rafmacalaba/datause-encoder-data huggingface dataset:
According to the simulation, 3.7 percent of the total population is vulnerable to fall into poverty in the presence of a crisis. This group would on average have a per capita household income almost 30 percent lower than in the absence of a crisis. The other groups would also experience a lower income but the magnitude of the income change is much lower.

Three interesting patterns emerge. First, households that are “always poor” (i.e., poor in year 2 under both scenarios) have a higher incidence of female headship and higher number of members than in the rest of the population. The same is true for those households that would fall into poverty were the crisis to occur. Second, the proportion of “always poor” and “vulnerable to crisis” households in rural areas is lower than the proportion of households categorized as “never poor”. Finally, families that are “vulnerable to crisis” seem to have not only higher unemployment rates than in the no-crisis scenario but also a higher proportion of people out of the labor forces altogether. Relative to the benchmark, those that would cease to work come from the commerce and services sectors (Figure 19).

**Figure 19: Changes in the labor market**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>No crisis Crisis No crisis Crisis No crisis Crisis<br>Vulnerable to crisis Always poor Never poor<br>Inactive Unempl Agriculture Manufactury Commerce Services<br><!-- End of picture text --> Source: WB staff calculations based on data from ECH 2011. Status is evaluated according to simulations for year 2: “Always poor” (poor with and without crisis), “never poor” (non-poor in both scenarios) and “vulnerable to crisis” (poor with crisis and non-poor without crisis) The decrease in the growth rate is the main contributor to higher poverty rates in the case of the simulated crisis relative to the absence of the shock. Following Datt and Ravallion (1992), increments in moderate poverty can be decomposed into those that are due to changes in the income level (growth) and changes in the income distribution (redistribution). Figure 20 presents the decomposition into these two components at the national, urban and rural areas comparing the year 2 benchmark (no crisis) and crisis scenarios. The lack of growth is the main driver of the poverty increments in both urban and rural areas, and thus at the national level. However, as the figure shows, taking into account _only_ the effect of negative growth would understate the rise on moderate poverty levels since the increased inequality of the income distribution plays an important role in explaining the predicted increment of poverty rates.

18
<!-- Start of picture text --> 55 Standard© 0.460 deviation in 2030<br>°° mai<br>Losi @® 4000<br>(®) 6.000<br>45 (9) 8.000<br>Locc (G9) 9.900<br>Data: Sample covered<br>0 1990_2014S<br>a + 2000_2014<br>85 35 % 2005_2014<br>o5 Countrywal typology<br>s38 30 —{-tose MiRoc nRoc<br>g 25 Hoc<br>£ Loge M Locc<br>A TM Mosc<br>= 20<br>45 ROC<br>10 Aig ges<br>AIX<br>5<br>QNROC<br>0 a<br>0 5 10 5; 20 25 30 35 40 45 sO 55<br>Median in 2015 (percentage)<br><!-- End of picture text --> <!-- Start of picture text --> 45 Standard deviation in 2030<br>@ 1450<br>44 (@® 4.000<br>() 6.000<br>(9 7.710<br>43 Data:© 1980_2014Sample covered<br>42 GC 1+ 1990_20142000_2014<br>+ X 2005_2014<br>a Country typology<br>froce | LOGC wall<br>ea RocROC<br>8g© 40 HOGC prose<br>e m LoGc<br>3 39 HOGC TM occ<br>3<br>NROG<br>38 All a O<br>NROC a Loce<br>37 MosEQ 3Xbait<br>nn x<br>36 mocc ROC x<br>x Loge<br>35 mocc O<br>ROC<br>34 4<br>34 35 36 37 38 39 40 41 42 43 44 45<br>Median in 2015<br><!-- End of picture text -->

<!-- Start of picture text --> 900M Stancors© cevieven0.460 2020)<br>3 @ im<br>8 800m (9) 6.000<br>Mw (GED 8.000<br>iS (GER) 2.200<br>cy53 700m fe}if Beeearenedoata-covered 1980_2014periose<br>ie fl X 2005_2014¢<br>3 600M Country typot<br>g x waountry typology<br>Ld2 coom 2. mM I nRocROC<br>io)&i osc Sel Mose +a MiBi vocehoscvoce<br>& a<br>3 400M Mosc<br>5 c<br>S2 300M<br>e5<br>3 200M Onroc<br>3g se(Conroe nvoc<br>= i00m NROC<br>=. Loce<br>om @<br>OM 100M 200M 300M 400M 500M 600M 700M _ 800M900M<br>Number of extreme poor corresponding to a median headcount in 2015<br><!-- End of picture text --> <!-- Start of picture text --> 24 0.00 Standard deviation in 2030<br>@ 18.17<br>22 @ 40.00<br>(®_ 60.00<br>20 (9) 80.00<br>= 100.00<br>18 (9) 115.82<br>16 + Data:© 1980_2014 Sample covered<br>14 RO 1 1990_2014<br>+ 2000_2014<br>12 oO X 2005_2014<br>°@ 10 — x ROC x CountryMall typology<br>&x HoGc Ri o Lose ee<br>e All Mi nroc<br>86 Xmoge OC | Bose<br>g M9GC >< NROC tose | MLocc<br>4 ste TM mocc<br>All MOGC<br>2<br>0 0.00 NR Ox<br>2 GC<br>4<br>HobC<br>6<br>8<br>-10 10.00<br>-10-8 6 -4 2 0 2 4 6 8 10 12 14 16 18 20 22 24<br>Median in 2016<br><!-- End of picture text -->
Figure 1: Impulse responses of seleceted variables following a positive one-standard deviation shock to the price of resource exports: All Consumption Regime <!-- Start of picture text --> Transitory shock on resource export price Impulse response of resource exports Impulse response of traditional exports<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>Impulse response of domestic sales Impulse responses of consumption Impulse responses of private investment<br> % deviation from initial steady state % deviation from initial steady state<br>0 50 100 150<br>periods 0 50 100 150 0 50 100 150<br>Impulse response of imports Impulse response of domestic absorption Impulse response of GDP at market prices<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>Impulse response of gross output Impulse response of labor supply Impulse response of wage rate<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>Impulse response of domestic good price Impulse response of consumption price Impulse response of traditional exports<br>0 50 100 150 0 50 100 150 0 50 100 150<br>periods periods periods<br>Impulse response of foreign savings and debt Impulse response of resources windfalls Impulse response of government transfers to households<br>0 50 100 150<br>periods<br>Foreign saving Foreign debt 0 50 periods 100 150 0 50 periods 100 150<br>15 8<br>−.2<br>6 −.4<br>10<br>4 −.6<br>−.8<br>5<br>2<br>% deviation from steady state % deviation from steady state % deviation from steady state −1<br>0 0 −1.2<br>.05<br>.4 1.5<br>0<br>.3 1<br>−.05<br>.2<br>% deviation from steady state −.1 C_ALLCONS .1 INV_ALLCONS .5<br>−.15 0 0<br>.8 .25 .2<br>.2 .15<br>.6<br>.1<br>.15<br>.4 .05<br>% deviation from steady state % deviation from steady state .1 % deviation from steady state 0<br>.2<br>.05 −.05<br>.1 .2 2.5<br>2<br>.05 .1<br>1.5<br>0<br>0 1<br>% deviation from steady state −.05 % deviation from steady state % deviation from steady state .5<br>−.1<br>−.1 0<br>2 1.2 −.2<br>1.5 1 −.4<br>.8 −.6<br>1<br>.6 −.8<br>% deviation from steady state .5 % deviation from steady state .4 % deviation from steady state −1<br>0 .2 −1.2<br>2 8<br>20<br>6<br>0 15<br>4<br>10<br>−2 2<br>deviation as % of steady state GDP 5 % deviation from steady state0<br>−4<br>deviation as % of steady state government revenue 0 −2<br><!-- End of picture text -->
- 11 -

accountability. The reason is that technical and nontechnical losses are not distinguishable without sufficient meters. Therefore, the system losses may or may not be considered as their faults. The lack of capital investment in the network, for instance, may not fall on utilities. In Andhra Pradesh, the utility’s efforts to reduce the system losses began with recognition of the existing technical losses and theft. The technical losses, of which the former official estimate was 18 percent, were projected to be 38 percent (Antmann, 2009). This is a politically challenging yet essential step to strengthen the utility’s accountability.

Second, the absence of metering may also weaken enforceability of payments, even if the norm is applied to unmetered customers. The reason is that people can always claim to have used nothing. In addition, given the weak metering practices, utility officials would likely have incentives to practice irregular billing and over-charging. On the other hand, customers may have incentives to pay a bribe and ask for falsification of meter readings. A survey in South Asian countries indicates that about 20 to 30 percent of customers are faced with endemic petty corruption through meter readers (Transparency International; World Bank, 2009a). The same problem has been observed in the water sector. More than 70 percent water utility staff responded that meter falsification is very common to lower bills. And 41 percent of interviewed customers actually made informal payment to falsify meter readings (Davis, 2004; World Bank, 2008).

To deal with these problems, the private sector participation in the distribution network is one of the options that governments can take. Experience shows that it can promote proper metering, reading, billing and collection quickly, thereby improving the sector’s governance and performance. Apparently, nontechnical system losses are an obvious revenue leakage from the systems.<sup>7</sup> Therefore, private operators are willing to make efforts toward proper metering.<sup>8</sup> In Colombia, when an electricity distribution company serving the capital Bogota

> 7 The other area is obviously technical losses in the system. The relative easiness to address the problems depends on country. In Albania, about half of the total system losses are estimated to be attributed to nontechnical losses.

> 8 Practically, a phased approach may be useful to address the metering issue. Because a small number of large consumers often represent a large amount of energy sales and revenue in the electricity sector. Hence, the
the equilibrium appreciation dictates or by leading to further depreciation (beyond the equilibrium level). Table 9 reports our results for RER undervaluations estimated using our panel data PMG estimator and shows that capital controls may have a significant effect for a larger value of the undervaluation threshold. That is, capital controls may influence the incidence of larger undervaluations.

For our Johansen time-series estimates of undervaluation, trade openness variable ( _open_ ) fails to yield a significant coefficient estimate and so do the outcome measures of financial policy while trade openness is positive and significant especially with a lower threshold with RER misalignments using PMG. Fiscal discipline with RER misalignments by Johansen, on the other hand, shows a negative and significant sign only when we consider thresholds of undervaluation of 5 and 10%. This implies that fiscal discipline reduces the likelihood of being able to sustain undervaluations. If the threshold is 20 or 25 percent, the fiscal variable becomes insignificant. This shows that fiscal policy is effective while the probability of the RER undervaluation is still closer to its equilibrium and fiscal policy likely becomes ineffective while the threshold gets more than 20 percent. Liability dollarization with RER misalignments by PMG shows negative and significant especially with a lower threshold although we did not find any significance in fiscal discipline.

Finally, the ability to sustain undervaluations granted by flexible exchange rate regimes and FOREX market intervention is robust for different thresholds of RER undervaluation with RER misalignments by Johansen (see Table 8). Higher values of the indicator of intervention in the foreign exchange market ( _Int2_ ) help signal a more active policy to keep the currency undervalued. The regressions in Tables 8 through 13 shows that with the 5 percent threshold the RER is more likely to undervalue in countries pursuing a more active intervention in the foreign exchange rate market. As the value of the threshold increases, the coefficients become insignificant. This means that the RER is less likely to be undervalued when pursuing a more active intervention when the RER gets too far from its equilibrium. With RER misalignments by PMG exchange rate regimes are robust but the results of FOREX market intervention varies.

Table 10 and 11 investigate the effects of the structural of external liabilities on the likelihood of generating and/or sustaining RER undervaluations using our Johansen and PMG estimates, respectively. Our findings in Table 10 are consistent with those of Table 4: equity-related liabilities enter with a negative sign whereas 21
to total household expenditure as proxy for consumption share. This methodology differs from the one used in this study. We use agricultural production information for individual commodities by drawing data from the Integrated Agricultural Survey (2015) rather than using information on net agricultural sales alone from the Household Income and Expenditure Survey (2014-15). As a result, this report is able to examine the welfare effect of price increases in three main staple food items.

Based on these proxy measures Arndt et al find that the NBR was 10 % for rural areas and -22 % for urban areas for a hypothetical, overall food price increase of 100%. Accordingly, based on the Deaton approximation, they find that a price increase negatively hurts urban consumers while benefitting rural households. In contrast, the findings from our analysis indicate a negative impact on both urban and rural households. Our results are consistent with evidence from other studies in Africa which have found that a larger than expected share of rural households also tend to be net buyers and are likely to lose from food price increases (Minot & Dewina (2015) in the case of rice and maize in Ghana; Wodon & Zaman (2008) in Ghana, Senegal and Liberia). Arndt et al also find regional differences, with urban households in south and central regions of Mozambique being the most negatively affected. Across region and expenditure quintiles, they find that the poorest households in Maputo are the worst affected, while middle income households in rural north and center are likely to benefit the most.<sup>1</sup> In order to overcome the limitations posed by their NBR analysis Arndt et al. extend their analysis by undertaking a general equilibrium analysis for Mozambique, shocking their model with prices that are reflective of the 2007-08 period. They use a CGE model calibrated to a 2003 Social Accounting Matrix (SAM) (see Arndt et al., and Thurlow(2008) for details of their model), along with the household survey data from 2002/03. The simulated price rises for food and fuel commodities range from 25-75%, under four different scenarios (varying the commodities experiencing the price rise and under differing assumptions for changes in land allocation for crops). Under their preferred scenario, they estimate a four percentage-point increase in national poverty rate, with the effect being larger in urban areas. The impact appears to be largely driven

> 1 Note that these observations by the authors are drawn based on the NBR values alone for each group of households (calculated based on the proxies for aggregate consumption and production ratios for households).

11
whereby the larger farmers may be operating closer to (or farther from) the frontier. The direction these components takes is an empirical issue, with each offering potentially important insights for policy.

Estimation of the returns to scale associated with the production frontier, the relationship between technical efficiency and farm size, and the magnitude of technical change provide evidence of the relationship between farm size and productivity in Bangladesh’s agriculture. In addition, the parameterization of the inefficiency function allows for directly testing additional policy variables that may be important in explaining technical efficiency – providing further insights to guide policy making.

The stochastic production function is specified as follows: where ��� is the value of output per hectare for farm i in period t, ��� is the total value of output ���� ~~�~~ and ��� is the farm size measured in hectares. The term ��� denotes the (set of) inputs used per ����� hectare. The inputs included in the estimation are fertilizer, hired labor, and an aggregate of other cash outlays (e.g. machine hiring cost, cost for draft animals, pesticide costs etc.) as other costs, all on a per hectare basis. To account for irrigation, the proportion of operated land under irrigation is included. As the data do not have the total amount of family labor used (either in quantity or value terms), the stock (number) of adult family workers, on a per hectare basis, is included as a proxy.<sup>10</sup> Finally, fixed farm capital is included as the value of all agricultural capital (all farm related machinery and equipment owned by the household) per hectare. All values are inflated to 2008 prices using the CPI index from the Bangladesh Bureau of Statistics.

> we find that neither the cost per unit of output, nor input proportions (fertilizer to labor, labor to other costs, and fertilizer to other costs) varies with the scale of operation/farm size (from regressions of each ratio on farm size, with and without household and time fixed effects). With this in mind, in the rest of this paper we use the terms scale and size interchangeably.

> 10 To verify the use of adult family workers as a reasonable proxy for family labor used on farms, separate regressions for rice plots were estimated for each of the two main growing seasons ( _Boro_ and _Aman_ ). As the dominant crop in Bangladesh, rice was of special interest to the survey team. More detailed information was collected on the main rice plot for each season, including on the actual family labor time spent on each plot. The results using the two options for family labor were very similar. Importantly, findings on the main relationships of interest to this study – those related to farm size – were found to be qualitatively the same using either variable.

9
# Table 64 (Con't.)

||Base<br>in|d on Value-A<br>Domestic Pric|dded<br>es|Base<br>in|d on Value-Added<br>World Prices|
|---|---|---|---|---|---|
||1|2|3|4|5<br>6|
|% Exports<br>Exponts<br>.pts:|-0320<br>(0.267)|-0.672e<br>(0282)|-0.427<br>(0.267)|-0.425<br>(0.312)|*0.s*<br>.08o<br>(0339)<br>(0326)|
|In (Senegalese Skilled<br>and Unsk$lled)|0.651<br>(0.115)|||0.673<br>(0.136)||
|In (Other African<br>Slcled h Unskilled)|1.1E-4<br>(1.SA4)|||2.6E-4<br>(18E-4)||
|In (Non-African Skied<br>and Unsilled)|4.0E4'<br>(1.4E-4)|||2.9E-4<br>(1.7E-4)||
|In (Senegle se Middle<br>and Upper Management)|19E-4<br>(1.9B-4)|||2A6i4<br>(2.4E-4)||
|tn (Otier African Midle<br>and Upper Management)|2.3E-4<br>(1.9B4)|||62E4'<br>(2.3A4)||
|In (NonAfrican Middle<br>and Upper Managemnt)|_-S 1E4'_<br>(3.12,4)|||-S.SE-4<br>(3A6E-4)||
|In (Unskilled Workes)||_0on_<br>(0.12)-l<br>0|<br>7*(0.14<br>_(0.116)_|6)|008<br>_OA_<br>(0.141)|
|In (Skiled Workers)||0.001<br>(4.7BE4)|||_-9.IPA_<br>(5.7E-4)|
**Figure 1. GDP cycle***

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>G.D.P. cycle (H&P) G.D.P. (annual growth)<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text -->

* H&P filter applied with smoothing parameter 10<sup>5</sup>

**Figure 2. Share of Out of the Labor Force* (O.L.F) and Unemployment* (U)**

<!-- Start of picture text --> 45 6<br>44<br>5<br>43<br>42 4<br>41<br>3<br>40<br>39 2<br>38<br>1<br>37<br>36 0<br>O.L.F. U<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text -->

*Out of working ages population.

**Figure 3. Share of Formal (F), Informal (I) and Self-employment (S.E.) sectors out of Employed workforce**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>S.E. I F S.E. + I<br>1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> 47
## **How Does Foreign Entry Affect the Domestic Banking Market?**

Stijn Claessens, Asli Demirgii9-Kunt, and Harry Huizinga<sup>1</sup>

### SECTORAL LIBRARY INTERNATIONAL BANK

# . JUN 2 6 1998

· FOR . RECONSTRUCTION AND DEVELOPMENT Keywords: foreign entry, domestic banking. JEL Classification: E44, G21

*** 8 HG 3881.5 .ws:_P63 N0.191 Claessens. St1Jn· How does f ore1 ·gn entry . n.king affect the domestic ba ~~market?~~ _~~I~~_

> 1 East-Asia and Pacific Region, The World Bank, Development Research Group, The. World Bank, and Development Research Group, The World Bank and center and Department of Economics, Tilburg University, respectively. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. We thank Anqing Shi for excellent research assistance.
#  _Expanding Intellectual Property_

As noted earlier, the Internet facilitates the abuse of intellectual property efficiently as it also spreads knowledge. Therefore, the need to do more to promote intellectual rights is crucial. But it must be noted that this challenge is a broad one, and it involves many other aspects, not to mention cost: The minimum costs of implementing [the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS)] in poor countries are about $1.5-2 million to build a rudimentary structure to enforce TRIPs-related rules, plus recurring operating costs. In contrast, the U.S. [Patent and Trademark Office] has a $1billion annual budget and 3,000 professionals. In addition, 600 judges preside over [Intellectual Property Rights (IPR)] litigations in the U.S.<sup>62</sup> As many studies suggest, the Intellectual Property debate is complex, wide-ranging, and inconclusive.<sup>63</sup> Nonetheless, with respect to creative work the following strategies, while hardly new, could be considered:

- Hold digital training workshops for artists and others in the domain of intellectual rights to understand the benefits and limitations of posting their work online.

- Enforce intellectual measures where possible, especially holding accountable public and private parties that abuse intellectual rights.

In Asia, the leading region with Internet users to date (see figure 6) — China leads here with over 600 million users<sup>64</sup> — the piracy problem is no secret. And China’s effort to curb piracy seems intractable. Though China has a long way to go, it took a step worth noting:

- China launched a “nationwide “Program for Special Campaign on Combating IPR Infringement and Manufacture and Sales of Counterfeiting and Shoddy Commodities” (“Special Campaign”). The program ran for 9 months (from October 2010 to June 2011), and it was “aimed to crack down on IP infringement and counterfeiting, raise awareness of IP rights and create a positive environment for intellectual property protection, and urge businesses to increase awareness of infringement and avoid infringing practices.”<sup>65</sup>

Such an approach is perhaps difficult to sustain. But it provides a mixture of hard and soft approaches developing countries could adopt. Moreover, by the time the “Special Campaign” ended, it had “solved 15,868 cases, bringing in 13.12 billion yuan ($2.08 billion

> 62 Minxin Pei, _Intellectual Property Rights: A Survey of the Major Issues_ (Hong Kong: Asia Business Council, September 2005), accessed March 31, 2015, <u>http://www.asiabusinesscouncil.org/docs/IntellectualPropertyRights.pdf.</u> 63 Ibid.

> 64 As of June 2014. See: “Internet Usage in Asia,” Internet World Stats, accessed March 30, 2015, <u>http://www.internetworldstats.com/stats3.htm#asia.</u>

> 65Vincent Brodbeck, “Streaming Media and Curbing Digital Piracy in China,” _Boston University Journal of Science and Technology Law_ , no. 19.1 (Winter 2013), accessed March 31, 2015, <u>http://www.bu.edu/jostl/files/2015/02/Brodbeck_Note.pdf.</u>

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