rafmacalaba/datause-encoder-data huggingface dataset:
# **Table A5: Comparing Income and non-agricultural output in the last 12 months at the individual's level for refugee’s hosting and non-hosting districts (excl. Kampala)**

||||**_In the base w_**<br>|**_ave=200_**|**_9/10_**||**Mean**||**_In the last_**<br>|**_wave=20_**|**_12_**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Variables**<br>**Levels**|**Mean**<br>**(Non-hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std.**<br>**Error**|**(Non-**<br>**hosting)**|**Obs.**|**Mean**<br>**(Hosting)**|**Obs.**|**Mean Diff.**|**Std.**<br>**Error**|
|**Main source of income**|||||||||||||
|Subsistence farming|0.560|12708|0.563|1805|-0.004|0.012|0.575|14760|0.616|2118|-0.041***|0.011|
|Commercial farming|0.032|12708|0.029|1805|0.003|0.004|0.016|14760|0.017|2118|-0.001|0.003|
|Wage employment|0.148|12708|0.143|1805|0.005|0.009|0.121|14760|0.148|2118|-0.027***|0.008|
|Non-ag. Self-employment|0.198|12708|0.202|1805|-0.003|0.010|0.210|14760|0.180|2118|0.030***|0.009|
|Remittances & others|0.062|12708|0.063|1805|-0.001|0.006|0.079|14760|0.040|2118|0.039***|0.005|
|**Other sources of income**|||||||||||||
|HH enterprises|0.764|12738|0.817|1805|-0.052***|0.010|0.742|14779|0.813|2118|-0.071***|0.009|
|Property & investments|0.049|12738|0.033|1805|0.016***|0.005|0.045|14779|0.016|2118|0.029***|0.003|
|Transfers & other benefits|0.187|12738|0.151|1805|0.036***|0.009|0.213|14779|0.171|2118|0.042***|0.009|
|Income received in cash|1001406.66|12738|755617.68|1805|245788.98***|55264.39|1462539.37|14716|827723.97|2094|634815.40***|<br>58819.71|
|Income received in kind|117858.05|12738|139507.10|1805|-21649.05**|10815.08|198902.89|14640|259817.51|2085|-60914.61***|11997.49|
|Av. monthly revenue from non-ag.<br>activities|532430.61|6859|276228.80|1014|256201.81***|61756.55|914609.66|6080|541996.37|909|372613.29***|<br>62027.61|
|Av. monthly employees in non-ag.<br>activities|0.619|6826|0.303|996|0.316***|0.058|43.095|6102|0.774|909|42.320***|11.806|

_Two sample t-test with unequal variances between refugee hosting and non-hosting districts. Significance level: *** p<0.01, ** p<0.05, * p<0.1_ 5
- Leard, Benjamin, Joshua Linn, and Yichen Zhou. 2021. “How Much Do Consumers Value Fuel Economy and Performance? Evidence from Technology Adoption.” _The Review of Economics and Statistics_ , forthcoming.

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- Xing, Jianwei, Benjamin Leard, and Shanjun Li. 2021. “What Does an Electric Vehicle Replace?” _Journal of Environmental Economics and Management_ 107: 102432.

37
15 -

-

Small companies will not have either audited or unaudited financials. Some will operate out of a business checkbook. These companies deposit receipts at the end of each day or week and write checks for purchases. The tax base for a cash flow tax can be determined from the bank records if they are complete. It is true that owners can skim cash, but this is a problem for either the income tax or a cash flow tax. If the tax base excludes all financial transactions, it would be necessa-y to determine whether a business check was for inventory or for a financial asset.

Income tax records are more difficult to construct from a business checkbook. Inventory and capital accounts must be established. It can be difficult to verify that inventory was destroyed or stolen or to determine appropriate reserves.

Smaller companies will operate out of a single personal and business checkbook. Here the additional problem is separating personal and business expenses, but presumably most of these businesses will not be subject to the cash flow tax.

The smallest companies may operate only in cash. For companies that have inadequate or no records, presumptive taxes can be used. The simplest form of a presumptive tax is a business license. Street vendors, small retail outlets, and cottage industries can be required to purchase annual licenses.
Table 1a: Female-owned Indian manufacturing and services establishments

|||Manufacturing|||Services||
|---|---|---|---|---|---|---|
||2001|2006|2010|2001|2006|2010|
||||A. Raw count of|data observations|||
|Total|244,258|116,108|134,617|351,916|186,481|109,631|
|Organized sector|25,532|35,096|33,072|8,044|4,928|3,922|
|Unorganized sector|218,726|81,012|101,545|343,872|181,553|105,709|
|Female-owned establishments|42,190|18,752|22,804|27,745|14,912|8,135|
|Female-owned share|0.20|0.25|0.23|0.08|0.09|0.08|
||||B. Estimated numbe|r of establishments|||
|Total|16,977,350|16,929,551|17,751,937|14,180,251|16,381,770|17,737,379|
|Organized sector|98,976|104,575|118,145|146,442|342,599|252,335|
|Unorganized sector|16,878,374|16,824,976|17,633,793|14,033,810|16,039,171|17,485,044|
|Female-owned establishments|4,417,408|6,174,508|6,647,471|1,081,446|1,289,543|1,419,807|
|Female-owned share|0.27|0.38|0.39|0.08|0.09|0.09|
||||C. Estimated em|ployment levels|||
|Total|40,678,774|40,299,063|43,161,148|26,064,374|32,607,152|34,091,395|
|Organized sector|6,716,239|7,469,639|9,593,975|3,179,267|6,339,472|4,964,826|
|Unorganized sector|33,962,535|32,829,425|33,567,173|22,885,107|26,267,679|29,126,569|
|Female-owned establishments|5,551,358|7,552,761|7,852,968|1,716,817|1,966,594|1,987,362|
|Female-owned share|0.17|0.25|0.25|0.08|0.09|0.08|
||||D. Estimated output|levels (in millions)|||
|Total|650,110|870,814|1,303,527|123,347|157,407|192,437|
|Organized sector|501,302|704,960|1,066,705|31,466|67,359|34,845|
|Unorganized sector|148,808|165,854|236,822|91,881|90,048|157,592|
|Female-owned establishments|7,128|10,371|18,486|4,615|4,777|7,522|
|Female-owned share|0.06|0.08|0.09|0.06|0.06|0.05|
|||E. District|count where female-o|wned establishments ar|e observed||
|Organized sector|n.a.|n.a.|n.a.|159|117|127|
|Unorganized sector|414|413|415|407|412|412|

Notes: Descriptive statistics taken from the Annual Survey of Industries (ASI) and National Sample Survey (NSS). Gender of owner is not present for organized sector of manufacturing. Organized sector for services is defined to be over 10 employees to mimic manufacturing definition. Female-owned share of establishments, employees, and output is calculated as a share of establishments for which we know the gender of the owner.
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- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), ―Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Russia.‖ Available at http://www.worldbank.org/trade/russia-wto.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004b), ―Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Maritime and Air Transportation Service Sectors in Russia.‖ Available at http://www.worldbank.org/trade/russia-wto.

- Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004c), ―Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in Financial Services Sectors in Russia.‖ Available at http://www.worldbank.org/trade/russia-wto.

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88
feared were the risk of losing one’s property and the risk of going bankrupt; hence the bankruptcy law might play an important role in preventing firm creation.

Although the cost of failure is difficult to measure, the World Bank has gathered information on the time and cost of going through an insolvency process in each of the countries under analysis. For more details, please refer to Appendix 2.

# **I.2. Which institutions and policies affect the survival and growth of new firms?**

Entrepreneurs face many obstacles. Complying with administrative regulations and their related costs is one of the most significant burdens. But also the red tape, the degree of flexibility of the labour market or the tax burden constraint business performance.

Institutions affecting the day-to-day operation of businesses are important for two reasons: first, they affect the expected benefits and costs from creating a firm, which are taken into account by would-be entrepreneurs. Moreover, even if they do not prevent entrepreneurship, they might force entrepreneurs into the informal economy, where their prospects of prospering and growing are low. Second, once the entry decision has been taken, they have an impact on survival and expansion –and therefore on job creation-- of existing businesses.

# **_Government interference_**

The government imposes price controls and cumbersome regulations, carries out over-inquisitive tax inspections and still holds or controls fundamental sectors of the economy. Another important set of factors may consist in the actions governments are not taking. Some examples are poor enforcement of the law, poor protection of property rights and laissez-faire with respect to bribes and corruption.

For operational purposes, data on government interference has been organized around four big issues: the **quality of the legal system** , **market regulation** (which includes credit and labor market regulations), **size of government** and **corruption** . Please refer to Appendix 2 for detailed definitions of each of those components.

**Box 2: Efficient but not independent judicial systems; the cases of Russia and Turkey**

The quality of the _legal system_ comprises aspects of the _efficiency_ of courts. But it also should take into account the _independence_ of the judicial system from political and economic agents and the _protection to property rights._ The World Bank offers information on issues related to the efficiency of courts to enforce contracts. They are derived from questionnaires answered by attorneys at private law firms. According to that information, courts in Russia and Turkey are more efficient than the European average when dealing with disputes about contract enforcement (figure 2.3).

8
elasticities in comparable units. We observe that a one percent change in learning is associated with a 6.5 percent change in annual growth. In contrast, a one percent change in the other human capital measures is associated with between a 1.6 to 3.3 percent change in annual growth. Moreover, the R-squared for the learning measure is highest at .275 relative to non-learning human capital measures which range from .240 to .261. We further observe when we include variables in the same multivariate regression that the relationship between learning and growth remains statistically significant between 4.7 to 5.5 percent, whereas other human capital variables have a reduced and statistically insignificant association with growth. However, we observe the overall model fit improves when all measures are included with an R-squared that increases, although only slightly, from .275 to between .281 to .298.

To this end, we observe that our measure of human capital individually and jointly appears to have a stronger relationship with economic growth. This is likely because alterative human capital measures rely largely on years of schooling and might underestimate the role of human capital in economic development by omitting learning.<sup>10</sup> However, their use remains standard practice in part since these data have the broadest coverage. By constructing learning data across 164 countries we fill a key gap: broad coverage over nearly two decades and a measure of human capital with strong links to economic development.

# IV. CONCLUSION

To understand and track human capital formation, a critical ingredient for development, there is need for globally comparable measurement of learning. The growth of international standardized achievement tests is a significant step in this direction. However, many of the countries that participate in these tests are often already rich. This limits the ability to track, compare or understand education patterns in developing countries – the countries that might have the most potential to gain from human capital formation.

We bridge this gap, constructing a globally comparable database of 164 countries from 20002017, representing more than 98 percent of the global population and over two-thirds of countries included are developing countries. We document a series of motivating stylized facts in a first application of the data. First, we show that global learning progress has been relatively limited to

> 10 This does not mean that schooling is not useful, but might lead to growth largely through the channel of learning.

14
Table 6. Average share of total income received by each ventile of national income distributions (unweighted average calculated from 115 household surveys)

|Ventile|Average ventile share<br>in total income(in %)|Standard deviation of<br>ventile share(in %)|
|---|---|---|
|First|1.00|0.52|
|Second|1.50|0.60|
|Third|1.80|0.63|
|Fourth|2.06|0.64|
|Fifth|2.31|0.65|
|Sixth|2.54|0.65|
|Seventh|2.78|0.64|
|Eighth|3.02|0.63|
|Ninth|3.28|0.62|
|Tenth|3.55|0.60|
|Eleventh|3.85|0.58|
|Twelfth<br>|4.18|0.56|
|13<sup>th</sup><br>|4.55|0.53|
|14<sup>th</sup>|4.99|0.48|
|15<sup>th</sup><br>|5.52|0.45|
|16<sup>th</sup><br>|6.18|0.41|
|17<sup>th</sup><br>|7.07|0.48|
|18<sup>th</sup>|8.36|0.75|
|19<sup>th</sup>|10.72|2.01|
|Twentieth(top)|20.74|7.35|
|Total|100||

Note: Distributions for the benchmark year 2002. Source: WYD database. Each country is one observation.

These results have implications for migration. If low income class people migrate to richer countries, and expect that they would end up there too among low income classes, then equality of the receiving country’s income distribution must be quite important for them. A very large increase indeed in the mean country income is needed to compensate for this “distributional premium”. But differently, if the nationally rich people (say, highly skilled) migrate from a poor to a rich country, and expect to be among high income groups in their new country too, then they might prefer to select highly unequal societies, even if their mean income is less than the mean income of an alternative migration destination.<sup>28</sup>

> 28 An interesting example is provided by Bustillo (2007, pp. 21-22). His results show that the percentage of immigrants monotonically decreases as one moves from poorer to richer deciles in Spain. But in the United States, the share of immigrants charts a U curve: it is very large in the bottom and top deciles.

29
Table 3b: Percentage change in the proportion of skilled workers by selected industry group over time <u>(Workers with positive reported earnings)</u>

|_Industry_|||% Upper S|econdary a|nd above|||
|---|---|---|---|---|---|---|---|
||Indonesia<br>1996-07|Philippines<br>1988-06|Thailand<br>1990-04|Vietnam<br>1992-06|Cambodia<br>1997-07|China<br>1999-05|Mongolia<br>1998-07|
|Agriculture|159.7|38.5|133.3|-11.4|-62.9|7.1|-8.1|
|Mining|23.5|-12.6|33.2|0.2|-|n/a|-|
|Manufacturing|19.7|30.8|76.2|5.6|-4.6|23.1|0.6|
|Utilities|10.4|9.0|9.1|1.1|-|0.0|-6.2|
|Construction|47.3|24.8|39.4|-10.7|35.5|53.1|-12.9|
|Trade|3.7|29.0|62.9|23.7|81.1|32.0|0.6|
|Transportation/Commun.|26.2|15.0|44.8|34.7|3.0|12.6|-7.4|
|Finance/Business|-2.7|1.7|-|13.1|-2.0|n/a|-|
|Public Admin.|19.0|-1.0|-|-|8.1|16.0|0.6|
|Other Services|-|-0.5|56.5|12.8|22.9|3.7|3.4|
|_Mean % change *_|-|-|-|-|-8.9|-|-|
|_Mean % change (1)_|25.6|32.9|62.7|26.1|-4.8|17.9|-3.2|
|_Mean % change (2)_|112.2|42.2|98.4|94.0|65.0|15.7|-43.6|

(1) Secondary education and above; (2) Tertiary education and above. * Lower secondary and above.

In Cambodia, once again the findings with respect to changes in skilled labor need to be contrasted between sample compositions (all employed workers vs. workers with reported earnings). In the case of the former, the proportion of skilled workers increased across the board and much more in agriculture, trade and construction (by 127, 79 and 69 percent respectively) and by only slightly in manufacturing and services. Overall, the proportion defined as those with lower secondary and higher qualifications increased by 60 percent, while the proportion of workers with upper secondary or tertiary qualifications increased by 45 percent. Once the sample is restricted to workers with reported earnings (Table 3), the findings change drastically, especially with respect to changes in agriculture. Now the proportion of skilled workers in agriculture exhibits a large decline of about 63 percent over the 1997-2007 period. Overall, the proportion of skilled workers independently of industry group registers a decline. On the other hand, the proportion of workers with tertiary qualifications exhibits a large increase.

Finally in Mongolia, small changes in skill composition are observed across industries, with most industries showing declines in the proportion of skilled workers. As in the case of Cambodia, here too there are data issues; as reported earlier, in the (lager) 2007 LSMS 10
# **Indonesia**

Table 4a : Returns to levels of education - Employed for wages

||1994|2007|Change (%)<br>2007-1994|
|---|---|---|---|
|_Basic controls only_||||
|- Primary|0.213|0.242|_13.6_|
|- Junior Sec./primary|0.234|0.128|_-45.3_|
|- Senior Sec./ primary|0.596|0.508|_-14.8_|
|- Tertiary/primary|1.125|1.084|_-3.6_|
|_Basic controls + Industry dummies_||||
|- Primary|0.195|0.243|_25.1_|
|- Junior Sec./primary|0.235|0.126|_-46.4_|
|- Senior Sec./ primary|0.610|0.498|_-18.4_|
|- Tertiary/primary|1.137|1.070|_-5.9_|

||1994|2007|Change (%)<br>2007-1994|
|---|---|---|---|
|_Basic controls only_||||
|- High school general/ primary|0.527|0.486|_-7.8_|
|- High school vocational/ primary|0.663|0.547|_-17.5_|
|- Tertiary/primary|1.123|1.084|_-3.5_|
|_Basic controls + Industry dummies_||||
|- High school general/ primary|0.545|0.479|_-12.1_|
|- High school vocational/ primary|0.678|0.526|_-22.4_|
|- Tertiary/primary|1.139|1.056|_-7.3_|

Table 4b:Returns to skill over time – Employed for wages

||1994|2007|Change (%)<br>2007-1994<br>(2007-1996)|
|---|---|---|---|
|_Basic controls only_||||
|- Upper Sec. and above/ Low sec. and below|0.696|0.683|_-1.9_|
|- At least some tertiary/Secondary and below|0.891|0.849|_-4.7_|
|_Basic controls + Industry dummies_||||
|- Upper Sec. and above/ Low sec. and below|0.677|0.622|_-8.1 (-6.2)_|
|- At least some tertiary/Secondary and below|0.819|0.771|_-5.9 (-2.1)_|

13
-18-

at lower rates or avoid taxing capital income altogether. Each of these measures will reduce effective tax rates and contribute to the removal of disincentives to save.

# _Fairness_

Equity and ability-to-pay considerations play a major role in public discussions on capital income taxation. As Mintz puts it, "perhaps the most important consideration behind the reluctance of governments to abandon the taxation of capital income is fairness."23 Should capital income, which is concentrated on the richest households, be taxed more heavily, on grounds of fairness, because labor income is heavily taxed and because workers have less opportunities to avoid paying taxes (because of withholding)?

Despite the fact that this is a passionately debated topic, neither theoretical arguments nor empirical evidence militate in favor of reinforcing capital income tax. At a theoretical level, intertemporal equity consideration (life-cycle hypothesis) must be taken into account. When capital income is taxed, people with otherwise identical economic characteristics (in particular, the same present value of lifetime earnings) will pay different amounts of lifetime taxes, depending on the amount and time pattern of savings. In particular, an individual will minimize his/her lifetime tax bill by not saving at all. From an distributional perspective, this obviously is a violation of the principle of horizontal equity since the ability to pay is the same in both cases.

The available empirical evidence shows that taxing capital income more heavily would only have a negligible distributional impact (in the sense of making income distribution more egalitarian) because the tax base is too small. Evidence from Hungary is provided by a recent study of the structure and distribution of income (see TARKI, 1995). _5_ Table 6 shows the distribution on income from work, transfers, income from abroad, other income, revenues accounted as expenses, and capital income.

23 See Mintz (1994, p. 1483). The whole debate on the flat tax is a good example. In the May-June 1996 issue of the magazine "Challenge", for instance, Roberts/Sullivan criticize the flat personal income tax proposal on the grounds that "under the flat tax, many of the wealthiest individuals would pay absolutely no tax. For example, a family whose income consists solely of millions of dollars of dividends, interest, and gains from the sale of stock held for investment would pay zero tax." (p. 25).

24 See Sorensen, 1994, for a simple numerical example. See also Mintz, 1994.

25 The survey is based on a random sample of all 4.1 million income tax returns filed by Hungarian taxpayers in 1994. Declared household income amounted to HUF 1,714.6 billion. The TARKI study was commissioned by the Ministry of Finance and financed by a PHRD grant from the World Bank supporting the Govemment's public finance reform.