rafmacalaba/datause-encoder-data huggingface dataset:
Before presenting our PVAR results, we first ascertain the presence of differences between ECA and the rest of the world. In doing so, we split our sample into ECA and non-ECA subsamples and run basic mean comparison t-tests to document statistically significant differences in the subsample means of an indicator of interest. By running t-tests for different periods, we are able to track the evolution of our variables in ECA and non-ECA before (2005-07), during (20082009), and after (2010-2011) the global financial crisis.

Table 4 presents the results. Panel A documents the ECA vs. non-ECA differences for the PVAR variables: PRIVATE CREDIT, FOREIGN LIABILITIES, GDP, and DEPOSITS. We observe that in the pre-crisis period, ECA was significantly different from the rest of the sample in terms of the growth of foreign liabilities, private credit, and deposits. The quarterly real rate of growth of foreign liabilities and private credit for ECA was more than double the respective rates of growth in non-ECA countries: 10.06% vs. 3.64% for foreign liabilities and 7.87% vs. 3.15% for private credit. These differences are statistically significant and reveal the distinctiveness of ECA’s business model, which consisted of rapid private credit growth sustained by large inflows of foreign funding. The resulting ECA credit boom contributed to higher average real deposit growth (5.42% vs. 2.65%) and in real GDP growth (0.95% vs. 0.51%) compared to non-ECA.

Next, we study the PVAR variables during the peak of the crisis in 2008-2009. During that period, credit growth declined in both regions, but relatively more in ECA, due to the sharp drop in foreign liabilities. Private credit growth remained higher in ECA (2.15% vs. 1.45%), but the difference is only marginally significant in this period, in contrast with the preceding period. Foreign liabilities growth declined significantly but remained higher in ECA (2.33% vs. 0.74%), reflecting the impact of the Vienna Initiative. However, the difference of foreign liabilities was not statistically significant in this period, reflecting substantial variations in the sample, including the ECA subsample. Note that economic activity contracted significantly in ECA during this period (-1.86% vs. -0.51%), driven by the sharper slowdown in credit and other factors (exports, remittances, tourism).

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After merging available precipitation data and dropping observations with missing data, the sample size in the 2000 IFLS3 for our analysis was reduced to 6,188 households from a total of 10,292. In our 2000 sample, 3,290 households were located in rural areas, and of these 2,159 were located on Java. Data from additional weather stations would benefit this analysis by improving the level of disaggregation of weather data, but these data could not be obtained.

**Figure 2: Variation in monsoon onset and post-onset rainfall**

<!-- Start of picture text --> 1996/1997 1999/2000 1996/1997 1999/2000<br>N. SumatraW. SumatraS. SumatraLampungJakartaW. JavaC. JavaYogyakartaE. JavaS. KalimantanBali N. SumatraW. SumatraS. SumatraLampungJakartaW. JavaC. JavaYogyakartaE. JavaS. KalimantanBali N. SumatraW. SumatraS. SumatraLampungJakartaW. JavaC. JavaYogyakartaE. JavaS. KalimantanBaliN. SumatraW. SumatraS. SumatraLampungJakartaW. JavaC. JavaYogyakartaE. JavaS. KalimantanBali<br>2<br>1<br>0<br>-1<br>-2<br>10<br>5<br>0<br>-5<br><!-- End of picture text --> Figure 2 shows variation by province in monsoon onset and post-onset rainfall. With respect to delays in monsoon onset, only provinces in Java experienced a delay greater than one standard deviation from the 25 year mean in 1999/2000. As for the amount of rainfall during the 90 day post-onset period, the data indicate that only provinces in Java experienced rainfall below two standard deviations from the 25 year mean in 1999/2000.

The summary statistics of household expenditures, household characteristics, and rainfall shock exposure in rural Java are shown in Table 1. The majority of household heads were married males who did not have more than an elementary education. The vast majority of households utilized electricity. Half of the households owned farmland, and 44% were engaged in non-farm businesses. Nearly 60% of households were engaged in a farm business, 38% with rice as the most valuable crop and 22% with another crop as the most valuable. 34% of households in our sample were exposed to the delay of onset shock and 45% were exposed to the post-onset low rainfall shock. The correlation coefficient between these two shock variables for our sample was not large at 0.38.

9
commodity prices? Could it be capturing a commodity price cycle which is not perfectly correlated with the cycle in economic activity that is already captured by GDP growth?

To analyze these issues, we follow the methodology of Céspedes and Velasco (2014) in separating GDP growth from the commodity price cycle by estimating a policy reaction function (i.e., equation (1)) that includes the price of commodities. By removing the potential comovements between fiscal policy and the price of commodities from the residual, it is possible that the volatility of commodity exporters gets closer to that of the other countries in the sample.

We run two variations of this exercise: first, we include the country-specific commodity export price index (from the IMF’s Commodity Terms of Trade database)—a weighted average of the export price of commodities, where the weight is the share of exports of a particular commodity. The second specification makes use of the price of oil.<sup>21</sup> Figure 8 shows the results for our four measures of fiscal policy volatility. As is clear from the four panels, the inclusion of the variation in commodity prices does not change the fact that commodity exporters have a much more volatile fiscal policy than other countries. In fact, there is almost no difference between the three specifications regardless of the fiscal policy variable being used. One potential explanation of our result is that the direct effect of commodity prices cannot be separated from the indirect effect that they have through economic activity. This is consistent with the evidence presented in Husain, Tazhibayeva, Ter-Martirosyan (2008). An alternative explanation is that the relationship between commodity prices and fiscal policy is much more complex than our simple fiscal policy rule.

# **5. Macroeconomic effects of fiscal policy volatility**

So far, we have documented the pattern of fiscal policy volatility with a focus on EMDEs and commodity exporters. We have shown that countries with certain institutional characteristics and policies have a more volatile fiscal policy. But what are the macroeconomic consequences of this additional volatility?

We measure these macroeconomic effects on both the volatility of GDP growth and the growth of GDP per capita by using the methodology of Fatás and Mihov (2013). We start with a regression of the type ∆y ε Where σi is the volatility of GDP growth and σi is the measure of fiscal policy volatility. Xi represents a set of controls.

Table 6 presents the results, with fiscal policy volatility measured using primary expenditures. Whether by itself or when including some of our standard controls, fiscal policy volatility is related to higher output volatility. Interestingly, neither EMDEs nor commodity exporters as a group

> 21 We use the average of Brent, Dubai and WTI oil prices (in nominal US dollars) from the World Bank’s Commodities Price Data (the “ _Pink Sheet_ ”).

15
18 the nature of regulations in the<sup>U.S. as well as in other countries. The problem</sup> is essentially one of incomplete<sup>and/or inaccurate information which is more</sup> serious in the case of new and<sup>relatively undertested drugs. The solution, or</sup> at least the reduction in health<sup>risks, lies in improved information on positive</sup> and negative effects of drugs.

Two ways to reduce health risks<sup>can be envisaged; market-oriented</sup> and government-oriented. In a market-oriented<sup>approach, the quality and quantity</sup> of information on drug therapy would<sup>rely mainly on the quality of the doctors</sup> and the professional management<sup>of manufacturing enterprises. Presumably, the</sup> degree of market success of these agents would be linked to their professionalism. Furthermore, potentially<sup>abusive behavior by these agents would</sup> be checked by other institutions;<sup>the law would define the boundaries under</sup> which behavior by doctors and<sup>firms could remain unpunished. The Government</sup> could also reinforce the market<sup>by directly providing or enticing private parties</sup> to improve and disclose the best information<sup>possible.</sup> An alternative approach would<sup>suggest that because the market is</sup> unreliable for these purposes,<sup>it is preferable to introduce a centralized</sup> regulatory agency. This alternative<sup>course of action is likely to be taken when</sup> the perceived risks of relying<sup>on the market remains very high.</sup> The U.S. pharmaceutical drug history demonstrates<sup>this latter approach; today the U.S.</sup> Government shares major responsibility<sup>for negative health consequences that</sup> could arise from the marketing<sup>of risky drugs.</sup> The first law regulating pharmaceuticals was the Pure Food<sup>and Drug Act of 1906. This law prohibited</sup> adulteration and mislabelling of<sup>food and drugs.</sup> The aim of the law was to improve the quality of information,<sup>and the role of government was essentially</sup>
means that transactions' demand for money is relatively stable. The slightly decreasing velocity of total savings (velocity-S) reflects only falling velocity of non-cash deposits (velocity-NC), which needs to be explained. On the one hand, this tendency could be brought about by higher speculative balances but, on the other hand, this may be evidence of some "forced" savings. The same tendency - rising ratio of savings or of saving increments to a macroeconomic variable - is confirmed by alternative indicators, such as average propensity to save (APSNC) and savingsto-sales ratio.

All these indicators also show relatively slower development in 1988-1989 and unusually large increases in 1990. The latter year reflects adverse expectations of the public concerning higher purchases and expenditures in general. It may be useful to recall the role of expectations: institutional and economic conditions, and especially anticipated changes in these conditions, may abruptly change the public's attitude to savings.

The question remains how these figures should be interpreted. From Table IV, we can see a clear trend for households to put aside more money than they desire, up to 1987. This means that the households were "forced" to save. We believe that the forced savings created a continuing pressure on the consumer market. This assumes, of course, that savings functions are stable over time, as we noted above. On this assumptio. , we estimate the forced savings as the difference between the actual savings and those estimated from the stable savings function and found that the share of "forced" savings was on average about 5 percent throughout the 1980s, which means approximately Kcs 1.5 billion per year. If prices were free to adjust during this period, the release of the "forced" savings would contribute by not more than 0.3 percentage point to the CPI. Furthermore, taldng into account the likely 5 percent share of "forced" deposits in aggregate household savings, this represents about 4-5 percentage points to inflation after full price liberalization - a fairly small impact.'

# 2.4 Conjuncture **Test**

A unique attempt to identify disequilibrium in the consumer market is the so-called "trade conjuncture" test. The test calculates the value of an indicator (imbalance) and is based on surveys of salesmen in which they are asked about the degree of demand satisfaction. The value 'Our estimates are very rough: we simply compute hypothetical savings based on APSNC from the previous year and the difference between the hypothetical and actual values are considered as "forced savings. Adding this amount to retail sales and comparing it with the actual sales gives an extra 0.3 percent inflation per annum on average. Similarly, cumulative "forced" savings are supposed to be 5 percent, which -placing the sum on the consumer market - should induce 4-5 percent inflation.

_15_
Collier, P. and J.W. Gunning (1999), Explaining African Economic Performance, _Journal of Economic Literature,_ 37, 64-111.

- Collier, Paul, and Stephen A. O’Connell. 2006. “Opportunities and Choices.” Draft chapter 2 of the synthesis volume of the African Economic Research Consortium’s Explaining African Economic Growth project. Nairobi, African Economic Research Consortium.

- Deaton, Angus, and Ronald I. Miller. 1995. “International Commodity Prices, Macroeconomic Performance, and Politics in Sub-Saharan Africa.” Princeton Studies in Intenational Finance 79, Princeton University, Princeton, NJ.

- Devarajan, Shantayanan, Delfin S. Go, John Page, Sherman Robinson, and Karen Thierfelder. 2008.  “The Macroeconomic Dynamics of Scaling-Up Foreign Aid.” In _Africa at a Turning Point? Growth, Aid, and External Shocks,_ ed. Delfin S. Go and John Page. Africa Development Essays Series, The World Bank. (forthcoming).

- Devarajan, Shantayanan, William Easterly, and Howard Pack. 2003. “Low Investment Is Not the Constraint on African Development.” _Economic Development and Cultural Change_ 51 (3): 547–71.

- Devlin, Julia, Michael Lewin, and Thilakaratna Ranaweera. 2008. “Harnessing Oil Windfalls for Growth in the Africa Region.” In _Africa at a Turning Point? Growth, Aid, and External Shocks,_ ed. Delfin S. Go and John Page. Africa Development Essays Series, The World Bank. (forthcoming).

- Dixit, Avinash. 2007. “Recipes for Development Success.” _World Bank Research Observer_ 22 (2): 131–57.

- Dufrénot, Gilles, Gilles Sanon, and Abdoulaye Diop. 2006. “Is Per-Capita Growth in Africa Hampered by Poor Governance and Weak Institutions? Examining the Case of the ECOWAS Countries.” West Africa Economic and Monetary Union,

- Ouagadougou.

- Easterly, William. 1998. “The Ghost of Financing Gap: Testing the Growth Model in the International Financing Institutions.” IMF Seminar Series 1998-2, International Monetary Fund, Washington, DC.

- ———. 2001. _The Elusive Quest for Growth_ . Cambridge, MA: MIT Press.

- Essama-Nssah, B., Delfin S. Go, Marna Kearney, Vijdan Korman, Sherman Robinson, and Karen Thierfelder. 2008. “Economywide and Distributional Impact of an Oil Price Shock on the South African Economy.” In _Africa at a Turning Point? Growth, Aid, and External Shocks,_ ed. Delfin S. Go and John Page. Africa Development Essays Series, The World Bank. (forthcoming).

52
are below versus above the chosen distance thresholds, consider, for instance, the walking time associated with the inclination-adjusted minimum cost distance between dwelling and plot locations in Malawi. For plots that are within the 500-meters and within the 1-kilometer threshold, the average walking time is 4 minutes and 6 minutes, respectively. Conversely, for plots that are outside the 500-meters and outside the 1-kilometer threshold, the average walking time is 33 minutes and 47 minutes, respectively.

Once the non-random, distant plots are identified according to one of the distance thresholds, we artificially create missingness in GPS-based areas among these plots at random, at a rate of 1 to 100 percent and with an increment of 1 percentage point and save these datasets separately. The choice of creating artificial missingness at random above an arbitrary distance threshold that identifies a non-random portion of the data is anchored in the specific way in which we see our findings would be operationalized, as explained below, particularly as part of multi-visit household and farm surveys that are in sync with a given agricultural season and that field a first, post-planting visit for parcel and plot demarcation and area measurement.

# **3.2 Multiple Imputation**

The second step in our analysis is to carry out Multiple Imputation (MI) to fill the gaps in GPS-based plot areas in each unique data set that is created by a given distance threshold-artificial missingness combination.

MI, first proposed by (Rubin, 1987), is a Monte Carlo technique that replaces missing values for a given variable with _m_ > 1 simulated alternatives. MI typically consists of three steps: (i) _m_ imputations (i.e. _m_ complete datasets) are generated based on an _imputation model_ that encompasses a vector of observable covariates that predict the missingness in a given variable, (ii) statistical analysis is performed separately with each of the _m_ complete datasets, and (iii) the results obtained from _m_ complete data analyses are combined into a single set of multiply-imputed parameter estimates and standard errors. The conditions 13
# **2. Enterprise Restructuring and Its Determinants**

The empirical literature on enterprise behavior during transition utilizes a wide array of restructuring measures, based either on accounting or qualitative information. There is no prevalent methodology, as the concept of restructuring differs across studies. For the purposes of this paper, we define restructuring as changes in operations, interactions, and motivation towards success in a changing market environment. This definition captures the essence of restructuring changes, some which will be forced by the behavior of other market players, while others will be pursued by management and owners. Restructuring in this context can lead to two different outcomes. Viable firms would increase their productivity and market share, attract more resources, and upgrade their production process. At the opposite, non-viable firms would shrink to a viable core or close down altogether. Their assets (both human and capital) could be used for alternative production. Thus the decline in business activity of an enterprise need not be associated with a lack of restructuring. It is part of the general movement of resources to more productive uses.

Following on this definition, we use three complementary measures of restructuring: increases in labor productivity (sales per worker growth), assets sales, and renovations at the factory to improve working conditions. The first measure is based on accounting data and is most often used in previous empirical work (Frydman et al, 1997; Pohl et al., 1997; Estrin and Rosevear, 1998) as it depends on both "defensive" or "passive" restructuring (reduction in excess employment), and on "active" restructuring (increase in sales volume).' The measure may, however, be misleading since substantial improvements in labor productivity need not arise from "pro-active" restructuring, particularly when it is accompanied by a substantial drop in output. Instead, labor productivity growth may simply account for a low initial level of efficiency, i.e., for the elimination of waste.

I A better measure, and one which is used in studies of developed countries, is the change in total factor productivity. This measure depends, however, on the proper accounting of fixed assets and its use is problematic in economies like Georgia and Moldova which have experienced high inflationary periods.

3
_Role of NGOs in Health Delivery Africa Health Policy Study_ .16-

# _Government Subsidies_

44. Government support for mission health facilities has been long standing, although as noted, costs have tended to escalate and the value of such grants to decline. Governments have a variety of options for such subsidies, such as providing an annual grant, depending on operating size or budget of the institution, or paying for particular services or particular items of expenditure such as salaries or drugs (Green 1987). Indirect subsidies to NGOs may be In the form of waiving import duties for medical supplies or pharmaceuticals. In Malawi, the government gives all NGOs subsidies and, in return, NGOs give the government audited accounts for grants and declare all external resources. In severml African countries, the government allocates a whole geographical area to the care of a particular NGO (usually mission) which it contracts out. District agency hospitals are run by missions in both Tanzania and Ghana, for example.

45. Through subsidies, the government can Influence NGOs performance and encourage uses of resources which conform most to nadonal priorities. The critical question for this and other types of government support is what strings are attached to the supportgiven-that accountabilityor control does the government have to ensure that national policies are followed? For example, matching funds may be the best incentive to encourage NGOs to serve in under-privileged areas.

# _Sector-Specific NGO Consortia_

46. The history of NGO consortia in Africa appears to be much stronger In population than In health, largely due to the Instigation of Internadonal Planned Parenthood Federatdon. Umbrella organizations, both international such as the Chrisdan Medical Commission and the League of Red Cross and Red Crescent Societies, and national, such as the Christian health associations and private health associations found in several African countries, have supported NGOs active in health. Nationally, these may provide a critical mass so that NGOs can approach government with a common voice. They can be very effective In providing a forum for exchaiage of information, and often provide training outreach and assistance to communities to organize PHC programs or procurement activities.

47. Their effectiveness depends crucially, however, on who provides the impetus for their creation and to whom they are accountable. The dangers of creating so-called "DONGOs," or donor.organized-NGOs, is exemplified by the case of Voluntary Agencies In Development Assistance (VADA) in Kenya. VADA was Irtended to provide a forum and to be a conduit for funds for health NGOs. After the disbursement of over $12 million In a seven-year period, without sufficient knowledge of the absorptive capacity of the NGOs, USAID withdrew from the progmm, contending that VADA had neither the legitimacy nor the ability to pursue its Intended role (Sheffield 1987).