rafmacalaba/datause-encoder-data huggingface dataset:
The estimated shock processes in the transitory and the growth rate of permanent components of Solow residual for India are sourced from table 3. The parameter for capital adjustment cost φ is set to 2.82 from Aguiar and Gopinath (2007).

_Effect of Financial Development on Relative Consumption Volatility_ The model predicts that a decline in the share of liquidity-constrained households in the population would allow more people to respond to permanent income shocks. They can increase current consumption more than the rise in current income. This is predicted to result in a rise in the relative consumption volatility.

Main findings are the following. The relative consumption volatility shows a rise in the post-reform period (table 6). This result supports the key prediction of the model. Since financial development allows more people to access savings instruments, when households perceive a permanent income shock which raises both current and future income, more people can respond to the shock by reducing current savings and raising current consumption more than the rise in current income. As a result of financial development, the volatility of consumption relative to volatility of output rises.

This model also replicates the pattern of changes in absolute consumption volatility successfully. The model also captures a decline in the absolute output volatility in the post-reform period as observed in the data. However, in terms of magnitude, the change in the output volatility is not substantial. With financial inclusion, more people can save, and, hence, investment volatility declines. The model shows a fall in the absolute volatility in investment in the post-reform period, as observed empirically. However, unlike the trend shown in the data, the simulated relative investment volatility declines in the post-reform period.

Next, the simulated correlation of consumption and investment cycles with 22
# Table 5 Savings Functions for the Panel Data of African Countries, 1960-90

|||Depen<br>(t-|dent Variab<br>Statistics in|le: Savings ra<br>parenthesis)|te||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|
|GDP per capita growth|3.821<br>(1.97)|0.494<br>(3.62)|3.820<br>(1.97)|4.917<br>(3.57)|3.760<br>(1.94)|4.715<br>(3.38)|3.536<br>(1.74)|4.892<br>(3.52)|
|LaggedGDPgrowthpercapita||2.453<br>(1.33)||2.405<br>(1.31)||2.133<br>(1.21)||0.942<br>(0.58)|
|Dependency ratio|-0.602<br>(-1.43)|-0.055<br>_(-0.15)_|-0.451<br>(-1.07)|0.045<br>(0.12)|-0.909<br>(-2.39)|-0.273<br>(-0.79)|-0.813<br>(-2.03)|-0.248<br>(-0.74)|
|Lagged Savings Rate||0.341<br>(2.34)||0.335<br>(2.26)||0.366<br>(2.64)||0.425<br>(2.98)|
|Overall Education Stock|-2.392<br>(-3.66)|-2.039<br>(-3.31)|||||||
|Lagged Overall Education Stock|1.846<br>(3.37)|1.860<br>(3.15)|||||||
|Primary Education Stock|||-2.536<br>(-3.84)|-2.074<br>(-3.18)|||||
|Lagged Primary Education Stock|||1.923<br>(3.52)|1.662<br>(3.02)|||||
|Secondary Education Stock|||||-1.580<br>(-3.26)|-1.116<br>(-2.71)|||
|Lagged Secondary Education Stock|||||1.279<br>(3.21)|-0.909<br>(2.62)|||
|Tertiary Education Stock|||||||0.056<br>(0.40)|0.024<br>(0.21)|
|Lagged Tertiary Education Stock|||||||-0.100<br>_(-1.05)_|-0.057<br>(-0.71)|
|AdjR<sup>2</sup><br>|0.163<br>|0.307|0.170|0.305|0.173|0.297|0.074|0.262|
|DW|1.67|2.02|1.68|2.02|1.62|2.02|1.85|2.03|
|Observations|109|109|109|109|109|109|109|109|

All variables are expressed im log (with the exception of the GDP growth rate) and in 5 year average. The TSLS have been used in all regression and fixedeffects have been introduced to account for countries' characteristics.
**19**

**27.** The respective premiums for the commercial (secondary) rate and the parallel (tertiary) rate are extremely high. Given such a high degree of deviation among the different rates, it is to be expected that the average of the multiple rates is significantly different from the official (Central Bank) rate. Perhaps what is more significant from the Bank's operational point of view is that with the shift (and therefore weights) away from the official (Central Bank) rate in recent years, its is likely that Egypt's per capita **GNP** will remain within the threshold for Bank's **IDA** eligibility."

**28.** The Syria example (Table **_5_** below) is intended to illustrate the types of relevant information **IEC** staff can be expected to extract from various Bank and Fund documents to estimate a trade-weighted average of officially recognized exchange rates. The Fund's Annual Report on Exchange Arrangements and **Exchange** Restrictions lists Syria as maintaining multiple rates, but these rates are not available in the **IFS.** On the basis of information in the Fund's Annual Report on Exchange Arrangements and Exchange Restrictions, and unpublished Recent Economic Development on member countries, Table **_5_** below illustrates the procedures **by** which **IEC** staff, in consultation with Regional staff, derives a time series of multiple rates and a corresponding time series of weights.

> **29.** As can be seen in Table **_5,_** the impact of the **30** percent premium in the secondary market is dampened **by** the fact that transactions in this market is only about one-third of total transactions. Thus, the "composite" rate does not exhibit the wide margin of deviation

> **"'** The Bank uses the weighted ("composite") exchange rate in the computation of Egypt's **GNP** per capita for the **Atlas** and the Operational Guidelines.
consumption model and, vice-versa, estimated the 2007 poverty rate using the 2001 consumption model. In both cases, we were able to closely match the official poverty rates with the imputation-based estimates. Whether we used the _forward_ approach or the _backward_ approach, we effectively obtained the same poverty estimates.

The application provides a number of new insights for Morocco. The imputation-based estimates show that poverty consistently declined between 2001 and 2007, and that the decline continued beyond 2007 and up to 2010. This confirms that Morocco has been able to withstand the global financial crisis, arguably due to the favorable agricultural production during that same time period. The estimates also show an urban-rural convergence in poverty, with rural poverty falling faster than urban poverty, thereby reducing the urban-rural gap. Interestingly, poverty rates in Morocco have not declined everywhere; disaggregating by region, we see both upward and downward trends in poverty that previous statistics for 2001 and 2007 were not able to capture. The quarterly poverty estimates have provided an entirely new perspective on the study of poverty in Morocco.

The potential for extensions and applications of this work is also promising. The estimated quarterly poverty series can be used for further cross-section and panel econometric work, for forecasting, and for simulation of policy reforms and economic shocks. These applications have the potential to substantially expand the toolkit of the welfare economist.

The paper is organized as follows. The next section describes the macroeconomic context, poverty trends and government policies in Morocco over the period 2000-2010. Section three illustrates the cross-survey imputation methodology adopted. Section four explains the HESs and LFSs data used. Section five, shows the empirical model, section six carries out some validation tests and section seven discusses the poverty estimations obtained. Section eight discusses possible extensions and applications of the methodology proposed and section nine concludes.

# **2. Growth, poverty and policies 2000-2010**

As an emerging economy that has increasingly opened to trade during the last decade, Morocco has become more dependent on the global economy. Global shocks that include the 2001 and 2007-2008 financial crises and the global increases in food and commodities prices in 2008 set the background for better understanding the performance of the domestic economy and the evolution of poverty between 2000 and 2010.

As shown in Figure 1, at the outset of the decade world growth rates were around 5% on average with relatively small differences between developed and emerging economies. The 2001 financial crisis is seen to be associated with a visible decline in world output affecting all groups of countries and reducing average growth rates by about 2-3 percentage points. We then see an exceptional period between 2001 and 2007 where all groups of countries experience increasing growth rates. During this 4
# ReferXnces

M. Bruno (1992) 'Shocks and Accommodation: The Dicothomy and Mechanics of High Inflation' mimeo.

V. Corbo and A. Solimano (1991) 'Chile's Experience with Stabilization Revisited' in M.

Bruno sL (eds.) Lessons of Economic Stabilization and Its Aftemath MIr Prcs. S. Commander and F. Coricelli (1992) 'Price and Wage Dynamics and Inflation in Socialist Economies: Empirical Models for Hungary and Poland' in The World Bank **_£=_** icRde January, Vol. 6, #1.

R. Dornbusch and S. Fischer (1993)'Moderate Inflation' The World Bank Ecnomic Review Jan, 7, 1-44.

R. Dornbusch (1986) 'Inflation, Exchange Rates and Stabilizaton' Princeton Essays in International Finance. #165, Octcber.

A. Solimano, 0. Sunkel and M. Blejer 1993 (eds), Rebuilding Capitalism. Alternatve Roads after Socialism and Diri&me. The University of Michigan Press (forthcoming). S. Fischo.r(1984) 'Real B.Lances, The Real Exchange Rate and Indexation: Real Variables in Disinflation' NBER Working paper # 1497.

M. Kiguel and N. Liviatan (1992) 'The Business Cycle Associated with Exchange RateBased Stabilization' The World Bank Economic Review. Vol. 6, 2, May. J. Kornai (1993) "Transformational Rccession. A General Phenomenon Examined Through the Example of Hungary's Development". Francois Perroux Lecture. Discussion Paper # 1. Collegium Budapest. Institute for Advanced Study, Budapest, Hungary.

34
center; the distance to the nearest post office, local market, bank, cinema, public transport, pharmacy, health center, hospital, or clinic.

# **5. Results and discussion**

_Is average enrollment associated with school availability?_ The last three columns of Table 1 report selected results from estimating the school enrollment model on 24 data sets in the 21 countries (variables other than distance are not reported—these are available from the author). The model is estimated as a Probit model with distances specified linearly (see below for a discussion of different specifications). The distance to the nearest primary school is statistically significantly negatively related to enrollment in all but nine of the data sets (at the 5 percent level). The distance to the nearest secondary school is positively associated with enrollment of 6 to 14 year olds in 7 of the 24 of the data sets.

The left panel of Figure 1 explores patterns in the results by showing, on the vertical axis, the elasticity of enrollment with respect to the distance to the nearest primary school, and on the horizontal axis the mean distance to the nearest primary school in each data set (solid points indicate those where the underlying coefficient on distance to the nearest primary school is significantly different from zero).<sup>3</sup> There is a strong negative association: as mean distance goes up the elasticity grows more negative (the correlation across the 24 data sets is 0.73). A large part of the association, however, comes from the fact that mean distance enters into the denominator of the calculation of the elasticity: the correlation between the marginal effect of distance on enrollment and mean distance is much weaker (0.002) and the association is illustrated in the right panel of Figure 1. Nevertheless, a general pattern does stands out: when the mean distance to the nearest primary school is large there tends to be a reasonably sized impact of distance on enrollment, but when mean distance is relatively low the impact varies a lot—some countries have a large impact and some have none.

> 3 The elasticity is defined as the average marginal effect of distance on enrollment, times mean distance, divided by mean enrollment. Its interpretation is that of the percent change in enrollment corresponding to a one percent change in distance.

8
# **Does Collective Action Sequester Carbon? The Case of the Nepal Community Forestry Program**<sup>1</sup>

## **1. Importance of the Issues and Introduction**

Evidence published in March 2013 suggests that the earth is now hotter than it was about three-quarters of the last 11,000 years (Marcott et al., 2013) and IPCC (2014) evaluated with medium confidence that the period 1983-2012 was hotter than the last 1,400 years. Based on ice core evidence, IPCC (2014) also notes that the concentration of greenhouse gases (GHG) in the atmosphere is now greater than at least the last 800,000 years and the rate of increase in the last 100 years is unprecedented in the last 22,000 years (high confidence).

GHG concentrations continue to rise and the climate will adjust to the existing concentrations by warming for over 1,000 years (Archer, 2009). In principle, therefore, all possible means need to be used to slow climate change. The United Nations Framework Convention on Climate Change (FCCC) of 1992 is the international agreement that governs these international efforts. It is generally agreed, that, despite the severity of the challenge, todate the FCCC has yielded insufficient results or agreements for concerted action. As a consequence, atmospheric carbon concentrations continue to rise and in 2011 increased approximately 3% (Gillis and Broder, 2012).

One reason for the relative lack of progress on climate change is that under the FCCC only the 41 countries listed in Annex 1 out of a total of almost 200 countries have obligations to reduce GHG emissions. This regulatory regime is in place even as non-Annex 1 country

> 1 Author affiliations are as follows: Bluffstone (Portland State University), corresponding author (bluffsto@pdx.edu); Somanathan (Indian Statistical Institute); Luintel (ForestAction Nepal and Portland State University); Jha (ForestAction Nepal and University of Venice Ca Foscari); Bista (ForestAction Nepal); Paudel (ForestAction Nepal); Adhikari (IDRC).  Financial support for this research was provided by the World Bank through the Knowledge for Change Program.  We thank seminar participants at REDD workshops in Dhulikhel and Kathmandu for comments.  The contents of the paper is the responsibility of the authors alone and should not be attributed to their institutions, the World Bank, or its member countries.

**2 |** P a g e
coefficients) obtained by average and household-level inflation reveals that the latter is consistently higher. Overall, these findings emphasize the importance of considering the distributional effects of inflation when projecting poverty and inequality to obtain more accurate estimates that account for differences in consumption patterns and price trends across households.

The flexibility of our modeling approach allows us to make subnational-level predictions that account for each region's economic structure in terms of share of urban population, sector of employment, and access to public and private transfers. Table 3 presents the evolution of poverty rates for each of the country's four provinces and urban and rural settings. The evolution of poverty rates across regions follows the overall trends at the national level. However, by FY23, KPK and Baluchistan are predicted to have about 3 percentage points higher poverty rates than those at the beginning of the period. At the same time, Punjab and Sindh lost all the past progress in poverty reduction during the same period. Considering that urban households are more likely to rely on sectors with more pronounced contractions (manufacturing and services), it is not surprising that between FY22 and FY23, they experience a larger increase in poverty (54 percent) vis-à-vis rural areas (26 percent).

**Table 3: Poverty** **<u>projections at provincial and urban/rural level</u>**

||FY19|FY20|FY21|FY22|FY23|FY24|FY25|
|---|---|---|---|---|---|---|---|
|**Punjab**|16.3|19.1|15.5|11.8|15.9|14.7|13.3|
|**Sindh**|24.1|26.0|21.9|18.4|24.0|22.1|20.0|
|**KPK**|29.5|32.3|29.3|25.4|31.5|30.3|27.8|
|**Balochistan**|42.7|46.7|40.8|36.7|45.9|43.4|39.7|
|**Urban**|10.9|13.4|11.1|8.1|12.6|12.5|11.3|
|**Rural**|28.2|31.0|26.4|22.2|27.8|25.5|23.2|

Source: Authors’ calculation from HIES 2018/19 data. Note: Preferred specification uses household-specific inflation as described in the methodological section until FY23. Due to the lack of disaggregated inflation projections, past FY23 projections have been deflated using headline inflation.

An advantage of a non-distributionally neutral model is that it provides information on which individuals will be most affected during a crisis. Given the modeling process, this is intrinsically linked to the evolution of sectoral growth and the initial distribution of households of different welfare levels across sectors. For reference, we present the changes in welfare levels across the expenditure distribution between FY22 andFY23. Figure 5 shows that while there is a reduction in expenditure for households across all quintiles, the highest losses occur for those at the bottom 40 percent.

13
## **Figure 7 - Optimal Coverage rate, Leakage rate and Cut point (%)**

<!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>14.6 23.3 30.9 38.5 46.8 57.5 73.3 100.0 155.0 1675.0<br>Poverty line (in JD)<br>Optimal coverage Optimal leakage Optimal cutpoint CDF<br><!-- End of picture text -->

Source: Authors’ estimations based on Jordan UNHCR data.

# 6. Conclusions

Humanitarian organizations such as the WFP and the UNHCR rely entirely on donors’ contributions to administer programs for refugees and IDPs. These contributions are increasingly hard to come by and, when crises are multi-dimensional, contributions are typically below the needs generated by the sum of these dimensions. This is the case, for example, of the recent crises in Nigeria, South Sudan, Somalia and the Republic of Yemen. The conflicts that affected these countries generated the displacement of millions of people who became IDPs or refugees. The more recent droughts that affected all these countries are contributing to further misery and the combination of conflict and droughts led to food shortages that are resulting in famine.

The needs of the affected populations, whether refugees and IDPs or host populations, grow disproportionally when these calamities occur simultaneously, donors find themselves stretched between 19
# **II. Methodological Issues**

Researchers face a number of challenges in studying the relation between demographic and socioeconomic status and the prevalence of HIV. This section examines issues related to the measurement of outcomes of interest, the construction of socioeconomic indicators, the design of appropriate samples, and the modeling of the correlates of HIV.

# **_Measuring Outcomes_**

For empirical work, understanding the correlates of HIV—a medical condition that is not easily detected or self-diagnosed—requires collecting data in ways that differ from those traditionally used in surveys. Customary sources for information on socioeconomic status (household surveys) rarely include medical testing for any health condition or information on risky behaviors correlated with HIV. Sources of information of HIV prevalence and sexual behaviors (such as antenatal clinics and other sentinel surveillance sites) tend to collect minimal information on only the basic demographic status of individuals, such as age and gender. In the rare cases in which a dataset offers both detailed demographic and socioeconomic status variables and includes measures of HIV status, samples are small or specialized, making it difficult to generalize the findings to the overall population. Important exceptions are the Demographic and Health Surveys [DHS] datasets.

Lack of data on individuals’ socioeconomic and HIV status may have resulted in continued speculation about the relation between the two. As a result of the advent of population-based surveys with both socioeconomic status (at least basic indicators of status) and HIV status, such as the DHS program, the past several years have seen a large increase in the number of empirical studies of this relation.<sup>3</sup>

> 3 For a discussion of some of the concerns about national population-based household surveys that include HIV testing (which relate to nonresponse rates), see García-Calleja, Gouws, and Ghys (2006). While the HIV prevalence estimates from large nationally representative sero-surveys such as the DHS might suffer from non-response bias if the willingness to be tested for the survey varies by HIV status, recent studies reviewing the DHS suggest that such bias, when present, is minimal (Mishra, Vaessen, Ties Boerma et al. 2006 ; Marston, Harris and Slaymaker 2008). However, Reniers and Eaton (2009) find that prior

3
Table 1: Sectoral Gravity Estimates CEFTA, 2013

||(1)<br>Manufacturing|(2)<br>Agriculture|(3)<br>Mining|(4)<br>Services|
|---|---|---|---|---|
|A. Gravity Estimates|||||
|DIST|-0.685|-0.826|-1.185|-0.322|
||(0.036)<sup>_∗∗_</sup>|(0.038)<sup>_∗∗_</sup>|(0.148)<sup>_∗∗_</sup>|(0.087)<sup>_∗∗_</sup>|
|CNTG|0.493|0.647|0.193|0.621|
||(0.060)<sup>_∗∗_</sup>|(0.070)<sup>_∗∗_</sup>|(0.210)|(0.131)<sup>_∗∗_</sup>|
|LANG|0.221|0.356|0.187|0.601|
||(0.043)<sup>_∗∗_</sup>|(0.055)<sup>_∗∗_</sup>|(0.155)|(0.083)<sup>_∗∗_</sup>|
|BRDR|-3.544|-5.542|-3.765|-5.656|
||(0.115)<sup>_∗∗_</sup>|(0.114)<sup>_∗∗_</sup>|(0.464)<sup>_∗∗_</sup>|(0.271)<sup>_∗∗_</sup>|
|FTA|0.401|0.380|0.781|0.266|
||(0.052)<sup>_∗∗_</sup>|(0.059)<sup>_∗∗_</sup>|(0.182)<sup>_∗∗_</sup>|(0.119)<sup>_∗_</sup>|
|BRDR_EU_CEFTA|-3.477|-5.905|-4.905|-6.591|
||(0.163)<sup>_∗∗_</sup>|(0.149)<sup>_∗∗_</sup>|(0.472)<sup>_∗∗_</sup>|(0.290)<sup>_∗∗_</sup>|
|BRDR_EU|-2.982|-4.592|-4.231|-5.470|
||(0.111)<sup>_∗∗_</sup>|(0.107)<sup>_∗∗_</sup>|(0.427)<sup>_∗∗_</sup>|(0.266)<sup>_∗∗_</sup>|
|B. CEFTA vs. EU Bor|der: _BRDR_EU_|CEFTA_-_B_|_RDR_EU_||
|CEFTA vs. EU|-0.495|-1.313|-0.674|-1.108|
||(0.132)<sup>_∗∗_</sup>|(0.124)<sup>_∗∗_</sup>|(0.291)<sup>_∗∗_</sup>|(0.125)<sup>_∗∗_</sup>|
|C. CEFTA vs. EU Bor|der: Tarif Equiva|lents|||
|%∆_tCEFTA,EU_|-7.923|-19.650|-10.619|-16.870|
||(2.020)<sup>_∗∗_</sup>|(1.665)<sup>_∗∗_</sup>|(4.349)<sup>_∗∗_</sup>|(1.736)<sup>_∗∗_</sup>|
|_N_|634838|117585|4698|29665|

**Notes** : Panel A of this table reports gravity estimation results for the four main sectors in the sample including Manufacturing, Agriculture, Mining, and Services. All estimates are obtained with data for 2013. The data for each main sector is constructed by pooling (not summing) the data for all individual products within the corresponding main sector. The estimator is PPML and the dependent variable is nominal bilateral trade. All estimations are obtained with exporter-product and importer-product fixed effects, whose estimates are omitted for brevity. Standard errors are clustered by country pair and are reported in + _∗ ∗∗_ parentheses. _p <_ 0 _._ 10, _p < ._ 05, _p < ._ 01. Panel B of the table reports the difference between the estimates of the border effects for trade within the EU vs. trade between CEFTA members and the EU. Panel C transforms the differences from Panel B into tariff-equivalent trade cost changes %∆ _tCEFTA,EU_ = ( _exp_ ( _BRDR_  EU_ ) _/exp_ ( _BRDR_  EU_  CEFTA_ ))<sup>1</sup><sup>_/_(1</sup><sup>_−σ_)</sup> _−_ 1) _×_ 100, where we set _σ_ = 7. The standard errors in Panels B and C are constructed with the Delta method. See text for further details.

17