rafmacalaba/datause-encoder-data huggingface dataset:
- Ciccone, A., Hall, R. (1996), ―Productivity and the Density of Economic Activity,‖ _American Economic Review,_ Vol. 86, 54-70.

- Copenhagen Economics, Osteuropa Institut Munchen and Institute for Economic Research (2005), ―Analysis of Economic Aspects of Ukraine‘s Accession to the WTO: Overall Impact Assessment,‖ mimeo, December.

- De Loecker, J. (2011), ―Product differentiation, multiproduct firms, and estimating the impact of trade liberalization on productivity,‖ _Econometrica_ , Vol. 79(5), 1407–1451.

- Eschenbach, Felix and Bernard Hoekman (2006), ―Services Policy Reform and Economic Growth in Transition Economies,‖ _Review of World Economics_ , Vol. 142(4), 746-762.

- Estevadeordal, A., and A. M. Taylor (2008), ―Is the Washington consensus dead? Growth, openness, and the Great Liberalization,‖ NBER Working Paper No. 14264.

- Fernandes, Ana M. (2009), ―Structure and performance of the services sector in transition economies,‖ _Economies of Transition,_ Vol. 17(3), 467-501.

- Fernandes, Ana M and Caroline  Paunov (2012), ―Foreign direct investment in services and manufacturing productivity: evidence for Chile,‖ _Journal of Development Economics,_ Vol. 97 (2), March, 305-321.

- Fink, Carsten, Aaditya Mattoo and Ileana Cristina Neagu (2005), ―Assessing the Impact of Communications Costs on International Trade,‖ _Journal of International Economics_ , Vol. 67(2), 428-425.

- Francois, Joseph F. (1990a), ―Trade in Producer Services and Returns due to Specialization under Monopolistic Competition,‖ _Canadian Journal of Economics_ , Vol. 23:109-124.

- Francois, Joseph F. (1990b), ―Producer Services, Scale, and the Division of Labor,‖ _Oxford Economic Papers_ , Vol. 42:715-729.

- Francois, Joseph and Bernard Hoekman (2010), ―Services Trade and Policy,‖ _Journal of Economic Literature_ , Vol. 48 (September), 642-692.

- Francois Joseph, O. Pindyuk, and J. Woerz (2009), ―International Transactions in Services: Data on International Trade and FDI in the Service Sectors,‖ University of Linz, Institute for International and Development Economics discussion paper 20090802.

- Francois Joseph and I. Wooton (2001), ―Trade in International Transport Services: The Role of Competition,‖ _Review of International Economics_ , Vol. 9, 249–61.

- Hall, R. and C. Jones, (1999), ―Why do some countries produce so much more output per worker than others?‖ _The Quarterly Journal of Economics_ , Vol. 114(1), 83–116.

25
Portney, P.R. and J. Mullahy. 1986. Urban Air Quality and Acute Respiratory Illness. J. of Urban Economics, Vol. 20, 21-38.

Rothenberg, S.J., I.A.P. Guerrero, E.P. Hernandez, L.S. Arrieta, S.C. Ortiz, D.S. Carcamo, J.F. Ortega, and S. Karchmer. Noviembre-Diciembre 1990. Fuentes de Plomo en Embarazadas de la Cuenca de Mexico. Salud Publica de Mexico, Vol.32, No.6, 632-643.

Rummo, J.H.,D.K.Routh, N.J.Rummo, and J.F.Brown. 1979. Behavioral and Neurological Effects of Symptomatic and Asymptomatic Lead-Exposure in Children. Arch.Env.He., Vol. 34 (2), 120-124.

Samet, J.M.,Y. Bishop, F.E. Speizer, J. D. Spengler, and B.G. Ferris. March 1981. The Relationship Between Air Pollution and Emergency Room Visits in an Industrial Community. J.Air Pollution Control Assoc., Vol. 31, 236-240.

Schwartz, J. and A. Marcus. October 1986. Statistical Reanalysis of Data Relating to Air Pollution During London Winters 1958-72. U.S.EPA, Washington, D.C., Working Paper.

Secretaria de Salud. 1988. Anuario Estadistico, Mexico.

Secretaria de Salud. 1988. Direccion General de Epidemiologia. Encuesta Nacional de Salud, Mexico.

Secretaria de Salud. 1990. Consejo Directivo del Programa Nacional de Enfermidades Diarreicas: Informacion Enidemiologica Sobre Enfermidades Diarreicas; Districto Federal, Mexico.

Secretaria de Salud. 1991. Direccion General de Epidemiologia: Estado Atual de la Vigilancia e Investigacion Epidemiologica de los Danos a la Salud por Efecto de la Contaminacion Ambiental en la Ciudad de Mexico; Districto Federal, Mexico.

Smith, V.K. (editor). 1984. Environmental Policy Under Reagan's Executive Order: the Role of Benefit-Cost Analysis. The University of North Carolina Press, Chapel Hill.

U.S.Agency for International Development. December 1990. Ranking Environmental Health Risks in Bangkok, Thailand. Washington, D.C., Working Paper, Vols. 1 and 2.

Whittemore, A.S. and E.L. Korn. July 1980. Asthma and Air Pollution in the Los Angeles Area. American Journal of Public Health, Vol. 70, 687-696.

27
points) more likely to invest an amount no larger than the small endowment when returns are observable; this is equivalent to a 5.4 percentage point decline in investment level. We find no similar tendency to hide income among men. The effect we observe among women appears to be driven primarily by the behavior of those with relatives attending the experiment, who would be able to observe their payoffs directly. Estimates suggest that these women invest 22.1 percent less when investment income is observable than when it is hidden; they are 38.9 percentage points more likely to invest no more than the amount of the small endowment, suggesting that their strategy is designed to keep the size of their endowment hidden. Consistent with a simple model of decisions in the experiment when subjects face pressure to share income and risk preferences are heterogeneous, we find that women receiving the large endowment are more likely to invest exactly the amount of the small endowment when investment returns are observable, and that this tendency is also more pronounced among women with relatives present. Impacts are unlikely to be driven by in-laws providing information to husbands: there is no direct impact of having one’s husband present, and choice patterns are similar in the sub-sample of unmarried women.

Among subjects given the opportunity to pay a randomly-assigned price to keep income hidden, 30 percent of those able to afford the cost of hiding income choose to do so. These subjects pay an average of 15 percent of their gross payout from the experiment. Though women are no more likely than men to pay to avoid the public announcement, their willingness to do so is strongly related to the amount of observable income they receive; this is not true for men. At the village level, women’s tendency to hide income within the experiment is negatively associated with durable asset accumulation by households, skilled and formal sector employment, and the probability of using fertilizer on crops, suggesting that social pressure to share may hinder growth and development.

After presenting our reduced form results, we estimate the magnitude of the “kin tax” parameter via maximum simulated likelihood in a mixed logit framework. In our setting, this extension is important because the size of the treatment effect of observability depends on individual risk preferences. Decisions in our private information treatments, in which investment returns are unobservable, suggest substantial heterogeneity in risk aversion across subjects.<sup>3</sup> After controlling

> 3See Hey and Orme (1994); Choi, Fisman, Gale, and Kariv (2007); Andersen, Harrison, Lau, and Rutstr ̈om

4
strength of the network effect varies with access to basic services such as water service, and electricity in rural areas only.<sup>12</sup> We do this by including an interaction term in regression equation (3) between the social network variable (SN) summarizing the quantity and quality of the network and a binary variable of indicating whether the household has no access to piped water inside the premises of its residence or no access to electricity.<sup>13</sup>

# **Table 5**

# **Social Network Effects and Access to Basic Services in Rural Areas: Indigenous Males and Females 20-65 years of age**

|**Dependent variable**|**SN**|**SN**|**x(noWater)**|**SN**<br>**S**|**Nx(noElec)**|
|---|---|---|---|---|---|
||||**MAL**<br>|**ES**<br>||
|**Internal migrant?**|**0.129**||**0.019**|**0.130**|**0.015**|
|t-value|**(6.22)*****||**(4.50)*****|**(6.19)*****|**(3.29)*****|
|Adjusted R-squared||0.5699||0.5699||
|Number of observations||145,464||145,464||
|**Receiving PROCAMPO/ OPORTUNIDADES ?**|0.017||**-0.026**|0.018|**-0.020**|
|t-value|(0.74)||**(-4.81)*****|(0.77)|**(-3.56)*****|
|Adjusted R-squared||0.2840||0.2839||
|Number of observations||142,962||142,962||
|**Occupied in agricultural activities (occup.**<br>**code=41)**|**0.028**||**-0.013**|**.0256096**|-.0035013|
|t-value|**(2.25)****||**(-5.88)****|**(2.02)****|(-1.53)|
|Adjusted R-squared||0.3210||0.3209||
|Number of observations||145,464||145,464||
|**Occupied in handicrafts, processing, repair &**<br>**maintenance (occup codes 52-54)**|**0.040**||**-0.019**|**.0392904**|**-.0126774**|
|t-value|**(1.79)***||**(-3.26)*****|**(1.74)***|**(-2.29)****|
|Adjusted R-squared||0.1700||0.1699||
|Number of observations||145,464||145,464||
||||**FEMA**|**LES**||
|**Internal Migrant?**|**0.093**||**0.023**|**0.094**|**0.018**|
|t-value|**(4.32)*****||**(5.18)*****|**(4.26)*****|**(4.04)*****|
|Adjusted R-squared||0.5106||0.5105||
|Numberofobservations||155,249||155,249||

Source: Authors’ estimates of the social network effect based on the 2000 Population and Housing Census (10% sample)

> 12  Results using access to sanitation services were very similar to the results for access to water, and they are not reported here.

> 13 The variables summarizing lack of access to basic services are already included the set of controls variables denoted by _X(i)_ in regression equation (3).

17
most conservative empirical estimates of such productivity kick (Topalova, 2004) and assume that a 10% decrease in protection leads to a 0.5 percentage points productivity gain. Table 3 illustrates the calculated trade weighted average reduction of tariffs and corresponding productivity gains in Indonesia for all simulations.

Assuming this productivity kick greatly enhances the positive economic impacts of all of the PTAs (see Figure 2 and Table 2 above). Compared to the standard setup, GDP increase is boosted by 0.22 percentage points in the case of RCEP and up to 7 percentage points in the case of EUIndonesia FTA. Due to the highest reduction in trade-weighted average tariff in the case of the EU-IDN CEPA (see Table 3), the productivity kick is the highest among all the simulations which leads to the strong increase of gains with the trade flows expanding by more than 13%.

**Table** **<u>3: Assumptions on productivity increase from import cost reductions in Indonesia</u>**

||**Aggregate tariff reduction in**<br>**Indonesia, percentage points**<br>|**Productivity kick, percent**<br>|
|---|---|---|
|**TPP-15**|0.39|0.12|
|**RCEP**|0.12|0.04|
|**FTAAP**|0.50|0.15|
|**EU-IDN**|3.19|0.96|

# 5. Distributional impacts of the PTAs

Next we translate these macro-economic effects into distributional effects using the GIDD microsimulation model. The key macro-micro results are reported in Table A2 in Annex 2. We first simulate the evolution of the distribution of income up to 2030 to create the baseline and then compare the results of the PTAs to this baseline.

Extreme poverty in Indonesia has steadily declined since the mid-1980s. The percentage of people living with less than PPP$1.90 a day fell from 70.3% in 1984 to 9.3% by 2013.<sup>17</sup> During this period, Indonesia successfully managed to keep the long-term pace of poverty reduction vis-à-vis other developing countries. Recent data show that Indonesia’s poverty rate was, nonetheless, 5.8

> 17 Data from PovcalNet <u>http://iresearch.worldbank.org/PovcalNet/povDuplicateWB.aspx# Poverty headcount ratio at</u> PPP$1.90 a day in 2014 is 8.3 percent.

22
**<u>Table 8: Inequality of Opportunity Indices for Household Consumption Expenditures (per capita)</u>**

||**C**|**OLOMBI**|**A**||**ECUADO**|**R**|**G**|**UATEMA**|**LA**||**PANAMA**|||**PERU**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**E(0)**|E(1)|E(2)|**E(0)**|E(1)|E(2)|**E(0)**|E(1)|E(2)|**E(0)**|E(1)|E(2)|**E(0)**|E(1)|E(2)|
|TOTAL INEQUALITY|**0.449**<br>**_0.018_**|0.503<br>_0.024_|1.013<br>_0.079_|**0.354**<br>**_0.015_**|0.375<br>_0.018_|0.574<br>_0.047_|**0.409**<br>**_0.024_**|0.436<br>_0.023_|0.676<br>_0.039_|**0.381**<br>**_0.016_**|0.374<br>_0.019_|0.539<br>_0.042_|**0.351**<br>**_0.015_**|0.384<br>_0.022_|0.660<br>_0.076_|
|NON PARAMETRIC<br>ESTIMATES<br>||||||||||||||||
|_N_<br>_d_<br>θ|**0.265**|0.275|0.177|**0.344**|0.347|0.270|**0.524**|0.536|0.440|**0.417**|0.385|0.285|**0.348**|0.339|0.229|
||**_0.017_**|_0.017_|_0.013_|**_0.021_**|_0.025_|_0.028_|**_0.023_**|_0.026_|_0.031_|**_0.016_**|_0.018_|_0.020_|**_0.017_**|_0.017_|_0.016_|
|_N_<br>_r_<sup>θ</sup>|**0.265**|0.304|0.456|**0.344**|0.353|0.427|**0.524**|0.542|0.630|**0.417**|0.405|0.475|**0.348**|0.389|0.533|
||**_0.017_**|_0.023_|_0.035_|**_0.021_**|_0.024_|_0.033_|**_0.023_**|_0.023_|_0.022_|**_0.016_**|_0.024_|_0.044_|**_0.017_**|_0.024_|_0.040_|
|PARAMETRIC<br>ESTIMATES<br>||||||||||||||||
|_P_<br>_r_<sup>θ</sup>|**0.244**|0.271|0.408|**0.321**|0.326|0.389|**0.503**|0.519|0.606|**0.386**|0.362|0.417|**0.340**|0.375|0.512|
|_J_<br>_r_<br>θ|**_0.017_**|_0.023_|_0.041_|**_0.022_**|_0.028_|_0.042_|**_0.020_**|_0.020_|_0.021_|**_0.016_**|_0.023_|_0.046_|**_0.017_**|_0.022_|_0.036_|
|_Race_|**0.001**|0.001|0.002|**0.032**|0.027|0.036|**0.141**|0.123|0.136|**0.121**|0.065|0.047|**0.054**|0.051|0.065|
||**_0.002_**|_0.002_|_0.003_|**_0.006_**|_0.005_|_0.006_|**_0.013_**|_0.011_|_0.013_|**_0.014_**|_0.012_|_0.016_|**_0.008_**|_0.007_|_0.008_|
|_Father's occupation_||||**0.106**|0.103|0.120|**0.073**|0.071|0.088|**0.071**|0.069|0.090||||
|||||**_0.010_**|_0.011_|_0.018_|**_0.011_**|_0.011_|_0.015_|**_0.011_**|_0.010_|_0.013_||||
|_Father's education_|**0.154**|0.179|0.288|**0.141**|0.148|0.192|**0.202**|0.219|0.285|**0.108**|0.109|0.146|**0.142**|0.147|0.199|
||**_0.016_**|_0.019_|_0.028_|**_0.015_**|_0.019_|_0.032_|**_0.021_**|_0.021_|_0.027_|**_0.014_**|_0.016_|_0.023_|**_0.013_**|_0.014_|_0.019_|
|_Mother's education_|**0.166**|0.194|0.314|**0.186**|0.193|0.244|**0.256**|0.279|0.353|**0.167**|0.177|0.241|**0.204**|0.222|0.306|
||**_0.014_**|_0.018_|_0.028_|**_0.015_**|_0.019_|_0.032_|**_0.019_**|_0.021_|_0.028_|**_0.017_**|_0.020_|_0.029_|**_0.014_**|_0.017_|_0.025_|
|_Birth region_|**0.040**|0.032|0.035|**0.040**|0.044|0.058|**0.102**|0.103|0.137|**0.096**|0.096|0.127|**0.109**|0.124|0.195|
||**_0.011_**|_0.016_|_0.033_|**_0.010_**|_0.014_|_0.025_|**_0.014_**|_0.015_|_0.020_|**_0.015_**|_0.016_|_0.023_|**_0.013_**|_0.019_|_0.038_|

Sample: individuals 30-49 with positive household consumption and information on a set of circumstances; standard errors in italics; father’s occupation is missing for Colombia and Peru.

41
common shock (  _sj_ ). In estimating (3.4) we apply a thana-level fixed-effects instrumental variable method (call it FE-IV) to eliminate bias due to unobserved thana-level common seasonal shock affecting the consumption and income variations, as well as controlling for the joint distribution of income and consumption due to time in-varying unobserved errors observed at the household and thana level .<sup>22</sup> One potential problem of estimating equation (3.4) is bias due to attrition of some thanas from the thana level fixed-effects analysis. As indicated, the HIES data sets of 2000 and 2005 are not meant to be panel even at the thana level. Out of a total of 504 thanas in Bangladesh, there were 250 thanas included in the survey year of 2000 and 291 thanas in 2005. When we merge the data at the thana level over two years, we get a common set of 184 thanas to be included in the thana level panel.<sup>23</sup> Thus, altogether 66 out of 250 thanas from 2000 survey were not available for panel analysis, implying an attrition rate of 26.4 percent. Attrition can bias estimates where it is nonrandom or selective. In that case, it may well ruin the advantages that seasonal panel data analysis is supposed to have and cross-sectional data may be a better choice. There is a large body of literature that demonstrates that even a high attrition rate is a non-issue as long as it is random (Alderman and others 2000; Fitzgerald, Gottschalk and Moffitt 1998; Thomas, Frankenberg and Smith 2001; Ziliak and Kniesner 1998). For example, Fitzgerald,

> 22 In practice, what we do is the following.  First, we multiply thana dummy with seasonal dummy to create thana-season dummy.  Since we have two seasons (monga and non-monga), the difference between these thana-season dummies cancels out season-specific unobserved effect within a thana (  _sj_ ).  Since these thanaseason dummies are also observed in two years, the difference between them in two years cancels out thanaspecific fixed effect (  _j_ ).  Yet there is a possibility that household-level heterogeneity which cannot be cancelled out in this process may be included in thana-level heterogeneity, part of which can vary over time. Therefore, we use an IV method to take care of this time-varying heterogeneity.  The IVs are the rainfall and other production constraints that affect only the household production and not the consumption directly. As before, we will include predicted total and seasonal income shares in estimating equation (3.4).

> 23 Note that the reduced samples for the thana level panel data consists of 5,040 households with 25% in season 1, 23% in season two, 27% in season three, and 25% in season 4.

18
publicly owned. Overall, the data clearly indicate that municipalities (which are in fact administering the privatization and lease applications for state lands as well) are still monopoly holders of this resource.

Of the remainder of the land in the 15 regions, some is owned by legal entities and some by individuals. St. Petersburg has the highest proportion of land owned by legal entities, at 11%, and Rostov Oblast has the highest proportion of land owned by individuals, at just under 65%. Moscow City has 0% of its land in private ownership of any kind, while Khabarovsk Krai has less than 1%.

**_Chart 1  Land owned/possesses by state and municipalities, 2004, %_**

<!-- Start of picture text --> Land Owned/Possessed by State and Municipalities, 2004, %<br>Moscow 100.0 0%<br>Khabarovsk Krai 99.9 2%<br>Sakhalin Oblast 99. 34%<br>Irkutsk Oblast 98 .50%<br>Tomsk Oblast 9 7.68%<br>Leningrad Oblast 94.33%<br>Sverdlovsk Oblast 92.76%<br>Novgorod Oblast 89.30%<br>Perm Oblast 89.14%<br>Saint-Petersburg 86.85%<br>Moscow Oblast 73.56%<br>Nizhny Novgorod Obl. 69.93%<br>Novosibirsk Oblast 68.61%<br>Kaliningrad Oblast 60.90%<br>Rostov Oblast 35.02%<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br><!-- End of picture text --> _Source: Based on data from Roskadastr State (National) Report on the Status and Use of Lands in the RF in 2004._ Data from recent surveys reinforce this message: Among SMEs surveyed by CEFIR in 2005, only 6% own land. From FIAS’ surveys for the current project in 15 regions of the Russian Federation of firms that have carried out land or real estate transactions in 2004 (i.e., mostly medium-sized firms), the proportion of respondents reporting ownership rights in land was about 18% (Table 1).

The survey also showed a large range of responses across regions, from zero private land ownership in Moscow City to a high of 47% in Irkutsk. Respondents in most regions indicated less than 25% of respondents owned land. **_Fully one-third of all survey respondents reported that they wanted to buy land but were deterred from doing so by local authorities._** While many firms had officially applied for land ownership rights in 2004, the success rate ranged from zero in Moscow City to a reported 97% in Rostov Oblast. By contrast, applications for lease-hold rights were usually much more successful, ranging from a low of 50% in Moscow Oblast to a high of 93% in Rostov Oblast.

7
|Fourth Export Finance Intermediation Loan(EFIL IV)|Ilias Skamnelos|
|---|---|
|Fourth Export Finance Intermediation Loan(EFIL IV)|Ilias Skamnelos|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Public Sector Reform TA|Hassan Aliev|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|Ningxia HighwayProject|Xiaoke Zhai|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(<br>|PNGeorge Soraya<br>|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|National Program for CommunityEmpowerment in Urban Areas(|PNGeorge Soraya|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|Shanghai Urban Environment(APL Phase III)|Alessandra Campanaro|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|
|China Bengbu Integrated Environment Improvement Project|ShunongHu|