rafmacalaba/datause-encoder-data huggingface dataset:
But with the Krugman model, the welfare change for China of using real data is positive (9.7% - 7.8% > 0).

# **5.4 Multiple Primary Factors, Sector Specific Factors and Labor-Leisure Choice**

Heretofore, we have assumed a single primary factor of production. In this section, we investigate the impact of allowing primary factors of production to include labor, capital and a resource factor which may be sector specific. Demand for primary factors is Cobb-Douglas. We also consider a case with laborleisure choice, using the structure explained in section 3.3. All scenarios assume: four monopolistically competitive sectors; CES demand for intermediates based on the data on intermediate shares by sector, and an elasticity of substitution equal to 0.5; initial trade imbalances; and initial tariffs. We take primary factor share intensities from the GTAP data. We simulate a global ten percent reduction in iceberg trade costs.

For the Armington, Krugman and Melitz market structures, we produce estimates for the welfare gains with a single primary factor in columns 1-3 and with three primary factors columns 4-6. Holding market structure constant, at a single decimal point of percentage welfare change, we see no change for the average for the world and almost no change for any of the ten regions. This extends the Costinot and Rodriguez-Clare result, of very little impact of multiple factors of production, to the Krugman and Melitz models. Our models in this section and the model of Costinot and Rodriguez-Clare incorporate differences in factor intensities across counties. We believe, however, that further research is warranted into the question of whether differences in factor endowments are adequately incorporated before we conclude that Heckscher-Ohlin forces are unimportant.

The results without labor-leisure choice (columns 1-9) show the same pattern as in table 4. That is, regarding the welfare impacts, we have _M K A_ for seven of the ten regions and for the average for the world. For the regions Australia-New Zealand, Low Income and Middle Income, we have that _A K M_ . The additional factors of production do not change the qualitative result.

<!-- Start of picture text --> K M<br><!-- End of picture text --> One quantitative difference, however, is that we find larger welfare gains in table 5. Comparing columns 1-3 of table 5 with columns 7-9 of table 4, the only difference is the presence of initial tariffs in table 5. The initial tariffs imply there is less trade than is optimal for the world, so the reduction of iceberg trade costs leads to larger gains from a second-best effect.

Labor-leisure choice, however, does reverse the qualitative result for Australia-New Zealand and the Low Income regions regarding the ranking of Armington compared to the monopolistic competition models. Without labor-leisure choice, these regions obtain larger estimated gain in the Armington model, but prefer the outcomes of the monopolistic competition models with labor-leisure choice. This result is 25
All households Assumed co-located with household location Housing

# **_3.3.1 Accessibility analysis_**

The accessibility of certain infrastructure services (education, hospitals, and health facilities) is modeled by connecting all households via the road network to the closest infrastructure asset. This is done by first creating a topologically correct road network using the road infrastructure data. Second, based on the road type ( _e.g._ , highways or zila road) and paving ( _e.g._ pucca or katcha), we assign a speed to the road segment. The processed road map with allocated speeds is shown in Figure E1. Third, we assign every household location in the synthetic household survey to the closest road and every infrastructure asset to the closest road. Then, for every household, we find the closest asset that this household can reach and derive the time and distance it takes to access these assets. Based on this, we find the total number of households that are using each individual asset and the road segments that are used to reach this asset.

For electricity services, we connect the urban households with electricity access to the closest substation, assuming that they will be connected to the substation via a local distribution network. We assume that rural households are not connected to the main grid, but instead use rural community electricity services, such as solar panels. As described in Section 3.2, we assume that tube wells and pit latrines are co-located with households, and the infrastructure service provision is directly modeled using the location of households in the SHSD data.

# **_3.3.2 Infrastructure services_**

Using the accessibility analysis, we can derive the system of infrastructure services across the coastal zone. Across the coastal zone, 71% of the urban households have access to electricity. Moreover, 88% of the rural households and 75% of the urban households are using dug wells for accessing drinking water, which, given a total of around 0.5 million functioning tube wells located in the coastal zone (based on DPHE data), equates to around 18 households per well. Moreover, 97% and 98% of the rural and urban households, respectively, have access to some form of sanitation, with 71% and 78% of rural and urban households having improved sanitation facilities.

An overview of the household allocation to assets and the roads per infrastructure type is shown in Figure 3, showing clear size differences in the service areas of different infrastructure assets. One can observe that access to educational facilities is very decentralized (median of 173 households per asset) given the large number of assets, while access to hospitals is very centralized (median 52,787 households per asset) with a relatively small number of main roads used to access these hospitals. Access to health facilities (median 8,619 households per asset) shows a more regional infrastructure services provision with each facility serving a relatively equal sized regional service area. The median number of households per electricity substation is 7,400 households, concentrated in the urban areas.

17
|**Table 5 -- Benchmark Distortions (%)**|||Regulat|orybarriers|
|---|---|---|---|---|
||Tariff|Sales Tax|All firms|Foreign firms|
|**Business Services**<br>|||||
|Telecommunication|||3.0|8.0|
|Insurance|||18.0|36.0|
|Banking|||37.0|25.0|
|Professional business services|||5.5|10.9|
|Air transport|||20.0|2.0|
|Road transport|||30.0|5.0|
|<br>Railway transport|||40.0||
|Water transport|||86.0|39.0|
|<br>**Dixit-Stiglitz Goods**|||||
|<br>Mining & quarrying|5.3|4.2|||
|<br>Grain milling|9.3|0.4|||
|<br>Processed food|8.5|7.1|||
|Beverages & tobacco products|33.3|11.4|||
|Textile & leather products|24.0|14.1|||
|<br>Manufacture of basic & industrial chemicals|3.2|14.2|||
|Manufacture of fertilizers & pesticides||0.1|||
|Petroleum refineries|2.6|1.5|||
|Glass & cement|6.1|18.5|||
|Iron steel & metal products|6.6|2.8|||
|**Agriculture**|||||
|Maize|0.8|0.1|||
|Paddy|20.8||||
|Sorghum or millets||0.0|||
|Wheat|8.7|0.9|||
|Beans|47.8|0.8|||
|Cassava|34.4|5.3|||
|Other cereals|8.8|2.0|||
|Oil seeds|5.5|0.2|||
|Other roots & tubes|0.6|5.0|||
|Cotton|1.2|2.0|||
|Coffee|16.8|27.8|||
|Tobacco|26.9|2.6|||
|Tea|22.2|28.0|||
|Cashew nuts|22.2|6.2|||
|Sisal fiber|||||
|Sugar|28.2|1.8|||
|Fruits & vegetables|8.2|0.1|||
|<br>Other crops|5.3|12.4|||
|<br>Poultry & livestock|10.5|2.8|||
|**Other CRTS**|||||
|Fish|24.3|2.5|||
|Hunting & forestry||3.6|||
|<br>Meat & dairy products|54.3|13.0|||
|<br>Wood paper printing|13.1|3.0|||
|Rubber plastic & other manufacturing|5.2|7.1|||
|Manufacture of equipment|5.3|0.0|||

Note: Utilities, Construction, Wholesale and Retail Trade, Hotels and Restaurants, Postal Communication, Real Estate, Other Services. Public Administration and Health Care and Tourism have no distortions and are excluded from the table.

Source: Author’s estimates. See Jensen, Rutherford and Tarr (2009) for details.

72
rate from the calculation, but summary statistics are also included for all choices including the implemented interest rate.

_Future-biased ratio_ is fraction of choices where allocation to sooner in near period is more than 100MK lower than allocation to sooner in far period (comparing choices in near and far periods for same interest rate). In regressions this variable excludes the implemented interest rate from the calculation.

_Fraction of decisions consistent with law of demand_ is fraction (out of 8) of pairs of adjacent choices (adjacent in interest rates) where allocation to later rises in rate of return.

_Spouse minus own allocation to sooner (MK)_ is spousal allocation to the sooner period minus corresponding allocation for respondent, for all choices excluding the randomly-chosen implemented choice.

_Implemented interest rate_ is rate of return to waiting 30 days for funds for the respondent’s randomly-selected choice (out of 10 choices made).

_HH total in bank_ is total value of balances in formal banks reported at baseline (in thousands of MK).

_HH total cash_ is total value of cash held at home reported at baseline (in thousands of MK). _HH items_ is total value of physical household items and assets owned, reported at baseline (in thousands of MK).

_HH animals_ is total value of livestock owned, reported at baseline (in thousands of MK). _Total HH wealth_ is sum of HH total in bank, HH total cash, HH items, and HH animals (in thousands of MK).

# A.2 Variables collected in revisit survey

_Indicator for death in family_ indicates death in respondent’s own household in the time since the baseline survey (reported in revisit survey).

_Shortfall in expected household income_ is expected household income minus actual household income, where expectation is reported in baseline and actual is reported in revisit survey. Both expectation and actual income reports refer to same period (time between baseline and revisit survey), reported in revisit survey.

# A.3 Variables from administrative (project) data

_Days to first disbursement at revisit (targeted)_ is randomized target number of days prior to first far period disbursement that revisit survey is planned. Randomization assigns days from 2 to 16 in unit intervals with equal probability.

_Days to first disbursement at revisit (actual)_ is actual number of days prior to first far period disbursement that revisit survey is carried out.

_Indicator for days to first disbursement (targeted) <=6_ equal to 1 if days to first disbursement at revisit (targeted) is less than or equal to 6, and 0 otherwise.

# **Appendix B: Simulations of stochastic choice**

In this section we assess the adherence of subjects’ optimizing behavior to the canonical model of Section 3.1.1 by comparing their choices to those of hypothetical subjects that choose randomly. Given that more than two thirds of individuals choose allocations that deviate at least 33
- 17 -

The paper reexamined such a nexus among crop production, transport infrastructure and agrobusinesses. The results show the particular importance of intensification in Malagasy rice production. Fertilizer use has the highest elasticity, followed by irrigation and market accessibility. Consistently, the results indicate the importance of agrobusinesses. Particularly, proximity to input-oriented agrobusinesses, such as input dealers and equipment suppliers, is found to be important: They can facilitate farmers’ adoption of new inputs and technologies.

Methodologically, the paper also attempted to apply newly available data and techniques. Spatial data are generally rich data sources, which allow to generate various information that would be unavailable or very costly to collect otherwise. The paper combined highly disaggregated crop production data and detailed geo-referenced road network data. Various transport accessibility measurements were developed, which are clearly useful to consider many other policy issues.
urban households or other regionally disparate populations facing different prices (e.g., Ravallion and Lokshin, 2006; Boom, Halsema and Molini, 2015). Deflators can be used to adjust for such price differences. However, they are sensitive to methodological choices (Ravallion and Bidani, 1994) and, usually, require large amounts of high-quality data on quantities and prices.

If income or consumption data for FDPs are missing from the data at hand but available in other data representative of the same population, one can also use cross-survey imputation techniques to estimate consumption using proxies of well-being. This method uses a baseline survey inclusive of consumption data to model consumption using a regression model inclusive of easily measurable household characteristics as independent variables. The estimated coefficients from the model are then used to predict consumption for FDPs using census data (usually FDPs registration data) that lack consumption but include the same household characteristics used with the baseline survey model. This method has been tested with refugees in Jordan and Chad (Dang and Verme, 2021, Beltramo et al., 2021) providing encouraging preliminary results. These works showed that accurate poverty estimations for refugees can be obtained with a relatively small number of proxies of well-being which are usually already available in the UNHCR proGres registration system. The quality of the imputed poverty estimates depends on the similarity of the population surveyed at baseline and the population used for imputations. Both populations might differ because of the time passed between baseline and imputation survey, and because of different characteristics if both surveys are not representative for the same populations. It is therefore essential for these types of exercises to use survey and census data from the same time period and with very similar predictors.

The treatment of aid is also particularly challenging. By definition, aid is included in consumption aggregates and, hence, will reduce estimated poverty among FDPs. This provides a fair assessment of the current situation and is particularly helpful in the context of comparison with host communities to ensure that they are not receiving less assistance while being more deprived. However, it can also easily be misinterpreted that FDPs do not require assistance as they are not poor. To avoid this misinterpretation, a second consumption estimate can be constructed that excludes aid from consumption to reflect livelihoods of FDPs in the absence of aid. A precise estimate of consumption excluding aid is not easily feasible. Even if consumed quantities for each item are split into aid vs. non-aid, in-kind assistance that is sold would make attribution difficult, while an income-focused angle would have similar problems as discussed before. A light-handed approach that asks for each item whether it was mainly provided for free can produce a consumption aggregate excluding aid that can be useful in re-thinking targeting of aid (Pape et al., 2019a).

17
because without taking these interactions into the analysis, the poverty and income distribution effects of biofuels may be overestimated.

**Table 4: Impacts on Household Labor and Land Income in 2020** **<u>(Percentage Change from BaU)</u>**

||Land I<br>|ncome<br>|Labor<br>|Income<br>|
|---|---|---|---|---|
||AT/BaU|ET/BaU|AT/BaU|ET/BaU|
|China|0.8|2.6|0.0|-0.1|
|Japan|1.6|4.6|0.0|0.1|
|Indonesia|1.0|3.5|0.0|0.0|
|Malaysia|0.6|1.5|-0.1|-0.2|
|Thailand|1.9|6.7|0.2|0.5|
|India|3.7|4.7|0.0|0.3|
|Canada|2.0|5.9|0.0|-0.1|
|United States|1.4|3.9|-0.1|-0.4|
|Argentina|1.8|5.0|0.1|0.2|
|Brazil|3.3|7.7|0.7|1.1|
|France|11.6|37.3|0.3|0.6|
|Germany|4.9|15.6|0.1|0.2|
|Italy|6.2|17.1|0.2|0.3|
|Spain|5.0|14.8|0.2|0.5|
|United Kingdom|9.6|27.1|0.1|0.2|
|Russia|0.8|2.1|-0.4|-0.9|
|South Africa|1.2|3.0|0.1|0.2|
|Rest European Union and EFTA|2.9|7.9|0.0|0.1|
|Rest of Latin America and|||||
|Caribbean|0.9|2.7|-0.1|-0.4|
|Australia and New Zealand|1.2|3.3|0.1|0.1|
|Rest of East Asia and Pacific|0.7|1.7|0.1|0.2|
|Rest of South Asia|0.6|1.5|0.0|-0.1|
|Rest of Europe and Central Asia|1.2|3.2|0.1|0.2|
|Middle East and North Africa|1.3|3.3|-0.7|-1.6|
|Rest Sub-Saharan Africa|0.4|1.1|-0.3|-0.6|

Relatively higher wages and demand for unskilled rural labor because of increased biofuel production have favorable effects on agriculture and rural households in developing countries. Higher land income also favorably affects them, although the impact is higher for richer farmers than for poorer farmers with less farm holdings. But food consumer prices increase as biofuel production and food production compete for feedstock. The increase in food prices is higher in developing countries than in developed countries. The next section will 25
Figure 29. Shapley Decomposition of the Dissimilarity in Savings (household level, LITS) <!-- Start of picture text --> 100%<br>90%<br>80% 41.1<br>70%<br>60%<br>9.7<br>50%<br>40% 15.0<br>30%<br>20%<br>33.8<br>10%<br>0%<br>Azerbaijan Bulgaria Turkey Tajikistan Moldova<br>Age Education Gender Income Urban<br><!-- End of picture text --> _Notes:._ LITS is household level. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. See Appendix for all countries.

_Sources_ : LITS 36
suggest that the EBA program was associated with more exports, holding other HighDimensional Fixed Effects (HDFEs) and RTAs constant. These estimates are slightly less than those for RTAs generally. In addition, the effects on Cambodian exports were especially acute. The interaction estimates in the second column suggest that the EBA program was associated with smaller increases in exports from Cambodia than from other countries.

**Table 3.6: Cambodia EBA Estimates**

|**VARIABLES**|**Zeros**|**Zeros**|
|---|---|---|
|RTA|0.581***<br>(0.099)|0.581***<br>(0.099)|
|EBA|1.103***<br>(0.090)|1.110***<br>(0.091)|
|Cambodia EBA||-0.323*<br>(0.181)|
|Constant|16.016***<br>(0.045)|16.016***<br>(0.045)|
|Observations|1,231,334|1,231,334|
|**Notes:**PPML HD<br>annual pair-wise C<br>spanning 1996-20<br>errors in parenthes<br>***p<0.01,**p<|FE estimation<br>OMTRADE/<br>21. Robust sta<br>es.<br>0.05,*p<0.1|using<br>Baci data<br>ndard<br>.|

One reason why the EBA program is associated with such large increases in exports from LDCs is that they explicitly reduce tariffs and quotas for LDC exports. Other agreements are more aspirational, such as the Trade and Investment Facilitation (TIF). The text of the TIF contains no reduction in any tariff or non-tariff barriers. The agreement is purely a framework for continued discussion and collaboration on trade issues. The agreement has no provisions that would reduce trade costs, and, therefore, no enforcement provisions. In other words, there is nothing in the TIF that should actually affect trade flows. Nevertheless, we estimate the relationship between the TIF and bilateral trade flows, controlling for other agreements and HDFE (see Table 3.7). Cambodia’s exports increased significantly after the agreement went into effect.

**Table 3.7: Cambodia TIF Estimates**

|**VARIABLES**|**Zeros**|**Non**<br>**Zeros**|
|---|---|---|
|RTA|0.581***|0.024|
||(0.099)|(0.042)|
|TIF|0.623***|0.199**|
||(0.099)|(0.077)|
|Constant|16.027***|16.397***|
||(0.045)|(0.019)|
|Observations|1,231,334|762,476|

20 | P a g e
about the demand for their products, they decide whether to move to the formal sector and expand, or to shut down.

The heterogeneity of the informal sector in low-income countries allows each side to cite at least anecdotal evidence supporting its own story. But we are unaware of any study which takes these arguments to the data in a systematic way with samples representative of both the informal sector and larger firm owners. In this chapter, we aim to provide such a systematic comparison of wage workers, own account workers, and employers, using data from surveys in Sri Lanka. The wage worker and larger firm surveys are cross-sectional; the own account worker data come from a survey begun in April 2005, with the most recent survey conducted in October 2007. We use both the cross sectional comparison and the dynamics from the microenterprise panel.

There are at least two lines of empirical research which are related to our analysis. William Maloney and coauthors use labor market surveys in several Latin American countries to study the pattern of transition into and out of self employment and the informal sector more generally. These data show high rates of mobility between formal wage work and informal self employment in all of the countries examined (Maloney 1999; Bosch and Maloney 2007; Bosch, Goni, Maloney 2007). Moreover, a large majority of own account workers report being self employed by choice rather than by force, for the lack of available wage jobs. Perhaps the strongest evidence that the self employed are drawn by opportunity is that in Mexico, at least, transitions from formal wage work to informal self employment are procyclical (Bosch and Maloney 2007). Collectively, the Maloney et al analysis indicates the lack of any hard barrier between wage work and self employment in the Latin American countries. But most of their findings are also consistent with a Lucas-style decision model (Lucas 1978), where those with low levels of entrepreneurial ability move between wage work and self employment as economic opportunities change. Because the Latin American labor data are limited to overlapping one-year panels, they tell us less about whether those transitioning to self employment subsequently grow to become employers.<sup>3</sup>

> 3 Woodruff (2007) questions how much of this mobility is sustained rather than representing short term churn.