rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A2B – Parallel Trend Test of GDP and Employment Across States of Varying Public Debt Before FD Law**

This table reports OLS estimates of the trends of GDP and employment across states of varying public indebtedness prior to the introduction of the FD Law. _GDPs,q_ is the GDP growth rate of state _s_ in quarter _q_ . _Employment Totals,q_ is the growth rate of employment in state _s_ in quarter _q_ . _Employment Primarys,q, Employment Secondarys,q and Employment Tertiarys,q_ are the growth rates of employment in the primary, secondary, and tertiary sectors of state _s_ in quarter _q_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendq_ is a linear trend over time from the start of our sample in 2014Q1 up to 2016Q1, the quarter prior to the implementation of the FD Law. Observations at the state-quarter level. Standard errors are reported in parentheses and are doubled clustered at the state and quarter levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the <u>Appendix.</u>

||GD|Ps,q|Emplo<br>Tot|yment<br>als,q|Emplo<br>Prim|yment<br>arys,q|Emplo<br>Secon|yment<br>darys,q|Emplo<br>Terti|yment<br>arys,q|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Trendq|-0.00*||-0.00||-0.00||0.00||-0.00||
||(0.00)||(0.00)||(0.01)||(0.01)||(0.00)||
|Trendq*DebtStates,16Q1|0.00|0.00|-0.00|-0.00|-0.00|-0.00|-0.02|-0.02|0.00|0.00|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.03)|(0.02)|(0.01)|(0.01)|(0.01)|(0.01)|
|Observations|210|210|150|150|150|150|150|150|150|150|
|R-squared|0.04|0.52|0.06|0.28|0.10|0.20|0.08|0.09|0.10|0.30|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|No|Yes|No|Yes|No|Yes|No|Yes|No|Yes|

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**Table 7: Relationship between the employment rate of older persons (55-64) and the employment rate of youth (15-24) and prime-aged persons (25-54) by education level**

||(1)<br>Youth<br>Primary|(2)<br>Youth<br>Secondary|(3)<br>Youth<br>Tertiary|(4)<br>Prime-aged<br>Primary|(5)<br>Prime-aged<br>Secondary|(6)<br>Prime-aged<br>Tertiary|
|---|---|---|---|---|---|---|
|Employment rate of old persons|0.106|0.355***|0.267***|0.634***|0.468***|0.169***|
|(55-64)|(0.126)|(0.051)|(0.088)|(0.050)|(0.048)|(0.026)|
|Natural log of hourly income of|-2.192|-2.427|-0.357|0.320|-2.885**|-0.990|
|younger persons (15-24/25-54)|(2.530)|(1.706)|(1.923)|(1.767)|(1.422)|(0.988)|
|Poverty rate|-0.146|-0.165***|-0.020|-0.016|-0.042|-0.029|
||(0.092)|(0.053)|(0.104)|(0.061)|(0.039)|(0.036)|
|Share of employment in the|0.191**|0.028|-0.289***|0.007|-0.109***|-0.060**|
|agriculture sector|(0.088)|(0.052)|(0.081)|(0.036)|(0.038)|(0.028)|
|Share of employment in the|-0.252*|0.191**|-0.054|-0.129**|-0.186***|-0.070|
|industrial sector|(0.149)|(0.082)|(0.172)|(0.056)|(0.052)|(0.045)|
|Share of population with a|0.510***|0.218***|0.443***|0.038|0.166***|0.239***|
|primary education or less|(0.078)|(0.051)|(0.094)|(0.029)|(0.033)|(0.030)|
|Natural log of GRP per capita|2.000*|3.645***|5.071***|0.228|0.266|-0.045|
|(constant 2010 IDR)|(1.140)|(0.628)|(1.466)|(0.533)|(0.468)|(0.403)|
|Growth in GRP per capita|-0.040|-0.164**|-0.149|-0.074|-0.003|0.069*|
||(0.090)|(0.066)|(0.118)|(0.056)|(0.042)|(0.040)|
|Number of observations|272|272|272|272|272|272|
|R-squared|0.530|0.406|0.292|0.754|0.590|0.527|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

23
<!-- Start of picture text --> 3500<br>2960<br>3000<br>2500<br>2000<br>1359<br>1500<br>1000 509<br>500 5 59<br>0<br>2002 2003 2004 2005 2006 2007 2008<br>Year<br>(accumulated)<br>Number of Projects<br><!-- End of picture text --> Figure 1: Accumulative number of CDM submitted projects 2003-2007.

34
corporate vulnerability in emerging economies has become dangerously high or not. This type of assessment might require analyzing how financial indicators among emerging market firms compare relative to other crisis periods. The AFC serves as a good benchmark. Financial distress in the nonfinancial corporate sector was a key factor leading to the AFC. In fact, some conditions in Asian nonfinancial corporate sectors in the run-up to the Asian crisis were very similar to the ones since the GFC, including a rapid increase in leverage coupled with deteriorating profitability and debt repayment capacity (Pomerleano, 1998; Claessens et al., 1999).

A comparison of leverage and debt repayment capacity of emerging market firms post-GFC relative to East Asian firms before the AFC suggests that solvency risk after the GFC remained below that on the eve of the AFC (Alfaro et al., 2017). The average leverage ratio (measured as debt to equity) and debt repayment capacity (measured as earnings to liabilities) among East Asian economies in the period 1996-98 were 146 and 24 percent, respectively. In contrast, during the period 2009-14, these values stood at 87 and 36 percent, respectively, among emerging economies. Furthermore, in no emerging economy was the average value of the Altman Z Score across firms in the so-called distressed zone (below 3.75) during the period 2009-14.<sup>4</sup> This pattern contrasts with the situation before the AFC, when the average Altman Z Score of firms in the Republic of Korea fell in the distressed zone.

# _3.2. Currency Risk_

Currency risk is another type of risk that is not well captured by the solvency risk measures. Currency risk could have increased if emerging market firms issued debt in foreign currency, but then used the proceeds to invest and generate earnings in domestic currency. Having foreign currency liabilities and

> 4 The Altman Z Score calculates the probability of a firm going bankrupt based on different balance sheet indicators. Based on the score, a firm can be categorized as safe (safe zone), with a mild probability of going bankrupt (grey zone), or with a high probability of going bankrupt (distressed zone).

14
21 charged an imbalance penalty. In addition, they receive credit or are charged for the gas withdrawn or injected in excess of the tolerance. A unit price of this gas is equal to the system marginal price.

The transition from monthly to daily balancing imposed substantial requirements on shippers. To phase in this change, the Network Code operated under a "soft landing" regime between April and September 1996. This regime allowed shippers to exceed their daily balance by 100 percent of the daily average of their deliveries and offlakes during the previous 30 days. The daily imbalance outside this tolerance was cashed out at the system marginal price. At the end of the month, if the monthly imbalance (the cumulative daily imbalances) did not exceed three times the tolerance level, it was cashed out at the system average price, equal to the average price of all accepted flexibility bids during the last 30 days. If the monthly imbalance was above the tolerance, the imbalance was cashed out at the system marginal price (figure 4).

**Figure 4 Balancing under the Soft Landing Regime of the Network Code**

<!-- Start of picture text --> Daily imbalance tolerance Monthly imbalance<br>Daily Imbalance<br>Monthly imbalance tolerance)<br>Days in month End of the<br>month<br>Source: Gas Matters, March 1996<br><!-- End of picture text --> Note: The daily imbalance is equal to the difference between offlakes and deliveries on the gas day. It is cashed out at the system marginal price if it exceeds daily imbalance tolerance; otherwise it is cashed out at the system average price. The daily imbalance tolerance is based on the average of the last 30 days of deliveries and offtakes. The monthly imbalance is equal to the cumulative daily imbalance at the end of the month. It is cashed out at the system marginal price if it exceeds the monthly imbalance tolerance; otherwise it is cashed out at the system average price. The monthly imbalance tolerance is equal to three times the daily imbalance tolerance.
# Figure 6. Regional potential growth

## A. Potential growth in EMDE regions

## B. Potential growth in EMDE regions

<!-- Start of picture text --> Percent Potential growth<br>10<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> <!-- Start of picture text --> Percent Potential growth<br>Actual growth<br>8<br>2000-2021 potential growth<br>6<br>4<br>2<br>0<br>MNA SAR SSA<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> C. Share of countries and GDP with potential growth below the previous decade average in EMDE regions D. Share of countries and GDP with potential growth below the previous decade average in EMDE regions <!-- Start of picture text --> Percent<br>100 Share of countries Share of GDP<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>EAP ECA LAC<br>E. Contributions to regional potential<br>growth<br>Percentage point<br>10 TFP Capital<br>8 Labor Potential growth<br>6<br>4<br>2<br>0<br>EAP ECA LAC<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Percent <!-- Start of picture text --> Share of countries Share of GDP<br>100<br>80<br>60<br>40<br>20<br>0<br>2011-21 2022-30 2011-21 2022-30 2011-21 2022-30<br>MNA SAR SSA<br><!-- End of picture text -->

- E. Contributions to regional potential growth

F. Contributions to regional potential growth <!-- Start of picture text --> Percentage point<br>10<br>TFP Capital Labor Potential growth<br>8<br>6<br>4<br>2<br>0<br>-2<br>MNA SAR SSA<br>2000-10 2011-21 2022-30 2000-10 2011-21 2022-30 2000-10 2011-21 2022-30<br><!-- End of picture text --> Sources: Penn World Tables, World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa. A. B. E. F. GDP-weighted arithmetic averages using potential growth estimate based on production function approach. C. D. Number of economies and their share of GDP in the region among 61 EMDEs with potential growth in each region.
# **Table S1.9: Robustness: including control for survey length as proxy for fatigue**

||DP|FDC|NRM|UPC|
|---|---|---|---|---|
|Enumerator age|-0.529***|-0.383**|-0.118***|-0.497***|
||(0.145)|(0.154)|(0.040)|(0.113)|
|Enumerator gender (male)|-0.020|-0.004|0.002|-0.032**|
||(0.020)|(0.019)|(0.005)|(0.015)|
|Enumerator education|0.110|0.137|-0.075**|0.065|
||(0.106)|(0.119)|(0.036)|(0.108)|
|Enumerator from urban locality|0.449**|0.571***|0.008|0.432***|
||(0.177)|(0.196)|(0.045)|(0.144)|
|Survey length|0.002|0.001|-0.001|0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Observations|6,038|6,126|6,341|6,111|
|R-squared|0.491|0.461|0.241|0.485|

_Source_ : Data for the study comes from a survey of farmers in northern Uganda. The survey was conducted by Innovations for Poverty Action.

Note: This table presents OLS regression results for regression (1). For each column, the outcome variable is (an increasing) categorical variable (1-4) which measures the openness to vote for a political party, namely, the Democratic Party (DP), the Forum for Democratic Change (FDC), the National Resistance Movement (NRM), and the Uganda People’s Congress (UPC). _Survey length_ is the time length of the interview in minutes. Each regression includes community fixed effects and a constant. Robust standard errors in parenthesis are clustered at the enumerator level. *** p<0.01, ** p<0.05, * p<0.10.

42
**Table 6: Antidumping Investigations in Developed and Developing Countries, 1978-2011**

||ByReporting|Member(no. of|cases)|ByExporting|Member(no. of|cases)|
|---|---|---|---|---|---|---|
|Year|Developed<br>Members|Developing<br>Members|Developed<br>as % of Total|Developed<br>Members|Developing<br>Members|Developed<br>as % of Total|
|**GATT:**|||||||
|1978|6|0|100|5|1|83|
|1979|58|0|100|54|4|93|
|1980|72|0|100|66|6|92|
|1981|94|0|100|77|17|82|
|1982|221|0|100|174|47|79|
|1983|178|0|100|130|48|73|
|1984|159|0|100|119|40|75|
|1985|206|0|100|131|75|64|
|1986|156|5|97|105|56|65|
|1987|101|19|84|86|34|72|
|1988|112|12|90|78|46|63|
|1989|87|9|91|55|41|57|
|1990|149|18|89|80|87|48|
|1991|209|19|92|139|89|61|
|1992|260|65|80|167|158|51|
|1993|149|157|49|140|166|46|
|1994|138|91|60|96|133|42|
|**WTO**|||||||
|1995|78|79|50|71|86|45|
|1996|79|147|35|95|131|42|
|1997|127|119|52|125|121|51|
|1998|90|176|34|135|131|51|
|1999|176|182|49|154|204|43|
|2000|138|160|46|130|168|44|
|2001|179|193|48|145|227|39|
|2002|109|206|35|127|188|40|
|2003|86|148|37|90|144|38|
|2004|113|107|51|61|159|28|
|2005|62|139|31|44|157|22|
|2006|71|133|35|49|155|24|
|2007|61|104|37|38|127|23|
|2008|75|138|35|48|165|23|
|2009|64|145|31|48|161|23|
|2010|38|133|22|66|105|39|
|2011|63|92|41|44|111|28|

Sources: GATT and WTO antidumping database.

Table 7 shows the number of anti-dumping, countervailing and safeguard measures taken by the 50 countries in our sample cumulatively over the period 1995-2011 as well the number products affected by these actions. The last four columns show the number of trade remedy 21
# **Table 2**

Richer Specification and Alternative Measures of Emigration: Main Results (Coefficient estimates; standard errors in parentheses)

|||Depend<br>|ent Variable:<br>Measure o<br>|Logof Month<br>f Emigration<br>|lyWages<br>||
|---|---|---|---|---|---|---|
||Baseline<br>Experience<br>Categories<br>2000-06|Baseline<br>Experience<br>Categories<br>2000-06|Baseline<br>Experience<br>Categories<br>2004-06|Broader<br>Experience<br>Categories<br>2000-06|Broader<br>Experience<br>Categories<br>2000-06|Broader<br>Experience<br>Categories<br>2004-06|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Ratio of emigrants to labor force|0.311***|0.134***|0.165***|0.756***|0.345***|0.611***|
||(0.086)|(0.045)|(0.062)|(0.193)|(0.091)|(0.154)|
|Sector fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Occupation fixed effects|No|Yes|Yes|No|Yes|Yes|
|Number of observations|22,899|22,667|22,667|22,967|22,735|22,735|
|R-squared|0.229|0.272|0.272|0.233|0.274|0.274|

*** p<0.01, ** p<0.05, * p<0.1.

Note: The standard errors are adjusted for emigration cohort clustering. The independent variables include marital status, gender, education, experience, and experience-squared. They also include occupation and sector fixed effects, as indicated.

Another concern is the possibility of reverse causality. As explained in Mishra (2007), if lower wages promote emigration, then this biases the estimated coefficient of emigration. Our use of individual data on wages from 2006 combined with emigration data on workers who mostly left during the _preceding_ years avoids the possibility of reverse causality due to contemporaneous changes in emigration and wages, though this still leaves the possibility of reverse causality due to _persistent_ changes in wages. However, if reverse causality is an issue, it means that if anything, we likely underestimate the impact of emigration on wages. The results here could then be treated as lower bound estimates.

# _Differences across Economic Sectors_

There are a few other reasons why the baseline results should be interpreted with caution. Equation (1) assumes the emigration of labor force is exogenous to the variation in monthly wages. Second, the emigration-induced labor shock is assumed to be a national 13
**Figure 12: Pre- versus Post-Reform Full-Cost Recovery (%)**

<!-- Start of picture text --> 140%<br>120%<br>Ukraine Philippines<br>Colombia<br>100%<br>Peru<br>Uganda<br>Vietnam Kenya<br>Morocco<br>80% India - Odisha Pakistan<br>Tajikistan SenegalDominican<br>60% Egypt, Arab Tanzania Republic<br>Rep.<br>India -<br>40% Rajasthan<br>India - Andhra<br>Pradesh<br>20%<br>0%<br>0% 20% 40% 60% 80% 100% 120% 140%<br>Pre-Reform Full-Cost Recovery<br>Post Reform Full-Cost Recovery<br><!-- End of picture text --> _Source:_ World Bank staff.

**Figure 13: Composition of Changes in Full-Cost Recovery Compared to Pre-Reform Period (Percentage Points)**

<!-- Start of picture text --> 100%<br>80%<br>8 out of 17 cases<br>60%<br>with net<br>improvement in 40%<br>cost recovery<br>20%<br>0%<br>-20%<br>9 out of 17 cases<br>-40%<br>with deterioration<br>in cost recovery -60%<br>-80%<br><!-- End of picture text --> <!-- Start of picture text --> Changes in tariff levels Difference between realized costs and LRMC Net change<br><!-- End of picture text --> _Source:_ World Bank staff.

27
# **For online publication: Appendix**

## **Data**

This section provides details on other data sources employed.

### **Credit: Reserve Bank of India**

We obtained data on bank branches from the Reserve Bank of India.<sup>27</sup> We obtained the number of commercial (which include both private and public sector banks) by district for the years 2002 and 2012 for both rural and urban areas.<sup>28</sup> . This measure provides a proxy for banking activity in the district.

### **Weather: University of Delaware**

We also aim to include control variables for adverse weather conditions in the years preceding the year under consideration. We utilized two databases of the University of Delaware’s Global Climate Database: Earth Precipitation: 19002017 Monthly Time Series in Grid, and Earth’s Air Temperature: 1900-2017 Time Series in monthly grid.<sup>29</sup> These databases contain monthly precipitation and air temperature for the 1900-2017 period, based on weather station data, but interpolated to a 0.5-degree by 0.5-degree latitude/longitude grid (with the grid nodes centered on the 0.25 degree).

Using the 2001 district census boundaries, we average the precipitation/air temperature data of all data points within each district boundary to create district-level averages for all the years between 1970 and 2012.

Using these district-level time series, we classify years (in each district) as to whether or not adverse weather conditions prevail, and then aggregate this information (at the district level) over five years.

We define an rain adverse event as a year in which the annual rainfall is below the 20th percentile or over the 80th percentile of the long term (30

> 27 see: https://www.rbi.org.in/ under the annual publications tab.

> 28The data from 2012 used the 2008 district boundaries; we mapped up the data points using our district harmonization method

> 29See: http://climate.geog.udel.edu.

54
- e) Whether transactions against extra-budgetary funds are processed through the FMIS;

- f) Whether transactions against internally generated funds are processed through the FMIS;

- g) The extent to which transactions related to donor funds are processed through the system.

For a set of 22 countries, the coverage of the FMIS has been estimated by trying to determine which payments and receipt transactions are routed through the FMIS and which bank accounts are covered by the FMIS. This was used by a quantitative study to assess whether FMIS systems have an effect on fiscal discipline and found a negative correlation between FMIS budget coverage and the deviation of planned to actual deficit targets, as well as a positive correlation between budget coverage and relevant PEFA scores. The authors stress that results only hold if budget coverage means budget subjected to FMIS internal budgetary controls, as expenditure controls may otherwise not be applied. [14] In Ghana and Zambia for example, wages and salaries were not managed through the FMIS and thus not subject to FMIS internal controls. The executive was therefore able to increase salaries substantially mid-year without legislative approval and adequate checks and balances. This put excessive strain on the budget and would not have been possible had these items been subjected to FMIS internal controls. [15-17] FMIS coverage and utilization also matters for the reliability of expenditure reports. Partial budget coverage in the FMIS means that any reporting from the system will also be partial, and therefore any resulting analytical work potentially biased or misleading. [10] Transactions can also be posted to the ledger without going through the FMIS to complement, but this process does not go through the same rigor and verifying the integrity of these is more difficult. Some of these transactions may be accurate, others may not be, and other transactions may not be posted at all, as was found during an assessment of the Malawi FMIS. [10, 18] Posting transactions into the system after they have occurred only gives the illusion of comprehensiveness and control, while integrity cannot be ensured, and controls are actually not applied.

Coverage and utilization of FMIS is important as it determines how well the system can support expenditure control and gives a measure of the integrity of expenditure reporting. What underpins coverage and utilization is the actual transactions database. Yet, actual government transactions data are rarely analyzed despite their obvious value proposition: (i) revealed preferences of the executive to meet expenditure targets, and (ii) a measure of confidence in the integrity of government expenditure data. This paper is the first in exploring governments’ expenditure transactions more closely and 6
**Chart 2: Defying Comparative Advantage (FDI) (2003-2007)**

<!-- Start of picture text --> SGP<br>IRL<br>ISR<br>ESPGBR<br>FRA<br>DEU<br>PRT<br>ITA USA<br>SAU<br>IND PHL ZAFBRABGRPANARGMYSMEX HUN KOR JPN<br>CHN TUR POL<br>IDN THA ROM CHL<br>7 8 9 10 11<br>Log GDP per capita (PPP, 2003)<br>.05<br>.04<br>.03<br>.02<br>.01<br>Outflows of total FDI to OECD as a share of GDP<br>0<br><!-- End of picture text --> _Sources: Thomson Financial SDC Platinum database and Financial Times’ FDI Intelligence database._ This chart plots FDI outflows from a country to OECD countries as a share of its GDP (averaged over the period 2003-2007) against its per capita income. The sample comprises selected industrial and emerging market countries.

13
affected by the crisis, which would, no doubt, have included households involved in these three sectors.

Prior access to and familiarity with the local health system helped households gain healthcards. Ownership of the _kartu sehat_ (pre-crisis healthcard) in 1997 had a quantitatively important, and statistically significant, impact on the probability of receiving a healthcard after the economic crisis. Given that both healthcards were distributed through the same network of health centers and midwives, it is not surprising that ownership of one enhanced the household’s chances of getting the other. Similarly, households that knew where the nearest hospital, health centre and midwife were located in the 1997 survey were more likely to receive healthcards: coefficients for all three variables pertaining to knowledge about health facilities in the area are positive and statistically significant.

The urban dummy is positive and statistically significant in the propensity score model. Urban areas were worse affected by the economic crisis than rural areas because the sectors of employment that were directly affected were more likely to be in urban areas. To the extent that the healthcard program was introduced as a social safety net to help households cope with the crisis, a higher probability of healthcard ownership among worse affected urban households is not surprising. On the other hand, it may be a consequence of the increase in the number of health centers and midwives - the main vehicle for distributing healthcards – being more accessible in urban or semi-urban areas.

35