rafmacalaba/datause-encoder-data huggingface dataset:
18 One can also use the results of Table 3 to estimate the deterioration in overall inequality which would be sufficient to eliminate the otherwise desirable effect of growth on poverty in Bangladesh. The headcounc index of poverty will not be very sensitive to growth associated with an increase in inequality, assuming that this entails a proportionate shift in the Lorenz curve, as discussed above. It would take a 13% annual increase in the Gini index to eliminate the desirable effect of a 2.4% growth rate on the headcount index of poverty, using the lower poverty line.<sup>11</sup> This would imply a substantial increase in the Gini index, from 0.27 to 0.50 over five years. It would clearly take a highly inequitable growth process to undercut the desirable impact of growth on the proportion of poor in Bangladesh.

However, that conclusion is not robust **to** the choice of poverty measure. One finds that, for the distributionally sensitive FGT poverty measure, an increase in the Gini coefficient of only 4.1% per year would be sufficient to wipe out the effect on poverty of a 2.4% annual growth rate in mean consumption. This would be equivalent to an increase in the Gini from 0.27 to 0.33 over five years, which is not inconceivable. Thus, even seemingly small deteriorations in overall equity associated with growth can substantially impede progress in alleviating the most severe extremes of poverty in Bangladesh.

# IV. Conclusions

The recent evidence of a decline in absolute numbers of poor in Bangladesh during the 1980s is unconvincing. The rate of growth in real consumption per capita of 10% per year implied by the underlying household expenditure surveys is too high to be believed. While i_ should not be
**<u>Table 4. The district fixed-effect RDD-DD regressions of households’ income and poverty</u>**

||||D<br>|ependent variable<br>|s<br>|||
|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|*|-0.0181|0.4116|0.8039**|-0.3774**|0.5492|0.8739***|0.9973**|
|Program Post-program period|(0.0869)|(0.5071)|(0.3499)|(0.1852)|(0.5740)|(0.3109)|(0.4132)|
|(Poverty rate – 50) * Post-|0.0052|-0.0756*|-0.0658*|0.0099|-0.0303|-0.0566*|-0.0153|
|program period|(0.0088)|(0.0442)|(0.0382)|(0.0216)|(0.0554)|(0.0317)|(0.0407)|
|Program * (Poverty rate – 50) *|-0.0092|0.0284|0.0511|-0.0052|-0.0064|0.0444|0.0215|
|Post-program period|(0.0093)|(0.0502)|(0.0414)|(0.0223)|(0.0607)|(0.0373)|(0.0456)|
|Age of household heads|0.0262***|0.0485***|0.0565***|0.1330***|-0.0451***|-0.0931***|-0.0598***|
||(0.0021)|(0.0135)|(0.0081)|(0.0128)|(0.0090)|(0.0099)|(0.0105)|
|A d f hhld hd|-0.0002***|-0.0008***|-0.0007***|-0.0013***|0.0006***|0.0012***|0.0007***|
|ge square o ouseo eas|(0.0000)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|(0.0001)|
|Gender of household head|0.0053|-0.0124|0.2031***|0.6791***|-0.4089***|-0.0837|-0.3800***|
|(male=1, female=0)|(0.0181)|(0.0987)|(0.0626)|(0.0753)|(0.0502)|(0.0556)|(0.0663)|
|Ehi iii =1 Kih=0|-0.6139***|-0.2264|-1.7972***|0.8630***|-0.5437***|0.6164***|-0.7285***|
|tnc mnortes (yes, n)|(0.0490)|(0.1793)|(0.1596)|(0.1217)|(0.1376)|(0.1165)|(0.1017)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.6202***|3.0156***|1.6227***|3.7472***|4.8958***|1.9215***|3.0930***|
||(0.0824)|(0.3525)|(0.2387)|(0.3254)|(0.2912)|(0.2996)|(0.2578)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.364|0.164|0.107|0.148|0.228|0.149|0.091|
|The mean value of the dependent<br>variables before the program|9.026|3.651|1.712|8.149|3.912|0.754|1.324|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

43
**Figure 6:  7-year EUR-USD cross currency basis**

<!-- Start of picture text --> 10<br>0<br>‐10<br>‐20<br>‐30<br>‐40<br>‐50<br>‐60<br>‐70<br>May‐01 Dec‐01 Jul‐02 Feb‐03 Sep‐03 Apr‐04 Nov‐04 Jun‐05 Jan‐06 Aug‐06 Mar‐07 Oct‐07 May‐08 Dec‐08 Jul‐09 Feb‐10 Sep‐10 Apr‐11 Nov‐11 Jun‐12 Jan‐13 Aug‐13 Mar‐14 Oct‐14 May‐15 Dec‐15 Jul‐16 Feb‐17<br><!-- End of picture text --> Source: Bloomberg

# **III. The markets for USD and EUR-denominated debt**

The market for EUR-denominated bonds for emerging market countries differs from the USDdenominated market regarding its size and depth, the investor base, issuers, standard tenors, and conventions. These differences bring significant implications for EM issuers both for strategy development and execution of transactions.

As discussed above, the market for USD-denominated bonds for emerging market countries is several times larger and more liquid than its equivalent in EUR. Even though the surge in EM bond issuance in EUR has brought the ratio of new issuance in USD to EUR down to historical lows, the ratio is still above 4:1 and its average since 2000 hovers around 8:1. The larger size of the market in USD tends to be accompanied by a higher turnover which in turn results in lower bidask spreads. For example, in the case of Mexico, the average bid-ask spread of the yield to maturity on outstanding USD-denominated international bonds is 7 basis points, compared to 10 basis points for outstanding EUR-denominated bonds; and Mexico is an example with very liquid benchmarks on both currencies.

Investors in EUR-denominated sovereign securities tend to be less sophisticated compared to those operating in the USD market and are likely to demand higher credit spreads to move outside their comfort zone. While hedge funds participate actively in both markets, real money investors such as pension funds and insurance companies dominate the EUR market. These institutional investors have at their disposal an extensive portfolio of securities from sovereigns that are in the Eurozone, in the path to joining the euro, or, with close economic and financial linkages with the Eurozone.

8
# **Figure 1. Emerging Economy Imports Affected by TTBs through 2011**

<!-- Start of picture text --> a. By policy<br><!-- End of picture text --> <!-- Start of picture text --> b. By category of affected trading partner<br><!-- End of picture text --> <!-- Start of picture text --> percent Argentina percent Argentina<br>4 14<br>3.5 12<br>3<br>10<br>2.5<br>8<br>2<br>6<br>1.5<br>1 4<br>0.5 2<br>0 0<br>1994 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1994 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Brazil percent Brazil<br>2.5 14<br>2 12<br>10<br>1.5<br>8<br>1 6<br>4<br>0.5<br>2<br>0 0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent China percent China<br>5 5<br>4.5 4.5<br>4 4<br>3.5 3.5<br>3 3<br>2.5 2.5<br>2 2<br>1.5 1.5<br>1 1<br>0.5<br>0.5<br>0<br>0 1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent India percent India<br>7 25<br>6<br>20<br>5<br>4 15<br>3<br>10<br>2<br>5<br>1<br>0 0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11 1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Stock: imports subject to any imposed TTB in effect All trading partners' exports under any TTB in effect<br>Stock: imports subject to AD only in effect China's exports under any TTB in effect<br>Flow: imports subject to any newly initiated TTB investigation Other emerging economies' (non-China) exports under any TTB in effect<br>Flow: imports subject to newly initiated AD investigation only High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

19
Market and product experience decrease the probability to exit foreign markets. The more experienced a firm is the lower the probability to stop selling product _p_ in market _j_ will be. But product experience seems to matter more than market experience when it comes to exit. As for the years as an exporter, the probability to stop a product in given a market decreases by one percentage point with an additional year of export experience. Also being a multi-product exporter in _t-1_ decreases the probability to drop a product-market combination in _t_ by 18.7% to 23.2%. This is the covariate with the largest effect on the probability to stop selling a product in a given market.

Table 4: Estimates on the probability to exit

|variables|**(1)**<br>exit|**(2)**<br>exit|**(3)**<br>exit|**(4)**<br>exit|**(5)**<br>exit|**(6)**<br>exit|
|---|---|---|---|---|---|---|
|tariff cut|-0.001***<br>(0.000)|-0.001***<br>(0.000)|-0.001***<br>(0.000)||||
|tariff||||0.000|0.000|0.000|
|||||(0.000)|(0.000)|(0.000)|
|share in sales (t-1)|-0.001***<br>(0.000)|-0.002***<br>(0.000)|-0.002***<br>(0.000)|-0.000***<br>(0.000)|-0.002***<br>(0.000)|-0.002***<br>(0.000)|
|market experience (t-1)||-0.003***|-0.003***||-0.005***|-0.004***|
|||(0.000)|(0.000)||(0.000)|(0.000)|
|product experience (t-1)||-0.018***<br>(0.001)|-0.016***<br>(0.001)||-0.020***<br>(0.001)|-0.018***<br>(0.001)|
|years as an exporter||-0.014***|-0.014***||-0.010***|-0.009***|
|||(0.001)|(0.001)||(0.002)|(0.002)|
|multi-product exporter (t-1)||-0.206***<br>(0.010)|-0.187***<br>(0.009)||-0.232***<br>(0.015)|-0.208***<br>(0.014)|
|ez|||-0.093***<br>(0.006)|||-0.134***<br>(0.009)|
|comparative advantage|||-0.052***<br>(0.004)|||-0.045***<br>(0.006)|
|comparative advantage_x_tariff cut|||-0.000<br>(0.000)||||
|comparative advantage_x_tariffs||||||0.000<br>(0.000)|
|Observations|90,261|90,223|90,149|47,145|47,113|47,102|
|R-squared|0.720|0.736|0.738|0.693|0.711|0.715|

Time, country and sectors fixed effects are used in all specifications, but not reported. Errors are cluster by product-country. Robust standard errors in parentheses. Significance levels: *** p<0.01, ** p<0.05, * p<0.1 Firms located in an export zone show a lower probability to exit from market _j_ . This is not surprising, as the requirements to be located in an EZ are more stringent than those required to be located in the national territory. Larger fixed costs in the EZ could therefore explain the better survival of EZ firms through a hysteresis mechanism (Baldwin and Krugman, 1989 and Dixit, 1989).<sup>31</sup> Another explanation could be that the firms in export zones are the most productive ones (i.e. firms self-select into export zones) and therefore also exhibit higher survival rates.

> 31 Hysteresis refers to the persistency of a firm’ export participation as a consequence of the sunk costs associated with the entry into new markets. The entry into new markets is generally costly, so if a firm enters a market following a shock (i.e. exchange rate depreciation) it won’t necessarily exit once the shock disappears.

20
# **Table 8B. Second stage estimations instrumenting the per capita amount of remittances received**

This table shows marginal effects from the estimations of a probit using instrumental variables. The dependent variables are dummy variables for whether the household has a deposit account (columns (1)-(3)), received a loan (columns (4)-(6)) and requested a loan (columns (7)-(9)). The variable being instrumented is the per capita amount of remittances received by households. The instruments used are _Real GDP per capita in US states_ (columns (1), (3), (4), (6), (7), and (9)) and _Poverty in US states_ (columns (2), (3), (5), (6), (8), and (9)) _._ All independent variables and instruments are defined in Table 1. Department fixed effects are estimated but not reported. Robust z-statistics are shown in brackets. *,**, and *** denote significance at 10, 5 and 1 percent, respectively.

|Variables||Deposit accou|nt||Loan receive|d|L|oan request|ed|
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Remittances|0.361|0.360|0.360|0.005|-0.035|-0.030|-0.014|-0.052|-0.047|
||[9.16]***|[10.30]***|[10.24]***|[0.10]|[-0.72]|[-0.56]|[-0.28]|[-0.93]|[-0.80]|
|Real per capita income|0.013|0.013|0.013|0.001|0.001|0.001|0.002|0.001|0.001|
||[3.36]***|[3.38]***|[3.38]***|[0.67]|[0.50]|[0.53]|[0.82]|[0.66]|[0.68]|
|House and land ownership|0.001|0.001|0.001|0.021|0.025|0.025|0.020|0.025|0.024|
||[0.05]|[0.04]|[0.05]|[2.03]**|[1.97]**|[1.90]*|[1.69]*|[1.75]*|[1.69]*|
|Adults average education|0.014|0.014|0.014|0.006|0.007|0.006|0.006|0.007|0.007|
||[2.76]***|[2.90]***|[2.91]***|[3.10]***|[2.70]***|[2.67]***|[2.82]***|[2.58]***|<br>[2.54]**|
|Agriculture|-0.016|-0.016|-0.016|0.012|0.013|0.013|0.015|0.016|0.016|
||[-0.64]|[-0.63]|[-0.63]|[1.03]|[1.03]|[1.03]|[1.17]|[1.15]|[1.15]|
|Electricity|0.000|0.001|0.001|0.018|0.029|0.027|0.028|0.039|0.037|
||[0.00]|[0.03]|[0.04]|[1.24]|[1.64]|[1.49]|[1.64]|[1.90]*|[1.76]*|
|Returned migrant|-0.137|-0.137|-0.136|0.023|0.067|0.061|0.046|0.090|0.084|
||[-2.58]***|[-2.75]***|[-2.73]***|[0.43]|[0.94]|[0.82]|[0.69]|[1.11]|[1.00]|
|Instrument: Real GDP per capita in US states|Yes|No|Yes|Yes|No|Yes|Yes|No|Yes|
|Instrument: Poverty in US states|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Observations<br>|2289|2289|2289|2261|2261|2261|2267|2267|2267|
|Wald test chi<sup>2</sup>|574.5|573.9|573.8|57.9|64.9|62.6|64.3|78.6|75.7|
|Wald test p-value|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Log likelihood|-4005|-3999|-3999|-3596|-3593|-3593|-3645|-3642|-3642|
|Over-identification test chi<sup>2</sup>|-|-|0.01|-|-|2.21|-|-|1.70|
|Over-identification testp-value|-|-|0.94|-|-|0.14|-|-|0.19|

29
the Afghan diet and households often purchase wheat in the form of flour. In the robustness tests, we use the prices of imported wheat flour, and domestic and imported wheat; the results are substantively the same.

Consumer theory indicates that decisions are based on the relative prices of goods. In order to capture this in our empirical specification, and to better isolate the effect of wheat flour prices as distinct from co-movement from other price changes, we include several other prices in our specification. In particular, we include the prices of milk, lamb, rice (domestic) and vegetable oil, as well as the price of kerosene, which is commonly used in cooking. These four food products plus wheat flour make up eighty percent of average food expenditure of the relatively poor (i.e., the reference population for defining the composition of food needs for the official government poverty line). Average prices are calculated for rural and urban areas within each province for each quarter.

Since each household is observed only once over the year, the household-level controls do not vary over time. The controls include the following: dummies for consumption quintiles (bottom quintile excluded) intended as proxies for income;<sup>34</sup> age of household head; dummy for households where heads are literate; dummy for households where heads are married. (In the robustness tests, we find that the inclusion of other household demographic characteristics does not qualitatively alter the general findings.) District-level variables include topography dummies (plateau and mountainous areas, plains excluded) and a rural dummy. Lastly, we include province fixed effects, Π, to control for observable and unobservable time-invariant province-level factors that could confound the results. These fixed effects control, for example, for differences in local governance efficacy, province-specific variation in relationships with the central government,<sup>35</sup> or differences in conflict and insecurity that are present throughout the survey year.

> 34 The NRVA does not collect detailed income data, but rather focuses on detailed consumption data primarily because it is generally assumed that consumption is more accurately measured than income in poorer countries. Total household consumption (Afghani per month) consists of expenditures on food, non-food, durables and rent, following guidelines in Deaton and Zaidi (2002).

> 35 As one candidate example, the World Bank (2008) identifies wide variation across provinces in the distribution of public expenditures from central funds as a key public finance concern. Comparisons of per capita, public expenditures by province produces unusually large (and unexplained) variation with a six-fold difference between provinces receiving the highest and lowest levels of per capita public expenditures.

13
**Table A.2. Unrestricted irrigation: comparison of the Karavarsamis-Hamilton method and the method used in the WHO Guidelines for estimating annual rotavirus infection risks from the consumption of wastewaterirrigated lettuce by 10,000 Monte Carlo simulations**<sup>_a_</sup>

|**Wastewater**<br>**quality**<br>**(****_E. coli_ numbers**<br>**per 100 mL)**||**Rotavirus infection risk**<br>**pppy estimated by the**<br>**method of Karavarsamis**<br>**and Hamilton (2010)**|**Rotavirus infection risk**<br>**pppy estimated by the**<br>**method used**<br>**in the 2006 WHO**<br>**Guidelines**|
|---|---|---|---|
|10<sup>3</sup>−10<sup>4</sup>|Median risk:|0.36|0.30|
||95%-ile risk:|0.39|0.71|
||Minimum:<sup>_b_</sup>|0.30|1.1 × 10<sup>−2</sup>|
||Maximum:<sup>_b_</sup>|0.44|0.97|
|100−1000|Median risk:|4.5 × 10<sup>−2</sup>|3.5 × 10<sup>−2</sup>|
||95%-ile risk:|4.9 × 10<sup>−2</sup>|0.11|
||Minimum:|3.5 × 10<sup>−2</sup>|1.0 × 10<sup>−3</sup>|
||Maximum:|5.5 × 10<sup>−2</sup>|0.27|
|10−100|Median risk:|4.6 × 10<sup>−3</sup>|3.5 × 10<sup>−3</sup>|
||95%-ile risk:|5.0 × 10<sup>−3</sup>|1.2 × 10<sup>−2</sup>|
||Minimum:|3.5 × 10<sup>−3</sup>|9.5 × 10<sup>−5</sup>|
||Maximum:|5.7 × 10<sup>−3</sup>|3.0 × 10<sup>−2</sup>|

> _a_ Assumptions: 100 g lettuce eaten per person per 2 days; 10−15 mL wastewater remaining on 100 g lettuce after irrigation; 0.1−1 rotavirus per 10<sup>5</sup> _E. coli_ ; no pathogen die-off; N50 = 6.7 ± 25% and α = 0.253 ± 25%.

> _b_ The lowest and highest values of the 10,000 risk simulations.

Tables A.3 and A.4 detail the QMRA-MC estimates of infection risk for restricted and unrestricted irrigation, respectively, in the same way as determined in the 2006 WHO Guidelines, but with two important differences: (a) use of the Karavarsamis-Harrison method, and (b) without any pathogen dieoff. The implications are:

- **_Restricted irrigation:_** if a maximum tolerable DALY loss of 10<sup>−4</sup> pppy is adopted for fieldworkers (rather than the 10<sup>−6</sup> DALY loss pppy used in the Guidelines), the tolerable rotavirus infection risk is ~10<sup>−1</sup> and a pathogen reduction of 2 log units is required, which has to be achieved by wastewater treatment.

- **_Unrestricted irrigation:_** if a maximum tolerable DALY loss of 10<sup>−4</sup> pppy is also adopted for consumers, and thus a tolerable rotavirus infection risk of ~10<sup>−1</sup> , then a pathogen reduction of 4 log units is required (from 10<sup>7</sup> −10<sup>8</sup> per 100 ml to 10<sup>3</sup> −10<sup>4</sup> per 100 ml), which could be achieved by wastewater treatment (2 log units, as for restricted irrigation) and, for example, die-off (2 log units).

81
Policy ReseaRch WoRking PaPeR 4394

# **Abstract**

This paper measures the extent to which firms in developing countries are the target of bribes. Using new firm-level survey data from 33 African and Latin American countries, we first show that perceptions adjust slowly to firms’ experience with corrupt officials and hence are an imperfect proxy for the true incidence of graft. We then construct an experience-based index that reflects the probability that a firm will be asked for a bribe in order to complete a specified set of business transactions. On average, African firms are three times as likely to be asked for bribes as are firms in Latin America, although there is substantial variation within each region. Last, we show that graft appears to be more prevalent in countries with excessive regulation and where democracy is weak. In particular, our results suggest that the incidence of graft in Africa would fall by approximately 85 percent if countries in the region had levels of democracy and regulation similar to those that exist in Latin America.

This paper—a product of the Enterprise Analysis Unit, Financial and Private Sector Development Vice Presidency—is part of a larger effort to study and promote reforms in the business environment. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at jlopezcordova@ifc.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
<!-- Start of picture text --> 35%<br>30%<br>Starting<br>25%<br>point<br>20%<br>15%<br>10%<br>5%<br>0%<br>Argentina Brazil Peru Dominican El Salvador<br>Republic<br><!-- End of picture text --> Source: Own calculations based on Household surveys Figure 4: Prevalence of Temporary employment among salaried employees (Mid-90s/Mid-2010s) <!-- Start of picture text --> 70%<br>60% Starting Point<br>Ending Point<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Argentina Brazil Chile Mexico El Salvador<br><!-- End of picture text --> Source: Own calculations based on Household surveys Analyzing the evolution of non-standard employment according to their age profile, we found a slight increase in the share of the older groups (Figure 5 and 6). This slight aging in the profile of nonstandard workers is observed in both part-time and temporary employment. This finding is striking since, in principle, it was expected that the non-standard modalities of employment would show an increasing participation of the younger groups of the population. However, this change in the age composition of NSE is consistent with the age profile observed in total employment. In fact, the Page 10 of 51