rafmacalaba/datause-encoder-data huggingface dataset:
# **1 Introduction**

In April 2024, the average price of one gallon of fresh fortified milk in US cities was $3.868.<sup>1</sup> Consumers in the northeast paid, on average, $4.395, and consumers in the west purchased milk at an average price per gallon of about $0.60 less ($3.757). In the same month, the urban consumer index of rent on a primary residence was about 5.4 percent higher in the west than in the northeast.<sup>2</sup> The existence of differences in the prices of consumption goods and services across space is well established. These variations are sometimes considerable between urban and rural areas, across regions, provinces or states, cities, and so on. The simple statistical evidence has, however, important effects on the purchasing power of money. The higher the prices, the lower the purchasing power of households.

Welfare economists, practitioners, statisticians, and analysts interested in living standards are aware that any meaningful comparison of material well-being among individuals living in different geographical areas requires some adjustment to transform a nominal welfare aggregate, be it consumption or income, into a metric that does not conceal spatial differences in the cost of living.<sup>3</sup> First, adjusting for spatial price differentials has important implications for monetary poverty and inequality. Examples of relevant statistics that are affected by spatial differences in prices include poverty headcount ratios, the depth of poverty, the number of the poor, poverty profiles, and indicators of inequality. Second, such statistics on living standards are key inputs to poverty reduction strategies. Adjustments for spatial price differences are therefore relevant to the design of policies aimed at helping people move out of poverty and to the allocation across ministries or across subnational state or provincial governments of resources derived from national budgets, international donors, or multilateral development banks.

Somewhat paradoxically, it is precisely low-income countries, in which the statistical information needed to measure price variability adequately is rarely available, that are characterized by a

> 1 See BLS Data Viewer (graphic), time series: Milk, fresh, whole, fortified, per gal. (3.8 lit), time period: 2024, series ID: APU0000709112.

> 2 The base period of the index is 1982–84, and the index is not seasonally adjusted. For an overview, see Measuring Price Change in the CPI: Rent and Rental Equivalence (web page), US Bureau of Labor Statistics, Washington, DC, https://www.bls.gov/cpi/factsheets/owners-equivalent-rent-and-rent.htm.

3 This is in addition to any required correction for differences in the prices of consumption goods and services over time (inflation) and differences in household size and composition (adjusted through the use of equivalence scales).

2
istics. _ατ_ , _βτ_ , _γτ_ , and _δτ_ are coefficients specific to the _τ_<sup>_th_</sup> quantile, and _tt_ represents time to account for time fixed effects. It is important to note that this specification doesn’t include a firm fixed effect, as any gender-related impact is captured by the gender indicator variable ( _Wi_ ).

Table 10: Quantile Regression Result for Tax Declarations and Payment Indicators

||(1)<br>Sales|(2)<br>Tax payable|(3)<br>Tax payment|
|---|---|---|---|
|Women-owned|-0.266<sup>_∗∗∗_</sup>|-0.212<sup>_∗∗_</sup>|-0.0991|
||(-4.61)|(-2.88)|(-1.57)|
|Constant|11.99<sup>_∗∗∗_</sup>|7.974<sup>_∗∗∗_</sup>|8.318<sup>_∗∗∗_</sup>|
||(143.99)|(79.56)|(88.83)|
|Pseudo R2|.16|.12|.12|
|Observations|3347|3347|3347|

_t_ statistics in parentheses All of the regression models above include a time dummy. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001 Table 10 delves into the insights derived from the analysis of pooled quantile regression at the median, focusing on the tax declarations and payment indicators while accounting for various firm characteristics. The analysis, as depicted in Table 10, incorporates potential controls, including the sector of operation (Agriculture, Industry, and Service), the year of adopting sales register machines, business categorization (A or B), geographical location, and temporal trends. These controls enable us to reduce the risk of picking up other explanations as gender-based disparities in taxation.

The findings would shine a light on distinct gender differences in sales and tax payable. It is important to note that the findings underscore no significant differences in tax payments between men and women. In simpler terms, when it comes to the actual amount businesses remit to the tax authorities, gender ceases to be a differentiating factor. The implications of these findings warrant careful consideration. Women-owned businesses have 22
A final point worth mentioning is the productivity growth of the very small firms in Vietnam. The census of establishments in Vietnam covers firms with fewer than 10 employees. Productivity growth in these very small Vietnamese firms was around 9 percent per year between 2000 and 2017; the corresponding growth rate in Ethiopia is imprecisely estimated at zero percent per year. We do not have an explanation for this difference. The firms with fewer than 10 employees in the Vietnamese enterprise survey are all registered; we do not know the registration status of the small-scale industries in Ethiopia but in 2014, more than 85 percent of these firms had a license. In any case, given the prevalence of small manufacturing establishments in Africa, this difference seems worth investigating.

The evidence discussed so far suggests that employment growth in Africa’s small and informal manufacturing firms is considerably more rapid than employment growth in manufacturing firms which employ 10 or more workers. However, labor productivity growth in the larger formal firms is reasonably strong. This mixture of outcomes is not all bad news. Productivity growth in formal sector firms is indicative of an advancing technology frontier. The rapid growth in small manufacturing firms is indicative of entrepreneurial spirit; indeed, anyone who has spent time in an African country understands the incredible ingenuity of African entrepreneurs. Nevertheless, if African governments want to expand manufacturing exports and employment opportunities for those who prefer formal wage work, they will need to grow their formal manufacturing sectors. How might this happen? In the next section, we consider some of the opportunities.

# **Opportunities**

_African Continental Free Trade Area_ The African Continental Free Trade Area was founded as a free trade area in 2018 with 54 of the 55 African Union nations as signatories (the exception being Eritrea). To date, 36 states have ratified the agreement, and trade under the agreement officially commenced at the start of 2021.<sup>4</sup> Its key functions include progressively eliminating tariffs on intra-African trade (with alternate timelines for implementation based on countries’ income status), implementing rules-of-origin, monitoring and

> 4 A useful source of background information on the African Continental Free Trade Area is the South Africa–based nonprofit Trade Law Center often called “tralac.” For details on the agreements and membership behind the African Continental Free Trade Area, a starting point is - <u>https://www.tralac.org/resources/by region/cfta.html (accessed June 1, 2021).</u>

14
- 8 -

fabric and yarn are subject to a cap of 1.5 percent of overall U.S. imports, growing to 3.5 percent of overall imports over an 8-year period.<sup>7</sup> In addition a number of customs requirements need to be satisfied. To receive the apparel and textile benefits of AGOA, a USTR-chaired inter-agency committee must determine, inter alia, that countries have an effective visa system and enforcement procedures to prevent unlawful transshipment and the use of counterfeit documents.

There is an interesting difference between the rules of origin under the Cotonou Agreement, which governs preferential access to the European Union, and AGOA. The Cotonou rule of origin is based is based on the concept of "double transformation" i.e., if two of the processing stages (yarn into fabric-weaving; and fabric into apparel-assembly) are done in the beneficiary country, duty free entry into the EU can be enjoyed. Under Cotonou, therefore, yarn can be sourced from anywhere in the world, whereas under AGOA the yarn must come from a beneficiary SSA country or from the United States.

## IV. ECONOMIC IMPACT OF AGOA's APPAREL PROVISIONS

# **A. AGOA's Apparel Provisions and Their Timing**

In order to quantify the economic impact of AGOA, it is necessary to understand the provisions and their timing, which are summarized in Table 9. In the apparel sector, AGOA distinguishes two categories of SSA countries.

Lesser Developed Beneficiary Countries (LDBCs), namely those with per capita GNP under $1500 in 1998 (based on the World Bank Atlas method), and other SSA countries will see their quotas on apparel exports eliminated beginning 2001.8 Both sets of countries will 7 Based on the growth trend of U.S. apparel imports in recent years, the cap on apparel imports from sub-Saharan Africa under AGOA could conceivably expand to $4.2 billion in eight years, from the current level of $584 million. It is unlikely that the cap will be binding for two reasons. First, the utilization rate, though growing, was around 40 percent in the first nine months of 2002, and is projected to reach a little over 50 percent for the whole year. Second, the recent passage of the Trade Promotion Authority bill by the U.S. Congress has doubled the size of the cap on imports of apparel made from regional fabric - which will now reach 7 percent of overall imports in 2008.

**8** Forty-two countries in sub-Saharan Africa fall below the specified GNP level and hence qualify as an LDBC under AGOA; another two countries-Botswana and Namibia-have recently been designated as LDBCs despite their high GNP levels. Thus, only the following four do not qualify: Gabon, Mauritius, Seychelles, and South Africa. As of end-200 1, 12 countries-Mauritius and Kenya (January), South Africa and Madagascar (March), Lesotho (April), Swaziland (July), Ethiopia, Malawi, and Botswana (August), Uganda (October), and
Finally, the annual data on the expenditures required for PSNP scale-up if the rainfall patterns in previous years were to repeat was extrapolated to give 5,000 years of simulated expenditures by fitting a Pareto distribution.

Under these hypothetical rules, on average 2.9 million people would be supported under the scalability component of PSNP and the average required expenditure under the scalability component is approximately US$139 million. Based on the historical data and hypothetical PSNP scale-up, expenditures would be highest if the 2009 drought was to repeat (5.3 million additional beneficiaries with expenditures of US$252 million) and lowest if weather in 2010 was to repeat (0.6 million additional beneficiaries with expenditures of US$30 million).

The average contingent liability as well as the contingent liability at different return periods (i.e. events with different probabilities of occurrence based on the 5,000 simulated years) are presented in Figure 2.2 below. Variations in the contingent liability were also considered, details of which can be found in Annex 6.

## **Figure 2.2: Hypothetical PSNP scale-up expenditure**

<!-- Start of picture text --> 600<br>500<br>400<br>300<br>200<br>100<br>0<br>Average 1 in 5 1 in 10 1 in 30 1 in 50<br>(20% probability of (10% probability of (3.3% probability of (2% probability of<br>occurrence) occurrence) occurrence) occurrence)<br>Return period of loss<br>Cost (US $m))<br><!-- End of picture text -->

# **3 Characterizing Risk Financing Strategies**

Having specified the illustrative contingent liability for the PSNP we now present three potential strategies for financing this contingent liability, along with a set of financial and economic assumptions.

The range of potential financial instruments<sup>**9**</sup> considered in this analysis include:

> 9 Further detail on the types of financing instruments is provided in the glossary in Annex 1.

6
# **4 Nonparametric evidence**

This section examines the age structure and health status of women by the sex of the first-born by using kernel-weighted local polynomial regressions. The resulting smoothed values are displayed in graphs with 95% confidence intervals. The age range of women considered in this section is 20–49.<sup>26</sup>

## **4.1 Share of women with a first-born girl**

The nonparametric graphs in this section plot the share of women with a first-born girl by their age at the time of the survey. This share is simply the inverse of the sex ratio for first births. Therefore, rather than plotting the sex ratio by birth year of the child (done in section 3), the following graphs show the age distribution of women with a first-born girl (due to pooling, these are independent of cohort effects, except for non-overlapping years of the NFHSs).

The left panel of figure 4 shows that the sex ratio for first births is in the biological range for women below the age of 30 (including the confidence bands). However, the share of women with first-born girls decreases almost monotonically for women older than 30, where it is below the biological range. This is equivalent to saying that, among women with at least one child ever born, the share of those who had a first-born girl is lower among older women, particularly among those older than 30. The right panel shows that the same pattern is found in each round of the NFHSs (though with larger confidence bands and more fluctuation likely due to smaller sample sizes when using each survey separately). Interestingly, the upper graph in figure 5 shows that the decreasing pattern is found only among women who did not complete any years of schooling, while the share is rather stable for women with at least one year of education at all ages.<sup>27</sup> Similar patterns are found when using each NFHS round separately (see bottom two graphs in figure 5).<sup>28</sup> Considering education as a plausible proxy for socioeconomic status, this suggests that poorer women suffer higher maternal and adult mortality. Having lower access to health services (and in particular to prenatal sex diagnostic techniques in more recent decades), uneducated women may rely more heavily on son-preferring fertility behavior to achieve the desired sex composition of children and thus suffer the adverse consequences on their health and survival.

**State-wise variation.** There is substantial state-wise variation in sex ratios at birth and excess mortality in adulthood within India. Anderson and Ray (2012) find a distinctive pattern: gender

> 26 Women aged 15–19 are dropped because of very large confidence bands due to a small fraction of them who already has had a birth –these represent 4.5% of the entire pooled sample.

> 27In Milazzo (2012), I find similar patterns when using DHS data for Nigeria. Compared to India, this is a context in which women are also found to engage in son-preferring fertility behavior (though not as strongly), and where the lifetime risk of maternal mortality is higher.

> 28Appendix figure A.1 also shows a decreasing pattern for women in rural areas (not found in urban areas).

12
- 10-

consumption has advantages over income as a direct tax base.<sup>2</sup> In fact, many tax systems which purport to tax income come closer to taxing consumption given the number of assets which are sheltered, such as pension funds, housing and other consumer durables, human capital accumulation, insurance and cash balances. If countries were to adopt a full consumption tax system, this withholding rationale would disappear. Even so, a corporate tax may still be needed to fulfill some of the other roles discussed below. If that role were the other withholding role, a system of imputation would still be desired.

# _b. Wholding Agais Foreign Shareholders_

Income accruing to foreign shareholders would also escape domestic personal taxation since the latter applies only to residents. If it is desired to tax foreign shareholders, a corporate tax could be used for this purpose (perhaps alongside withholding taxes). The ability to extract tax revenues from foreigners depends upon the tax systems facing foreigners in their home countries. Specifically, if the host country into which the foreign capital is imported is small relative to world capital markets, which wil typically be the case, taxes can only be obtained from foreigners to the extent that the taxes are creditable against tax liabilities in the home country. Otherwise, any attempt to tax foreigners wil result in capital leaving the country until the rate of return before tax rises to cover tax liabilities. Effectively, the tax is being shifted back to domestic factors of production. If host country taxes can be credited against home country tax liabilities, a pure tax transfer can be affected from the home country treasury to that of the host country. Since this is costless it should be fully exploited.

Typically, there are two sorts of capital income taxes which are creditable. Pure withholding taxes are creditable to the extent specified by tax treaties. As well, many corporate tax systems provide credits on taxes paid abroad. The credits are limited by the amount of home country tax liabilities, are calculated using the home country tax system and are available when dividends are repatriated. As well, full credit is usually only available on shares held in fbreign-controlled affiliates, which accounts for most foreign direct investment. To exploit this tax transfer fully requires that the host tax system conform with the foreign one. If host country tax rates are too high, some foreign investment will be discouraged. If they are too low, the host country is forgoing costless tax transfer. Since most countries tax corporations on the basis of some notion of equity income, this is also the sensible tax base for host countries to adopt, despite the fact that for domestic withholding purposes only retained earnings need be taxed. It would not be possible for the corporate tax to apply differently to domestic and foreign 2 llhe arguments an wel-known and are **presented** in Meade (1978) **and** U.S. Treasury (1977), among **other "Jlac".**
CHART 12 TRENDS IN RETURNED CHECKS CHART **13** REAL GROWTH OF DEPOSITS, LOANS, AND FOREIGN SHARE IN FUNDING <!-- Start of picture text --> 10YO"- > ,<br>wm' a-r= -4w 9i'., ghi dw is i'9~ ~ '<br><!-- End of picture text --> <!-- Start of picture text --> rmowF<br>3 o.0 or s.sd 9v s9901 9903 90 9907 9909 p9,,2 s0*<br>Deposit growth Loan gowth e Foreign share in funding<br><!-- End of picture text --> Chart Il and I3 show that, contrary to what one might expect in a credit crunch, loan growth was strong even in real terms and the ratio of excess reserves to deposits did not increase. Loan growth, however, was not financed via regular deposit growth (which became negative in real terms), but via funds denominated in foreign currency whose share on total resources --i.e. foreign funds/(deposits + foreign funds)-- strongly increased from less than 30 to beyond 50 percent at the peak of the crisis."<sup>2</sup> Indeed, the negative trend in deposits seems to reflect a widespread change in depositors' attitude towards banks and a declining desire to keep their funds in Rupiah.

Chart **14** RATIO OF EXCESS RESERVES TO DEPOSITS Chart 15 MARKET SHARE TIME & SAVINGS DEPOSITS <!-- Start of picture text --> 250 - -<br>2. =<br>In -~~~~~~~~~~~~~~~ -------<br>03 & s Wm , .,do r QM W ., ,ot, sIs Ootun .In 9o,' b' s tod.<br>Foreign banks . Regional govembn,nt banks<br>Private nanosmi banks w State banks<br><!-- End of picture text -->

- 22 We include in foreign finds both foreign currency deposits by residents and banks' borrowing abroad.

17
function of consumption and leisure of household members, as well as the quality and quantity of their children. A household maximizes its utility subject to standard budget constraints and the restrictions imposed by the health production function. The household demand for child health (child’s height and weight) depends on a set of exogenous characteristics of a child _Xi_ , household characteristics _Xh_ , characteristics of its mother _Xm_ , community characteristics _Xc_ , and some unobserved factors captured by random error<sup>(Thomas, Strauss and Henriques, 1991). This relation can be expressed as:ε</sup> _i_ _s s Zi_ = _Zi_ ( _X i_ , _X m_ , _X h_ , _X c_ , ε _i_ ), _s_ = _WAZ_ , _HAZ_ . (1) The child’s characteristics include age, sex, birth order, and month of birth. The mother’s characteristics include her age, educational attainment and employment status. Characteristics of a household include household size, socio-demographic composition, household wealth index, religion, and caste. Community characteristics include availability of community services and infrastructure. In the linearized form, the production function of child’s health (1) can be expressed as: where _Mik_ is a dummy variable equal to 1 if child _i_ is born in month _k_ , vector _X i_ combines the child’s, mother’s, household’s and community’s characteristics, and α _ks_ and π _s_ are parameters. Table 2a shows the coefficients of the linear regressions for WAZ and HAZ for children younger than 36 months, and for three rounds of NFHS. The ‘month-ofbirth’ effect persists when we control for a wide set of exogenous characteristics in (2). Relative to December, children born in other months have worse health outcomes, with the largest negative differences showing up in May, June and July. In Table 2b, we add three variables on the access to health facilities: distances from the village to a hospital, a primary health center and a health sub-center. Table 2b shows the estimates for 1992 and 1998, as the information on these distances was collected only for rural population in the first and second rounds of the NFHS. We still find a strong month-of-birth effect under this extended specification.

The coefficients on the control variables in the regressions used in Tables 2a and 2b reveal the expected relationship between child health outcomes and the characteristics 7