rafmacalaba/datause-encoder-data huggingface dataset:
how it is that the values for these variables will be determined to "close" the model. There ale indeed different ways in which this can be achieved, and the user is referred to Ventura [19911 for a thorough discussion. We will nevertheless discuss two of the most useful closure rules: the normative and the positive. In our model we allow for these two types of closure rules. It is therefore important to examine their implications, specially in terms of the distribution of domestic credit. The solution of the model under these closures is illustrated in Figure 1, which we now describe.

# Normative **Closure**

In the normative approach, the user selects a set of target values for some variables, and lets the model determine the resources needed to achieve the targets. For example, in our model we allow the user to determine the growth of income and inflation, private credit and investment, imports (thorough income an price elasticities) and government foreign borrowing. Given the said targets, the technology available to the economy, in the form of the ICOR (Incremental Capital-Output Ratio) parameter, and fiscal and consumption propensity rates, the model computes endogenously investment and savings, and thus borrowing needs for the whole economy. The financing gap, computed endogenously, represents the difference between total foreign financing needs, and foreign financing available.

Government saving (S 8 ) is defined as fiscal revenues--tied to income and expenditures--minus current expenditures. From the total level of investment needed in the economy, and the pre-determined level of private investment (Ip), it follows how much the government needs to invest (49). The difference between government investment and savings represents the financing needs of the government. The latter are also known as the Public Sector Borrowing Requirements (PSBR), although in our case public sector is limited to central government. Disposable income for the Private Sector is defined as GDP at factor cost, plus net subsidies and transfers (domestic and foreign), minus taxes and net interest payments (domestic and foreign). Private saving _(Sp)_ is computed as the proportion of disposable income that is not used for consumption.

In the money market, the given inflation rate and nominal interest rate determine the real interest rate, while real output is determined hy the growth assumption. Together with the assumption on money velocity, they determine the trajectory of money demand. This yields the required path of money stock and, through the multiplier, the money base. Foreign reserves and foreign debt of the Banking System, which includes the Central Bank and Analysis Banks, are determined exogenously. In turn, private deposits are given by money demand and the deposit to money ratio. Using the reserve requirements 19
**Table 4 – Papers, Results and Gini by Outcome**

||**Papers**|**Results**|**Res/Paper**|**Gini**|
|---|---|---|---|---|
|Employment|24|334|13.9|0.35|
|Prices|13|120|9.2|0.16|
|Wages|23|244|10.6|0.37|
|Well-being|13|64|4.9|0.35|
|**Total/Average**|21.0|762|36.2|0.32|

The Gini index is calculated using number of papers (as “individuals”) and number of results (as “income”). A lower Gini indicates more spread of results across papers. _ 30
we directly test the hypothesis that trade openness helps attenuate the RER volatility after controlling for fundamental volatility. Fifth, we evaluate whether the composition of trade and capital flows may play a role in explaining the openness-RER volatility relationship. Finally, as opposed to periods of tranquility, we test whether the relationship between openness and RER volatility changes in times of currency crisis (i.e. _turbulent times_ ).

Interestingly, we consistently find that higher trade openness leads to more stable RERs while higher international financial integration generates more volatile RERs. We also find that the composition of flows of trade and capital matters for explaining the openness-RER volatility link. Why? Due to the different nature of the shocks that govern: (a) manufacturing vs. non-manufacturing trade, and (b) equity- vs. loan-related financial openness. Specifically, we find that the ability of trade openness to smooth shocks to the RERs is mainly driven by manufacturing trade while non-manufacturing trade plays a limited and, in most cases, negligible role. The results on the composition of capital flows are striking: we not only find that financial openness may reduce RER volatility in countries with low debt-to-equity ratios, but also that higher share of debt in foreign liabilities may amplify RER volatility and increase the likelihood of currency crisis episodes.

This paper consists of the following sections: Section 2 shows the main stylized facts of the model that draws the testable implications on the relationship between RER volatility and openness. Section 3 discusses the data and methodology of estimation. Section 4 presents the regression analysis of the RER volatility in the baseline scenario and the sensitivity analysis to different samples and different measures of the dependent variable. Section 5 explores in more detail the nature of the openness-RER volatility link by analyzing the role of the composition of trade and capital flows as well as vulnerabilities in the external sector while section 6 concludes.

# **2. Some Theoretical Insights**

In this section we present some theoretical foundations on the relationship among the volatility of RER fluctuations, the volatility of RER fundamentals (which we will also call _fundamental volatility_ ), and (trade and financial) openness. Rather than calibrating the model, we will conduct econometric tests for a wide sample of countries on the relationship between: (a) RER volatility and trade openness, and (b) RER volatility and financial openness. Our theoretical framework is _redux_ model augmented by the presence of the

> 2 Hau (2002) gathered data for 48 countries (of which 23 were OECD countries) over the 1980.01-1998.12 period.

5
Policy Research Working Paper 5169

# **Abstract**

Malawi has experienced several catastrophic droughts over the past few decades. The impact of these shocks has been far reaching, and the resulting macroeconomic instability has been a major constraint to growth and poverty reduction in Malawi. This paper describes a weather risk management tool that has been developed to help the government manage the financial impact of drought-related national maize production shortfalls. The instrument is an index-based weather derivative contract designed to transfer the financial risk of severe and catastrophic national drought that adversely impacts the government’s budget to the international risk markets. Because rainfall and maize yields are highly correlated, changes in rainfall—its timing, cumulative amount, and distribution—can act as an accurate proxy for maize losses. An index has been constructed using rainfall data from 23 weather stations throughout Malawi and uses daily rainfall as an input to predict maize yields and therefore production throughout the country. The index picks up the well documented historical drought events in 2005, 1995, 1994, and 1992 and a weather derivative contract based on such an index would have triggered timely cash payouts to the government in those years. This innovative risk management instrument was pioneered in 2008/2009 by the Government of Malawi, with the assistance of the World Bank, and was a first for a sovereign entity in Africa. Several piloting seasons will be necessary to understand the scope and limitations of such contracts, and their role in the government’s strategy, contingency planning, and operational drought response framework.

This paper—a product of the Southern Africa Poverty Reduction and Economic Management Unit, Africa Region—is part of a larger effort in the department to analyze constraints to growth and poverty reduction. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at anucifora@worldbank. org or joanna.syroka@wfp.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
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In terms of maturity (see Table 7), in 1987-89 on average about 12 percent of GDP consisted of currency, 33 percent of demand deposits and the remaining 55 percent of GDP consisted oS savings and time deposits. Most demand deposits are held by public sector enterprises--about 30 percent of GDP--while most of the savings and time deposits are held by households. In fact, the single most important type of deposit is savings deposits held by the household sector (chiefly accounted for by the SSB's deposits); these accounted for more than 46 percent of GDP. It is also important that most of M2 is denominated in Levs. Foreign currencydenominated deposits (in convertible and non-convertible currencies) accounted for about 10 percent of GDP. The authorities allowed deposits denominated in US dollars, in Swiss Francs or Deutsche Marks, only in January 1990.

Tabte 7: Composition of Broad Money 1/ (Percentage of GDP)

||. ...<br>.....<br>..<br>1986|<br>.<br>...<br>..<br>....<br>1987|....<br>...........<br>1988|.<br>1989|1990|
|---|---|---|---|---|---|
|BROAD MOHEY, M2 (=1+2+3) 2/|97.4|96.6|101.4|<br>101.1|<br>83.3|
|1. Money, Ml|41.0|44.5|48.1|50.1|45.8|
|Currency Outside Banks|10.0|11.0|<br>12.3|14.0|<br>12.4|
|Demand Deposits|31.0|33.6|35.8|36.1|<br>33.4|
|In Leva|28.6|31.1|32.9|32.5|29.6|
|in Hon-Convertible<br>Currencies|<br>0.7|0.8|0.9|1.1|<br>0.8|
|In Convertible Currencies|1.7|1.7|1.9|2.4|3.0|
|2. Quasi-Money|55.4|51.0|52.4|50.1|<br>35.9|
|Time Deposits|3.7|1.3|<br>1.5|2.3|6.4|
|Savings Deposits|46.7|45.1|46.3|43.1|29.1|
|Car Deposits|3.7|3.7|3.9|3.8|--|
|Other Deposits|1.4|0.9|0.5|0.7|--|
|Foreign Currency Deposits|0.0|0.0|0.1|0.1|0.4|
|3. Import and Restricted Deposits|1.0|1.1|1.0|1.0|1.6|
|In Non-Convertibte<br>Currencies|<br>0.3|0.3|0.3|0.3|0.2|
|In ConvertibLe Currencies<br>..........<br>.....................<br>..<br>.........|0.7<br>...............|0.8<br>......|0.7<br>....|0.7<br>. .......|i3<br>----|

Source: National Bank of Bulgaria, Monetary Survey.

Notes: (1) This is the IMF-corrected Monetary Survey. Corrections consist of: removing Municipalities deposits from the definition of demand deposits; and re-classifying interest-remunerated savings SSB deposits as Quasi-Money (rather than as demand deposits). (2) To correct for price differences arising from coaputing a ratio of an end-period stock and a GDP flow at average prices, stocks were converted at average-1989 prices using the end-period consumer price index and coqmuting its ratio to **9** real GDP with base average-1989. Real GDP was estimated using GDP implicit price deflator.

A combination of factors make possible this high involuntary demand for money, thus leading to a "monetary overhang" problem. The two most important factors were the disequilibrium in the goods market and the small number of financial assets offered by the financial system. The diaquilibrium in the goods market resulted from the authorities' policy of guaranteeing a stable income to the income earners by controlting prices of goods, the nominal exchange rate, the nominal interest rate and the nominal wage. As was explained before, the
remittances. Section 3 then describes our dataset of immigrant surveys with remittances. Section 4 provides results, and Section 5 concludes.

# **2. Theoretical background**

Theoretically there are several reasons to believe that there will be differences between the remitting patterns of highly-skilled emigrants and less-skilled emigrants. On one hand there are several factors which would tend to lead highly skilled migrants to be more likely to remit and/or send a larger amount of remittances. First, highly skilled individuals are likely to earn more as migrants, increasing the potential amount they can remit. Second, their education may have been funded by family members in the home country, with remittances providing a repayment of this family investment. Third, skilled migrants are less likely to be illegal migrants, and more likely to have bank accounts, lowering the financial transactions costs of remitting. However, on the other hand there are several factors which may lead highly skilled migrants to be less likely to remit or to remit less. First, highly skilled migrants may be more likely to migrate with their entire household, so not have to send remittances in order to share their earnings abroad with other household members. Second, they may come from richer households, who have less need for remittances to alleviate liquidity constraints. Third, they may have less intention of ever returning to their home country, reducing the role of remittances as a way of maintaining prestige and ties to the home community. A priori then, it is not clear which direction will dominate, and thus whether the highly skilled will remit more or less on average.

One key point to note from all of these theoretical channels is that education doesn’t enter directly as a determinant of remittances; rather, education is associated with other things that affect remitting behavior. Before we turn to the empirical analysis, it may therefore be useful to summarize the existing literature and clarify the theoretical relationship between education and remittances, and the implied testable predictions regarding education. In this section we present a brief summary of the theoretical literature based on Rapoport and Docquier (2006) and focus the discussion on the role of education.

Thanks to the new economics of labor migration (Stark, 1991), migration is now recognized as an informal familial arrangement, with benefits in the realms of mutual insurance, consumption smoothing, and intergenerational financing of investments, including education. Remittances are an integral part of such implicit arrangements and can be seen as combining an 4
humanitarian crises and evaluated during the humanitarian crises were excluded from the review.

We limit our analysis to programs that provided humanitarian assistance to crisis-affected populations in the form of unconditional cash transfers (UCTs), conditional cash transfers (CCTs), food transfers (FT), vouchers, and public works (PW), as these are the most widely used forms of transfers and, therefore, an evidence priority in the humanitarian sector. We excluded other types of interventions in humanitarian settings such as microfinance, WASH, community-driven development programs or peacebuilding activities, and health and medical assistance. We decided to focus primarily on non-contributory humanitarian assistance interventions because many governments and humanitarian actors have recently started to use social protection tools in humanitarian settings, but very little is known about their effectiveness in such contexts.<sup>5</sup> Studies included in this review were required to employ either experimental or quasiexperimental designs that can isolate the causal impact of humanitarian assistance programs on outcomes of crisis-affected populations. We required that studies were either randomized control trials or that they estimated intervention effects using one of the following quasi-experimental methods: difference-in-difference, propensity score matching, instrumental variables, regression discontinuity design, or another causal estimation technique. We also included experimental studies that did not have a pure control group, which are particularly relevant in humanitarian settings. Studies that did not establish any counterfactual were excluded.

The search did not impose restrictions on the timing of publication, although focusing on experimental and quasi-experimental evaluation designs effectively limits our attention to studies published after 2007.

Regarding types of studies, publication in a peer-reviewed journal was a preferred requirement for inclusion. We also included studies from working paper series (e.g., World Bank Policy Research, CDG, IFPRI) and technical reports if they included a suggested formal institutional citation. This analysis is also restricted to low- and middle-income countries (as

> 5 It is important to note that the evidence from development contexts cannot be applied directly to complex humanitarian contexts since the latter are characterized by harsher conditions such as increased economic, social, institutional, and security challenges. This situation creates an important knowledge gap, particularly in terms of the design and implementation of effective social protection measures in humanitarian settings.

7
paid regularly, who are eligible for recognition schemes, and who are in schools with better infrastructure are less likely to be absent (Table 4 - Column 2). However, none of these correlations are significant in multiple regressions or in specifications with state or district fixed effects suggesting that these measures are correlated with each other, and that states that have a longer history of investing in education may have better indicators of school and teacher quality and lower teacher absence, but that these metrics of teacher quality do not predict teacher absence within states or districts. Overall, there are few robust correlations across all specifications except that schools that have been inspected recently have significantly lower rates of absence. One important result in the correlations is that there appears to be no significant relationship between teacher salary and the probability of teacher absence. Since salary data were not collected in the 2003 survey, this variable is not included in the panel analysis below.

# **4.2 Correlates of Changes in Teacher Absence between 2003 and 2010**

The main identification challenge in the cross-sectional correlations presented in Table 4 (and in Kremer et al. (2005)) is that we cannot rule out the possibility that the results are confounded with village-level omitted variables. The use of panel data helps mitigate these concerns since our correlations are now identified using changes in village-level measures of school inputs. Table 5 (columns 4-6) presents results from the following regression: where ∆ _Absi_ is the change in the mean teacher absence rate in government schools in village _i_ between 2003 and 2010, **∆Ti** is the change in village-level means of measures of teacher attributes, **∆Si** is the change in village-level means of measures of school facilities, and **∆Mi** is the change in village-level means of measures of school monitoring and supervision. **Zi** represents different levels of fixed effects (state or district) and _εi_ is the error term. Since changes in the measures of school quality included above may be correlated, we report both bivariate regressions with only covariate at a time (columns 1-3) as well as multiple regressions that include all of these covariates (columns 4-6).

Since equation 1 differences away fixed unobserved heterogeneity at the village level (and therefore at the state and district level as well), the inclusion of state and district fixed effects in the specification controls for average state and district specific _changes_ over time in both the left-hand and right-hand side variables. Thus our panel results with state and district fixed effects are least likely to be confounded with time-invariant and time-variant 12
To characterize further the advance clearance procedure, we show in Table 9 the set of imported intermediate products (at the HS 2-Digit level) that most often enter Peru under that customs clearance regime from 2000 to 2012.

**Table 9. Products Imported under Advance Clearance Procedure**

|**Products**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|**2012**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|EXPLOSIVES|5%|5%|12%|5%|42%|35%|47%|39%|29%|49%|47%|56%|59%|
|IRON & STEEL|22%|27%|27%|36%|25%|25%|35%|32%|23%|23%|31%|29%|46%|
|VEHICLES NON RAILWAYS|11%|11%|17%|17%|17%|15%|14%|18%|21%|24%|33%|39%|43%|
|WADDING, SPECIAL YARNS, ETC|11%|14%|15%|13%|4%|4%|5%|4%|1%|1%|5%|53%|38%|
|PHOTOGRAPHIC/CINEMATOGRAPHIC GOODS|15%|29%|30%|27%|33%|14%|6%|8%|2%|6%|15%|26%|38%|
|STONE, CEMENT, ASBESTOS|14%|10%|14%|23%|25%|18%|9%|6%|2%|7%|14%|25%|37%|
|WOOD|1%|4%|2%|1%|2%|0%|1%|1%|1%|9%|5%|21%|35%|
|SOAPS, WAXES, SCOURING|2%|7%|18%|14%|8%|3%|4%|4%|4%|17%|22%|32%|35%|
|ZINC|0%|44%|54%|55%|42%|35%|18%|4%|19%|9%|9%|27%|33%|
|ARTICLES OF IRON & STEEL|6%|17%|5%|17%|18%|20%|16%|14%|7%|9%|12%|16%|29%|

Among exporter-importers in Peru, the main users of the advance clearance procedure are the large firms, classified as such based on their total sales (domestic and foreign) as provided by SUNAT.<sup>16</sup> Also, large firms seem to be the only group of firms which has consistently increased their use of the advance clearance procedure for their imports of intermediates from 2006 to 2012.

**<u>Figure 6. Imports of Intermediates under Advance Clearance Procedure by Firm Size</u>**

<!-- Start of picture text --> Imports under Advanced Clearance by large Imports under Advanced Clearance by smaller<br>firm (sales size) firms (sales size)<br> 4.5 30.0<br> 4.0 25.0<br> 3.5<br> 20.0<br> 3.0<br> 15.0<br> 2.5<br> 2.0 10.0<br> 1.5 5.0<br> 1.0 ‐<br> 0.5 (5.0) 2006 2007 2008 2009 2010 2011 2012<br> ‐<br>2006 2007 2008 2009 2010 2011 2012 Micro Small Medium<br>Billions of $US Millions of $US<br><!-- End of picture text --> Focusing on the two sectors that we analyze separately, agribusiness and apparel, note that their exporter-importers do not appear to be particularly intensive users of the advance clearance procedure when taken as a whole. Figure 7 shows for exporter-importers in each HS 2-digit sector

> 16 For the purposes of this analysis SUNAT provided us with a variable which indicates for each exporting firm its size category based on total sales in the currency “nuevos soles”. The categories are defined based on a unit of measurement for sales called Unidades Impositivas Tributarias (UIT) which is defined in each year by SUNAT and is available at http://www.sunat.gob.pe/indicestasas/uit.html. Firms are defined as micro if their annual sales are smaller than 150 UIT, small if their sales are larger than 150 UIT but smaller than 1700 UIT, medium if their sales are larger than 1700 UIT but smaller than 2300 UIT, and large if their sales are larger than 2300 UIT.

25