rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 9353

# **Abstract**

Export responses to real exchange rate (RER) depreciations in Pakistan are lower than those to appreciations. This paper empirically documents this asymmetric response using macro-level data. It then relies on a disaggregated export product–level data set for 2003–17 to test, within a panel fixed-effects framework, three hypotheses explaining the low export response to depreciations, focusing on information costs, supply constraints, and pricing to market. The analysis finds that (i) exports of differentiated products grow more slowly when the RER depreciates than they fall when it appreciates; (ii) exports from sectors with relatively greater supply constraints—in particular related to accessing finance- respond less to depreciations than to appreciations; and (iii) dollar prices for Pakistani exports tend to fall after nominal depreciations of the Pakistani rupee, in violation of the Dominant Currency Paradigm and consistent with pricing-to-market behavior, further accounting for the low response of exports to RER depreciations.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gvarela@worldbank.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
Policy Research Working Paper 5154

# **Abstract**

This paper analyzes changes in agricultural production and economic welfare of farmers in rural Peru resulting from a large irrigation infrastructure rehabilitation project. The analysis uses a ten-year district panel and a spatial regression discontinuity approach to measure the causal effect of the intervention. While general impacts are modest, the analysis shows that the project is progressive--poor farmers consistently benefit more than non-poor farmers. Farmers living in districts with a rehabilitated irrigation site experience positive labor dynamics, in terms of income and agricultural jobs.

Poor farmers increase their total income by more than $220 per year compared with the control group, while rich farmers do not experience such an income gain. The results also show crop specialization patterns in the economic status of farm households; poorer farm households increase their production of staple crops, such as beans and potatoes, while non-poor beneficiary farmers cultivate more industrial crops. Findings from this evaluation have important implications for pro-poor policy design in the agricultural sector.

This paper—a product of the Corporate, Global and Methods Division, of the Independent Evaluation Group—is part of a larger effort in the department to assess impacts of World Bank projects in Agriculture. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xdelcarpio@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. 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
variables collected in 2013 and the 16 variables from 2016 used to test overall balance. Out of 39 variables, no variables from 2016 were unbalanced and only three from 2013 were not balanced at the 5% level based on Kruskal-Wallis tests. Respondents with Mirt stoves were more likely in 2013 to have metal roofs (p-value = 0.0019), which is the same as the overall sample, are less likely to report being food secure over the previous year (p-value = 0.049), and are more likely to drink alcohol (p-value = 0.052).

We find significant effects of the Mirt stove on average PM2.5 concentrations if households have their primary baking area in their main house, but not for households with separate cooking/baking areas. When cooking/baking is done in the main living areas, average mean concentrations of PM2.5 are estimated to be 64% lower for those who have the Mirt ICS than households without Mirt stoves (592  g/m<sup>3</sup> versus 1622  g/m<sup>3</sup> , two-tailed t-test p value=0.03, Kruskal-Wallis rank sum test with ties prob > <sup>2</sup> =0.10).

There are no statistically significant differences in maximum concentrations, but in percentage terms, effects are very large if central tendency is measured at the median. We find that the average of the median PM2.5 concentrations is less than 1⁄4 of what they are in houses that do not have a Mirt stove (19  g/m<sup>3</sup> versus 85  g/m<sup>3</sup> , Kruskal-Wallis rank sum test prob > <sup>2</sup> =0.01). As the distributions of all three PM2.5 concentration measures are skewed, medians may be considered better measures of central tendency than means. By this measure, the Mirt stove reduces PM2.5 concentration by 78%, which is at the higher end of ICS interventions and may offer health benefits to those in baking areas (Quansah et al., 2017).

**Table 2 Mean, Median and Maximum PM2.5 Concentrations in Cooking/Baking Areas of Households who did and did not Receive Mirt Stoves in 2013 and who Bake and Cook in their Main House Rather than in a Separate Cooking/Baking Area**

||Mean|Median|Maximum|
|---|---|---|---|
|Mirt, Generally with<br>Traditional Too|595.85 (157.23)<br>(21)|19.12 (9.25)<br>(21)|45,579.62 (6828.47)<br>(21)|
|Traditional Only|1622.93 (421.22)<br>(32)|85.18 (48.77)<br>(32)|43,796.72 (6828.47)<br>(32)|

Standard deviations and N in parentheses It is important to emphasize that there are no statistically significant differences in average PM2.5 concentrations when comparing our three concentration measures by cooking location only (main house versus separate cooking/baking area) rather than treatment vs. control assignment 12
Table 4: Estimates of catch-up <u>growth in HAZ scores (conditional model)</u>

||||||Be|tween||Withi|n|Both|
|---|---|---|---|---|---|---|---|---|---|---|
|||_α_0|_β_0|_ραβ_|H0_α_0|H0_β_0|?|H0_ραβ_|?|?|
|Ethiopia|POLS|-1.390<br>(0.022)|-0.016<br>(0.004)|-0.246<br>(0.023)|0.00|0.99|N|0.00|Y|N|
||CRE<br>FEIS|-1.402<br>(0.022)<br>-1.381<br>(0.023)|-0.002<br>(0.004)<br>0.003<br>(0.003)|-0.529<br>(0.023)<br>-0.303<br>(0.023)|0.00<br>0.00|0.69<br>0.20|N<br>N|0.00<br>0.00|Y<br>Y|N<br>N|
|India|POLS|-1.438|-0.034|0.367|0.00|0.99|N|0.99|N|N|
||CRE|(0.020)<br>-1.451|(0.004)<br>-0.025|(0.023)<br>-0.295|0.00|0.99|N|0.00|Y|N|
|||(0.020)|(0.003)|(0.023)|||||||
||FEIS|-1.440<br>(0.021)|-0.015<br>(0.003)|-0.193<br>(0.023)|0.00|0.99|N|0.00|Y|N|
|Peru|POLS|-1.290<br>(0.019)|0.020<br>(0.004)|0.335<br>(0.023)|0.00|0.00|Y|0.99|N|N|
||CRE|-1.327|0.045|-0.067|0.00|0.00|Y|0.00|Y|Y|
|||(0.019)|(0.003)|(0.023)|||||||
||FEIS|-1.244<br>(0.022)|0.046<br>(0.003)|-0.130<br>(0.023)|0.00|0.00|Y|0.00|Y|Y|
|Vietnam|POLS|-1.128<br>(0.019)|-0.005<br>(0.004)|0.347<br>(0.023)|0.00|0.91|N|0.99|N|N|
||CRE|-1.130|0.003|0.024|0.00|0.13|N|0.85|N|N|
|||(0.019)|(0.003)|(0.023)|||||||
||FEIS|-1.124<br>(0.023)|0.020<br>(0.002)|0.036<br>(0.023)|0.00|0.00|Y|0.94|N|N|

Note: the first three columns report parameter estimates (and cluster-robust standard errors, in parentheses) from alternative estimators (POLS, CRE and FEIS); all estimates include a set of time-varying controls; columns denoted ‘H0’ report the probability associated with individual tests of the one-sided null hypotheses associated with each parameter (see Table 2); columns denoted ‘?’ report the conclusion of (composite) hypotheses regarding specific forms of catch-up growth; probability estimates larger than 0.99 have been rounded down to the latter value. Source: own estimates.

17
## _<u>Salinity Estimates</u>_

To generate a baseline profile of aquatic salinity for the Indian Sundarbans,<sup>5</sup> a high‐ resolution, spatial point file covering four years (2012–15) was created, based on data compiled from field measurements taken by the Nature Environment & Wildlife Society (NEWS) and World Wildlife Fund‐India (WWF‐India). Given the limited amount of salinity information available for the Indian Sundarbans, this analysis also drew from the most comprehensive study to date on salinity impacts for the Bangladesh Sundarbans—which accounts for 60 percent (about 6,000 km<sup>2</sup> ) of the wetland forest—in order to arrive at a broader picture of the Sundarbans’ current and future salinity (Dasgupta, Sobhan, and Wheeler 2018).<sup>6</sup>

# **3. Methods**

To get a broader picture of the migration of mangrove species with progressive aquatic salinization in a changing climate, a five‐step analysis was conducted. In step 1, Landsat 8 (OLI), sentinel, and hyperspectral data from Hyperion were used to generate a basemap (2015) to understand the current spatial distribution of mangrove species. In step 2, a high‐resolution spatial point file of aquatic salinity covering four years (2012–15) was created from the best available, location‐specific monitored salinity data to understand the baseline aquatic salinity profile of the area. In step 3, salinity tolerance ranges for the mangrove species were computed, combining the baseline mangrove distributions and the baseline, high‐resolution aquatic salinity profile. In step 4, projections of location‐specific aquatic salinity were made for 2050. Finally, in step 5, the impacts of progressive aquatic salinization on mangrove species by 2050 were projected.

_<u>Step 1. Generate a Basemap (2015) of Mangroves in the Indian Sundarbans from Satellite Data</u>_ A high‐resolution map of mangroves was prepared from hyperspectral data from Hyperion, Landsat 8 Operations Images (OLI), and field survey data using a multi‐step procedure.

First, a field survey was conducted at 141 GPS locations to collect geographic location information on five dominant genera of Indian Sundarban mangroves— _Avicennia sp_ ., _Ceriops sp_ ., _Excoecaria sp_ ., _Heritiera sp_ . and _Sonneratia sp_ .—and their assemblages. This information was later used to generate a spectral signature of mangroves and validate the interpretation of satellite images.

Second, spectral profiles for the various mangrove species were generated from hyperspectral imagery from Hyperion. To start, 67 of 242 bands of Hyperion images were

> 5 At present, there is no geo‐coded database on aquatic or soil salinity for the Indian Sundarbans.

> 6 This paper draws extensively on spatial data from the Aquatic Salinity Information System (RSIS) for southwest coastal Bangladesh, including the Sundarbans. The RSIS provides location‐specific salinity estimates for December 2011, January–June 2012, December 2049, and January–June 2050 under 27 climate‐change scenarios (http://sdwebx.worldbank.org/climateportal/index.cfm?page=websalinity_dynamics&ThisRegion=Asia&ThisCcode =BGD).

5
Table 11: Extensive margin I: Probability of export participation

|**Dependent Variable:**Probability|of Export Participation_f pjt_<br>||||||
|---|---|---|---|---|---|---|
||PCA|Baseline||Robus|tnesstests||
||||Lags|w/o large firms|w/o concentrated sectors|Placebo|
||(1)|(2)|(3)|(4)|(5)|(6)|
|NTM Principal Component_i_,_j_,_p_,_t_|-0.1448||||||
||(0.008)***||||||
|Log (Tariff_i_,_j_,_p_,_t_)||-0.0483|-0.0571|-0.0512|-0.0488|-0.0242|
|||(0.011)***|(0.011)***|(0.013)***|(0.011)***|(0.022)|
|SPS_i_,_j_,_p_,_t_||-0.0280|-0.0283|-0.0363|-0.0307|-0.0219|
|||(0.013)**|(0.013)**|(0.016)**|(0.013)**|(0.024)|
|TBT_i_,_j_,_p_,_t_||-0.0343|-0.0424|-0.0496|-0.0348|-0.0098|
|||(0.012)***|(0.012)***|(0.014)***|(0.012)***|(0.013)|
|Pre-Shipment_i_,_j_,_p_,_t_||0.0099|-0.0142|0.0169|0.0089|-0.0030|
|3||(0.009)|(0.009)|(0.012)|(0.009)|(0.013)|
|QuantityControl_i_,_j_,_p_,_t_||-0.0537|-0.0454|-0.0391|-0.0501|-0.0193|
|||(0.021)***|(0.022)**|(0.022)*|(0.021)**|(0.022)|
|PriceControl_i_,_j_,_p_,_t_||-0.0043|0.0018|-0.0073|-0.0044|0.0008|
|||(0.014)|(0.014)|(0.016)|(0.014)|(0.014)|
|OtherNTMs_i_,_j_,_p_,_t_||-0.0091|0.0248|-0.0053|-0.0172|-0.0254|
|||(0.022)|(0.020)|(0.030)|(0.022)|(0.019)|
|Log (Import Demand_f _,_p_,_j_,_t_)|0.0184|0.0110|0.0105|0.0138|0.0114|0.0107|
||(0.001)***|(0.001)***|(0.001)***|(0.002)***|(0.001)***|(0.002)***|
|Observations|253212|253212|227964|200738|250213|219733|
|Adjusted_R_<sup>2</sup>|0.13|0.15|0.15|0.15|0.15|0.15|
|Fixed Effects|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|_f _−_p _−_j_,_f _−_p _−_t_,_j _−|_t_<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_|<br>_f _−_p _−_j_,_f _−_p _−_t_,_j _−_t_<br>_f _|−_p _−_j_,_f _−_p _−_t_,_j _−_t_|

This table estimates equation 1 for the extensive margin I of Colombian firm-level exports. Columns (2-6) show coefficients standardized with zero mean and unit standard deviation. All columns use firm-HS6 product-destination, firm-HS6 product-year and destination-year fixed effects. All columns also control for the Inverse Mills Ratios for each NTM type following estimation of probit model of NTM selection based on NTM intensity in neighboring countries. Clustered standard errors at the HS6 product-year level are presented in parenthesis. ∈ _p <_ 0.10,∈∈ _p <_ 0.05,∈∈∈ _p <_ 0.01
# **References**

- Alm ̊as, Ingvild, Alexander W. Cappelen, Erik Ø. Sørensen, and Bertil Tungodden (2010). “Fairness and the development of inequality acceptance,” _Science_ , 328(5982): 1176–1178.

- Andreoni, James, Deniz Aydın, Blake Barton, B Douglas Bernheim, and Jeffrey Naecker (2016). “When fair isn’t fair: Sophisticated time inconsistency in social preferences,” Working Paper.

- Andreoni, James and John Miller (2002). “Giving according to GARP: An experimental test of the consistency of preferences for altruism,” _Econometrica_ , 70(2): 737–753.

- Bellemare, Charles, Sabine Kr ̈oger, and Arthur van Soest (2008). “Measuring inequity aversion in a heterogeneous population using experimental decisions and subjective probabilities,” _Econometrica_ , 76(4): 815–839.

- Bowles, Samuel and Herbert Gintis (2011). _A cooperative species: Human reciprocity and its evolution_ , Princeton University Press.

- Cappelen, Alexander W., Astri Drange Hole, Erik Ø. Sørensen, and Bertil Tungodden (2007). “The pluralism of fairness ideals: An experimental approach,” _American Economic Review_ , 97(3): 818– 827.

- Cappelen, Alexander W., James Konow, Erik Ø. Sørensen, and Bertil Tungodden (2013a). “Just luck: An experimental study of risk taking and fairness,” _American Economic Review_ , 103(3): 1398–1413.

- Cappelen, Alexander W., Karl O. Moene, Erik Ø. Sørensen, and Bertil Tungodden (2013b). “Needs versus entitlements: An international fairness experiment,” _Journal of the European Economic Association_ , 11(3): 574–598.

- Cappelen, Alexander W., Erik Ø. Sørensen, and Bertil Tungodden (2010). “Responsibility for what? Fairness and individual responsibility,” _European Economic Review_ , 54(3): 429–441.

- Charness, Gary and Matthew Rabin (2002). “Understanding social preferences with simple tests,” _Quarterly Journal of Economics_ , 117(3): 817–869.

- Chen, Yan and Sherry Xin Li (2009). “Group identity and social preferences,” _American Economic Review_ , 99(1): 431–457.

- Engelmann, Dirk and Martin Strobel (2006). “Inequality aversion, efficiency, and maximin preferences in simple distribution experiments: Reply,” _American Economic Review_ , 96(5): 1918–1923.

- Falk, Armin and Urs Fischbacker (2006). “A theory of reciprocity,” _Games and Economic Behavior_ , 54(2): 293–315.

- Fehr, Ernst, Helen Bernhard, and Bettina Rockenbach (2008). “Egalitarianism in young children,” _Nature_ , 454(7208): 1079–1083.

- Fehr, Ernst and Urs Fischbacher (2004). “Social norms and human cooperation,” _Trends in Cognitive Sciences_ , 8(4): 185–190.

- Fehr, Ernst and Simon G ̈achter (2000). “Cooperation and punishment in public goods experiments,” _American Economic Review_ , 90(4): 980–994.

- Fehr, Ernst, Daniela Gl ̈atzle-R ̈utzler, and Matthias Sutter (2013). “The development of egalitarianism, altruism, spite and parochialism in childhood and adolescence,” _European Economic Review_ , 64(1): 369–383.

6
households’ cooking/baking areas for 72 hours. Because households typically cook injera at least twice per week, all households should have baked injera during the 72-hour monitoring period.<sup>5</sup> PATS+ results were calibrated using gravimetric filters co-located for exactly 24 hours in 50 of the sample households. Half of these households received Mirt stoves and half did not. Such calibration is necessary when using light scattering particle sensors (Berkeley Air Monitoring Group, 2017; Chow et al., 2002). Based on these findings, OLS regression was used to estimate an adjustment factor of 0.8065, which was applied to all PATS+ measurements. In gathering HAP information, field enumerators were instructed to follow strict protocols developed by Berkeley Air Monitoring Group. These protocols, which focus on placement of the equipment in cooking areas, maintenance of the equipment and timing of the samples, are included in the Appendix to this paper.

In addition to measuring mean, median and maximum PM2.5 concentrations, the PATS+ monitors also measured humidity and temperature. Key data on cooking/baking area characteristics, including size, shape, presence of windows and location (in the main living area or separate), were collected as well as information on the status of Mirt stoves, including if they were broken or unused, and whether traditional stoves were still in place. We also collected updated information on fuels used.<sup>6</sup>

# 3.2 Empirical Methods

With sufficient sample size and a randomized intervention, if the randomization is successful across the treatment and control groups, we can simply compare sufficient statistics of those in the treatment and control groups to identify the treatment effect. To evaluate the quality of the randomization we compare households receiving Mirt stoves with those who did not receive stoves along 39 dimensions, with a special emphasis on cooking/baking area and environmental characteristics that are very likely to affect HAP concentrations. We also test for balance on smoking behaviors, socioeconomic characteristics, and cooking/eating frequency. We find that only one of the 39 variables is statistically different across the groups, a rate that is just as likely to be due to chance given a 5 percent significance threshold.

> 5 Only 2 households (<1%) report on average cooking injera less than twice per week.

> 6 The research design allowed for the possibility that households cooked in two places in their homes (e.g. in main house and a separate kitchen), but no households had two kitchens. Two HAP-monitored households reported they did not use their Mirt stove and one had purchased a replacement.

8
# **Table 9 – Local banks: Adverse reform spillovers affecting domestic financial products and services provided by your bank**

_Please indicate the regulatory reforms which contributed to a significant reduction or tightening of your institution’s activities in emerging markets and developing economies (EMDEs), particularly those that are jurisdictions not required to implement the reforms (you can select more than one reform area)._

|**N = 20**|**Risk-based**<br>**capital**<br>**reforms**|**Leverage**<br>**ratio**|**Liquidity**<br>**measures**|**Measures**<br>**dealing with**<br>**systemically**<br>**important**<br>**banks**|**Measures**<br>**dealing**<br>**with OTC**<br>**derivatives**|**Any other**<br>**G20**<br>**financial**<br>**reform**|**No**<br>**Significant**<br>**Impact of**<br>**any reform**|**Don't**<br>**know**|
|---|---|---|---|---|---|---|---|---|
|Infrastructure finance and other<br>forms of long-term investment financing|13|6|9|5|2|1|5|1|
|Syndicated lending|10|8|6|4|2|1|8|1|
|Consumer credit|8|9|7|3|2|1|9|1|
|Mortgages or Housingfinance|9|9|7|4|2|1|8|1|
|Corporate credit|16|14|9|4|3|1|3|1|
|SME credit|9|8|7|4|2|0|6|2|
|Trade credit for SMEs|6|5|5|3|2|1|8|2|
|Trade credit for other companies|9|7|5|4|2|2|7|1|
|Agricultural credit|5|4|6|2|2|0|10|3|
|Islamic finance|1|2|3|1|0|1|8|**9**|
|Foreign currencylending|13|5|12|5|3|2|2|**1**|
|Hedging and risk management products and<br>services|8|3|8|5|8|1|4|1|
|Corresponding banking services provided<br>byforeign banks inyourjurisdiction|11|5|10|7|3|2|6|1|
|Remittances services provided by foreign<br>banks inyourjurisdiction|6|4|6|5|2|3|7|4|

30
**Figure 7: Equity multiplier**

_(Outstanding guarantees/ Equity)_ <!-- Start of picture text --> 25<br>20<br>20<br>15<br>15<br>12.4<br>10<br>10<br>8.5<br>5.79<br>5 5 5<br>5 3.75 4 4.1<br>1.2<br>0.45 0.7<br>0<br><!-- End of picture text --> _Source: Respective PCG schemes_

**Figure 8: Net loss ratios** _(Average of 2007-09)_

<!-- Start of picture text --> 4.5<br>4<br>4<br>3.7<br>3.5 3.3<br>3.2 3.2<br>3 3<br>3<br>2.5<br>2.5<br>2<br>2<br>1.5 1.5<br>1.5<br>1<br>1<br>0.6<br>0.5<br>0.5 0.3<br>0.1<br>0<br><!-- End of picture text --> _Source: Respective PCG schemes_ 32