rafmacalaba/datause-encoder-data huggingface dataset:
poverty line, the same amounts were used for the pre-payment and the post-payment lines-750 and 1287 thousand Dong for 1993 and 1998 respectively. In the case of the broader poverty line, a lower line was set for post-payment income, reflecting the fact that health care payments have to be met from pre-payment income but have already been met at the post-payment stage. The pre-payment and post-payment poverty lines for 1993 were set at 1160 and 1091 respectively, while the corresponding lines for 1998 were set at 1790 and 1700 respectively.

Figure 8 shows the chart of Pen's parade for households (individuals are used in the analysis that follows) for pre-payment income and extreme poverty in 1998. Overlaid on the chart are the out-of-pocket payments of each household. In some cases, households are clearly pushed further into extreme poverty by out-of-pocket payments, whilst in others they are pushed below the extreme poverty line having started out "before" out-of-pocket payments above it.

Table 6(a) shows that in the case of the food poverty line, out-of-pocket payments increase the headcount ratio by 4.4 percentage points in 1993 and by 3.4 percentage points in 1998. The poverty gap comparisons across years are most meaningful when _normalized_ poverty gaps are used (i.e., poverty gaps are divided through by the poverty line). Out-of-pocket payments increase the normalized gap by only 1.4 percentage points in 1993 and by only 0.8 percentage points in 1998. In both years, around three quarters of the addition to the poverty gap was from previously poor people being further impoverished by out-of-pocket payments, and only one quarter was attributable to previously non-poor people being pushed into extreme poverty as a result of out-ofpocket payments.

From Table 6(b) it is clear that out-of-pocket payments have a smaller impact on the headcount in the case of the broader-based poverty line. This reflects the lower poverty line for post-payment income. Indeed, there is no assurance-as indicated above-that the impact of out-of-pocket payments on the headcount will be positive in this case. In the event, out-of-pocket payments increase the headcount ratio but by only 0.4 percentage points in 1993 and 0.5 percentage points in 1998. These low increases reflect the fact that the percentages of the sample becoming poor through out-of-pocket payments (1.9% in 1993 and 2.3% in 1998) are almost matched by the percentages of persons who were among the pre-payment poor but _not_ among the post-payment poor (1.5% of the sample in 1993 and 1.7% in 1998). The need for the use of the normalized poverty gap is, of course, even greater in this case than in the case of the food poverty line, given that the poverty line is different pre-payment and post-payment, as well as across years. In 1993, the normalized poverty gap is 0.4 percentage points higher post_29_
_Appendix Table 10: Program impact on nonbeneficiary 6-60-month-old children's physical growth by age category_

||Height-for-<br>Age Z score|Stunted|Severely<br>stunted|Weight-for-<br>Age Z score|Underweight|
|---|---|---|---|---|---|
|Proram villae|-0.016|0.012|0.025|0.058|-0.030|
|g g|(0.148)|(0.071)|(0.042)|(0.125)|(0.046)|
|Dummy: 6-24 months|0.213|-0.049|-0.059|0.290|-0.038|
|old|(0.445)|(0.156)|(0.083)|(0.416)|(0.115)|
|Dummy: 24-36 months|0.159|-0.112|-0.006|0.262|0.039|
|old|(0.301)|(0.108)|(0.055)|(0.252)|(0.081)|
|Program village*6-24|-0.622**|0.131|0.062|-0.166|0.198**|
|dummy|(0.259)|(0.098)|(0.060)|(0.247)|(0.078)|
|Program village*24-36|-0.165|0.097|-0.040|-0.157|-0.035|
|dummy|(0.255)|(0.095)|(0.063)|(0.247)|(0.082)|
|Control Observations|242|242|242|265|265|
|Treated Observations|265|265|265|287|287|
|Control Mean|-1.305|0.335|0.070|-0.966|0.181|

note: *** p<0.01, ** p<0.05, * p<0.1 Age measured to the day of birth, therefore age categories are not overlapping All specifications include quadratic terms for child age (in months), and municipality fixed effects. Standard errors are clustered at village level; there are 130 villages.

Source: 2011 _Pantawid_ impact evaluation survey 59
Table S.9: Channels - Proportion of ex-combatant households owning a shop

||_Shop k_|_eepers_||
|---|---|---|---|
||2006|2010|p-value|
|CNDD+|0.558|0.307|0.292|
||(0.287)|||
|FNL|0.12|0.448|0.126|
||(0.126)|||

_Notes:_ Authors’ analysis based on data described in the text. Clustered-robust standard errors are in parentheses. Outliers are excluded (as explained in appendix). Sampling weights are accounted for. For household ex-combatants belonging to each group, the _p_ -value corresponds to a test of equality of means of the share of kiosk owners between both surveys.

# **Appendix B. Online Appendix - The Construction of Variables of Interest**

# _Appendix B.1. The Construction of Consumption Aggregates_

In this appendix, we describe how we cleaned the data and constructed the relevant consumption aggregates for our analysis. First, we introduce the construction of the reference price vector and the local price vectors associated with each household. Second, we explain in detail how we corrected typing mistakes, how we treated missing and vague information, and how we dealt with outliers. Finally, we describe the construction of our main consumption measure which is a consumption aggregate per adult equivalent calculated using constant prices.

_Appendix B.1.1. Prices_ Prices faced by households are crucial in each stage of our analysis. First, in the data cleaning process, prices are an important to fill missing information as well as to detect and correct outliers. Second, in the construction of consumption aggregates, prices are used to make consumption data comparable across space and time. Following Deaton and Zaidi (2002), the harmonization of consumption data is done by scaling consumption values by a Paasche index of prices. Finally, finding a price increase in primary units where demobilized ex-combatants are numerous would corroborate the hypothesis that positive spillovers of the DDR program are due to a local economy effect.

We distinguish two sets of prices. On the one hand, the reference price matrix, denoted _p_<sup>0</sup> , is a 47×1 vector such that each line is a reference price for one of our 47 consumption products. This matrix is common for all households and both periods of time. We used the 2010 panel survey as the reference year. For each one of the 47 products, the reference price was computed as follows. First, the most widely used unit of measurement was selected by considering the 44
<!-- Start of picture text --> = ° i Ly<br>cc . . "<br>— t ' 1<br>z= on<br>2 |<br>od. it ‘! / . \A<br>-4 -2 0 2 4 6<br>Log hourly earnings<br>——__ 1993 Private --—--—- 1993 Public<br>cos BOO Private —-— - 2006 Public<br><!-- End of picture text -->
overseeing FO included a larger branch canal (3 FOs in all), leaves 2,860 channels. Of these, 1,007 are in FO circles, covered by 394 FOs, and 1,853 are in non-FO circles. The excess of channels over FOs in the former case reflects the fact that most distributaries have offtaking minors (and sub-minors) for which we also have discharge data. A distributary-level FO manages all of these minor canals as well. Appendix Table B.1 presents descriptive statistics for all channels by FO status of the circle. FO and non-FO circles look quite similar across design features, which include the number and location of outlets as well as position along parent channel (e.g., a minor canal’s “parent” is a distributary canal).

Table 1 gives a timeline of FO operationalization in each of the circles where they have been formed. Between 2006 and 2014, FOs in LCC East and LCC West had completed one full tenure and started their second tenures, while FOs in Bahawalnagar, LBDC, and Derajat were in their first tenure. We do not have pre-reform data for LCC East because FOs there began their first tenure just prior to 2006. Also, because of delays in the election process, there was an interregnum between the two FO tenures in both LCC East and West. During this period, control of the channels reverted back to the irrigation department under a caretaker administration. Finally, note that legal action (court stays) extended the first tenure of 27 FOs (82 channels) in Bahawalnagar and extended the second tenure of 41 FOs (117 channels) in LCC East. An empirical concern addressed below is that these extensions may have occurred precisely in FOs where rent-seeking was intensifying.

# **2.3 Canal water discharge data**

Punjab Irrigation Department’s Program Monitoring and Implementation Unit (PMIU) has maintained daily records of authorized (designed) and _actual_ discharge at the head and tail of each channel since 2006. Figure 2 illustrates the typical location of PMIU discharge gauges. Since tail discharge is measured at the last watercourse outlet of the channel, design discharge at the tail is never zero; all sanctioned outlets are entitled to off-take canal water.

10
Dao, Thu Hien, Frédéric Docquier, Chris Parsons and Giovanni Peri (2018) “Migration and development: Dissecting the anatomy of the mobility transition”, _Journal of Development Economics_ 132: 88-101.

de Janvry, Alain, Kyle Emerick, Marco Gonzalez-Navarro, and Elisabeth Sadoulet (2015) “Delinking Land Rights from Land Use: Certification and Migration in Mexico” _American Economic Review_ , 105 (10): 3125-49 Deryugina, Tatyana, Laura Kawano and Steven Levitt (2018): “The Economic Impact of Hurricane Katrina on its Victims: Evidence from Individual Tax Returns,” _American Economic Journal: Applied Economics_ , 10(2): 202–233.

Dustmann, Christian, Hyejin Ku and Tanya Surovtseva (2021) “Real Exchange Rates and the Earnings of Immigrants”, CREAM Discussion Paper no. 10/21 Dustmann, Christian and Ian Preston (2019) “Free Movement, Open Borders, and the Gains from Labor Mobility”, _Annual Review of Economics_ 11: 783-808.

The Economist (2021) “Automatic for the people”, August 14.

Fernandez, Raquel and Dani Rodrik (1991) “Resistance to Reform: Status Quo Bias in the Presence of Individual- Specific Uncertainty”, _American Economic Review_ 81(5): 1146-55.

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Gazeaud, Jules, Eric Mvukiyehe and Olivier Sterck (2021) “Cash transfers and migration: Theory and evidence from a randomized controlled trial”, _Review of Economics and Statistics,_ forthcoming.

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23
we should find it in countries where women face higher entry barriers. We thus allow the two FDI terms to depend on two available measures of barriers to female entrepreneurship: the number of days required to start a business for female entrepreneurs, which measures entry costs for female entrepreneurs; the share of females age 15 and older with financial institution account, which measures financial access.<sup>8</sup> Table 10 reports the results.

While higher entry cost for women, by itself, is not significantly associated with female entrepreneurship,<sup>9</sup> it is associated with a more pronounced within-industry FDI crowding out effect. The interaction of c and the entry barrier indicator for females is statistically significant for both specifications. By column (1), the c effect on _Female_ FF _Entrep_ is -0.09 at the mean of the entry costs for females (i.e., 22.1), but -0.17 when the FF entry costs for females increase by one standard deviation (i.e., 26.5) from the mean, which implies that increasing the average share of foreign firms in an industry by one standard deviation (0.082) is associated with lower _Female Entrep_ by 0.014, or 4.5% of one standard deviation of _Female Entrep_ (0.312). Higher entry cost for females is associated with relatively higher effects. This is consistent with the general equilibrium reallocation of female entrepreneurship across cc FF industries: when the entry barrier is high for females, in high-FDI industries, women face even harsher conditions for competition relative to men, and opt to reallocate or start their businesses to other industries. The findings in this paragraph and the last one, when combined, are suggestive that where entry costs for females are high, women entrepreneurs have a strong tendency to shift away from FDI industries to non-FDI industries.

When women’s access to finance, as measured by adult women with financial institutions accounts, is better, _Female Entrep_ is higher, as indicated by the total effect of this variable evaluated at the means of the FDI interaction variables. Increasing this variable by one standard deviation (i.e., 25.1) is associated with higher _Female Entrep_ by 5 percentage points, almost half of the mean level, indicating a huge effect. Interestingly, the FDI variables do not interact with women’s access to finance in influencing _Female_

> 8 The original data sources are Doing Business for “time required to start a business for female entrepreneurs,” and Global Findex of World Bank, both available from World Development Indicators.

> 9 This is based on evaluating the effect of the “barriers indicator” at the mean of the interaction terms.

15
[Ol.l(Y RIsIAIR(.HI WORKING. PAZ'1kI1 1409

# Summary findings

Uganda's commercial debt huy-back tiperation was financed by thc Inteiriational Development Association's Debt Reduction Facility (IDA Facility), with cofinancing from the governments of Germany, the Netherlands, and Switzerland and thc Furopean Union.

Commercial dcbt scrvice is a seriouis burden to many low-income economies. Yet, althnugh the IDA Facilit has existed since 1989, only a handfiul of countries have been able to avail themselves of its resources. That is not altogether surprising: commercial debt buy-backs can be extremely complex, requiring substantial preparation for a well-articulated external debt strategy based on comprehensivc data.

One objective in describing Uganda's operation is to) discuss the requiremcnts of the IDA Facility and to shed light on how Uganda satisfied them and on which areas needed special attention.

Uganda's offeT to buy back irs uninsured commercial debt at a discount of 88 percent of face value was extremely successful. Of the total eligible debt of US$188 million, Uganda bought back debt worth _US$1S3_ million, reflecting a participation rate by creditors of 80

> percent. As a result, most of Uganida's commercial debt hias been eliminated. The remaining commercial obligarions accrue to creditors that cither have work in progress or that hold some form of security or collateral for their claims.

Uganda's debt problems are far from resolved. Much of the country's Paris Club debt is ineligible for rescheduling because it was contractcd after the cutoff datc. Arrears owed to non-OECD bilateral creditors have continued to mount, as these creditors have resisted restructuring Uganda's obligations to them. And servicing that part of multilateral debt that is nonconcessional or Iess concessional also is a burden.

Resolving Uganda's external debt problem will require concerte. action on several fronts:

* Continued economic reform.

* Vigorous pursuit of export diversification and growth.

*No letup in seeking creative ways **to.** restructure the debt portfolio. Uganda must find grant funding wherever possible, or contract only the most concessional debt.

- - This paper a product of the CoLntry Opcrations Division, Eastern Africa Department is part of a larger effort in the department to help Ulganda address its debt problems. Copies of the paper are available free from the World Bank, 18 18 H Street NW, Washington, DC 20433. Please contact Edith Spano, room J i 0-169, extension 35538 (30 pages). January 1995.

_The Policy Research VWorking Paper Soics disseminates thP fidings of work in progrnss to encoararcan_ **_g_** _the e e of ideas about developmnent issues. An objective of the series is to get tec findings out quickly, cen if the prcentations are less than fully polisheds The papers carry the names of the authors and sbKruld be used and citcd accordingly. The findings, interpretations and conclusions are the authors- oumn and should not be attribused to the World Bank, its Executive Board of D recors. or any of its member counstries._ Produced by the Policy Research Dissemination Ccnter
|g Tail 5%<br>1978-1998|(8)<br>2.007***|(0.000)|-3.358***|(0.000)|-0.864|(0.208)<br>0.081*|(0.06)|-0.012***|(0.001)|52|0.528||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Trimmin<br>1952-1998|(7)<br>0.767***|(0.008)<br>-1.985***|(0.000)|-0.193|(0.349)|0.046*|(0.064)|-0.004**|(0.012)|53|0.432|tical signifcance.|
||(6)<br>2.102***|(0.002)|-4.506***|(0.000)|-0.842|(0.282)<br>0.143***|(0.007)|-0.010***|(0.005)|57|0.645|% and 1% statis|
|1978-1998|(5)<br><br>2.103***|(0.003)|-4.634***|(0.000)|0.839|(0.299)||||57|0.49|indicate 10%, 5|
|ample|(4)<br>3.166***|(0.000)||||||||57|0.089|* respectively|
|Full S|(3)<br>0.907***|(0.003)<br>-2.127***|(0.000)|-0.417|(0.136)|0.055**|(0.015)|-0.004***|-0.008|57|0.58|owth Rates.<br>ported. *, **, **|
|1952-1998|(2)<br>0.925***|(0.004)<br>-1.876***|(0.000)|0.217|(0.464)|||||57|0.403|Annual Gr<br>epts are not re|
||(1)<br>0.987***|(0.004)||||||||57|0.049|Factions on<br>nthesis. Interc|
||_Power Structure_<br>YRD|_Initial Conditions_<br>Ln_GVOPC_52|Ln_GVOPC_78|Ln_Pop_52|Ln_Pop_78|_Geography_<br>Share of Plain (%)||Distance to Xiamen|(Km)|Obs.|_R_2|Table 6: Efects of YRD vs. FA3<br>Note: White Standard Errors are in pare|

27
- 34 -

affect the demand for the variable inputs: If capital and labour (materials) are substitutes, the increase in investment expenditures reduces the<sup>demand for the</sup> variable inputs. If capital and labour (materials) are complements, the increase in investment expenditures increases the eemand for the variable inputs. In addition, the increase in investment expenditures temporarily reduces output, hence the demand for the variable inputs labour and materials. The reason for this temporary reduction in output is that there are adjustment costs associated with investment expenditures, which manifest themselves as a short-run loss of output [see Treadway (1970) and (1974) on the desirability of this specification of adjustment costs as internal and non-separable].

Table **3** tbJ Impact of a 10% Increase in Investment Tax Credit on Textile Industry Investment and Government Revenues - in 1980.

Increase in Investment = Rupees 276,053 Reduction in Corporate Tax Revenues = Rupees 16,497,204 Incremental Benefit-Cost Ratio= Increase in Investment/Reduction in Corporate Tax Revenues = 0.017

# Source : Model-based calculations

The results presented in Table 3 (b) suggest that an increase in<sup>the</sup> investment tax credit had trie predicted but quite small effects on the endogenous variables of our model: Ii. increased investment, and reduced output and the demand for labour and for materials. If the investment tax credit had been 10% larger ir. 1980, investment in the textile industry would have been approximately 276,000 Rupees higher during that year at a revenue loss of 16<sup>million rupees.</sup> Thus the incremental benefit-cost ratio for investment tax credit was quite small.
# **Figure 1: World Development Indicators:  Share of Agricultural Employment Out of Total Employment vs Log GDP for Select Countries**<sup>**2**</sup>

<!-- Start of picture text --> 100<br>90<br>80<br>Argentina<br>70<br>Bolivia<br>60<br>China<br>50<br>France<br>40<br>Japan<br>30<br>Netherlands<br>20<br>United States<br>10<br>0<br>Log GDP per Capita<br>Figure 2:Urban Population Share of Select Countries 3<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1500 1529 1560 1590 1621 1651 1682 1713 1743 1774 1804 1835 1866 1894 1925 1955 1986 2016<br>Year<br>Germany India Japan South Korea<br>Turkey United States Uzbekistan<br>Share of Agricultural Employment<br>Percent Urban<br><!-- End of picture text -->

**Figure 2:Urban Population Share of Select Countries**<sup>**3**</sup>

2 Notes: Courtesy of Our World in Data. Employment is defined as persons of working age who were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).

> 3 Notes: World Development Indicators, courtesy of Our World in Data.

5
Similarly for provisions, I estimate where _MFN_ equals one if the services agreement between the country pair ( _ij_ ) at time ( _t_ ) includes a MFN provision. Likewise, _NT_ , _NonEst_ , and _Move_ are dummy variables for having a provision on national treatment, non-establishment, and movement of natural persons.

# **4 Data**

The bilateral indicators of GVC participation are calculated from the OECD Inter-Country Input-Output tables (ICIO).<sup>29</sup> These international inputoutput data allow one to decompose gross trade, using the method devel— oped in Wang, Wei, and Zhu (2013) with a large coverage 61 countries, 34 industries, and 7 years.<sup>30</sup> For the empirical analysis in this paper, I exclude countries that do not have a manufacturing export base (i.e. less than 20 percent of total exports in manufacturing) since I am interested in manufacturing GVCs.<sup>31</sup> Also, I exclude years 2008, 2009, and 2010 in which trade flows were heavily affected by the global financial crisis. This is also to be consistent with the literature in using 5-year intervals. The resulting dataset has 56 countries and 4 years (1995, 2000, 2005, and 2011).<sup>32</sup> For data on trade agreements, I use the Economic Integration Agreement (EIA) database by Baier and Bergstrand (2007) and the Design of Trade Agreements (DESTA) database by D ̈ur et al. (2014). The latest version of the EIA database covers the period 1950–2012. They code the trade agreements as following: (1) non-reciprocal preferential trade arrangements, (2) preferential trade arrangements, (3) free trade areas, (4) customs union, (5) common market, and (6) economic union. The dummy variable for the existence of an RTA equals one if a country pair has an FTA or stronger, as often used in the literature.

> 292015 edition.

> 30The wide coverage of developing and developed countries makes the OECD ICIO database most suitable for the analysis of heterogeneous effects of services trade agreements by income level. Alternative data sources for ICIOs include the World Input Output Database (WIOD) which provides more detailed sectoral data but limited mostly to advanced economies.

> 31These are Brunei Darussalam, Cyprus, Hong Kong SAR, China, Luxembourg, and Saudi Arabia. See Appendix A.1 for the full list of countries.

> 32The baseline regressions include years 1995, 2000, 2005, and 2011 instead of 2010 for two reasons: (i) In 2010, many countries’ trade flows were still heavily affected by the crisis, and (ii) by using 2011 instead of 2010, the number of services agreements included in the sample increases significantly.

16