws, such as the Employment Relations Act 2000. This law promotes collective bargaining and at the same time helps with mediation to enable early resolution of disputes. It insists on minimum labor standards but protects the integrity of individual choice. The consequence was a period of rapid growth in employment up to 2009. This fine combination of policies has meant that New Zealand has one of the highest rates of firm creation and destruction and job turnover.<sup>3</sup> With all these advantages, it is still running against the tide of technological shifts, and so the wage bill as a share of GDP rose only marginally between 1995 and 2014, from 45.8 to 46.1.<sup>4</sup>

**Table 1. Wage as a share of GDP**

||1975|1995|2014|
|---|---|---|---|
|_High income countries_<br>||||
|Australia|66.5%|58.0%|53.0%|
|Canada|60.6%|57.0%|55.1%|
|European Union (15<br>countries)|66.0%|57.6%|56.3%|
|Japan|77.3%|67.3%|59.6%|
|New Zealand|..|45.8%|46.1%|
|United States|61.4%|59.8%|57.0%|
|_Emerging Economies_||||
|China|..|52.8%|47.0%*|
|Mexico|..|42.2%|36.0%|
|Russian Federation|..|40.8%|42.1%*|
|Turkey|..|41.2%|32.3%|

_Notes: Wage Bill is the adjusted labor income as a share of GDP at current price. The unadjusted labor income only includes compensation of employees, whereas the adjusted labor income share makes an adjustment to account for the self‐employed as well. For China, the share is unadjusted. The Russian Federation’s share excludes hidden wages and mixed income. *Latest data available for China is 2011 and 2012 for the Russian Federation._ 3 This is mirrored in the fact that New Zealand has second rank among the 189 economies (which includes all major nations in the world), behind Singapore, in terms of the World Bank’s Ease of Doing Business indicator (see World Bank, 2015).

4 The other small puzzle is the Russian Federation, but the nation’s inclusion of “hidden wages” in the aggregate wage bill makes this harder to interpret.

5
# Stochastic Modeling of Food Insecurity

Dieter Wang<sup>∗1</sup> , Bo Pieter Johannes Andrée<sup>†1</sup> , Andres Fernando Chamorro<sup>‡2</sup> , and Phoebe Girouard Spencer<sup>§1</sup> 1 World Bank Group; Fragility, Conflict and Violence 2World Bank Group; Development Data Analytics and Tools

**JEL:** C01, C14, C25, C53, O10

**Keywords:** Food insecurity, famine risk, variable selection, stochastic simulation, panel vectorautoregression, expert opinion.

- ∗Corresponding author. E-mail: dwang5@worldbank.org

- †E-mail: bandree@worldbank.org

> ‡E-mail: achamorroelizond@worldbank.org

> §E-mail: pspencer1@worldbank.org

This work was prepared as background for the Famine Action Mechanism (FAM). Support from the State and PeaceBuilding (SPF) Trust Fund (Grants N. TF0A7049 and TF0A5070 ) is gratefully acknowledged. The authors would like to express their gratitude to Zacharey Carmichael, Aart C. Kraay and Nadia Piffaretti for their valuable comments, guidance and continued support. The authors also thank Cathrine Ansell, Bledi Celiku, Harun Dogo, Nicholas Haan, Therese Norman, John Plevin, Nicola Ann Ranger, as well as workshop participants of the 14th Annual Workshop of the Household Conflict Network (Medellin, Colombia) for their comments and feedback.
Faruqee, Hamid and Aasim Husain (1998): "Saving trends in Southeast Asia: a cross-country analysis", _Asian Economics Journal_ 12, 195217.

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Ghura, Dhaneshwar, and Thomas Grennes (1993): "The real exchange rate and macroeconomic performance in Sub-Saharan Africa", _Journal of Development Economics_ 42, 155-174.

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- Hausmann, Ricardo, Lant Pritchett, and Dani Rodrik (2004),<sup>“</sup> Growth accelerations,<sup>”</sup> mimeo, John F. Kennedy School, Harvard University.

- Jappelli, Tullio and Marco Pagano (1994), "Saving, growth, and liquidity constraints," _Quarterly Journal of Economics_ , Vol. 109, pp. 83109.

Johnson, S., J. Ostry and A. Subramaniam (2007):<sup>“</sup> The prospects for sustained growth in Africa,” IMF Working Paper 07/52.

- Kraay, Aart (2000), "Household saving in China," _Journal of Development Economics_ , Vol. 14, No. 3, pp.

- Kouri, Pentti (1978): "Profitability and growth in a small open economy", Cowles Foundation Discussion Paper 490.

- Lee, J. and M. Tang (2007):”Does productivity growth appreciate the real exchange rate?” _Review of International Economics_ 15, 164-187.

- Levy-Yeyati, Eduardo, and Federico Sturzenegger (2007a): “The effect of monetary and exchange rate policies on development”, forthcoming in _Handbook of Development Economics._

- Levy-Yeyati, Eduardo, and Federico Sturzenegger (2007b). “Fear of floating in reverse: exchange rate Policy in the 2000s”, paper presented at the LAMES-LACEA Annual Meetings.

- Loayza, Norman, Klaus Schmidt-Hebbel and Luis Serven (2000a): "What drives private saving across the world?", _Review of Economics and Statistics_

- Loayza, Norman, Klaus Schmidt-Hebbel and Luis Serven (2000b), “Saving in developing countries: an overview,” _World Bank Economic Review_ , Vol. 14, No. 3, pp. 393-414.

Montiel, Peter (2007), "The real exchange rate and the saving rate in an intertemporal model of the current account," Williams College, mimeo.

32
# **1. Share of the Stock of Employment**

To define some basic terms for discussion, we refer to the total employment of each establishment _i_ , by using _E_ . We present the more general case with survey weights ( _wt_ ), which account for establishment _i's_ varied probability of selection relative to the total number of establishments in an economy,<sup>4</sup> for census or administrative data sources, _wt_ would be equal to one for all establishments.<sup>5</sup> We start with the question of where people are employed, using a metric that is defined as the share of employment ( _s_ ) over each analytical category (e.g. firm size, age, exporting status) or “cut” ( _c_ ), given by: Where �<sup>�</sup> �,� is the sum of establishment-level employment in a given analytical cut, given by �<sup>�</sup> �,� � ∑�� �,� ∗���, where ��,� ∗��� is the (weighted) establishment-level estimate of total employment at time _t._ The share of employment by various cuts has garnered particular attention in the literature, yet this measure is partial as it only gives a measure of the current state of employment and not how it is changing over time.

# **2. Net Job Growth Rate, Job Creation Rate, Job Destruction Rate**

To measure the changes in employment, we start with the measure of employment growth at the establishment level. In its most basic form, employment growth between period _t_ and _d_ , can be expressed as _gi,t_ = ��,�����,��,��� Where ��,� is employment in a base period, _b_ . A natural choice for this base is the beginning period of measurement, here employment at a lagged time, that is _b_ = _t-d_ (where _d_ is the time difference between the two periods). Davis et al (1996), however, note that using the initial size as the base can lead to upward bias in the growth estimate for smaller establishments: firms that reduce employment (say due to an adverse shock) are likely to be the quickest to increase employment after emerging from the shock. These same firms will then have a faster growth rate as they approach previous employment levels, showing a “regression to the mean” or “regression fallacy” effect. Using the end-period, _t_ , as the base conversely may have an undesirable downward bias for smaller firms. Davis et al., in response, use a measure to mitigate this bias, where the difference in employment between the two time periods is divided by the average employment over the two periods.

> 4 Other weighting methodologies can include an establishment’s relative contribution to value added or sales. 5 Official and administrative data sources, such as those produced by national statistical offices, are often considered as exhaustive and thus are by definition nationally representative. Where such sources are not available, nationally representative surveys can be employed; as these rely on randomly selected samples, though, they must be considered with their survey weights.

4
of the poor over and above their impact on mean income. Equivalently from Equation (2), a2 measures the impact of these other variables on the share of income accruing to the poorest quintile, holding constant average incomes.

Simple ordinary least squares (OLS) estimation of Equation (1) using pooled country-year observations is likely to result in inconsistent parameter estimates for several reasons.'<sup>4</sup> Measurement error in average incomes or the other control variables in Equation (1) will lead to biases that are difficult to sign except under very restrictive assumptions.'5 Since we consider only a fairly parsimonious set of right-handside variables in X, omitted determinants of the log quintile share that are correlated with either X or average incomes can also bias our results. Finally, there may be reverse causation from average incomes of the poor to average incomes, or equivalently from the log quintile share to average incomes, as suggested by the large empirical literature which has examined the effects of income distribution on subsequent growth. This literature typically estimates growth regressions with a measure of initial income inequality as an explanatory variable, such as: This literature has found mixed results using different sample and different econometric techniques. On the one hand, Perotti (1996) and Barro (1999) find evidence of a negative effect of income inequality on growth (i.e. 01>0). On the other hand, Forbes (2000) and Li and Zou (1998) both find positive effects of income inequality on growth, (i.e. 1<sup>3</sup> i<<sup>0</sup> ). Finally, Bannerjee and Duflo (1999) modestly, and perhaps most appropriately, conclude that there is at best weak evidence of a U-shaped correlation between income inequality and growth and that very little can be said about causation in

> 14 It should also be clear that OLS standard errors will be inconsistent given the cross-observation correlations induced by the unobserved country-specific effect.

> '5 While at first glance it may appear that measurement error in per capita income (which is also used to construct our measure of incomes of the poor) will bias the coefficient on per capita income towards one in Equation (1), this is not the case. From Equation (2) (which of course yields identical estimates of the parameters of interest as does Equation (1)) it is clear that we only have a problem to the extent that measurement error in the first quintile share is correlated with average incomes. Since our data on income distribution and average income are drawn from different sources, there is no a priori reason to expect such a correlation. When average income is taken from the same household survey, under plausible assumptions even measurement error in both variables will not lead to inconsistent coefficient estimates (Chen and Ravallion (1997)).

16