rafmacalaba/datause-encoder-data huggingface dataset:
# **4. Conclusion**

There are often large regional differences in poverty and other social indicators within a country. Geographic poverty profiles based on household surveys tend to be limited to broad areas because survey sample sizes are too small to permit analysts to construct valid estimates of poverty at the local level. At the same time policymakers often need finely disaggregated information at the neighbourhood, town, or village level in order to implement anti-poverty programs. It is for this reason that poverty maps have become popular in developing countries to provide better information for the targeting of various types of public transfers. Yet while most of the work on poverty maps has focused on assessing patterns of poverty at one point in time, the technique of the poverty map can also be used to assess the geographic impact of shocks, and thereby to help inform policy responses to such shocks.

After providing summary data from a new poverty map for Guinea, we have focused in this paper on assessing the geographical impact on poverty of an increase in the price of rice. The impacts differ substantially between areas. In some countries which import most of their rice such as Senegal for example, these differences in impacts between areas pose a difficult dilemma for policy makers. On the one hand, the desire to help households cope with the increase in food prices may lead policy makers to implement projects or provide relief in the hardest hit areas which tend to be urban. On the other hand these hard hit areas may not be among the poorest in the country, and when a country imports essentially all of its consumption of a basic staple, the rural poor suffer as well. In such case, one may wonder if for poverty reduction, interventions should not remain focused to the poorest areas (as measured after the shock), instead of the hardest hit ones by the shock.

In the case of Guinea, the dilemma is perhaps less present than in some other countries in West and Central Africa. Given the substantial production of rice in the country, some of the poorest areas, which are also the rice producing regions, may benefit from the 14
IPGt+7 = α 2 + λ F2FORTGRt + ?T2TOTGR + λ I2IMCUMt + λ S2SKt + λ R2RDt + φ 2 tTOTPATSTOCKt + δ B2BD + _θ_ 2tTIMEt + η 2t In the first equation the dependent variable is total patent applications as a percentage of the U.S. labor force, five years after inputs are employed. These inputs include foreign graduate students as a percentage of total graduate students (FORTGR), total graduate students as a proportion of labor force (TOTGR), skilled immigrants as a proportion of labor force (IMCUM), PhD's employed in science and engineering as a percentage of labor force (SK), and real research and development expenditures as a percentage of labor force (RD). We employ both FORTGR and TOTGR to permit identification of the impact of foreign graduate students, holding constant the total relative presence of graduate students in U.S. universities.

Regarding skilled immigrants we wish to have a measure that is comparable to such other variables as graduate students and engineers and scientists, which are defined as the total amount in activity (e.g., added over all enrollments for students rather than new enrollments). Therefore, we define the variable IMCUM, which is the number of skilled immigrants cumulated over the preceding six-year period, divided by the labor force.

The estimation also includes the knowledge stock, as proxied by cumulative total patent stock over the past five years (TOTPATSTOCK), again divided by the labor force. Finally, there is a dummy variable capturing the Bayh-Dole Act, which takes on the value zero before 1980 and unity from 1980 onwards. The second equation is for patent grants and has the same structure, except that the independent variables enter with a seven-year 14
- -. 1990b. A _Profile of the Elderly in Costa Rica_ Washington D.C.

Palacios, Robert. 1996 (forthcoming). "Intemational Patterns of Public Pension Spending and Coverage." Working paper. World Bank, Washington, D.C.

Palmer, J.,T. Smeeding and B. Torrey eds. 1988. _The Vulnerable._ Washington D.C.: Urban Institute Press.

Pathak, J.D. 1978. _Our Elderly._ Bombay, India: Medical Research Center.

Petkov, Krastyu and Doukhomir Minev. 1989. "Work and Income of the Bulgarian Pensioner." _International Social Security Review;_ 1(2): 70-86.

The Planning commission dar es salaam. 1992 November. Tanzania. The Economic Survey, 1991. Tanzania.

Provida (Administrativo de Fonds de Pensiones). 1993. _Informe Annual._ Chile.

Psacharopoulos, George. 1993. "Returns to Investment in education: A Global Update." Working Paper WPS 1067. World Bank, Policy Research Department, Washington D.C.

Psacharopolous, George, Samuel Morley, Ariel Fiszbein, Haeduck Lee, and Bill Wood. 1993. "Poverty and Income Distribution in Latin America: The Story of the 1980s." World Bank, Human Resources Division, Washington D.C.

# Psacharopolous and Ng. 1992

Queisser, Monika. 1991. "Social Security Systems in South East Asia: Indonesia, the Philippines and Singapore." _International Social Security Review_ 44: 121-135.

Reisen, Helmut. 1994. "On the Wealth of Nations and Retirees." OECD Development Centre. Paris.

# Revista de Seguridad Social, No. 43-44, 1989, Ministerio de Trabajo y Seguridad Social.

Rofman, Rafael (Centro de Estudios de Poblacion). 1993. "Diferencias de Mortalidad Adulta en Argentina" Unpublished. March 24.

-.1994. "La Reforma Previsional en Argentina: Que Podemos Esperar?" Centro de Estudios de Poblacion. Buenos Aires, Argentina.

-.1994. Crisis de la seguridad social y reforma previsional en Argentina. Un analisis de sus causas y consecuencias. Buenos Aires. Argentina.: Centro de Estudios de Poblacion.

> Schmidt-Hebbel, Klaus and Luis Serven. "Structural Reforms and their Fiscal Implications." Unpublished.

137
individuals often resort to other methods of reducing vulnerability, some of which can be quite costly. For instance, people may choose less profitable but less risky investments (Morduch 1995) or sell productive assets in bad times (Rosenzweig and Wolpin 1993).

In this paper, we explore whether transactional sex provides inter-personal insurance in addition to a higher income level, and whether such insurance affects the decision of women to enter (or continue) in transactional sex. In particular, we are interested in the insurance that sex workers receive from “regular” clients. Regular clients differ from “casual” clients in that they tend to have longer-term relationships with a particular woman. Over time, women who supply transactional sex typically develop relationships with one or more such regulars, and these relationships can extend beyond just sex. For instance, some regulars are thought of as akin to boyfriends or lovers, and it is not unheard of for women to marry regular clients.

We examine whether regular clients become part of a woman’s risk-coping network and provide insurance through transfers when negative shocks occur. To do this, we utilize a unique panel dataset constructed from 192 daily sex worker “diaries.” The diaries were essentially preprinted questionnaires that were filled by the women on a daily basis. In the diaries, women recorded each transaction that they had with a client, including all of the sexual activities performed, the price paid, whether a condom was used, and whether the client was a regular or a casual. They also recorded any shocks that they experienced, their income, expenditures, and, most important for this paper, the transfers they had given to or received from other individuals.

To measure this, we asked women to record all the transfers they received from other people, whether from regular clients or from anybody else (usually friends or relatives). For transfers from regulars, women separately recorded whether the transfer was in cash, for rent, for other household expenses such as school fees, or whether the transfer was in-kind (such as a gift of 3
# Measurement Issues

## Sampling strategies

There are some technical reasons that make surveying refugees and IDPs complex. Sampling is one of these reasons. Refugees and especially IDPs are mobile populations which can move into host areas, even if they are initially residing in camps, and might settle outside camps in previously uninhabited areas. These factors make the inclusion of these populations in national master samples for surveys difficult. For refugees, this problem is partly overcome by the UNHCR registration system (proGres) which requires all registered refugees to provide a set of basic information including location and socio-economic characteristics of the household’s members. However, using the proGres registration system to sample refugees has its own challenges. This system is available in some countries but not in others where a system may be missing or managed by the local authorities and not available to others. Where the system exists, many refugees are not registered, some are registered but their information is outdated or missing, while identifying the unit of observation (household, family, case) may be challenging although in other contexts such as Bangladesh, the registration system may be up to date and almost universal. In other words, extracting a representative sample of refugees in many countries, even when the proGres registration system is available, is not simple.

Sampling IDPs can be even more complex because they no longer reside in the place where they are registered in the census. If they reside in previously uninhabited places, they will be excluded from the sampling frame. International organizations that collect information on IDPs, such as the International Organization for Migration (IOM), tend to collect information on IDP communities, but sampling frames are often not updated accordingly. Security reasons also hamper proper sampling and data collection as many destination areas for IDPs are themselves unsecure locations. In essence, the sampling infrastructure that is typically available for national populations, and even the basic infrastructure that is available for refugees, is lacking for IDPs. Definitions of IDPs can also be problematic as shown by a recent JIIPS study.<sup>6</sup> Former IDPs deciding to remain in their location of displacement but no longer being displaced can easily be mis-classified as IDPs, while IDPs returning to their place of origin without overcoming their specific displacement vulnerabilities can be mis-classified as non-IDPs.

In general, two classes of sampling approaches are used for FPDs: area-based sampling and list-based sampling. The area-based sampling partitions the area of interest into smaller enumeration areas. In the first stage of the sampling, enumeration areas are randomly selected. All households in selected enumeration

> 6https://www.jips.org/jips-publication/identifying-internally-displaced-persons-in-surveys-approaches-andrecommended-questionnaire-modules-jips-2021/

7
decade of transition, but less so during the latter stages, as reform programs have advanced in many of the transition economies.<sup>10</sup> In 1995, only one country had an IRP above 30 (Hungary); in 2005, ten countries have IRPs above this threshold. The shifting relative importance of drivers of growth may also help explain why the early literature transition economy growth did not yield consistent findings on the importance of fiscal balance yet in our baseline results the positive impact of fiscal balance on economic growth is unambiguous and robust.

The results are presented in Table 3 and provide some evidence of the timevarying impact of fiscal balance and reform progress. The results under column (2), for example, based on fixed effects, imply that the net impact of fiscal balance on economic growth through 1998 is about 0.36 and more than double at 0.77 thereafter. As for IRP, its net impact on growth is 0.50 through 1998, and not statistically different from 0 thereafter.

Table 3. Determinants of Economic Growth, 1992-2004: Accounting for Time-Varying Effects

||OLS<br>(1)|Fixed Effects <br>(2)|Random Effects<br>(3)|OLS<br>(4)|Fixed Effects <br>(5)|Random Effects<br>(6)|
|---|---|---|---|---|---|---|
|War dummy|-4.18|-5.33***|-4.65***|-4.51|-7.20***|-4.51***|
||(1.43)|(2.75)|(2.60)|(1.64)|(3.76)|(2.70)|
|Ln(inflation) (IMF)|-1.89***|-1.47***|-1.83***|-1.65***|-1.71***|-1.65***|
||(4.39)|(3.77)|(5.59)|(4.18)|(4.30)|(4.97)|
|Fiscal balance (%GDP) (IMF)|0.57***|0.77***|0.65***|0.44***|0.75***|0.44**|
||(6.02)|(3.79)|(3.48)|(4.27)|(3.58)|(2.36)|
|Fiscal balance (%GDP) * Dummy (years through 1998)|-0.3|-0.41*|-0.35*|-0.21|-0.44**|-0.21|
||(1.59)|(1.94)|(1.74)|(1.20)|(2.00)|(1.04)|
|Index of Reform Progress|-0.33**|0.09|-0.27**|-0.33**|-0.88***|-0.33***|
||(2.24)|(0.40)|(2.04)|(2.47)|(3.21)|(2.92)|
|Index of Reform Progress * Dummy (years through 1998)|0.66***|0.50***|0.62***|0.45**|0.39**|0.45***|
||(3.04)|(3.18)|(4.11)|(2.17)|(2.53)|(2.93)|
|Constant|19.39***|8.06|17.89***|5.32|7.77|5.32|
||(4.36)|(1.32)|(4.76)|(0.94)|(1.37)|(1.36)|
|Observations|305|305|305|305|305|305|
|R-squared|0.54|0.58||0.6|0.64||
|Number of countries||25|25||25|25|

Robust t statistics in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%

# C. Lagged Effects

Using equation (4), we can re-run the baseline regressions but allowing for the lagged impact of reform progress. The results are presented in Table 4 and suggest negative contemporaneous effects (though insignificant) and lagged positive effects from

> 10 This may also explain, in part, why Godoy and Stiglitz (2006) why, using more recent data, some variables which thought to “play a large role in the earlier literature, appear to have at most a marginal effect.”

16
are routinely included in transport tariffs. According to anecdotal evidence, in the case of Georgia, they account for from a quarter to a third of the highway tariffs.

# **3. Trade after Conflict Resolution**

In this chapter, we discuss the potential for trade in the region in the event of conflict resolution. We focus on non-energy trade here. Energy trade is relegated to the next chapter.

# **3.1. Export Opportunities**

There are three missing export flows in the region -- Armenia to Azerbaijan, Azerbaijan to Armenia, and Armenia to Turkey (which is, formally speaking, positive but, for all practical purposes, zero) and one restricted export flow -- Turkey to Armenia.

In order to put into perspective the current and potential export flows in industrial products outside of natural resources, we applied the gravity model developed by Baldwin to assess the potential integration of East and West European trade<sup>4</sup> . Baldwin's model was estimated on trade flows among the EC, EFTA, the USA, Canada, Japan, and Turkey using the random effect method with maximum likelihood correction for first-order autoregressive error. The model has the following form: _Ln Xij = -17.5 - 0.88 Ln Dy + 0. 77 Ln Pop i +1.16 Ln GDPCapi + 0. 79 Ln Popj (12) (11) (26) (13) (25) +1.22n GDPCapj + 0.28 Adj] + 0.53 Block0j, (16) (2) (3)_ _R_<sup>_2_</sup> = _0.99, DW= 2.39, t-statistics in parenthesis,_ where i is the exporting country,j is the importing country, _Pop_ is population, _GDPCa p_ is per capita GDP (in PPP), _Adj_ is the dummy for adjacent countries, and _Block_ is a dummy that takes on the value if the trading partners belong to a trading block.

The distances are straight lines between the capital cities ("as the crow flies"). GDP (in PPP) is expressed in 1985 U.S. dollars based on the Penn World Tables.

We used this model to calculate export flows among the three South Caucasus countries and their major partners, further referred to as "potential". The countries' GI)P (in PPP) for 1996, developed by the European Comparison Project, were rebased to 1985 US dollars. In order to be compatible with the Baldwin's model, the results were converted into 1996 US dollars.

> 4 Baldwin (1994).

20
FIGURE 7. DURATION OF PROTECTIVE IMMUNITY PROVIDED BY MATERNAL TETANUS TOXOID IMMUNIZATION AGAINST 4-14 DAY MORTALITY: MATLAB COMPARISON AREA, 1973-89 <!-- Start of picture text --> 40<br>D<br>E<br>A /<br>T 30<br>H<br>s<br>R<br>0 K<br>I~~~~~~~~~~~~~~~,<br>v~~~~~~~~~~<br>E<br>T<br>H<br>~°QTT1 TT-'2T<br>73 74 7576 77 787980 8182 8384 8586 87 8889<br><!-- End of picture text --> YEAR

**-**<sup>0</sup> T T -*-1TT ~2TT

**SOURCE: KOENIG, ET AL.** (1991b)
**Table 13: Comparison of biofuels projection from reviewed studies**

|<br>|Units<br>|2015<br>|2020<br>|2025<br>RSL<br>|2030<br>|2035<br>|2040<br>|2045<br>|2050<br>|
|---|---|---|---|---|---|---|---|---|---|
|Max|Mtoe|8.89|16.26|28.58|40.90|48.18|55.45|62.73|70.00|
|Min|Mtoe|2.52|4.00|6.50|9.00|12.00|15.04|16.72|18.24|
|||||PSL||||||
|Max|Mtoe|15.66|29.80|58.55|87.30|100.73|114.15|127.58|153.58|
|Min|Mtoe|2.00|5.00|8.00|12.00|16.00|35.14|42.11|49.08|

As to the sub-targets of fuel ethanol and biodiesel, almost all scenarios provide optimistic projections on biodiesel, while great difference on fuel ethanol projections by 2020 (Table 14). The minimum projection is from M1 scenario of Chang et al. (2012), which assumed a most pessimistic development of fuel ethanol by 2020. The maximum projection is from Most Optimistic Scenario (MOS) of Li & Chan-Halbrendt (2009), which is identified as the most optimization scenario by the authors.

A Bundle of additional policies were modeled to show the effectiveness to trigger biofuels development in the following years by 2020. These policies include less regulation coupled with sustainable guidelines(MOS from Li & Chan-Halbrendt (2009); L1&L2 from Chang, et al.(2012)); speeding up of technology development (M2 & L2 from Chang et al. (2012); BA from Zhang, et al. (2012); MS & AS from RGCMLEDS (2011); MOS from Li & Chan-Halbrendt (2009)); increasing renewable energy share binding target (NPS from IEA(2011a); AS from RGCMLEDS (2011)); energy security (Qiao, et al., 2010); and climate change addressing (450 Scenario from IEA(2009), NPS and 450 Scenario from IEA(2010), NPS and 450 Scenario from IEA(2011a), AS from RGCMLEDS (2011)). Notwithstanding the modeling methodological difference, all scenarios demonstrate the perspective of biofuels development by adopting different policy options.

**Table 14: Comparison of fuel ethanol and biodiesel projections in 2020 under reviewed scenarios.**

|Storyline|Target|||Scenario com<br>|parison<br>||
|---|---|---|---|---|---|---|
|||Max|Min|Average|Variance|Standard Deviation|
||||Fuel ethano<br>|l<br>|||
|Unit|Mtoe|Mtoe|Mtoe|Mtoe|-|-|
|RSL|6.38|12.00|2.44|5.81|18.80|4.34|
|PSL|6.38|32.00|3.48|10.72|69.17|8.32|
|All Scenarios|6.38|32.00|2.44|9.08|55.58|7.46|
||||Biodiesel<br>|<br>|||
|Unit|Mtoe|Mtoe|Mtoe|Mtoe|-|-|
|RSL|1.81|3.00|1.80|2.64|0.32|0.56|
|PSL|1.81|13.55|2.80|6.59|14.27|3.78|
|All Scenarios|1.81|13.55|1.80|5.15|12.64|3.56|

21
Policy Research Working Paper 5916

# **Abstract**

This paper validates a recently proposed method to estimate intra-generational mobility through repeated cross-sectional surveys. The technique allows the creation of a “synthetic panel”—done by predicting future or past household income using a set of simple modeling and error structure assumptions—and thus permits the estimation of lower and upper bounds on directional mobility measures. The authors validate the approach in three different settings where good panel data also exist (Chile, Nicaragua, and Peru). In doing so, they also carry out a number of refinements to the validation procedure. The results are broadly encouraging: the methodology performs well in all three settings, especially in cases where richer model specifications can be estimated. The technique does equally well in predicting short and long-term mobility patterns and is robust to a broad set of additional “stress” and sensitivity tests. Overall, the paper lends support to the application of this approach to settings where panel data are absent.

This paper is a product of the Poverty, Equity and Gender Unit, Latin America and the Caribbean Region. 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://econ.worldbank.org. The authors may be contacted at rvakis@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
2023). Additional evidence looking at specific outcomes helps qualifying these results. As shown in the lower panel, in developing countries we find higher elasticities for microlevel digital studies (0.17), consistent with for example the evidence in Hjort and Poulsen (2019). The outcome-level analysis also reinforces the evidence for the strong impact of transport investments, especially for labor market outcomes (0.19).

# **4.4 Robustness**

Table 7 presents a number of robustness checks. In meta-analysis estimations, more precise estimates carry a larger weight. One issue is that observations with extremely small standard errors then have a on the outcome. A visual ex- may disproportionate impact amination of the data shows a few observations that have very small standard errors (i.e., values of 1/standard error of more than 200, going up to 7,000 in some cases). Of course, very small standard errors may simply correspond to equally small coefficients. However, an examination of the data shows in a majority of cases these do correspond to estimates that report extreme t-statistics of up to 245. In light of this, in panel A we run robustness checks excluding observations with 1/standard error larger than 200 and a t-statistics larger than 4. Overall, this tags 52 observations, 6 cross-sectoral, 30 in digital, 4 in energy and 12 in transport. In panel B, we restrict the sample to the set of estimates that were reported as elasticities in the original studies, excluding semi-elasticities based on dummy independent variables. In panel C, we include only estimates that explicitly address endogeneity concerns, thus eliminating about half of the sample. Finally, panel D considers only single-country estimates, excluding cross-country estimates.

While the different robustness exercises introduce some variation in the resulting metaelasticities, these remain broadly consistent overall. In the full sample, the estimated coefficients are very stable, only slightly smaller in panel A, while in the cross-sectoral sample, they remain comparable to previous results, between 0.09 and 0.16. Focusing on the three main sectors, the overall elasticity for digital is now slightly higher, between 0.03 and 0.06, which is perhaps not surprising given that studies focusing on this sector concentrate most of the outliers in the full sample. For energy, estimates are very stable, only about 19