rafmacalaba/datause-encoder-data huggingface dataset:
exporters not expanding sales, and average employment increases modestly. This group of firms can be thought of as those that sell more to the domestic market. What is interesting is that when we compare this case (ii) to the one where sales are increasing and export sales are not (iii), change in employment is almost identical.

Indeed, looking at Figure 10, which depicts the kernel densities of change in log employment for each of the four cases, we see that the density of (i) lies to the right of the pack. Interestingly, the distributions of (iii) and (iv) have a high degree of overlap, lying to the left of the pack, indicating firms with decreasing sales being likely to reduce employment. Of firms with reducing sales, those that export tend to employ more workers, and are thus more likely to increase employment. The case where sales do not increase and export shares do (ii), lies in the middle between the (iii) and (iv) pair on the left and (i) on the right. It would seem that increasing sales is a stronger predictor of employment change rather than the degree of those sales going to exports. While these results are not consistent with the theory that exporting firms are shifting sales from domestic to foreign markets, they are not conclusive enough to reject the theory outright. Overall, while some firms shift production from the domestic to the foreign market without increasing employment, it has a minor effect on the disconnect between export flows and the labor market.

Table 6 reports changes in aggregate employment between 2013 and 2020 in survey firms grouped by export status. We divide the firms into four groups: (i) “always” are firms which exported in all three years (2013, 2016, and 2020); (ii) “new” are firms that did not export in 2013 but were exporting by 2020 (starting in either 2016 or 2020); (iii) “stopped” are firms that switched from exporting to not;<sup>12</sup> and (iv) “never” are firms that did not export in any of the three years. What is interesting is that overall employment appears to have decreased between 2013 and 2016

> 12 Notice this group does not appear in the simulation because under the assumptions of the model, firms would not have any incentive to switch from exporting to not.

17
MONGOLIA'S INFORMAL SECTOR - Contents v

# Tables

|1.|Degrees of Informality ....................................................|3|
|---|---|---|
|B1.|Monthly Income of Taxicabs .............................................|38|
|B2.|Kiosk Sampling .......................................................|41|
|B3.|Monthly Income of Kiosks .<br>.............................................|42|
|B4.|Generalized Informal Sector Surveys-<br>Activities Represented in the Sample ....|.. 45|
|B5.|Characteristics of Informals .<br>.............................................|48|
|B6.|Infonnal Ownership .<br>...................................................|49|
|B7.|Informal Working Arrangements .50||
|B8.|Informal Income and Expenditure<br>.5||
|B9.|Informal Perceptions of Employment Status .52||
|B10.|Coverage of Social and Health Insurance .53||

# Figures

|1 .|"Not Employed" in Ulaanbaatar, by Reason .................................|10|
|---|---|---|
|2.|Able-bodied People of Working Age and Official Employment in Ulaanbaatar ....|.<br>10|
|3.|Percentage of Household Income, by Type ..................................|13|
|4.|Monthly Incomes ......................................................|25|
|_5._|Monthly Income Taxes ..................................................|25|
|6.|Kiosks and Retail Shops in Ulaanbaatar ....................................|28|
|7.|Ulaanbaatar's Transportation ................<br>.............................|31|
|8.|Hours Used as a Taxicab in a Week ...........<br>............................|36|

# Boxes

|1 .|"Unemployment" and Informal Income ...................|I|
|---|---|---|
|2.|Estimates of the Size of the Informnal Sector .................|16|
|3.|An Escape from Poverty.........................1|8|
|4.|Coverage Under Social and Health Insurance in Mongolia's Informal Sector.....20||
|_5 ._|The Market's Response to the Difficulty of Finding Premises...........2|1|
|6.|The Popular Revolt Against the Informnal Sector Income Tax Law.........23||
|7.|Kiosks.................................29||
|8.|Indoor Markets .............................|29|
|9.|TheI[nformal Sector in Mongolian Comedy..................30||
|10.|The Federation..............................33||
# **INSTITUTIONAL AND POLICY ANALYSIS OF RIVER BASIN MANAGEMENT**

# **_The Fraser River Basin, Canada_**<sup>1</sup>

**William Blomquist Ken S. Calbick Ariel Dinar**

## World Bank Policy Research Working Paper 3525, February 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 This paper is a product of the study, “Integrated River Basin Management and the Principle of Managing Water Resources at the Lowest Appropriate Level – When and Why Does It (Not) Work in Practice?” The Research Support Budget of the World Bank provided major funding. The project was carried out by the Agriculture and Rural Development Department at the World Bank. The Water Resources Management Group and the South Asia Social and Environment Unit at the World Bank have provided additional support. The study core team includes Karin Kemper and Ariel Dinar (Co-Task Team Leaders, World Bank), William Blomquist and Anjali Bhat (consultants, Indiana University), and Michele Diez (World Bank), William Fru (consultant), and Gisèle Sine (International Network of Basin Organizations). Basin case study consultants include Maureen Ballestero (Tárcoles - Costa Rica), Ken Calbick and David Marshall (Fraser - Canada), Rosa Formiga (Alto Tietê and Jaguaribe - Brazil), Consuelo Giansante (Guadalquivir - Spain), Brian Haisman (Murray Darling - Australia), Kikkeri Ramu and Trie Mulat Sunaryo (Brantas - Indonesia), and Andrzej Tonderski (Warta - Poland). Useful comments on this paper by Neil Peters, Hans Schreier, Allen Domaas, and Daryl Fields are appreciated. We are grateful to the individuals whom we interviewed in the course of this research. None of those individuals is responsible for the findings and conclusions in this paper.
# Appendix 3: Linear probability models:

## Table 18

|Dependent variable: Nee|d of fiscal ad|justment b|ased on Esc|olano et al|.(2014)||||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Rigid expenditure minus|0.038***|0.032***|0.039***|0.032***|0.041***|0.041***|0.010|0.026|
|structural as % GDP|(0.007)|(0.006)|(0.006)|(0.006)|(0.008)|(0.014)|(0.020)|(0.016)|
|**Economic factors**|||||||||
|GDP growth (t-1)||-2.034***||-1.901***|-1.807***|-1.869***|-1.860***|-1.909***|
|||(0.422)||(0.419)|(0.410)|(0.423)|(0.411)|(0.422)|
|Inflation (t-1)||-0.046||-0.001|-0.013|-0.004|-0.008|-0.003|
|||(0.063)||(0.052)|(0.056)|(0.052)|(0.056)|(0.054)|
|U.S. Interest rate||-0.016||-0.022*|-0.024*|-0.023*|-0.025**|-0.022*|
|||(0.011)||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|**Political factors**|||||||||
|Gini|||0.007|0.006|0.005|0.007|0.006|0.006|
||||(0.006)|(0.006)|(0.006)|(0.007)|(0.006)|(0.006)|
|Rule of law|||0.001|0.021|0.017|0.020|-0.026|0.021|
||||(0.031)|(0.032)|(0.031)|(0.031)|(0.035)|(0.032)|
|Margin of Majority|||0.082|0.052|0.068|0.052|0.061|-0.030|
||||(0.128)|(0.129)|(0.128)|(0.130)|(0.129)|(0.152)|
|Election's year|||0.021|0.029*|0.031**|0.029*|0.030*|0.029*|
||||(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|(0.015)|
|**Interactions**|||||||||
|EMEs * Deviation|||||-0.020*<br>(0.011)||||
|Gini * Deviation||||||-0.000<br>(0.000)|||
|Rule of law * Deviation|||||||0.005<br>(0.004)||
|Majority * Deviation||||||||0.011|
|||||||||(0.026)|
|Constant|-0.226***|-0.041|-0.581*|-0.379|-0.355|-0.446|-0.183|-0.327|
||(0.065)|(0.070)|(0.323)|(0.295)|(0.281)|(0.340)|(0.268)|(0.290)|
|Observations|1,467|1,451|1,318|1,304|1,304|1,304|1,304|1,304|
|R-squared|0.104|0.164|0.118|0.171|0.179|0.172|0.176|0.172|
|Number of ifscode|83|83|74|74|74|74|74|74|
|CountryFE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Robust standard errors cl|ustered by c|ountry in pa|rentheses||||||
|*** p<0.01, ** p<0.05, *|p<0.1||||||||

- 47 -
Table 2: Details of household surveys<sup>10</sup>

|**Wards**|**Type of**<br>**area**|**Population**|**Chronic**<br>**flooding**<br>**spots**|**Localized**<br>**flooding**<br>**spots**|**Flooding**<br>**Depth**<br>**(feet)**|**Sample h**|**ouseholds**|
|---|---|---|---|---|---|---|---|
|||||||**2010**<br>**household**<br>**survey**<br>**(N=1168)**|**2014**<br>**household**<br>**survey**<br>**(N=200)**|
|**K East**|Reclaimed<br>area|823,885|2|7|1.0‐3.6|241|30|
|**H East**|River flood<br>plain|557,239|0|14|0.8‐6.0|176|36|
|**F North**|Low lying<br>area|529,034|2|9|1.0‐3.0|177|29|
|**F South**|Low lying<br>area|360,972|1|8|2.0‐4.0|103|35|
|**L Ward**|River flood<br>plain|902,225|3|13|1.0‐4.0|231|35|
|**P North**|Reclaimed<br>area|941,366|3|6|1.0‐2.6|240|35|

# 4. Results and discussion

This section discusses the findings based on the primary and secondary data to understand the exposure of poor and non‐poor households to recurrent floods, direct and indirect impacts on them and their ability to respond to such events.

## 4.1 Exposure to floods

In order to understand the nature of exposure of poor and non‐poor households to recurrent floods in Mumbai, the study has examined the demographics and land use pattern in nine administrative wards that are prone to floods every monsoon. The details of the chosen wards are given as Annexure A.1. Also provided in are the tables showing the flood exposure and demographics in each of the 9 selected wards in Annexure A.1. These wards represent three types of flood‐prone areas – low‐lying areas, reclaimed areas and areas in the Mithi River flood plain, as seen in Table A.1.1. The information on the total ward population, slum population and the number and location of flooding spots has been obtained from the Disaster Management Cell (DMC) of the MCGM. All the selected wards experience recurrent floods and have a number of chronic and localized flooding spots located within. As seen in Table A.1.1, each ward has a large residential density and also a significantly high percentage of the residents living in slums. For instance, F North, L Ward, M West and P North have more than 50% people residing in slums followed by K East, where the slum population is 49% and H East, where it is 42%.

> 10 Information on ward population, chronic and localized flooding spots and average depth of flooding obtained from Disaster Management Cell, MCGM.

10
**Table 1: Medical Research Council, General Population Cohort, Rural Uganda.**

**Regressions and summary statistics at Rounds 1 and 11**

**Logit estimation: Mean of Marginal Effects, observation by observation**

Dependent variable: HIV positive =1, HIV negative = 0

||**Round 1**|**(11/89-11**|**/90)**||**Round 1**|**1 (11/99-9**|**/00)**||
|---|---|---|---|---|---|---|---|---|
||**Age 18 -29**||**Age 30 and**|**above**|**Age 18 -29**||**Age 30 an**|**d above**|
||**Logit**<br>|**Means**|**Logit**|**Means**|**Logit**<br>|**Means**|**Logit**|**Means**|
|**HIV positive**|Dep. Var|0.144|Dep. Var|0.077|Dep. Var|0.075|Dep. Var|0.085|
|||{0.010}||{0.005}||{0.006}||{0.005}|
|**no education**|omitted|0.133|omitted|0.408|omitted|0.068|omitted|0.247|
|||{0.010}||{0.011}||{0.006}||{0.008}|
|**primary**|-0.021|0.742|-0.008|0.504|-0.051|0.643|0.049|0.599|
||[-0.76]|{0.013}|[-0.60]|{0.011}|[-1.39]|{0.011}|[1.40]|{0.009}|
|**secondary**|-0.012|0.124|0.006|0.087|-0.088|0.287|0.035|0.0153|
||[-0.34]|{0.009}|[0.33]|{0.006}|[-1.89]|{0.011}|[1.10]|{0.007}|
|**male**|-0.011|0.445|0.04|0.468|-0.035|0.439|0.037|0.457|
||[-0.52]|{0.014}|[1.81]|{0.011}|[-1.49]|{0.011}|[1.60]|{0.009}|
|**single**|omitted|0.399|omitted|0.124|omitted|0.46|omitted|0.045|
|||{0.014}||{0.007}||{0.012}||{0.003}|
|**married**|0.035|0.542|-0.44|0.638|0.037|0.45|0.001|0.604|
||[1.24]|{0.014}|[-1.55]|{0.010}|[1.14]|{0.012}|[0.04]|{0.009}|
|**previously**|0.034|0.057|0.021|0.237|0.074|0.086|0.085|0.35|
|**married**|[0.74]|{0.007}|[1.14]|{0.009}|[1.84]|{0.006}|[1.83]|{0.009}|
|**catholic**|omitted|0.581|omitted|0.623|omitted|0.62|omitted|0.64|
|||{0.014}||{0.011}||{0.011}||{0.009}|
|**protestant**|0.002|0.111|0.001|0.144|-0.102|0.131|0|0.129|
||[0.10]|{0.009}|[0.09]|{0.008}|[-2.14]|{0.008}|[-0.00]|{0.006}|
|**muslim**|-0.036|0.304|-0.04|0.22|-0.064|0.246|-0.023|0.224|
||[-1.26]|{0.013}|[-1.62]|{0.009}|[-1.98]|{0.010}|[-1.07]|{0.008}|
|**house soft**|omitted|0.431|omitted|0.366|omitted|0.146|omitted|0.159|
|**materials**||{0.014}||{0.015}||{0.09}||{0.007}|
|**house mixed**|-0.036|0.38|-0.001|0.478|-0.039|0.292|-0.019|0.334|
|**materials**|[-1.56]|{0.014}|[-0.15]|{0.011}|[-1.43]|{0.012}|[-0.94]|{0.009}|
|**house hard**|-0.01|0.187|-0.0009|0.154|-0.075|0.56|-0.009|0.506|
|**materials**|[-0.38]|{0.011}|[-0.05]|{0.008}|[-2.58]|{0.013}|[-0.57]|{0.010}|
|**N =**|1052||1549||1156||1611||
|**Pseudo R-2**<br>Note:<br>Robust z-stat|0.069<br>in square brac|kets (regre|0.12<br>ssions), stand|ard errors in|0.153<br>curly bracke|ts (means).|0.149||

<u>Note: Robust z-stat in square brackets (regressions), standard errors in curly brackets (means).</u> Age and village dummies included. Primary is some primary education (grades 1 to 7), secondary is some secondary education (grades 8 to 13) or above. The type of housing materials (soft roof and house, mixed or hard roof and house) serves as proxy for wealth. Type of housing is measured at Round 12 in the case of Round 11.

39
The dynamic effects of trade liberalization might imply that the losses from protectionism are a multiple of the values displayed in Figure 3. To the extent that dynamic effects are similar across deciles, the losses from high-income country protectionism would still follow the pattern in the figure displayed below.

**Figure 3: Effects of High Income Country Protectionism**

<!-- Start of picture text --> High Income Country Protectionism: Percent<br>Impact on GNI Per Capita by Decile<br>0.00%<br>-0.10%<br>-0.20%<br>-0.30%<br>-0.40%<br>-0.50%<br>-0.60%<br>-0.70%<br>-0.80%<br>D1 D2 D3 D4 D5 D6 D7 D8 D9 D10a D10b<br><!-- End of picture text --> Note: No productivity enhancement. Change in real household income from the model. Dotted line represents aggregate world impact. Source: The Linkage Model (van der Mensbrugghe 2005).

It is interesting to discuss the combined impacts of the two sources of international redistribution discussed above – aid and trade protection. To do so, we compare the redistribution effected via existing aid levels versus the lost income caused by the existing barriers to trade in rich countries. In this way, we have a rough approximation of the impact of two international policies (aid and trade) on global distribution of income. For the bottom deciles, part of the gains from aid is negated by the detrimental impact of trade barriers via lost income, but the overall effect remains positive. For upper-middle income deciles, the lost 18
the personal income tax by following the OECD’s assumption that income distribution is lognormal, and allowing for a distribution of income based on each country’s Gini coefficient (taken in most cases from the World Bank’s Poveal database) around average wage income (taken from national sources). We then located within this stylized income distribution the tax brackets as outlined in the tax code. The average marginal tax rate is the average across tax brackets of marginal tax rates weighted by total income in each bracket. As an example, Figure 2 illustrates the calculation of the average marginal income tax rate for Brazil in 2008. The figure shows the approximated cumulative distribution function (CDF) of earnings. At each wage rate along the X-axis, the CDF gives the percentage of total earnings in Brazil earned by workers with wages below the indicated wage. There were three income-tax brackets in Brazil in 2008: 0%, 15%, and 27.5%. Although 78% of workers in Brazil fell into the lowest tax bracket, their earnings comprised only 39% of total earnings. Similarly, 14% of the working population faced a 15% marginal tax rate; this group earned 25% of Brazilian income. Finally, the remaining 7% of the population, and 36% of national income, faced the highest marginal rate. The average marginal tax rate in Brazil in 2008 was calculated as the sum of the three marginal rates, each multiplied by the share of total wage income that faced that marginal rate. To assure the reader that our methodology is consistent with earlier estimates in the literature, Figure 3 compares the time series of the average marginal income tax rate calculated by Barro and Redlick (2009), to that obtained using our methodology. The two series are broadly similar, with <u>~ 1aez (2001) and others have argued</u> that the Pareto distribution better characterizes the actual income distribution in the United States than does the lognormal distribution. Lopez and Serven (2006), in contrast, cannot reject a lognormal distribution of income in a large sample of countries. To allow a direct comparison with the income elasticities of tax revenues estimated by the OECD, we follow their methodology and assume a lognormal income distribution.

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**Table 11: Views on the Current Environment Indicators Panel Regression with Household and Temporal Fixed Effects**

|||**xt.O**|**LS: Current Mi**|**grant= 1, No M**|**igrant= 0**||
|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Rising Prices|-0.003<br>(0.008)||||||
|Optimistic Country Direction||0.012**<br>(0.006)|||||
|Cannot Afford Enough Food|||0.021<br>(0.016)||||
|Worse Fin. Sit. this Year||||0.057**<br>(0.025)|||
|Worried about Losing Job|||||0.042***<br>(0.012)||
|No Restrict Movement||||||0.018**<br>(0.008)|
|Constant|0.023***|0.019***|0.022***|0.022***|0.019***|0.020***|
||(0.004)|(0.004)|(0.004)|(0.003)|(0.004)|(0.004)|
|Number of observations|12,137|12,137|12,137|12,137|12,137|12,137|
|Calendar Month FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Household FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Adjusted R2|0.006|0.006|0.006|0.007|0.009|0.006|
|r2_w|0.006|0.007|0.006|0.007|0.009|0.007|
|r2_o|0.003|0.008|0.005|0.007|0.016|0.011|
|r2_b|0.006|0.003|0.000|0.001|0.020|0.009|
|sigma_u|0.119|0.119|0.119|0.119|0.118|0.118|
|sigma_e|0.177|0.177|0.177|0.177|0.177|0.177|
|rho|0.313|0.311|0.312|0.312|0.308|0.310|

note: .01 - ***; .05 - **; .1 - *; 25
Table 11: Deep trade agreements and GVC integration, indirect effect PPML

||(1)<br>DVA|(2)<br>DVA final|(3)<br>DVA<br>intermediate|(4)<br>DVA int.<br>re‐exported|(5)<br>FVA|(6)<br>FVA final|(7)<br>FVA<br>intermediate|
|---|---|---|---|---|---|---|---|
|No. of provisions|0.0133***|0.00982***|0.0138***|0.0150***|0.0104***|0.00815***|0.0107***|
||(0.00209)|(0.00255)|(0.00213)|(0.00231)|(0.00201)|(0.00241)|(0.00184)|
|BIT|0.380***|0.322***|0.359***|0.334***|0.309***|0.291***|0.276***|
||(0.0944)|(0.0947)|(0.0914)|(0.0933)|(0.0785)|(0.0817)|(0.0820)|
|PTA (in force or||||||||
|inactive|0.143|0.359***|0.0224|0.127|0.221**|0.500***|0.0208|
||(0.113)|(0.0943)|(0.115)|(0.120)|(0.102)|(0.110)|(0.0942)|
|Observations|26,520|26,520|26,520|26,520|26,520|26,520|26,520|
|R‐squared|0.980|0.987|0.974|0.972|0.988|0.988|0.986|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Table 12: Deep trade agreements and GVC integration‐ interval estimations, PPML

||(1)<br>DVA|(2)<br>DVA final|(3)<br>DVA<br>intermediate|(4)<br>DVA int. re‐<br>exported|(5)<br>FVA|(6)<br>FVA final|(7)<br>FVA<br>intermediate|
|---|---|---|---|---|---|---|---|
|No. of provisions|0.00431***|0.00308*|0.00488***|0.00565***|0.00337**|0.00227|0.00379***|
||(0.00137)|(0.00173)|(0.00151)|(0.00179)|(0.00142)|(0.00179)|(0.00130)|
|BIT|0.123***|0.0827|0.145***|0.119**|0.0794**|0.0491|0.104**|
||(0.0452)|(0.0580)|(0.0486)|(0.0551)|(0.0385)|(0.0513)|(0.0414)|
|PTA (in force or||||||||
|inactive)|‐0.0457|0.154***|‐0.148***|‐0.138*|‐0.000971|0.233***|‐0.149***|
||(0.0514)|(0.0559)|(0.0570)|(0.0715)|(0.0486)|(0.0592)|(0.0478)|
|Observations|9,360|9,360|9,360|9,360|9,360|9,360|9,360|
|R‐squared|0.995|0.996|0.992|0.990|0.997|0.996|0.996|
|Country‐pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country‐time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Period|3‐year|3‐year|3‐year|3‐year|3‐year|3‐year|3‐year|

Clustered standard errors at the country pair level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 30
In short, _Zrt_ supposedly captures an exogeneous component (namely foreign demand) of the growth in the tradable service sector, by district.

Figure 1 visually depicts the instrumental variable. Figure 1.1 depicts our “shift” component, i.e., log global import demand (excluding India) for tradable services. As can be seen, global demand for all tradable services has been growing strongly in the time frame of our sample. Figure 1.2 shows the share of tradable service employment by district in India, i.e. the “share” component of the instrument. The districts with a higher share would have a stronger exposure to services trade and therefore be more affected by the increase in global demand.

10