rafmacalaba/datause-encoder-data huggingface dataset:
in non-farm participation like role model<sup>effects,learning externalities,and transferof reputation</sup> capital. We explicitly control for education,<sup>assets,andnetwork variables intheregressions,and</sup> the parental occupation variables thus<sup>capturethe 'intergenerationallinkages'overandabove</sup> these more tangible (relatively easily<sup>measurable)factors.To separate outtheeffectsof parents</sup> as role models, and of learning externalities<sup>andtransferof reputationcapital,wealsoneedto</sup> control for genetic ability transmission<sup>whichis presumablyan importantintangible factorin any</sup> type of intergenerational link. While it is very difficult, if not impossible,<sup>tofindreasonably</sup> good controls for ability, we use parental<sup>andspousal educationto control forability correlations.</sup> The inclusion of spousal education is predicated<sup>onthe largeempiricalliteratureonassortative</sup> matching in marriage market.<sup>7</sup> The econometric<sup>results,usinghouseholdlevelsurveydata from</sup> Nepal, indicate strong intergenerational<sup>linkagesin non-farmparticipationrunning alonggender</sup> lines (mother-daughter, father-son) if, following<sup>the extantliterature,weignorethesimultaneity</sup> between education and occupation decisions.<sup>Theintergenerationalcorrelationsare pretty robust</sup> with respect to the inclusion of an array<sup>of controlvariables.However,theevidenceunambigu-</sup> ously rejects the null hypothesis of exogeneity,<sup>bothforeducationandassets,inthenon-farm</sup> participation decisions of sons. The estimated intergenerational occupational<sup>linkagesbecome</sup> numerically small and statistically insignificant<sup>for sonswhenendogeneity of education andassets</sup> is corrected by utilizing the Two-Stage<sup>ConditionalMaximumLikelihood(henceforthTSCMLE)</sup> approach (Blundell and Smith, 1986; Rivers<sup>andVoung,1988).Whendisaggregatedaccording</sup> to skill levels, there are evidence of a<sup>positivecorrelationbetweenfatherandson(s)onlyin the</sup> case of skilled jobs, thus indicating that<sup>theaggregateresults arepartlydrivenbythelackof</sup> intergenerational correlations in unskilled<sup>jobs.Fordaughters,thereis convincingevidencethat</sup> the endogeneity problems are not important,<sup>andthattherearestrongeffectsofparents',in</sup> particular mother's, non-farm participation<sup>regardlessoflevelsof skill.Havinga motherinthe</sup> non-farm sector raises a daughter's probability<sup>of non-farmparticipationby 200percentwhenthe</sup> sample consists of both skilled and unskilled<sup>groups.The effectsaretrulydramaticin thecaseof</sup> skilled jobs; a daughter's probability of being<sup>in skilled job increasesby 1200percent if her mother</sup>

> *<sup>7</sup> We do not have any data on cognitive tests,<sup>likeRaven'stest.</sup> However, it is not clear to what extent the results of a very rudimentary test like Raven's<sup>cancapturethe typeof abilitycorrelations relevantfor occupational</sup> choice.

3
|**Table A19: IFAD Rural Sector**|**Perfor**|**manc**|**e Ass**|**essm**|**ents (**|**IFD)**||
|---|---|---|---|---|---|---|---|
|**Data Provider**<br>International Fund for Agricultural De|velopm|ent||||||
|**Description**<br>Multilateral development institution<br>investments in developing countries.<br>|headqu|artere|d in|Rome|, Ital|y, financing|agricultural|
|**Website**<br>www.ifad.org||||||||
|**Data Source**<br>Rural Sector Performance Assessment|s|||||||
|**Type**<br>Expert assessment||||||||
|**Respondents**<br>IFAD country economists, subject to c|entraliz|ed rev|iew|||||
|**Frequency**<br>Annual since 2004||||||||
|**Coverage**<br>IFAD client countries||||||||
|**Public Access**<br>Yes||||||||
|**Description**<br>This source assesses 12 dimensions o<br>assessments are used in IFAD's p<br>resources across countries.|f the ru<br>erform|ral po<br>ance-b|licy e<br>ased|nviro<br>alloc|nment<br>ation|on a 6-poin<br>system for|t scale. The<br>distributing|
||2008|2007|2006|2005|2004|2003<br>2002|2000<br>1998<br>1996|
|**Voice and Accountability**||||||||
|Policy and legal framework for rural organizations|X|X|X|X|X|..<br>..|..<br>..<br>..|
|Dialogue between government and rural organizations|X|X|X|X|X|||
|**Political Stability and Absence of Violence**||||||||
|NA|..|..|..|..|..|..<br>..|..<br>..<br>..|
|**Government Effectiveness**||||||||
|Allocation & management of public resources for rural development|X|X|X|X|X|||
|**Regulatory Quality**||||||||
|Enabling conditions for rural financial services development|X|X|X|X|X|||
|Investment climate for rural businesses|X|X|X|X|X|||
|Access to agricultural input and produce markets|X|X|X|X|X|||
|**Rule of Law**||||||||
|Access to land|X|X|X|X|X|||
|Access to water for agriculture|X|X|X|X|X|||
|**Control of Corruption**||||||||
|Accountability, transparency and corruption in rural areas|X|X|X|X|X|..<br>..|..<br>..<br>..|
|**Country Coverage**|**90**|**87**|**100**|**121**|**124**|**..**<br>**..**|**..**<br>**..**<br>**..**|
|**Year of publication**|**2008**|**2007**|**2006**|**2005**|**2004**|**..**<br>**..**|**..**<br>**..**<br>**..**|

58
instance, reduced tariffs will yield welfare gains of 0.2 percent for AfCFTA country members. Cutting NTBs in half would increase welfare gains by 1.6 percent while the full implementation of the WTO’s Trade Facilitation agreement would lead to overall welfare gains of 5 percent by 2035 (World Bank 2019b).<sup>15</sup>

# **6 Conclusions**

The goal of this paper is to provide evidence on the nature of the relationship between international trade integration for Sub-Saharan Africa in a comprehensive and systematic manner. Unlike previous research in this strand of the literature, this paper first examines the impact of trade integration on growth as well as on the sources of growth —that is, physical capital accumulation and TFP growth. Second, it evaluates the impact on growth (and its sources) of the different dimensions of trade integration (trade volume, trade diversification, and natural resource dependence). This implies that the nature of the tradegrowth relationship depends not only on how much you trade but also on what you export and to whom you export (or have trade with). Third, it examines the impact of growth of the composition of trade volumes by product (primary goods vs. manufacturing goods) and the composition of trade volumes by destination (inter- vs. intra-regional trade).

We have collected a comprehensive data set on international trade integration indicators, growth per worker, capital accumulation, and TFP growth for 174 countries across the world, including 45 SubSaharan African countries, from 1970 to 2014. The relationship between international trade integration and growth is estimated using the GMM-IV system estimator (Arellano and Bover 1995, Blundell and Bond 1998). This estimation method accounts for unobserved country and time fixed effects as well as the likely endogeneity (or reverse causality) of explanatory variables, thus providing evidence of a causal relationship between trade integration and growth. The GMM-IV estimation is also corrected for the likely downward-biased estimation of the asymptotic standard errors of the two-step GMM estimator in small samples (Windmeijer 2005) and the overfitting of endogenous variables due to the existence of too many instruments (Roodman 2009).

The econometric analysis finds a causal relationship from (the different dimensions of) trade integration to growth (including the sources of growth). The amount of trade is not the only dimension

> 15 The estimated aggregate welfare gains mass the cross-country heterogeneity in the impact of the AfCFTA. Welfare gains by 2035 are estimated to range from 0.4 percent (Mozambique) to 19 percent (Togo). In this context, the welfare effects of the AfCFTA depend on the depth and extent to which the policy changes cover non-trade barriers and services (especially in transport and logistics) and on the structure of the export basket and the economy.

40
This means that there is statistically significant evidence that oil price volatility RV has a causal impact on economic activity, i.e. contemporary RV is useful in forecasting future industrial production. With respect to the USA it should be noted that the p-value 0.059 is only marginally excessive of the 0.05 significance level. At a 10% (or in fact 6%) significance level the null of ‘no Granger causality’ would also be rejected for the USA.

|||**GrangerCaus**<br>|**ality**<br><br>_′_||
|---|---|---|---|---|
||Η0:_“IP does not_|_Granger cause RV”_|Η0<br>:_“RV does not_|_Granger cause IP”_|
||F-statistic|p-value|F-statistic|p-value|
|USA|0.977|0.480|1.589|0.059|
|JPN|0.829|0.620|2.492|0.004|
|GER|0.632|0.814|2.053|0.020|
|KOR|0.230|0.875|2.329|0.009|
|IND|0.499|0.751|1.565|0.048|
|MYS|0.499|0.683|6.554|0.001|

**Table 5.** Results for Granger Causality Tests: investigating the causal relationship between Industrial Production and Realized Volatility in both directions.

# **3.5. Impulse Response Functions**

To understand the nature of the IP-RV relationship, it is crucial to analyze how a volatility shock transmits to industrial production through the dynamic lag structure of the VAR process. Impulse response functions (IRF) trace out the effect of a realized volatility shock to industrial production over time – and can yield interesting insight for policy makers. While VAR coefficients and Granger causality inform about the sign, extent and causal direction, impulse response functions inform about the persistence and dynamics of the oil-GDP relationship. To find the impulse response function, the previous VAR is transformed into its ‘Wold representation’, i.e. an infinite vector moving average process VMA�∞�, which expresses exogenous variables as a function of all past shocks. The previous VAR(q) can be rewritten using Lag-operators L, such that By defining the matrix lag polynomial where I2 is a 2 × 2 identity matrix, the original VAR(q) can be expressed as This VAR process can be rewritten as an infinite vector moving average process. To do so, a necessary condition is invertibility of the Φ(1) matrix. Since Xm = [IPm RVm] _′_ is stationary, invertibility can easily be shown: For the unconditional expectation of Xm (defined μ≡ E{Xm}) it must hold that The VAR process can thus be expressed as a vector moving average process by pre-multiplying with Φ(L)<sup>−1</sup> : While the first term is equivalent to μ, the second term can be expressed as a weighted sum of past and current innovations by defining Φ(L)<sup>−1</sup> = I2 + A1L+ A2L+ ... : where As is a matrix of coefficients, given by 9
- 5 -

may reflect Nepal’s fiscal year cycle, which runs from July 16 through July 15. There may be some administrate pressure to execute the budget toward the end of each fiscal year.<sup>4</sup> On the other hand, there is also another peak to start public works in the first quarter of the fiscal year.

In Nepal many road contracts are made before the end of the fiscal year (July) and the actual works tend to be started after the rainy season. Rural road works can last several months to more than one year (Figure 4). For technical reasons, the works can be affected by the climate, depending on work specification. Concrete or bitumen work cannot be implemented during the rainy season. The data indicate that cost overruns and project delays seem to depend on when the work is started. Suppose that the sample is divided into two seasons: rainy (May-September) and dry (October-April). More cost overruns and delays occurred in the rainy season. The distribution of project delays in the rainy season has a relatively thicker tail on the right hand side (Figure 5).

**Figure 2. Average monthly precipitation for the past 10 years**

<!-- Start of picture text --> 1200<br>Siraha<br>Makwanpur<br>1000<br>Banke<br>800<br>600<br>400<br>200<br>0<br>1 2 3 4 5 6 7 8 9 10 11 12<br>Month<br>Monthly precipitation (mm)<br><!-- End of picture text --> Source: Author’s calculation based on data from the Ministry of Environment, Nepal.

> 4 For some of the sample projects, there is a view that the work schedules in contracts are determined by the remaining number of days in the current fiscal year, regardless of the work period estimate from the engineering point of view. This may attribute to the observed massive project delay on average.
Policy Research Working Paper 9499

# **Abstract**

This study looks at the redistributive effects of fiscal policy —in particular of direct taxation and expenditures—in Uruguay. This fiscal incidence analysis applies a widely recognized methodology to household survey data and government data for fiscal year 2017 and compares the results with the policies seen in 2009 to study the evolution of the distributional impacts of fiscal policy in the country. The study finds evidence that Uruguayan fiscal policy continues to reduce inequality, with government expenditures in the form of in-kind transfers leading to the largest decreases. While expenditures in basic education are benefitting the poorest, expenditures in tertiary education remain largely regressive. The personal income tax is found to be largely progressive, with the top quintile paying more than fourfifths of this tax. Uruguay’s fiscal policies also lead to a reduction in poverty, mainly due to well-targeted direct transfers.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at glaraibarra@worldbank.org, marisa.bucheli@cienciassociales.edu.uy, and dtuzmanfernandez@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
income (<sup>_∂lnP_</sup> _∂lnyit_<sup>_<u>it</u>_</sup> _−_<sup>_−_</sup> _τ_<sup>_<u>τ</u>_)18;and,thesamplemeansof</sup><sup>_Pit−τ_and</sup><sup>_gi_(</sup><sup>_yit_),thecomputationof</sup> (9) yields virtually the same food poverty convergence rates calculated above (see table 6, panel a).

For instance, the poverty convergence rate calculated based on (9) is _−_ 0 _._ 011 during 1992–2014, which is very close to the coefficient of _−_ 0 _._ 012 computed based on equation (6) for the same period. The decomposition of that rate reveals that the convergence effect accounted for _−_ 0 _._ 007 and that poverty was actually responsive to growth, with a poverty elasticity effect of _−_ 0 _._ 005. By contrast, the initial poverty rates of municipalities exerted an adverse, yet moderate effect of 0.001. In the 1990s only, unsurprisingly, a convergence effect of _−_ 0 _._ 024 was more than offset by both the initial poverty effect (0.024) and the poverty elasticity (0.015) effect, thus confirming the significant poverty divergence of 0.014 found in those years. Meanwhile, in 2000–14, both convergence and poverty elasticity effects explain in similar magnitudes ( _−_ 0 _._ 016 and _−_ 0 _._ 020, respectively) the totality of the speed of poverty convergence ( _−_ 0 _._ 034), with only a slightly adverse effect of initial poverty, at 0.002. These results confirm that the process of income convergence and the efficiency of growth in reducing poverty effectively translated into poverty convergence during 1992–2014 in general, but particularly after 2000.

Focusing on the first five years of the 2000s, probably the most revealing period under study, the decomposition offers a remarkable result: the three effects moved in the same favorable direction. The convergence rate of _−_ 0 _._ 055 was mostly explained, again in similar magnitudes, by the convergence effect ( _−_ 0 _._ 024) and the poverty elasticity effect ( _−_ 0 _._ 022). But, saliently, the initial poverty rates of municipalities also contributed an effect of _−_ 0 _._ 009, equivalent to 16 percent of the speed of poverty convergence. This result supports the evidence in tables 7 and 8 for this period, which suggest plausibly that starting out (very) poor in 2000 was associated with high growth rates in mean per capita income in the next five years. In a context of disappointing economic growth, such high rates could have been the result of the explosive expansion of cash transfers among the extreme poor and of social spending in general, potentially having the double effect of bolstering per capita incomes enough to have reduced food poverty, while fostering progressive changes in the distribution, which, in turn, may promote poverty reduction (see figure 4, panel b).

To shed some light on the latter issue, the analysis also explored the role of inequality. Initial inequality in municipalities tends to exert sizable and significant adverse effects

> 18The computation of these elasticities through ordinary least squares yields _−_ 1 _._ 505 in 1992, _−_ 1 _._ 662 in 2000, _−_ 1 _._ 664 in 2005, and _−_ 1 _._ 553 in 2010.

31
further technological progress, result in additional conversion of scarceland resource to o↵set the impact of potentially lower yields. Owing to intertemporal substitution, some of that conversion takes place even in the absence of actual realization of the climate shocks or technology outcomes. This expansion is accompanied by the changes in the consumption of processed food, livestock, and biofuels - the land-based products most a↵ected by changes in crop productivity.

This study is primarily a methodological contribution, and the chosen model illustration (FABLE) seeks to balance computational complexity and economic tractability. It thus ignores many features standard in more advanced computational land and other resource use models. The future research should focus on integrating economic decisions under uncertainty into large dynamic natural resource models that feature spatial disaggregation at the regional or zonal level, a more extensive representation of the energy sector, and di↵erent types of resources and their production derivatives. Another promising research direction would be to incorporate a more detailed representation of uncertain states backed by an econometric analysis that recovers underlying distributions of uncertain natural resource drivers over time.

# **References**

- Alexander, P., R. Prestele, P. H. Verburg, A. Arneth, C. Baranzelli, F. Batista e Silva, C. Brown, A. Butler, K. Calvin, N. Dendoncker, et al. (2017). Assessing Uncertainties in Land Cover Projections. _Global Change Biology 23_ (2), 767–781.

- Babiker, M., A. Gurgel, S. Paltsev, and J. Reilly (2009). Forward-looking versus Recursivedynamic Modeling in Climate Policy Analysis: A Comparison. _Economic Modelling 26_ (6), 1341–1354.

- Benayas, J., A. Newton, A. Diaz, and J. Bullock (2009). Enhancement of Biodiversity and Ecosystem Services by Ecological Restoration: a Meta-analysis. _Science 325_ (5944), 1121–1124.

- Brock, W. A. and M. Majumdar (1978). Global Asymptotic Stability Results for Multisector Models of Optimal Growth under Uncertainty when Future Utilities are Discounted. _Journal of Economic Theory 18_ (2), 225–243.

- Bulte, E., D. van Soest, G. Van Kooten, and R. Schipper (2002). Forest Conservation in Costa Rica when Nonuse Benefits are Uncertain but Rising. _American Journal of Agricultural Economics 84_ (1), 150–160.

- Burke, M. B. and K. Emerick (2016). Adaptation to Climate Change: Evidence from US Agriculture. _American Economic Journal: Economic Policy 8_ (3), 106–140.

- Cai, Y. (2019). Computational methods in environmental and resource economics. _Annual Review of Resource Economics_ (11), 59–82.

- Cai, Y. and K. Judd (2013). Shape-preserving Dynamic Programming. _Mathematical Methods of Operations Research 77_ (3), 407–421.

25
1

# SHORT TERM SUPPLY RESPONSE TO A DEVALUATION: A **MODEL** 'S IMPLICATIONS FOR **PRIMARY** COMMODITY-EXPORTING DEVELOPING COUNTRIES

# Bruno Boccara and Fabien Nsengiyumwa 1/ Country Operations, Western Africa World Bank

# 1 Introduction

While it is widely accepted that in the long run, a depreciation of the real exchange rate will improve the current account and stimulate economic activity, t'-are is a growing consensus that devaluations can have, in the shortrun, contractionary effects on output. The nurpose of this paper is to evaluate the circumstances under which, in the short term, a devaluation in primary commodities exporter less developed countries could have contractionary effects.

To do so, a model capturing the principal features of these economies has been used to evaluate the short-run impact of a devaluation. The model itself is not meant to be used as a forecasting tool but rather to illustrate and show the relative magnitude of various effects which are relevant in countries where the initial supply response to a devaluation would mostly come from an increased production of an export commodity. In particular, the paper analyzes the difference between the producer's response under a "wrong' timing (in the sense that the pre-devaluation price is the price signal on which production decisions are based) and under the presence of middlemen (rentiers in the export sector whose presence affects the devaluation's passthrough to producers).

The model is simulated for two economies, that of a "middle-income" country and that of a "low-income', country. The economic structure of the "low-income" country is less flexible (lower supply elasticity in production, lower elasticity of substitution between domestic production and imported inputs) than that of the "middle-income" country. Section 2 discusses the channels identified in the literature through which a devaluation could be _1/ The views expressed in this paper do_ not necessarlly _reflect_ the _views of_ the _World Bank and its affiliates. Any errors In this paper are the sole responsibility of the authors._