rafmacalaba/datause-encoder-data huggingface dataset:
|2) FD is "First Difference" panel estimator<br>3) WG is the WG estimator<br>4) WG-Comp is the FD estimator (unweighted) with com<br>5) All specifications (except WG) use village-level data,<br>6) All coefficients are the coefficient on lagged inequality<br>7) FD-IV is the instrumented version of FD (with instrum<br>1) OLS is the pooled OLS panel estimator<br>_Notes:_|**Four-Period Differences**<br>_N_|**Three-period Differences**<br>_N_|**Two-period Differences**<br>_N_|_N_<br>490|**OLS**<br>**First Differences**<br>-0.012<br>(0.081)|
|---|---|---|---|---|---|
|parable specification to WG<br>weighted by the number of households<br>, with the standard village-level specification<br>ent sets described in the appendix)|0.402<br>(0.258)<br>162|0.290<br>(0.159)<br>244|0.260<br>(0.140)<br>326|408<br>326<br>408<br>326<br>490|**FD**<br>**FD-IV**<br>**WG**<br>**WG-Comp**<br>**OLS**<br>0.145<br>1.094<br>0.141<br>0.193<br>-0.003<br>(0.156)<br>(1.189)<br>(0.188)<br>(0.205)<br>(0.003)<br>**Table 5**<br>**Village Panel Specification (OLS, FD, FD-IV, WG, WG-Comparable)**<br>**The Effect of Lagged Inequality on Subsequent Growth**<br>**Mean Log Deviation**|
||0.019<br>(0.011)<br>162|0.010<br>(0.009)<br>244|0.007<br>(0.007)<br>326|408<br>326|**FD**<br>**FD-IV**<br>0.001<br>0.034<br>(0.008)<br>(0.040)<br>**90-10 Ratio**|
|||||408<br>326|**WG**<br>**WG-Comp**<br>-0.001<br>-0.002<br>(0.009)<br>(0.010)|
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Microenterprise<br>Participation|-|Core<br>Respondent|No|4|=1 if the core respondent controls the use of<br>profits from any household microenterprise.<br>1 if th dt i ild i|
||||||= e core responen s nvove n|
|Livestock Control|-|Core<br>Respondent|No|4|<br>decision-making around any of the household's<br>livestock.|
|Ratio of Wife to Husband<br>Consumption<br>(Standardized)|-|Core<br>Respondent|No|4|Standardized index of the ratio of wife's to<br>husband's consumption focusing on gender<br>specific goods.<br>Standardized quality super index for husbands<br>of core respondents. Includes nine<br>components listed below: highest grade|
|Husband Quality Super<br>Index (Standardized)|V|Husband|Yes|4|completed, passed PSLC, passed JCE, passed<br>MSCE, cognitive test score, typical wage in<br>the past three months, employment status,<br>sexual activity and marital fidelity, and<br>physiological distress.|
||||||Highest grade completed is the self-reported|
|Highest Grade Completed|V|Husband|No|4|highest completed by the husband at the time<br>of the household survey.|
|=1 if Passed Primary<br>School (PSLC)|V|Husband|No|4|=1 if husband passed the Primary School<br>Leaving Certificate (PSLC) at the end of 8th<br>grade.|
|=1 if Passed Junior<br>Secondary School (JCE)|V|Husband|No|4|=1 if husband passed the Junior Certificate of<br>Education at the end of 10th grade|
|=1 if Passed Secondary<br>School (MSCE)|V|Husband|No|4|=1 if husband passed the Malawi School<br>Certificate of Education at the end of 12th<br>grade|
|Cognitive Test Score<br>(Standardized)|V|Husband|No|4|The cognitive test score is based on Raven’s<br>Colored Progressive Matrices.|

70
# **Making Poor Haitians Count Poverty in Rural and Urban Haiti Based on the First Household Survey for Haiti**

By

**Dorte Verner** World Bank<sup>1</sup>

> 1 I am very grateful to Nadim Khouri for inviting me to join the Haiti Rural Development Team, Michael Justesen for excellent and invaluable research assistance, and the Haiti Rural Development Team for answering many questions. Moreover I am especially grateful to Willy Egset for data information and other support with the household data set. Without the help of all these people this paper would not have existed. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author.
9 traceability. HACCP and GMP certification became a mandatory requirement for entry into the EU, and pellet production lent itself more favorably to this form of certification (Box 2).

# **Box 2: Standards governing production in the Thai dried cassava value chain**

Minimum export standards required by the Thai Ministry of Commerce For both pellets and chips, there are a number of technical export standards required by the Thai Ministry of Commerce (MoC), based on Notification of MoC B.E.2545 (2002) for sale into the domestic animal-feed market. The main ones are minimum starch content of 65 percent, maximum crude fibre of 5 percent, maximum moisture of 14 percent, maximum sand of 3 percent, and free from foreign materials. However, despite these export standards, some exports (including residue pellets exports to Korea) are shipped to a lower starch content (see below).

Standards governing product entry into the EU EU ‘farm to fork’ policy, introduced in 2000 requires the traceability of products used in food and feed production. Compliance for traceability and hygiene requirements is obtained through HACCP and GMP certifications on the pellet plants.

- HACCP ("Hazard Analysis Critical Control Point") certification is required as cassava pellets are part of the animal feed-food chain.

- GMP (”Good Manufacturing Practice”) relates to the sanitary and processing requirements necessary to ensure the production of wholesome food.

The requirement for HACCP is based on European Council Directive 89/397/EEC of 14 June 1989 on the official control of foodstuffs, and Council Directive 93/43/EEC of 14 June 1993 on the hygiene of foodstuffs. The HACCP system has to be implemented in relation to products and production processes.

Standards governing product entry into China No official standards govern product entry into China. However, since cassava chips are used as a biofuels feedstock, Chinese buyers customarily specify a 67 percent starch content compared to the 65 percent level required to meet Thai Ministry of Commerce export standards. This also tends to be higher than the starch levels required by EU and Korean buyers where cassava exports are sold to the animal-feed market.

Standards governing product entry into Korea No distinct standard certification is required for entry into the Korean market, but there are technical standards specified by buyers (notably a 55 percent starch content).

However this attractiveness of Thai cassava as an animal-feed input for EU livestock producers was undermined by a series of trade restrictions introduced in the 1980s and early 1990s, and then particularly by the 1992 reform of the CAP. EU domestic cereals became increasingly competitive with cassava pellets. By 2005, pellets export to the EU had collapsed to 250,000 tonnes, compared to a peak of over nine million tonnes in 1989 (COMTRADE, accessed 26 November 2009; TTTA, 2009) and price
- **Black, Sandra E. and Chinhui Juhn** , “The Rise of Female Professionals: Are Women Responding to Skill Demand?,” _The American Economic Review_ , May 2000, _90_ (2), 450–455.

- **Braun, Matias and Claudio Raddatz** , “The Politics of Financial Development: Evidence from Trade Liberalization,” _The Journal of Finance_ , 2008, _63_ (3), 1469–1508.

- **Do, Quy-Toan and Andrei A. Levchenko** , “Comparative Advantage, Demand for External Finance, and Financial Development,” _Journal of Financial Economics_ , December 2007, _86_ (3), 796–834.

- **and** , “Trade, Inequality, and the Political Economy of Institutions,” _Journal of Economic_

- _Theory_ , 2009, _144_ , 1489–1520.

- **Frankel, Jeffrey A. and David Romer** , “Does Trade Cause Growth?,” _American Economic Review_ , June 1999, _89_ (3), 379–399.

- **Galor, Oded and David N. Weil** , “The Gender Gap, Fertility, and Growth,” _The American Economic Review_ , June 1996, _86_ (3), 374–387.

- **Hall, Robert and Charles Jones** , “Why Do Some Countries Produce So Much More Output per Worker then Others,” _Quarterly Journal of Economics_ , 1999, _114_ , 83–116.

- **Helpman, Elhanan, Marc Melitz, and Yona Rubinstein** , “Estimating Trade Flows: Trading Partners and Trading Volumes,” _Quarterly Journal of Economics_ , May 2008, _123_ (2), 441–487.

- **Heston, Alan, Robert Summers, and Bettina Aten** ,“Penn World Table Version 6.1,”October 2002. Center for International Comparisons at the University of Pennsylvania (CICUP).

- **Jones, Ronald** , _A Three-Factor Model in Theory, Trade, and History_ Trade, Balance of Payments, and Growth: Papers in International Economics in Honor of Charles P. Kindleberger, Amsterdam: North Holland, 1971.

- **Levchenko, Andrei A.** , “Institutional Quality and International Trade,” _Review of Economic Studies_ , 2007, _74_ (3), 791–819.

- **Mussa, Michael** , “Tariffs and the Distribution of Income: The Importance of Factor Specificity, Substitutability, an Intensity in the Short and Long Run,” _Journal of Political Economy_ , 1974, _82_ , 1191–1203.

- **Oostendorp, Remco** , “Globalization and the Gender Wage Gap,” _World Bank Economic Review_ , January 2009, _23_ (1), 141–161.

- **Pitt, Mark M., Mark R. Rosenzweig, and Nazmul Hassan** , “Human Capital Investment and the Gender Division of Labor in a Brawn-Based Economy,” _SSRN eLibrary_ , 2010.

- **Qian, Nancy** , “Missing Women and the Price of Tea in China: The Effect of Sex-Specific Earnings on Sex Imbalance*,” _Quarterly Journal of Economics_ , July 2008, _123_ (3), 1251–1285.

27
(NAMA) were being discussed. At the signaling exercise, participating ministers indicated that they might significantly improve their services offers.

**Table 1:  Offer improvement, offer gap and binding gap**

||**Actual**<br>**policies**|**Overall Offer**<br>**improvement**<br>**(UR**<br>**commitment**<br>**-Doha Offer)**|% of UR<br>commitm<br>ent|**Overall Offer**<br>**gap (Doha**<br>**Offer -Actual**<br>**Policy)**|% of<br>policy|**Overall**<br>**Binding gap**<br>**(UR**<br>**commitment -**<br>**Actual Policy)**|% of<br>policy|
|---|---|---|---|---|---|---|---|
|SAR(3)|36.0|15.5|18%|32.3|90%|47.8|133%|
|EAP(7)|36.8|1.7|3%|24.3|66%|26.0|71%|
|MENA<br>(5)|36.9|3.8|7%|17.4|47%|21.2|57%|
|AFR(6)|16.9|0.7|1%|52.9|314%|53.6|318%|
|LAC (9)|16.6|6.3|10%|41.9|253%|48.2|290%|
|OECD<br>(20)|14.9|9.4|46%|3.8|25%|13.1|88%|
|ECA(5)|10.6|5.4|19%|4.4|41%|9.8|93%|
|World<br>(55)|21.0|6.4|**13%**|20.8|**99%**|27.2|**130%**|

Note: The numbers in bracket indicate the number of countries covered per region. See annex table 4 for the list of countries in each region.

# III. CONCLUSION

Our analysis suggests that negotiators have been content to let services lag. The best market access offers do not even reflect the liberalization that has already taken place. The “requestoffer” negotiating process, bilateral and plurilateral, seems to have resulted in a low-level equilibrium trap.

More effort to liberalize trade and investment in services at the multilateral level is needed. Perhaps greater progress could be made by turning the negotiating progress on its head, and instead of the incremental and unproductive request and offer process, Members could strive directly to define a final package. To be both worthwhile and attainable, such a “package” on services would have to be balanced from a mercantilist perspective, commercially relevant from a business perspective, and offer substance rather than rhetoric from a development perspective (Mattoo, 2005). Indeed, the WTO’s Hong Kong Ministerial Declaration sketched out similar ambitious aims. An agreement could follow the precedent of the WTO’s Information Technology Agreement, where participation is limited to a “critical mass” of signatories who would extend the benefits also to non-participants.

Such a package could have three elements. First, a promise not to impose new restrictions on trade in services. This would dispel the specter of protectionism that hangs over outsourcing of business services – which is producing huge cost savings in the North and ever-widening export opportunities for the South.

12
|**Table 23: Sensitivity Ana**<br>|**lysis of Keny**<br><br> <br>|**a-Africa FTA**<br> <br><br>Parameter Value|**% Wel**|<br>**fare Chan**|**ge (EV)**|
|---|---|---|---|---|---|
|Parameter|Lower<br>|Central<br>Upper|Lower|Central|Upper|
|σ(qi, qj)– services sectors|1.5|3<br>4.5|5.02|0.29|0.16|
|σ(qi, qj)–goods sectors||see below|0.32|0.29|0.28|
|σ(va,bs)|0.625|1.25<br>1.875|0.25|0.29|0.33|
|σ(D,M)|2|4<br>6|0.28|0.29|0.29|
|σ(L,K)|0.5|1<br>1.5|0.28|0.29|0.29|
|σ(A1,...An)|0|0<br>0.25|0.29|0.29|0.29|
|σ(D,E)|2|4<br>6|0.28|0.29|0.29|
|εKEN|Central v|alues of all 4 sets of eta|0.31|0.29|0.27|
|εEU|paramet|ers are listed in table 6B|0.29|0.29|0.29|
|εAFR|Lower value|s are 0.5 central values and|0.14|0.29|0.43|
|εROW|upper values|are 1.5 times central values|0.29|0.29|0.29|
|share of rents captured|0|0<br>1|0.29|0.29|0.05|
|CRTS--share of rents captured|NA|0<br>1|NA|0.14|-0.06|
|θm|0.025|0.05<br>0.075|0.29|0.29|0.29|
|σ(qi, qj)–goods sectors||||||
|sugar and bakery|2.12|2.93<br>3.74||||
|beverages and tabacco|1.52|2.33<br>3.14||||
|chemicals|2.01|2.82<br>3.63||||
|metals and machines|8.345|16.69<br>25.035||||
|gain milling|2.43|3.24<br>4.05||||
|nonmetallicproducts|2.805|5.61<br>8.415||||
|petroleum|2.75|3.56<br>4.37||||
|Source: Authors' estimates||||||

67
Figure 3.1: Sexual Initiation and First Child, by Age and Gender (percent of those having initiated sex <u>or have at least one child), Argentina 2005</u> <!-- Start of picture text --> 25.0<br>Sexual Initiation Female<br>Sexual Initiation Male<br>20.0 First Child Female<br>First Child Male<br>15.0<br>10.0<br>5.0<br>0.0<br>Source : Own calculations on ESCVJA (four urban areas)<br>11 years 12 years 13 years 14 years 15 years 16 years 17 years 18 years 19 years 20 years 21 years 22 years 23 years 24 years<br><!-- End of picture text --> The risk of early motherhood or STDs is confirmed by the lack of use of contraception among young females (Table 3.4). While males initiate sex earlier than females, they are also better at protecting themselves by the use of condoms; of males, 31 percent below 18 years use condoms, compared to only 16 percent of their female peers. Thus, 10 percent of young women—age 18 or less—are exposed to the risk of STDs or teenage pregnancy. This number increases to 39 percent above 18 years, naturally explained by the higher proportion in a steady relationship or in marriage.

Table 3.4: Contraception use among Argentine Youth, 2005

||Below|18|years|18 years o|r above|
|---|---|---|---|---|---|
||Female||Male|Female|Male|
|Never had sex|74.2||66.2|21.9|11.1|
|Safe sex (condoms)|15.8||30.6|39.1|80.7|
|Unsafe sex|10.0||3.2|39.1|8.2|

_Source_ : Own calculations on ESCVJA (four urban areas) Not surprisingly, early sexual initiation and marriage increase the likelihood of having children. Figure 3.1 shows that the proportion by age when having the first child follows the sexual initiation by a lag of a couple of years. Ten percent of youth are married and 13 percent have at least one child. Unsafe sex and marriage are naturally intertwined, and married youth are much more likely to have a child than singles are (Figure 3.2). While 20 (68) percent of married females (males) have a child, 10 (3) percent of single females (males) do.

8
33 importance of economic reform and political stability for poverty reduction; the role of geography and remoteness in conditioning how the benefits of growth are distributed; the significance of private endowments (especially education and land) for the ability of households to take advantage of new opportunities, and the consequent poverty outcomes; and finally the need to account for shocks in understanding distributional outcomes and poverty changes over time.

The 'emerging picture' described by Demery and Squire (1996) appears to be confirmed with the better data (reflecting also a longer time perspective than previous work). Improvements in the macroeconomic balances are associated with reductions in poverty in the region. There is also an emerging micro-picture concerning the consumption poverty impact of market liberalization. The analysis of household panel data by Dercon (2001) for Ethiopia and Deininger and Okidi (2001) for Uganda provide the most systematic and empirically convincing cases that policy-induced changes in relative prices can have marked poverty-reducing effects. Micro-evidence from Ghana provides some corroboration from West Africa.

The second policy message is the need for a geographical perspective on poverty. Whilst the various rounds of poverty assessments have established that the incidence of poverty varies considerably across different regions of a country, this recent work on poverty dynamics has shown that some regions, by virtue of their sheer remoteness, have been left behind somewhat as growth has picked up. Households with limited access to markets and public services have not benefited from growth during the 1990s. Public policy, and the provision of public goods (notably infrastructure services-from the Ethiopian case, especially roads and from the Ugandan case, electricity) must address these fundamental regional inequalities.

Third, both education and access to land emerge as key private endowments to enable households to escape poverty. The importance of education for poverty reduction is brought out in all our case studies-in rural and urban areas-with the marginal returns to education typically increasing by educational attainment. While land redistributions may not be appropriate in all countries, as argued by Dercon (2001) for Ethiopia, it is ultimately the productive capacity of land which matters. A more efficient organization of agricultural services and agricultural inputs, such as fertilizer, could go a long way towards improving productivity of land (Kherallah et al., 1 999).

Finally, the empirical evidence reviewed here underscores the importance of social protection in a poverty reduction strategy. The impact of rainfall variations and ill health are the two risk factors
questionnaire, and the geographic distribution of relatively poor persons was significantly different, suggesting some re-ranking of households based on survey design. Pradhan (2001) found that the shorter consumption module in the _Susenas_ questionnaire in Indonesia results in average consumption that is 1220 percent below the levels from the longer consumption module, although the ranking of households based on observable characteristics was not any different. Earlier experimental work, which led to the introduction of the short-form consumption module in the _Susenas_ , also found that a reduction in the number of items from more than 100 to 15 decreased consumption but did so proportionally, thus not reranking households (World Bank, 1993). In Jamaica, the shortened consumption modules produced mean per capita consumption results that were about 20 percent lower than the standard modules (Statistical Institute and Planning Institute of Jamaica, 1994). In Ecuador, where two versions of the food module were piloted, the ratio of food expenditures in the long module to the short module was 1.67 (Steele, 1998). Again there appear to be plenty of examples where aggregated commodity lists result in a lower mean and presumably less accurate consumption measure. What is not clear is the relative savings in survey resources, and the effects on distributionally sensitive measures, such as inequality, from such a design.

# _Reference Period_

In recall modules there are also important issues associated with the choice of reference period to be used. The direction of bias introduced by lengthening or shortening the reference period is ambiguous _a priori_ . Respondents may have difficulty recalling consumption expenditure with longer reference periods due to diminished capacity to remember (memory lapse). On the other hand, short recall periods may produce over-estimates (telescoping errors) if respondents include consumption/expenditures just outside the reference period. The extent to which expenditures are misreported in these ways is presumed to depend on the item in question and the characteristics of the household.

In India, there has been debate in recent years as to the impact on measured poverty and inequality of having altered the reference period over which (recall-based) consumption data has been collected in two key rounds of the National Sample Survey (see Deaton and Kozel, 2005). Lanjouw (2005) argues that, in Brazil, the application of an inappropriately short reference period over which purchases of foods are recorded in the 2002 _Pesquisa de Orçamientos Familiares_ consumption survey diary might account for the surprisingly high frequency of households who report zero consumption of certain key food items.

In Ghana, Scott and Amenuvegbe (1991) ran a well-documented experiment for 144 households on the impact of recall duration on reported expenditure (not consumption value) of the 13 most
either be formal or everyone informal. If there are heterogeneous effects, those who are informal will not be appropriate controls for those who are formal.

However, equation (1) also shows that some firms for which it is profitable to become formal will not do so if the initial costs of registering are too high. In the Bolivian case, the monetary costs of registering for a NIT are very low (zero for the registration itself, with only the costs of photocopying and obtaining accompanying documents needed). However, the time and information costs will depend on how closely located the firm is to the office where they have to register, and on how much general information is available about the registration process. This provides a potential source of identification: comparing two firms with the same potential net change in profits (and same psychic benefits) from obtaining a tax identification number, but with different time and information costs will enable estimation of the increase in profits resulting from formalization.

# **4.2 Identification**

Our basic identification strategy is therefore to use the geographic location of the firm relative to that of the tax office as a source of variation on the information and time costs of registering, assuming that after other locational controls are added, that distance to the tax office has no independent impact on profits.

The latitude and longitude of each firm was obtained using GPS receivers. In each city there is a single location of the tax office where firms can register for a NIT. We also obtained the coordinates of these tax offices, and of the city centers. We then calculate the straight-line distance from the firm to the nearest tax office and to the city center. Since the sample is contained in dense urban neighborhoods, straight-line distances will be good approximations for actual travel distances (Gibson and McKenzie, 2007). We have restricted the sample to firms within a 10 kilometer radius of the tax office: the mean firm is 3.2 kilometers away. The assumption is that for two firms of equal distance from the city center, the one that is closer to the tax office has better knowledge of the registration process.

- 10 -
- 14 -

Table 3: Per Capita Real Consumption,lnequality and Poverty Rural India 1973-74 to 1986-87

||||||Annu|al Grow|th Rates|(%)|
|---|---|---|---|---|---|---|---|---|
|Indicators|73-74|77-78|83|86-87|<br>73-74<br>to<br>77-78|<br>77-78<br>to<br>83|<br>83<br>to<br>86-87|<br>73-74<br>to<br>86-87|
|Per Capita Real Consume<br>Expenditure|r<br>51.6|56.5|62.5|67|2.3|1.9|<br>2|2|
|Theil's Inequality Measure|||||||||
|Within state inequality<br>Between state inequality|12.3<br>(93.2)<br>0.9|<br>15.6<br>(91.2)<br>1.5|13.6<br>(88.3)<br> <br>1.8|14.5<br>(92.9)<br> <br>1.1|<br>6.1<br>13.6|-2.5<br>3.4|1.8<br>-13.1|1.3<br>1.6|
||(68)|(88)|(117)|(71)|||||
|Total inequality|.<br>13.2<br>(100)|.<br> <br>17.1<br>(: 00)|.<br> <br>15.4<br>(100)|.<br> <br>15.6<br>(100)|<br>**-** 6.7|-1.9|0.4|1.3|
|Poor|||||||||
|Head-count ratio(%)|60.5|<br>56.2|<br>47.7|41.5|<br>-1.8|-2.9|-3.9|-2.9|
|Number of poor(millior's)|270.7|<br>271.1|<br>253.4|<br>233|<br>0|-1.2|-2.4|-1.1|
|Poverty gap ratio(%)|18.8|<br>17.4|<br>13.4|<br>10.5|<br>-1.9|-4.6|-6.7|-4.4|
|Ultra poor|||||||||
|Head-count ratio(%)|41.2|<br>38|<br>29.6|<br>23.8|<br>-2|-4.4|-6|-4.1|
|Number of poor(millions)|184.7|<br>183.5|<br>157.6|<br>134|<br>-0.2|-2.7|-4.5|-2.4|
|Poverty gap ratio(%)|10.6|<br>9.9|<br>7.1|<br>5|<br>-1.7|-5.9|-9.5|-5.6|