rafmacalaba/datause-encoder-data huggingface dataset:
**This** paper is about fiscal policy issues that arise during macroeconomic stabilization in developing economies. The empirical background comes from a recent study of stabilization episodes in 18 countries, organized by the World Institute for Development Economics Research (WIDER) in Helsinki. The results are reviewed in a summary monograph by Taylor (1988), and the country papers are available fron WIDER.

Fiscal policy cannot be described outside a macroeconosic framework. Section 1 sets out the basics, as tbey apply in the WIDER studies. Section 2 takes up tbree theories about the fiscal deficit -- it **may** rise for political or structural reasons, or perhaps because the relevant authorities want to maximize revenue trom the inflation tax. Section 3 is about the effects of deficit reduction, a central component of orthodox stabilization packages. Section 4 takes up other fiscal topics brought up in the WIDER country papers.

# 1. Macroeconomic Backoround

There are four classic modes of macroeconomic adjustment in the short run -- changes in the level of output, the trade balance, income distribution, and real wealth. We begin with the first, familiar from the General Theory and countless macro textbooks in its wake. a demand injection, say greater government spending or reduced taxes, makes economic activity rise. The increased production generates additional income, which feeds back into more consumption, still greater output, and so on until the multiplier chain converges to an equilibrium level of supply. This story is well-known, and emphasized in the WIDER 1
10

# _3.1. Financial Depth (Size)_

For financial institutions, the variable that has received much attention in the empirical literature on financial development is private credit, defined as deposit money bank credit to the private sector as a percentage of GDP.<sup>5</sup> There is a wide literature demonstrating the link between financial depth, approximated by private sector credit to GDP, on one hand, and long-term economic growth and poverty reduction on the other hand (e.g., Demirgüç-Kunt and Levine 2008). Indeed, cross-country regressions confirm a strong positive linkage between financial depth and economic growth. To illustrate, Table 2 shows the results of regressions of various proxies for financial depth on proxy variables for economic growth, updating an earlier analysis by King and Levine (1993b) with extended datasets.

Table 2. Financial Depth and Economic Growth, 1960-2010

|Dependent Variable|Depth|Bank|Privy|
|---|---|---|---|
|Real per Capita GDP Growth|2.4**|3.2**|3.2**|
||(0.007)|(0.005)|(0.002)|
|R2|0.50|0.50|0.52|
|Real per Capita Capital Growth|2.2**|2.2**|2.5**|
||(0.006)|(0.008)|(0.007)|
|R2|0.65|0.62|0.64|
|Productivity Growth|1.8**|2.6**|2.5**|
||(0.026)|(0.010)|(0.006)|
|R2|0.42|0.43|0.44|

Source: Authors’ update on King and Levine (1993b), Table VII _Notes_ : King and Levine (1993b) define 2 percent growth as 0.02; here, 2 percent growth is 2.00. * significant at the 0.10 level, ** significant at the 0.05 level, p-values in parentheses, Observations: 77 _Variable definitions:_ DEPTH = Liquid Liabilities/GDP, BANK = Deposit bank domestic credit/(deposit bank domestic credit + central bank domestic credit), PRIVY = Gross claims on the private sector / GDP Productivity Growth = Real per capita GDP growth - (0.3)*(Real per capita Capital growth) _Other explanatory variables included in each of the nine regression results reported above:_ logarithm of initial income, logarithm of initial secondary school enrollment, ratio of government consumption expenditures to GDP, inflation rate, and ratio of exports plus imports to GDP.

> 5 The data source is IMF‘s _International Financial Statistics_ . Private credit isolates credit issued to the private sector and therefore excludes credit issued to governments, government agencies, and public enterprises. Private credit also excludes credits issued by central banks.
caused changes in cropland. Thus, for some cells, changes in NPP may be due in part to changes in land use rather than productivity-enhancing or productivity-reducing effects of irrigation dams. To roughly investigate the role of such measurement error, we attempt to disregard grid cells that are more likely to have had a different land use classification prior to 2000. More precisely, we exclude all cells (approximately 8 percent) whose 1981 NPP value was either two standard deviations above or two standard deviations below the mean value of NPP for cropland in 2000. Two standard deviations below corresponds roughly to that what would be the mean value of the category “Sparse Herbaceous or Sparse Shrub Cover,” whereas two standard deviations above is close to the mean value of the category “Tree Cover, Broadleaved, Evergreen.” The results, shown in the fifth column, of using the mean basin values of NPP for this subset of cells demonstrate that the negative effect of own large dams is somewhat smaller, the positive effects of small dams and their interaction with large dams are also somewhat smaller, and the positive impact of upstream large dams is now marginally larger. Again, simple z-tests suggest that these differences are not statistically significant. Thus, there is little evidence that our use of the 2000 land use classification produces noticeable biases in our results.

Thus far, we have restricted our sample to include areas from 1 km<sup>2</sup> grid cells from the GLC 2000 that are characterized as “pure” cropland and to exclude those cells categorized as containing cropland within the mosaic of other vegetation. Feasibly, this approach may ignore smaller cropland and limit the interpretation of our results beyond very large or agglomerated plots. Thus, we reestimate our base specification from specification (13) using the mean NPP value of all cropland, including that classified as mosaic.<sup>17</sup> As shown, there is little change in the coefficients for local small dams or 36
# **I Introduction**

Does economic growth in developing countries attract more capital inflows? In turn, do capital inflows foster further economic growth? These are important questions for many developing countries. If the answers are yes for both, this is great news because this implies that economic growth and capital flows can form a positive feedback cycle where one reinforces the other. For Sub-Saharan Africa (SSA), the issue is becoming very important, in light of the stronger economic growth, and capital flows into the region in recent years. For example, FDI has grown from below 1% of GDP in the 1990s to as high as 4% of GDP in recent years, while sovereign debt inflows have grown from almost nothing in the early 2000s to about 1% of GDP in recent years (see Figure 1).

<!-- Start of picture text --> Private debt inflows Public debt inflows<br>ODA inflows FDI inflows<br>6<br>4<br>2<br>0<br>1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> Figure 1: Capital flows to SSA, % of SSA’s total GDP 2
_Table 7 - The asymmetric effect of good and bad shocks on expenditure and income. Shock is measured as the year-on-year difference in cyclical component of log(USD to IRR rate) in each month. The cyclical component is computed using Hodrick-Prescott filter. Bad shock is defined as shock * (shock > 0) and good shock is defined as – shock * (shock < 0). All regressions are estimated for the matched sample of HEIS households._

||**Total ex**|**penditure**|**Food exp**|**enditure**|**Total**|**income**|**Earned**|**income**|**Wag**|**e rate**|
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|**Afghan**|-0.050***|-0.160***|-0.006|-0.036|-0.042**|-0.114***|0.196***|0.200***|0.073***|0.058*|
||(0.018)|(0.034)|(0.017)|(0.033)|(0.017)|(0.033)|(0.021)|(0.040)|(0.017)|(0.033)|
|**Shock -Cyclical Xrate**|0.085**||0.068*||0.142***||0.093*||-0.094**||
||(0.041)||(0.040)||(0.040)||(0.049)||(0.040)||
|**Bad shock**||-0.061||-0.211***||-0.028||0.025||-0.244***|
|||(0.069)||(0.067)||(0.067)||(0.081)||(0.067)|
|**Good shock**||-0.403***||-0.678***||-0.514***||-0.239||-0.217*|
|||(0.127)||(0.124)||(0.124)||(0.150)||(0.120)|
|**Afghan * Shock**|-0.132***||-0.074||-0.102**||-0.004||0.058||
||(0.050)||(0.049)||(0.049)||(0.059)||(0.048)||
|**Afghan * Bad shock**||0.122||-0.001||0.066||-0.013||0.097|
|||(0.084)||(0.082)||(0.082)||(0.099)||(0.081)|
|**Afghan * Good shock**||0.702***||0.206||0.469***||-0.025||-0.002|
|||(0.157)||(0.153)||(0.153)||(0.185)||(0.148)|
|**Constant**|14.727***|14.789***|13.390***|13.509***|18.157***|18.229***|17.649***|17.678***|9.922***|9.985***|
||(0.014)|(0.028)|(0.014)|(0.027)|(0.014)|(0.027)|(0.017)|(0.033)|(0.014)|(0.027)|
|**Observations**|4122|4122|4119|4119|4125|4125|3979|3979|3014|3014|

29
column 4), which indicates that a less regulated labor market can increase the effects on domestic value added. This is broadly in line with the channels discussed in section 2.1. The positive effects from flexible labor markets arise from increased labor market turnover, which facilitates spillovers from knowledge embodied in foreign value added, supporting the general findings by Javorcik and Spatareanu (2005) and Hale and Long (2011).

**Table 6. The role of financial and labor market policies for economic upgrading through GVCs**

|VARIABLES|(1)<br>_DVA_|(2)<br>_DVA_|(3)<br>_DVA_|(4)<br>_DVA_|
|---|---|---|---|---|
|**Backward links**|||||
|_FVAX_|-0.0033|0.0470***|0.128***|-0.0016|
||(0.0366)|(0.0161)|(0.0470)|(0.0387)|
|_FVADP_|0.271***|0.252***|0.220***|0.271***|
||(0.0240)|(0.0270)|0.128***|-0.0016|
|_FVAX*Financial freedom_|0.0011*<br>(0.0006)||||
|_FVAX*Private Credit_||0.0003*<br>(0.0002)|||
|_FVAX*ULC_|||-0.0008**<br>(0.0003)||
|_FVAX*Labor freedom_||||0.0010*<br>(0.0006)|
|_Constant_|5.748***|5.624***|5.840***|5.711***|
||(0.164)|(0.169)|(0.379)|(0.161)|
|Observations|7,177|7,156|3,093|7,177|
|R-squared|0.858|0.854|0.650|0.858|
|**Forward links**|||||
|_DVAR_|0.0832|0.189***|0.275***|0.0962**|
||(0.0526)|(0.0265)|(0.0638)|(0.0464)|
|_FVADP_|0.249***|0.232***|0.210***|0.249***|
||(0.0229)|(0.0259)|(0.0292)|(0.0225)|
|_DVAR*Financial freedom_|0.0023**<br>(0.0009)||||
|_DVAR*Private Credit_||0.0004*<br>(0.0002)|||
|_DVAR*ULC_|||-0.0008**<br>(0.0004)||
|_DVAR*Labor freedom_||||0.0020***<br>(0.0007)|
|_Constant_|4.874***|4.986***|5.230***|5.087***|
||(0.155)|(0.173)|(0.428)|(0.166)|
|Observations|7,180|7,164|3,093|7,180|
|R-squared|0.877|0.872|0.678|0.876|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

Regarding forward links, we find positive and significant mediating effects of lower unit labor cost, and more flexible labor markets from GVC integration as a seller in the OECD sample (table 6, columns 3 and 4). A potential explanation is that lower labor unit costs increase the competitiveness of sellers in GVCs, while higher labor market flexibility allows sellers to react faster to the changing demands of their customers in the value chain.

Income group specific results, again, suggest that financial and labor market policies work as facilitators mainly in high-income countries, but, in summary, the results suggest that financial and labor market 25
cooperative income for 2015 was about a million Rwandan Francs (RWF) with an average expenditure of 330,000 RWF.

The results of the follow-up survey show that during the implementation of the program, the cooperatives were indeed reorganized according to the guidelines. Most of the cooperatives (about 80 percent) reported their number of members to be exactly three times the number of villages they serve. In addition, the ratio of females to males was two to one, as expected.

Our data show overall increase in utilization of maternal and child health services between the baseline and follow-up surveys. These trends are best portrayed by focusing on the sectors that were not exposed to either of the two interventions. Although the rate of women attending at least one antenatal consultation was already almost universal in the baseline (98 percent), the rate of women who initiated antenatal care within the first four months of their pregnancies increased from 63 percent to 72 percent. The percentage of women who attended four or more consultations increased from 36 to 40. The in-facility delivery rate increased from 79 to 94 percent. The rate of children 6-59 months who have been measured for nutritional status in the six months preceding the survey has increased from 50 to 79 percent.

During the follow-up survey, the field teams managed to complete interviews with 99 percent of the target number of women with recent births and 93 percent of the target number of CHWs in charge of maternal and neonatal health. There are no statistically significant differences in these rates between the treatment arms. However, the attrition rate of ‘baseline women’ - those who were recently pregnant at baseline - is not balanced. As can be seen in Table A1 in the appendix, successful reinterviews were conducted at the follow-up round with almost 93 percent of the baseline women in the sectors that did not implement the CHW cooperative performance payments and with 89 percent of the women in the sectors that implemented the intervention. Exposure to the performance payments intervention is significantly correlated to higher attrition also when controlling for a range of baseline characteristics of the women.

# **Impact of the CHW Cooperative Performance Payments**

Given the experimental design and the balance between the study arms, the effects of the CHW cooperative performance payments can be estimated simply by regressing outcomes from the follow-up survey on an indicator of exposure to the intervention. Our main empirical specification is therefore 10
**<u>Table 6: Lease price regressions for agricultural land for personal use</u>**

||<br>**All tran**|<br>**sactions**|<br>**With c**<br>**dur**|<br>**ontract**<br>**ation**|**With**<br>**charact**|**tenant**<br>**eristics**|**Tenant**<br>**contract**|**details &**<br>**duration**|
|---|---|---|---|---|---|---|---|---|
|Area (ha)|-0.007**<br>(0.003)|-0.017***<br>(0.003)|-0.006**<br>(0.003)|-0.017***<br>(0.003)|0.030***<br>(0.003)|0.021***<br>(0.004)|0.030***<br>(0.004)|0.022***<br>(0.004)|
|Area^2|-0.006**|-0.009***|-0.002|-0.005*|0.003|0.002|0.006**|0.005*|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Moratorium lifted|0.193***<br>(0.005)|0.192***<br>(0.005)|0.141***<br>(0.006)|0.140***<br>(0.006)|0.222***<br>(0.007)|0.221***<br>(0.007)|0.183***<br>(0.008)|0.182***<br>(0.008)|
|Legal entity (renter)|0.331***<br>(0.009)|0.316***<br>(0.009)|0.360***<br>(0.009)|0.344***<br>(0.009)|||||
|Legal entity * 2024|<br>0.028|<br>0.028|<br>0.004|<br>0.004|||||
||(0.018)|(0.018)|(0.019)|(0.019)|||||
|# transactions|<br>0.036***|<br>0.030***|<br>0.031***|<br>0.026***|0.040***|0.036***|0.038***|0.034***|
|in village|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|<br>Parcel part of field||<br>-0.009||<br>-0.008||<br>0.033***||<br>0.026***|
|||(0.007)||(0.007)||(0.009)||(0.009)|
|Field area (ha)||<br>-0.001||<br>-0.001||<br>-0.005*||<br>-0.002|
|||(0.002)||(0.002)||(0.003)||(0.003)|
|Field area^2||<br>0.006***||<br>0.006***||<br>0.004***||<br>0.004***|
|||(0.000)||(0.000)||(0.001)||(0.001)|
|Unmapped area||<br>-0.086***||<br>-0.091***||<br>-0.099***||<br>-0.095***|
|share in field||(0.017)||(0.017)||(0.020)||(0.021)|
|Lease term 14-20|||0.028***|<br>0.024***|||-0.058***|<br>-0.059***|
|years|||(0.006)|(0.006)|||(0.007)|(0.007)|
|Lease term > 20|||-0.279***|-0.281***|||-0.297***|-0.298***|
|Years|||(0.006)|(0.006)|||(0.008)|(0.008)|
|Tenant's cult.|||||-0.011**|-0.010**|-0.008*|-0.006|
|area (ha)|||||(0.004)|(0.004)|(0.004)|(0.004)|
|<br>Tenant's area^2|||||<br>0.010***|<br>0.010***|<br>0.010***|<br>0.010***|
||||||(0.001)|(0.001)|(0.001)|(0.001)|
|Tenant uses <33%|||||<br>-0.070***|<br>-0.071***|<br>-0.068***|<br>-0.069***|
|of village land|||||(0.008)|(0.008)|(0.008)|(0.008)|
|<br>Tenant uses 33-50%|||||<br>-0.040***|<br>-0.044***|<br>-0.031***|<br>-0.035***|
|of village land|||||(0.010)|(0.010)|(0.011)|(0.011)|
|<br>Tenant uses >50%|||||<br>-0.036***|<br>-0.042***|<br>-0.031***|<br>-0.038***|
|of village land|||||(0.011)|(0.011)|(0.011)|(0.011)|
|<br>No. of obs (farms)|236,644|236,644|219,904|219,904|<br>151,474|<br>151,474|<br>141,921|<br>141,921|
|Adj R-squared|0.346|0.347|0.354|0.356|0.379|0.379|0.388|0.389|

_Note:_ Parcel characteristics, year dummies, OTG fixed effects and a constant included but not reported (see appendix table 3 for estimated coefficients). Standard errors in parentheses (* p<0.10, ** p<0.05, *** p<0.010).

27
IWM (2018). Technical Report on Storm Surge, Wave, Hydrodynamic Modelling and Design Parameters on Drainage System and Embankment Crest Level. Volume-II: Package 2 (Appendix B: Storm Surge). Bangladesh Water Development Board, Ministry of Water Resources.

Jakobsen, F., Zeaul Hoque, A.K.M., Paudyal, G.N., Bhuiyan, Md. S., (2005) Evaluation of the ShortTerm Processes Forcing the Monsoon River Floods in Bangladesh, Water International, 30:3, 389-399, DOI: 10.1080/02508060508691880 Jiang, Z., Guan, W., Xiong, Y., Li, M., Chen, Y., Liao, B., (2019). Interactive Effects of Intertidal Elevation and Light Level on Early Growth of Five Mangrove Species under Sonneratia apetala Buch. Hamplantation Canopy: Turning Monocultures to Mixed Forests. Forests, 10(2), 83.

Kaimal, J.C., Finnigan, J.J. (1994). Atmospheric Boundary Layer Flows: Their Structure and Measurement. 1st ed., Oxford University Press, New York.

Kalloe, S.A., Hofland, B., Antolínez, J.A., van Wesenbeeck, B.K. (2022). Quantifying FrontalSurface Area of Woody Vegetation: A Crucial Parameter for Wave Attenuation. Frontiers in Marine Science 23.

Kathiresan, K., Rajendran, N. (2005). Coastal mangrove forests mitigated tsunami. <u>Estuarine, Coastal and Shelf Science. Volume 65, Issue 3, November 2005, Pages 601-606</u> Keulegan, G.H., Carpenter, L.H., (1958). Forces on cylinders and plates in an oscillating fluid. J. Res. Natl. Bur. Stand. 60, 423–440 Khan, R.S. (1992). Cyclone Hazard in Bangladesh. Community Development library, Dhaka, 86– 102. National States Geographic Information Council. Available online: <u>www.adpc.net.</u> Krauss, K.W.; Doyle, T.W.; Doyle, T.J.; Swarzenski, C.M.; From, A.S.; Day, R.H.; Conner, W.H. (2009). Water level observations in mangrove swamps during two hurricanes in Florida. Wetlands, 29, 142–149.

Lewis III, R.R. (2009). Methods and criteria for successful mangrove forest restoration. In: Gerardo M. E. Perillo, Eric Wolanski, Donald R. Cahoon, Mark M. Brinson, editors, Coastal Wetlands: An Integrated Ecosystem Approach. Elsevier, 2009, p. 787. ISBN: 978-0-444-53103-2 Lewis, R.R., Brown, B. (2014) Ecological mangrove rehabilitation – a field manual for practitioners. Version 3. Mangrove Action Project Indonesia, Blue Forests, Canadian International Development Agency, and OXFAM. p 275.

30
ate lag order. The international Stress scenario based on the actual historical cross-country crisis experience is presented under the “Stress Int.” heading. In addition, we compute the corresponding probabilities of the “Stress Int.” values vis-a-vis the VAR(1) model forecast distributions, and report them in the last column of Table 3 under the Pr(Stress Int.) heading.<sup>40</sup> The bank level balance sheet data are as of 2010:Q4.

Notice from Table 3 that under the TTC, PIT and Stress VAR scenarios the change in the nominal exchange rate is set to 0%. This is due to the fact that the economy operates under a fixed exchange rate regime vis-a-vis the Euro, and we assume that the current conversion rate to the EUR is sustained under the TTC, PIT and country specific VAR Stress scenario. Nevertheless, since there exists ample historical evidence suggesting that rate break down or are abandoned the authorities severe exchange pegs by during financial crisis periods (Reinhart and Rogoff, 2004; W ̈alti, 2005) we also consider a 31.5% depreciation of the local currency relative to the Euro under the “Stress Int.” scenario. The magnitude of this depreciation was determined from historical cross-country data as described in Section 3.3.

# **7.1.2. Bank Level Data**

The construction of the idiosyncratic (bank specific) risk component as described in Section 4 requires bank level data on credit growth at the peak of the last credit cycle or the most recent period of positive credit growth, and if available, data on the share of foreign currency denominated lending (SFX). Table 4 below shows summary statistics of annual credit growth in each asset class across the considered ten banks as of end-2007. The year

> 40These probabilities were constructed by taking the values under the “Stress Int.” heading in Table 3 and plugging them into the four step-ahead multivariate normal forecast density of the VAR(1) model. The median probability is 99.7 percent so that the banks that need to preserve at least a BBB rating during times of a severe downturn should withstand this scenario without having their CAR fall under the insolvency threshold.

51
procedure.<sup>15</sup> There are few significant effects, and the negative effect of the subsidy on raw weight is puzzling and may be a random outlier. A lot of precautions were taken to minimize measurement error, partly informed by the challenging measurement conditions (e.g. no flat, hard surfaces to place a scale). Each child was weighed twice, and the two measurements compared. If there was a difference of more than 0.1kg, a third measurement was taken.<sup>16</sup> Weakly, columns (2) and (4) suggest that the CHW might have some effect on the incidence of malnutrition danger signs. The age specific effects in Figure 4 of Appendix B for weight for age (significant reductions in dangerously low weight for age in the 0-2 age group in the CHW-only and the subsidy-only group) suggest again that program effects occur primarily at the lowest ages, likely due to the incidence profile of diarrhea, but we do not see a corresponding effect in the full program group.

# **5 Discussion and Conclusion**

Overall, our results are encouraging for combined interventions of both removal of user fees and employment of community health workers who visit households in an urban context.

The CHW have significant effects on indicators of preventive knowledge and behavior, such as breastfeeding practice and knowledge of ORS. Even though we do not find significant impacts on objectively measured water disinfection, self-reported disinfectant use is higher in the CHW treatment arms. There is a possibility that self-reports are inflated due to experimenter demand effects. However, the significant effects of the CHW on (objective) mosquito net hanging are not consistent with mere over-reporting and suggest that the problem could also be incorrect use of the disinfectant by the household, or of the water tests that our enumerators used. It will be important to understand better why self-reported and measured disinfection diverge, especially in the context of evidence from Kenya that chlorine solution dispensers at water sources increase use and greatly reduce child mortality

> 15We used the standards defined by the Unicef IGROWUP macro for weight for age, see latest version at Unicef [2019].

> 16Children were weighed on their own if over two and with their mother if below two years of age.

26