rafmacalaba/datause-encoder-data huggingface dataset:
# **2.3 Background: Government Responses and Economic Impacts**

Governments around the world have responded to COVID-19 by implementing lockdowns and mobility restrictions to slow the spread of the virus. The pandemic itself and government restriction policies have disrupted normal economic activity in a multitude of ways, impacting virtually all major parts of the economy. Consumption was reduced, investment activities have in some cases been slowed down, import and export relationships have become strained. Both the demand and supply sides of labor markets have been heavily impacted in many countries. Table 2 shows measures of government responses to the pandemic, including the economic support index, the stringency index, and the workplace closing index published by the Oxford COVID-19 Government Response Tracker (OxCGRT).<sup>4</sup> On average, countries in the ECA and LAC regions provided the most economic support, though at the same time the stringency of lockdowns and workplace closings ordered by the government were also quite severe in the LAC region. SSA countries provided the least economic support, but they also had the least stringent lockdowns and workplace closing measures on average.

The overall economic impact of the pandemic has been severe: Columns 9-11 of Table 1 show the International Monetary Fund’s estimate of the economic impact of the pandemic in the countries included in our data. Column 9 shows the October 2019 World Economic Outlook (WEO) projection for 2020 GDP change, Column 10 shows the same projection from the October 2020 WEO, and Column 11 shows the difference. For every country in our data, the projection has worsened by at least 4 percentage points, and in many cases by much more. The Latin America & the Caribbean region saw the biggest negative economic impact according to the IMF, with decreases in projections ranging from 5.5 percentage points in Guatemala to 17.6 percentage points in Peru.

> 4The OxCGRT data for the three indices were averaged for the period January 1 to May 31, 2020. The OxCGRT measures aim to analyze the government responses to the pandemic. These indicators are compiled to measure the various policies, such as school closures and restrictions, across more than 180 indicators, resulting in several indices and sub-indices. The economic support index records measures of income support and debt relief from 0 to 100. The stringency index records the strictness of ‘lockdown style’ policies that primarily restrict people’s behavior from 0 to 100. The workplace closing index, a subindex, records the closing of workplaces from 0 to 3. In each of these indices, a higher number implies more restrictions.

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# Reschedulings

A rescheduling of the debt is a negotiation in which the contractual streams of amortization and/or interest payments are modified. The conditions of the rescheduling are negotiated among creditors and borrowers. Two types of reschedulings are possible. Following standard practice, we will label these two cases as "pure rescheduling" and "refinancing". In the "pure rescheduling" case, creditors and borrowers negotiate new streams of amortization and interest payments, which replace the contractual ones. We define the difference between the old and new streams of amortization and interest payments as PRJ1 - and PRJIp1 respectively. In the "refinancing" case, the rescheduled - amortization and interest payments defined as RFJAP and RFJIp, - respectively are capitalized at negotiated conditions. These are embodied in the interest rate (iirs), maturity (Mjrs) and grace period (GJrs) applicable to the refinanced payments.

The DM is able to calculate the effects of both types of - reschedulings. First, the user must enter exogenously the values - 1 or equations for the following variables: PRJ , PRJIp, RFJApi RFpJ i Jr Mjr and G<sup>1</sup> rs' This is done in the jth-CREDITOR worksheet (see figure 3). Second, the DM determines AGDJrs, AAP<sup>1</sup> rs and AIPJrs.

Gross disbursements will be affected insofar some "refinancing" takes place. If this is the case, 1 (3.11) AGDJ _ = RFJAP+ RFJIP 47
such a system would require clarification of the rules under which a central bank operates, its functions and its relationships with various governments. For example, when Brazil in 1988 introduced a decentralized federal constitution, it significantly enhanced the independence of the central bank (Shah, 1991, Bomfim and Shah, 1994). Yet, independence of the central bank in Brazil remains relatively weak compared to other federal countries (see Huther and Shah, 1996). On the other hand, in centralized countries the role of the central bank is typically shaped and influenced by the Ministry of Finance. In an extreme case, the functions of the central bank of the UK (a unitary state), the Bank of England, are not defined by law but have developed over time by a tradition fostered by the UK Treasury. Only in May 1997, has the newly elected labor party government of Prime Minister Tony Blair assured the Bank of England a free hand in its pursuit of price stability. Such independence may still on occasions be compromised as the Chancellor of the Exchequer still retains a presence on the board of directors as a voting member. New Zealand and France (unitary states) have lately recognized the importance of central bank independence for price stability and have granted independence to their central banks. The 1989 Reserve Bank Act of New Zealand mandates price stability as the only function of the central bank and expressly prohibits the government from involvement in monetary policy. The People's Bank of China, on the other hand, does not enjoy such independence and often works as a development bank or as an agency for central government "policy lending" and in the process undermines its role of ensuring price stability (see World Bank, 1995 and Ma, 1995).

For a systematic examination of this question, Huther and Shah (1996) relate the evidence presented in Cukierman, Webb and Neyapti (1992) on central bank independence for 80 countries to indices of fiscal decentralization for the same countries. Cukierman et al. assess independence of a central bank based upon an examination of 16 statutory aspects of central bank operations including the terms of office for the chief executive officer, the formal structure of policy formulation, the bank's objectives as stated in its charter, and limitations on lending to the government. The correlation coefficient in Table 1 shows a weak but positive association confirming our a priori judgment that central bank independence is strengthened under decentralized systems.

4
# **5.1.3 Aggregated data**

When aggregating, a similar method as in section 4 is used, but now the aggregation is done directly by nightlight cells instead of by basin. The equation is: where _t_ is year, _l_ is all nightlight cells in the province or district _j_ and _ED_ is the damage from equation 7.

Table 9 provides the full overview of damage by province, showing the large differences between the provinces and how they vary from year to year, as one can expect with highly randomized events such as earthquakes. Using Yogyakarta - which was only impacted by earthquakes in 2006 - as an example, there was a loss of 0.4 percent of the total building mass, causing damages estimated to be approximately 3.1 billion US dollars in addition to more than 5,000 deaths and tens of thousands of injured and displaced people. Apart from that, provinces on Sumatra make up 6 of the top 10 most damaging years. Even though Indonesia is often hit by severe earthquakes, even in the worst of years they only destroy about 1 percent of the buildings in a province. That being said, 1 percent of total building mass and infrastructure being damaged does constitute a significant portion of local budgets. As another example, the September 2009 earthquake in West Sumatra inflicted damages for an estimated 2.3 billion US dollars, with repair costs and losses of 64 million US dollars on government buildings (Raschky, 2013).

The numbers per district are shown in Table 10, and they suffer much more damage than the provinces, with the most impacted district losing 5 percent of building mass.

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Figure 8: Relative Prices & Depreciation <!-- Start of picture text --> (A) Relative Capital Prices (B) Depreciation Rates<br>ICT Capital<br>Non-ICT Capital<br>ICT Capital<br>Non-ICT Capital<br>1950 1960 1970 1980 1990 2000 2010 1950 1960 1970 1980 1990 2000 2010<br>Year Year<br>2<br>20<br>1.5<br>15<br>1<br>Depreciation Rate (%) 10<br>.5<br>Price Index Relative to GDP Deflator<br>0 5<br><!-- End of picture text --> _Notes:_ Panel A graphs implicit price deflators by capital type expressed as a fraction of the GDP deflator, which were constructed directly from the BEA’s detailed fixed-asst accounts. The BEA GDP deflator is taken from FRED. Panel B depicts asset-specific depreciation rates constructed directly from the BEA’s fixed asset accounts. See Tables F.7 and F.8 in Appendix A and the text for our grouping of assets. The dashed vertical lines indicate the year 1968.

Tables F.7 and F.8 in Appendix A for complete lists of the detailed assets grouped into the two types of non-residential capital. Within consumer durables we classify the following assets as ICT: PCs and peripherals (1RGPC); software and accessories (1RGCS); calculators, typewriters, other information equipment (1RGCA); telephone and fax machines (1OD50).

To construct the income shares of these assets, we further need an estimate of both the depreciation rate, _δi,t_ , and expected capital gains, _E_ [ _Pi,t_ +1 _/Pi,t_ ], for each type of capital. We measure depreciation rates directly form the BEA’s nominal values of depreciation for each type of detailed asset.<sup>18</sup> We then employ implicit price deflators that we construct for each type of capital based on chain type price indices provided by the BEA, to measure capital type specific inflation.<sup>19</sup> Panel A of Figure 8 depicts the path of prices for different types of assets, where we have aggregated residential and non-residential non-ICT capital, since the prices for these types of assets largely

> 18In particular, we measure depreciation rates based on the BEA’s nominal values for depreciation and net capital stocks. That is, we compute _δi,t_ = ( _Pi,tDep i,t_ ) _/_ ( _Pi,t_ ( _NetStock i,t_ + _Dep i,t_ ). Since both measures are reported in yearend nominal values, the price terms cancel.

> 19Notice that this involves constructing appropriate chain type quantity aggregates and associated implicit price deflators for each capital type, derived from the BEA’s estimates of stocks and prices for the detailed assets listed in Tables F.7 and F.8 in Appendix A.

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Table A.2: Distribution of In-Kind rewards

||Proportion of<br>teachers|Proportion of<br>annual salary|
|---|---|---|
|None|36%|0%|
|Rice & radio|18%|4%|
|Rice & radio & cellphone|10%|8%|
|Rice & TV & cellphone|11%|32%|
|Rice & TV & cellphone & generator|25%|49%|

Table A.3: Quality of Implementation (teacher data)

||(1)|(2)<br>**Midlin**|(3)<br>**e**|(4)|(5)|(6)<br>**Endli**|(7)<br>**ne**|(8)|
|---|---|---|---|---|---|---|---|---|
||Control|In-Kind|Recog.|Obs|Control|In-Kind|Recog.|Obs|
|School participates|0.967|0.972|0.964|1055|0.788|0.918|0.832|1100|
|_Which teachers targeted?_|||||||||
|Only grade 2|0.032|0.051|0.065|999|0.104|0.111|0.115|861|
|Only grade 3|0.388|0.369|0.376|1016|0.296|0.157|0.254|861|
|Only grade 4|0.386|0.367|0.387|1023|0.052|0.071|0.062|861|
|Grades 3 and 4|0.704|0.712|0.753|1023|0.341|0.519|0.384|864|
|_Type of award_|||||||||
|Financial|0.484|0.355|0.462|1000|0.129|0.098|0.174|864|
|In-Kind|0.721|0.916|0.794|1023|0.414|0.739|0.217|864|
|Certifcate|0.455|0.497|0.620|1005|0.169|0.115|0.504|864|
|Ceremony|0.204|0.179|0.331|1002|0.064|0.056|0.244|864|
|_Performance metric_|||||||||
|Student test scores|0.756|0.840|0.794|1019|0.863|0.932|0.790|863|
|Inspection score|0.943|0.956|0.929|1022|0.860|0.921|0.795|865|
|French evaluated|0.943|0.947|0.969|1017|0.976|0.986|0.964|897|
|Math evaluated|0.931|0.924|0.959|1017|0.960|0.989|0.957|895|
|Science evaluated|0.334|0.337|0.376|993|0.195|0.123|0.148|779|
|_Abs vs relative performance_|||||||||
|Relative—Other teachers in school|0.238|0.181|0.184|1027|0.383|0.295|0.304|906|
|Relative—Other schools|0.158|0.164|0.146|1027|0.199|0.187|0.204|899|
|_Receipt of award_|||||||||
|Teacher received|||||0.066|0.458|0.504|889|
|Money|||||0.012|0.067|0.126|845|
|In-Kind|||||0.008|0.387|0.077|868|
|Certifcate|||||0.008|0.027|0.386|851|
|Ceremony|||||0.008|0.030|0.125|847|
|School received|||||0.058|0.817|0.754|936|

5
# FMIS Budgetary Coverage

Information on the total number of expenditure transactions processed through it in a given period (e.g. an FY) and the amount of budgetary resources transacted by these transactions give the extent of its use and scope of its coverage. When these figures are calculated for the center and the provinces or other subnational levels of government separately, they provide information on the percentage of budgetary resources spent at the center and the percentage at the provinces.

The total amounts processed through the FMIS represent a high percentage of the total domestically financed budget for 2017 and this shows that the coverage of the FMIS is very good on an overall basis. Donor funds are not processed through the FMIS at present.

Table 8 shows that 62.9 % of the budgetary amounts are transacted at the National level and 37.1% at the provinces. However, the total number of transactions processed at the national level is only 8.7% compared to 91% at the provinces. This suggests that this area needs to be investigated further. This has been done and the reason for this disparity are explained below by looking at the granularity of the transaction data.

_Table 8 Total Amount Processed through the FMIS and Values they Represent_

||**Total no of**<br>**transactions**|**Share of total**<br>**no**|**Total amount**<br>**(Riels)**|**Share of the total**<br>**amount**|
|---|---|---|---|---|
|National level|19,004|8.70%<br>|12,794,095M|62.90%|
|Provincial|||||
|treasuries|198,470|91.30%|7,530,580M|37.10%|
|Total|217,474|100%<br>|20,324,676M|100%|

_Source:_ Authors.

# Analysis of the Cambodia Expenditure Transaction Profile

It is observed that only 15% central expenditure transactions in 2017 are below $2,500 or 10 million Riel, while 71.6% of the Provincial transactions are below 10 m Riel or $2500. From this appears that at the central level the system may be used mainly for drawing out advances from Treasury which are then processed off-line. This can be tested to determine the granularity of the transactions. The transaction profiles for Cambodia are given at Annex II.

The granularity of the transactions would then show whether there is a wider control problem with the payment approval process in the country. In this case the line ministries and SUs would resort to taking advances, since it takes an inordinate time to process payment requests through the Treasury.

20
Table A.2: Spatial Disparity, Access to Infrastructure and Sorting of household Attributes Regression Coefficients from separate regressions (each panel reports result from one regression)

|||2000|||2010||
|---|---|---|---|---|---|---|
||All|Urban|Rural|All|Urban|Rural|
|Mean Rainfall|-0.000**|-0.000|-0.000|0.000|0.000***|0.000|
||(-2.276)|(-1.285)|(-0.851)|(1.572)|(3.176)|(0.345)|
|Rain Standard Deviation|-0.001***|0.000|-0.000**|-0.000|0.001*|-0.000***|
||(-2.763)|(0.288)|(-2.414)|(-1.581)|(1.933)|(-2.686)|
|Monsoon Rain Coeff. Of variation|-0.431|2.593**|-1.948***|-1.830***|-0.768|-2.107***|
||(-0.683)|(2.340)|(-3.461)|(-4.220)|(-0.867)|(-4.981)|
|% household with electricity|0.577***|0.862***|0.557***|0.443***|0.877***|0.329***|
||(17.705)|(9.811)|(10.230)|(15.405)|(9.548)|(9.650)|
|Access to santitation|||||||
|% of hh with sanitary latrine|0.702***|0.865***|0.459***|0.548***|0.715***|0.253***|
||(13.364)|(8.226)|(5.084)|(11.192)|(6.592)|(4.238)|
|% with water seal latrine|1.028***|1.141***|0.960***|0.463***|0.509***|0.339***|
||(10.593)|(7.605)|(3.102)|(7.994)|(4.269)|(4.691)|
|% with paved pit latrine|0.365***|0.510***|0.276***|0.165***|0.071|0.213***|
||(5.760)|(4.461)|(2.710)|(2.886)|(0.553)|(3.560)|
|% unpaved permanent latrine|0.153**|0.177|0.210***|0.112**|0.026|0.144**|
||(2.439)|(1.279)|(3.274)|(1.992)|(0.189)|(2.555)|
|Access to drinking water|||||||
|% using tap water|0.777***|0.928*|0.403|0.587***|0.626***|0.602***|
||(5.541)|(1.931)|(1.290)|(6.879)|(3.886)|(3.319)|
|% using tubewell water|0.100|0.345|0.001|0.059|0.213|-0.011|
||(0.727)|(0.713)|(0.006)|(0.802)|(1.390)|(-0.140)|
|% of household with phone|2.511***|2.124***|4.631***|0.825***|1.434***|0.570***|
||(16.438)|(11.724)|(5.550)|(18.844)|(14.274)|(10.751)|
|Access to large urban markets|-0.131***|-0.079***|-0.116***|-0.154***|-0.145***|-0.114***|
||(-9.273)|(-3.536)|(-4.639)|(-9.566)|(-5.901)|(-5.253)|
|Average years of education|0.104***|0.108***|0.073***|0.126***|0.161***|0.081***|
||(21.879)|(15.454)|(7.415)|(20.916)|(18.528)|(7.571)|
|Skill composition of workers|||||||
|% of workers skilled|1.141***|0.735***|0.673***|1.076***|1.281***|0.236|
||(8.936)|(3.946)|(2.706)|(12.220)|(9.339)|(1.506)|
|% of workers semi-skilled|0.257***|-0.289**|0.214**|0.136**|0.143|0.031|
||(3.757)|(-2.066)|(2.143)|(2.573)|(1.346)|(0.445)|
|Sectoral composition|||||||
|% of workers in manufacturing|0.408***|0.059|0.297**|0.175**|0.481***|-0.065|
||(3.889)|(0.276)|(2.134)|(2.565)|(3.632)|(-0.712)|
|% of workers in services|0.519***|0.220|0.299***|0.493***|0.788***|0.259***|
||(8.440)|(1.411)|(3.207)|(11.042)|(7.119)|(4.153)|

Robust z statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%
2006. Note that the data cover all the modals of the imports, including air, ocean and land shipments.

**Table 3:** **_Ad valorem_ Rates of Import Charge**

<u>(Unit: percent)</u>

||_Jap_|_an_|_United_|_States_|
|---|---|---|---|---|
||1996-2000|2001-2006|1996-2000|2001-2006|
|_Indonesia_|7.34|7.13|7.12|7.68|
|_Malaysia_|11.10|11.54|2.93|2.93|
|_Phlippines_|15.69|17.64|3.57|4.37|
|_Singapore_|7.34|6.81|1.68|1.80|
|_Thailand_|14.30|15.94|4.81|5.82|
|_Viet Nam_|12.83|11.23|7.33|8.28|
|_China_|7.65|9.29|6.46|6.72|
|_Korea_|10.91|14.27|3.36|3.79|
|_Hong Kong_|28.29|na|4.08|4.69|
|_Taiwan_|16.11|21.32|3.92|4.32|
|_Canada_|7.41|7.48|1.79|1.49|
|_Australia_|8.18|6.54|6.13|4.78|
|_New Zealand_|9.35|11.81|9.36|7.36|
|_Japan_|--|--|2.53|2.67|
|_United States_|13.43|13.89|--|--|

(Source) Japan: Customs Office, Bank of Japan, US: Department of Commerce (Note) 1. The rates are defined as: (Import Charge) / (Import in FOB/Custom Value) * 100.

2. The Bank of Japan reported negative imports from Hong Kong for 2003-2006, and the figures are omitted in this table.

Table 3 shows a significant difference between the levels of the rates reported by Japan and U.S. Japan generally reports higher rates of _ad valorem_ transport costs than the U.S. The variations across countries do not correlate with each other<sup>17</sup> , and do not appear to follow the exporters’ relative proximity to the U.S. and Japan. The difference is smaller in Indonesia, Australia and New Zealand. Aside from the possible economic reasons, this difference in total may come from the sources of statistics: the import charges of the U.S. are declared by the importers who may be subject to strict supervision by the authorities, while the data of Japan are simply estimated on

> 17 The correlation coefficient between the Japan and U.S. data across the exporting countries is -0.13 for the first period and 0.04 for the second period.

17
sulfate (an unlisted action) 96 percent of the time. In contrast, participants often missed other actions that are always appropriate: monitoring contractions and the amniotic fluid, tracking the fetal heart rate and the mother’s vital signs, and recording the fluids and drugs administered. Performance is also generally higher for clinically unnecessary or incorrect actions. For example, participants almost never recommended administering magnesium sulfate or augmenting labor, which are unnecessary in all cases.

# **3.2 Study arms**

The experiment was embedded in a survey of primary care clinics fielded as part of an impact evaluation of health facility financing modalities (see Section 4.1 below). Toward the end of the survey, we randomized respondents in each clinic into one of three trial arms (information, rewards, penalties), stratified by clinic. All participants received a printed list of seven randomly selected common clinical actions that are shown in Table 1, for example, to monitor the fetal heart rate or prepare for imminent delivery. The list shown to participants is purposively incomplete: there are eight additional actions not on the list but may be appropriate. In the rewards arm, participants were offered payments if they recommended five of the seven actions when they were indicated for the patient.<sup>3</sup> In the penalties arm we deducted the same payment amount for each action that is indicated but was not identified by the participant. The reward and penalty contracts are isomorphic: the same actions lead to the participant being paid the same in either arm. We randomized the payment amounts across the incentivized actions, and the listed and incentivized actions include some that are unnecessary or even harmful in some cases.

All participants received a flat payment that varied by arm such that the maximum, minimum, and, in expectation, average payouts are all the same. The flat payment in the information arm was 1,750 Nigerian Naira or about USD 5.80. The participation fee allows us to account for an endowment effect, which may be important because most public sector contracts have fixed remuneration scales and PFP interventions at scale aim to be budget neutral (Fritsche et al., 2014).

> 3 While we randomly selected actions to be listed, due to the small overall number of actions, the three groups of actions (listed, paid, unlisted) could still be systematically different. We address this issue below.

11
<!-- Start of picture text --> I.1 European bank leverage soared before the crisis. I.2 ... and their reliance on wholesale funding has<br>It has decreased significantly since, but remains exhibited a similar pattern<br>elevated...<br>Bank Leverage Ratios Bank Loan to Deposit Ratios<br>Weighted tangible assets to tangible common equity Weighted total gross loans to customer deposits<br>60% 150%<br>130%<br>50%<br>110%<br>40%<br>90%<br>30%<br>70%<br>20%<br>50%<br>10% 30%<br>EU UK EU UK<br>US Traditional Banks US Investment Banks US Traditional banks US Investment banks<br>Source: Bloomberg Source: Bloomberg<br><!-- End of picture text -->

**_I.3_ Total loans in peripheral Europe have been** **_I.4_ ... Foreign activity of European banks has contracting, while lending in the core has slowed contracted significantly, particularly in developed** **<u>down significantly compared to pre-crisis levels... economies</u>** Total loans stock Total Cons. Foreign Claims of European Banks _Jan 2009=100% (Jan 2008- Dec 2012)_ _<u>$ trillions (2000Q1-2012Q3)</u>_

<!-- Start of picture text --> 120% 25<br>110%<br>20<br>100%<br>90% 15<br>80%<br>10<br>70%<br>60% 5<br>50%<br>0<br>Germany+France Italy+Spain EMDEs Advanced Europe Other Advanced<br>Source: ECB Source: BIS<br>2005Jan 2005Jun 2005Nov 2006Apr 2006Sep 2007Feb 2007Jul 2008Jan 2008Jun 2008Nov 2009Apr 2009Sep 2010Feb 2010Jul 2010Dec 2011May 2011Oct 2012Mar 2012Aug 2000Q1 2000Q4 2001Q3 2002Q2 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1<br><!-- End of picture text --> 6