rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 6: U**<br>Dependent variable:SBM Efficiency|**se of tempora**<br>**(1)**|**ry workers**<br>**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|Volatility in Occupancy*Temporary|-0.001|-0.234*|-0.280**|-0.408***|
|workers Y:1 N:0|(0.197)|(0.121)|(0.129)|(0.125)|
|Volatility in Occupancy|0.080|0.567***|0.541***|0.584***|
||(0.056)|(0.124)|(0.127)|(0.110)|
|Temporary workers Y:1 N:0|0.076|0.253**|0.317**|0.479***|
||(0.175)|(0.114)|(0.138)|(0.123)|
|Volatility in Occupancy*Number of||-0.187***|-0.156***|-0.167***|
|workers (logs)||(0.046)|(0.043)|(0.035)|
|Number of workers (logs)||0.119**|0.123**|0.111**|
|||(0.053)|(0.054)|(0.042)|
|Central region Y:1 N:0||-0.042|-0.088|-0.050|
|||(0.044)|(0.058)|(0.110)|
|Eastern region Y:1 N:0||0.227***|0.253***|0.251**|
|||(0.067)|(0.072)|(0.090)|
|Northern region Y:1 N:0||0.018|0.111|0.089|
|||(0.047)|(0.075)|(0.150)|
|Sabah & Sarawak Y:1 N:0||0.151*|0.133|0.131|
|||(0.086)|(0.081)|(0.087)|
|Age of hotel (logs)|||0.027|0.098***|
||||(0.041)|(0.034)|
|Part of a chain Y:1 N:0|||-0.179*|-0.178*|
||||(0.092)|(0.094)|
|Women's ownership (proportion)|||0.067|0.199|
||||(0.121)|(0.156)|
|Finance major obstacle Y:1 N:0|||0.019|0.026|
||||(0.073)|(0.047)|
|Occupancy rate|||0.308*|0.292*|
||||(0.153)|(0.169)|
|Number of power outages|||-0.007|-0.024|
||||(0.019)|(0.023)|
|Largest owner is manager Y:1 N:0|||-0.047|0.002|
||||(0.051)|(0.056)|
|Website Y:1 N:0|||-0.141*|-0.109|
||||(0.075)|(0.078)|
|Low competition Y:1 N:0|||0.015|-0.015|
||||(0.055)|(0.069)|
|Intermediate competition Y:1 N:0|||-0.005|0.033|
||||(0.102)|(0.100)|
|Two-star hotel Y:1 N:0||||-0.077|
|||||(0.061)|

47
|**Item**|**Example**|
|---|---|
|Numbering of the province TT (I–XXVIII), numbering for<br>each stretch(1,2,etc)|I.1|
|Province and location|Quang Ninh, dike system in Quang Yen<br>town: Ha An dyke|
|Location bykilometer|K0–K8+500|
|Length(in kilometers)|8.5|
|Flood protection level according to QĐ 58 (Grades 9–12,<br>Tide 5%)|Grade 9, Tide 5%|
|Floodprotection level based on new design(Grades I–V)|Grade IV|
|Type(sea dike,estuarydike,revetment)|Estuarydike|
|Current crest height(in meters)|+4.5 ÷ +5.0|

The Technical Standards in Sea Dike Design (MARD 2012) have been used to extract relevant information, including the sea dike grade classification (table 2.2). This document provides guidance for the design and rehabilitation of various types of sea dike and other relevant structures in Vietnam and includes information on the grading classification system. The grade determines the safety standard, which depends on the area and population the section protects: the larger the protected area and number of people; the higher the safety standard. In areas of industrial or economic importance, the safety level can be upgraded on a case-by-case basis. The classification system is used to set the safety standard for each stretch of the coastal protection system. Grade I offers protection for a 150-year return period; Grade V for 10–30 years. This assessment has applied a 20-year return period for Grade V as a starting point.

**Table 2.2 Sea dike grade classification**

|**Area**|**Protected area**<br>**(hectares, thousands)**|**Population**<br>**(thousands)**|**Safety standard**<br>**(years)**|
|---|---|---|---|
|Developed industrial urban<br>area|Over 100|Over 200|150|
|Rural areas with developed<br>industryand agriculture|50–100|100–200|100|
|Developed rural and<br>agricultural area|10–50|50–100|50|
|Medium developed rural<br>and agricultural area|5–10|10–50|30|
|Underdeveloped rural and<br>agricultural area|Below 5|Below 10|10<SS<30|

# _Source:_ MARD 2012.

_Notes:_ Developed industrial and agricultural areas are determined on the percentage of economic structure in the protected area. Areas with a greater industrialization are classified as a developed industrial area and so on. The protected areas are first classified according to industrial/agricultural development. The criteria about size and population size then determine the safety standard. If the protected area meets only one criterion, the level is lowered by one. Spatial planning must consider the country’s socioeconomic development plan for 2016–2020 and Vision to 2030.

Sea dike grading, introduced relatively recently in Vietnam, is an important step in putting an explicit risk-based grade system in place. Most sea dike designs were originally based on a typical protection level against storm surge and waves with a 20 to 25-year return period (Mai et al. 2008). The current sea dike grade classification system differentiates between areas of high and 5
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deciles. The second and third columns of this table show the distribution of both pre-tax and after-tax income. The wealthiest deciles receives more that 40 percent of pre-tax income and about 41 percent of after-tax income while the poorest receives about 1.4 percent of each type of income.

Table 2.1. The Distribution of the Tax Burden in Chile (1996)

|Decile|Pre-Tax<br>Income|After-Tax<br>Income|Whole<br>System|Tax|Income<br>Tax|VAT|Other<br>Taxes|
|---|---|---|---|---|---|---|---|
|1|1.45|1.40|14.44||0.00|11.0|3.42|
|2|2.74|2.63|16.0||0.00|11.8|4.20|
|3|3.77|3.61|15.8||0.00|11.4|4.33|
|4|4.73|4.59|15.2||0.00|10.9|4.25|
|5|5.57|5.47|15.0||0.01|10.7|4.21|
|6|6.76|6.64|14.3||0.04|10.2|4.07|
|7|8.22|8.20|13.8||0.11|9.7|4.0|
|8|10.60|10.61|13.1||0.23|9.0|3.85|
|9|15.42|15.75|12.2||0.62|8.0|3.54|
|10|40.75|41.09|11.8||2.54|6.3|2.96|

Source: Engel, Galetovic and Raddatz (1999); Note: the entries are in percentage.

The last three columns of the table show the percentage of income that each decile pays in taxes considering respectively the whole tax system, the income tax, the valueadded tax (VAT) and other taxes. These results reveal the tax burden is disproportionately borne by the poor. For instance, the second decile is the group that pays the highest percentage of its income in taxes, 16 percent compared to the 12 percent paid by the wealthiest group.

Shah and Whalley (1991) caution that, a mechanical application of the conventional incidence analysis to developing countries can lead to significantly erroneous results and hence to wrong policy recommendations. Indeed, this standard analysis assumes an institutional setting that may not prevail in developing countries, namely a competitive market economy. For a proper tax incidence analysis in developing countries, these authors urge that account be taken of the following special features such 9
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20
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42
Table A3: Long differences: 5-year intervals

|||OLS|||IV||
|---|---|---|---|---|---|---|
|Dependent variable:|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|_log_(1 +_imp_)|_log_(1 +_exp_)|_log_(<sup>1+</sup><sup>_imp_</sup><br>1+_exp_ <sup>)</sup>|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_log_(1 + <sup>_robots_</sup><br>_hours_ <sup>)</sup>|0.1466*|0.3796***|-0.2329***|0.7252***|1.3597***|-0.6345***|
||(0.0793)|(0.1273)|(0.0624)|(0.2217)|(0.3390)|(0.1938)|
|First stage:|||||||
|_Replaceability_ * _initial GDPpc_ * _global robot stock_||||0.0041***<br>(0.0004)|0.0041***<br>(0.0004)|0.0041***<br>(0.0004)|
|Observations|204,656|204,656|204,656|204,656|204,656|204,656|
|_R_<sup>2</sup>|0.6361|0.7762|0.3935|0.6307|0.7634|0.3896|
|Kleibergen-Paap rk Wald F-stat||||88.479|88.479|88.479|
|Importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, using data on 5-year intervals from the baseline estimation sample. Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
female workers), firm-level average wage (total and female workers), the share of exports in firmlevel total sales, and the share of exports to specific countries in firm-level total exports.

We use the National Statistics Office of Georgia (GEOSTAT) Statistics Survey of Enterprises for 2006-2017, which is a panel with the same firm identified across years. The data are then merged with customs-level transactions data using unique firm IDs. The GEOSTAT survey covers more than 61,000 firms and 132,000 firm-year observations during this period, with data on employment (total and female), wages (total and female), and sales. Customs data record covered more than 81,000 entries, with information on export value and destination for each transaction. Data on effective exchange rates are collected from the International Monetary Fund’s International Financial Statistics.

**Export share:** Share of establishment’s sales that are exported. Firm-level export shares for Georgia are collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, calculated as total sales by each type of activity without VAT and excise divided by total exports (converted from USD to GEL using the corresponding year’s official exchange rate reported by the World Bank World Development Indicators). Note that because a panel dimension is necessary to conduct the analysis of changes in export shares and changes in labor market outcomes, the selection of firms was limited to those observed in the data across multiple years. A firm was included in the sample if it was observed in any two sample years, had exported in one of those years, and had non-zero exports in the first year or a previous year.<sup>7</sup>

**Market share:** Share of establishment’s exports that are exported to a particular country market. Firm-level market shares for Georgia are collected from the GEOSTAT Statistics Survey of Enterprises for 2006-2017, calculated as exports to a particular market (e.g., the European Union, the Russian Federation, non-EU non-Russia ECA, high income, upper-middle income, lowermiddle income, and low income) divided by total exports. Exports were converted from USD to GEL using the corresponding year’s official exchange rate reported by the World Bank World Development Indicators.

> 7 The first year a firm appears in the sample is when it first exported, such that the initial share of exports is not zero for any firm.

5
33 PHN 1991 Sector Review I ~~~.

.............................

TabLe 3: Poputation, Health and Nutrition Lendfig Volumn, FY80-91 Amount and Percent of Total PHN Sector Lending by Region (Commitment in USS millions)

|..........<br>|..........<br>|...........<br>||.........<br>Three<br>|....<br>year av<br>|..........<br>erage|..................<br>s|||.....................<br>............|....<br>...........|Percent<br>...............|....................................<br>age<br>change|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||..........|..............|..........|..............|............|....-|||||FY80-|82<br>to|FY83-85<br>t|<br>o FY86-88<br>to|
||FY80|-82|FY83-8|5|FY86-|88|FY89-91|||FY83-|85|FY86-88|FY89-91|
|AFRIC|A<br>7.7||39.3||78.|0|248.|8||413.|0|98.3|219.0|
|ASIA|47.7||89.0||105.3||330.C|||86.7||18.4|713.3|
|EMENA|<br>4.3||26.7||4.|3|163.|0||515.|4|(83.8)|3,661.5|
|LAC|4.3||30.7||71.|7|275.2|||607.|7|133.7|284.0|
||.........||-----..--|...|.......||.........|||.......|.|.....<br>-------|...................|
|TOTAL|$64.0||$185.7||$259.|3|$1,017.|0||190.|1X|39.7%|<br>292.2X|
|||FY80|<br>||FY81|<br>||FY82|<br>||FY83|||
||||No. of|||No. of|||No. of|||No. of||
||S|X|ProJ.|$|%|Proj.|<br>$|X|Proj.|S|X|Proj.||
|AFRICA|<br>0.0|0.0|(0)|0.0|<br>0.0|(0)|23.0|63.9|<br>(1)|<br>22.0|18.|3 (2)||
|ASIA|143.0|100.|0(.J|0.0|<br>0.0|(0)|0.0|0.0|<br>(0)|<br>27.0|22.5|**_(",_**||
|EMENA|<br>0.0|0.0|(0)|13.0|100.|0 (1)|0.0|0.3|<br>(0)|<br>37.0|30.8|(3)||
|LAC|0.0|0.0|(0)|0.0|<br>0.0|(0)|13.0|36.1|<br>(1)|<br>34.0|28.3|(1)||
|TOTAL|143.0|100.|0 (4)|13.0|100.|0 (1)|36.0|100.0|(2)|<br>120.0|100.|0 (7)||
|||FY84|||FY85|||FY86|||FY87|||
||||No. of|||No. of|||No. of|||No. of||
||S|X|Proj.|S|X|Proj.|<br>S|X|ProJ.|$|X|Proj.||
|AFRICA|31.0|12.7|(3)|65.0|33.7|(3)|82.0|19.5|<br>(5)|<br>31.0|57.4|(4)||
|ASIA|155.0|63.5|(2)|85.0|44.0|(2)|242.0|57.6|<br>(4)|<br>0.0|0.0|(0)||
|EMENA|<br>0.0|0.0|(0)|43.0|22.3|(2)|0.0|0.0|<br>(0)|<br>13.0|24.1|(1)||
|LAC|58.0|23.8|(1)|0.0|<br>0.0|(0)|96.0|22.9|<br>(2)|<br>10.0|18.5|(1)||
|TOTAL|244.0|100.0|~6)|193.0|100.|0 (7)|420.0|100.0|(11)|<br>54.0|100.|0 (6)||
|||FY88|||FY89|||FY90|||FY91|||
||||No.of|||No. of|||No. of|||No. of||
||S|**_K_**<br>|Proj.|S|K|Proj.|<br>S|K|Proj.|S|K|Proj.||
|AFRICA|121.0|39.8|(5)|81.0|14.7|(4)|232.7|24.9|<br>(8)|<br>432.8|27.6|(12)||
|ASIA|74.0|24.3|(2)|290.0|52.7|(4)|192.5|20.6|<br>(2)|<br>507.5|32.4|(5)||
|ENENA|0.0|0.0|(0)|80.0|14.5|(2)|119.0|12.7|<br>(2)|<br>290.0|18.5|C6)||
|LAC|109.0|35.9|(1)|99.0|18.0|(1)|389.2|41.7|<br>(6)|337.3|21.5|(5)||
|TOTAL<br>...............|304.0<br>...............|100.0<br>...........|(8)<br>...............|550.0<br>..............|100.0<br>.............|(11)<br>...........|933.4|100.0|(18)|1567.6|100.0|(28)||
spell, employment is measured using the same sample and coverage. The average duration of the short run spells is 2.2 years with the longest spell being 8 years.

Table 1 describes the covered sample. There are few low income countries in the sample, and a low number of countries in Sub-Saharan Africa and in the Middle East and North Africa category. The main data limitations come from the employment data, which have very low coverage in these regions. This implies that the analysis may better describe the behavior of middle income countries. Nevertheless, we are able to capture 15 percent of all low income countries.

**Table 1: Sample Description**

|**_Countries Grouped by Region_**<br>**_and Income Level_**|**_No. of_**<br>**_Countries in_**<br>**_the Region_**|**_No. of_**<br>**_Countries in_**<br>**_the Sample_**|**_% of_**<br>**_Countries in_**<br>**_the Sample_**|
|---|---|---|---|
|**_By Region_**||||
|Sub-Saharan Africa|48|4|8.3%|
|East Asia and the Pacific|24|5|20.8%|
|Europe and Central Asia|27|9|33.3%|
|Latin America and the||||
|Caribbean|31|17|54.8%|
|Middle East and North Africa|14|1|7.1%|
|South Asia|8|3|37.5%|
|**Total**|152|39|24.3%|
|**_By Income Level_**||||
|Low Income|53|8|15.0%|
|Lower Middle Income|58|20|34.5%|
|Upper MiddleIncome|41|11|26.8%|
|**Total**|152|39|24.3%|

# **3 Results:  What Do the Stylized Facts Say?**

Figure 1 presents stylized facts on the empirical link between growth and changes in poverty in all countries for which data were available. The first row of plots shows how poverty changes are correlated with changes in _productivity_ , _employment rates_ and _the share of the population of working age_ . The second row of plots illustrates how per capita value added growth in that country is correlated with the same three variables. Each data point in the figure corresponds to a specific growth spell in a country. Productivity is defined as value added per worker. Employment rates are measured as the employed as a fraction of the working age population. The share of the population of working age is the ratio of the working age population to the total population. A growth spell is simply the percentage change in the variables of interest between two points in time. The spells are short and medium run: on average, each spell lasts two years, and most spells are between one and four years. The first row of plots in Figure 1 illustrates the correlation between _per capita value added_ and percentage changes in productivity (first plot), employment rates (second plot) and the inverse of the dependency rate (third plot). The 14
## **2.3 Landowners**

Landowners have similar preferences to workers, although they may spend a different fraction of income on housing: with a budget constraint of _Pnc_<sup>_K_</sup> _n_<sup>+</sup><sup>_RnhK_</sup> _n_<sup>=</sup><sup>_Rn_.Because we assume homothetic preferences,</sup> when we aggregate the model it does not matter how many landowners there are. We therefore assume that all landowners own one unit of land. After optimization, the aggregate utility of all landowners is:

## **2.4 Equilibrium in Relative Changes**

The equilibrium is defined in changes by comparing the counterfactuals with the baseline. The counterfactuals consider shocks to trade costs _τ_ ˆ _ni_ , where _x_ ˆ denotes the value of variable _x_ in the counterfactual equilibrium relative to the initial equilibrium.<sup>1</sup> As shown in the appendix B, following standard steps as in Redding and Rossi-Hansberg (2017), the equilibˆ rium in changes is defined for employment, price indexes and wages _Ln, P_<sup>ˆ</sup> _n, w_ ˆ _n_ , and the � � welfare of workers _v_ ˆ<sup>_W_</sup> as a function of calibrated parameters and data.

Solving the system requires information on the parameters � _σ, γ, βL, βK, εW , αC_<sup>_W_</sup> � and data ˆ on import shares, export shares, and employment. Having computed _Ln, P_<sup>ˆ</sup> _n, w_ ˆ _n_ in the � � counterfactuals, we can then compute all the outcomes in each region.

# **3 Spatial Data and Calibration**

The information needed to calibrate the model comes from traditional data sources, such as surveys and geo-coded information on the transport network. It includes state- and districtlevel data on transport costs, land size, labor force, and wages.

The labor force includes the working-age population (people 15 and older). All wages are monthly and expressed in dollars. Data on labor force and wages in India come from the 2011 Periodic Labor Force Survey; data on Bangladesh come from the 2010 Labor Force Survey.

> 1 ˆ _<u>x</u>_<sup>_′_</sup> That is, _x_ = _x_<sup>,where</sup><sup>_x′_isthecounterfactualvalueand</sup><sup>_x_istheinitialvalueofvariable</sup><sup>_x_.</sup>

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