rafmacalaba/datause-encoder-data huggingface dataset:
Figure 9: Change in Gini after fiscal policy (including in-kind transfers) <!-- Start of picture text --> 0<br>-5<br>LIC<br>-10<br>HIC UMIC LMIC<br>-15<br>-20<br>-25<br>High income Upper middle income Lower middle income Low<br>income<br>Point Change<br>Poland Croatia Panama Uruguay Chile Romania Namibia Argentina South Africa Costa Rica Brazil Mexico Iran Mexico Mexico Ecuador Belarus Colombia Colombia Venezuela Russia Albania Dominican Republic Paraguay Armenia Peru Peru Indonesia Guatemala Jordan Ukraine Tunisia Kenya El Salvador Bolivia Tanzania Nicaragua Egypt Ghana Comoros Honduras Sri Lanka Cambodia Burkina Faso Uganda Ethiopia<br><!-- End of picture text --> Source: CEQ and World Bank databases and World Bank calculations (see World Bank, 2022b). Note: HIC = high-income country, UMIC = upper middle-income country, LMIC = lower middle-income country, LIC = low-income country.

At the same time, poorer households pay more in tax than they receive in cash transfers; short-term poverty is slightly higher due to indirect taxation. Overall, the modelled fiscal system increases short-term poverty by 2.1 percentage points, as shown by the transition from _market income_ to _consumable income_ (Figure 10). The net transfer achieved through direct transfers and direct taxes on income are too small to change average household incomes, and thus poverty. However, the net transfer resulting after subsidies and indirect taxes on consumption is negative and large, thereby increasing poverty measured today. In other words, all households face higher prices on goods from indirect taxes. For poor and nearpoor households, the higher price reduces purchasing power, potentially reducing net expenditures below the poverty line. It is important to note that poorer households are only out-of-pocket in the short-term; this analysis does not include the economic benefits of health and education services which households will enjoy in the longer-term and which will help reduce poverty.

Figure 10: Poverty headcount before and after fiscal interventions <!-- Start of picture text --> 25<br>19.8<br>20<br>17.7 17.8<br>15<br>10<br>Market income Disposable income Consumable income<br>Poverty headcount (%)<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data. Note: Per CEQ technical recommendations, in-kind health and education transfers are not included when estimating the impact on poverty.

18
crossing the fixed standard of living captured by an absolute poverty line (and its associated vulnerability lines). Hence the rise in mobility captured in this way is likely real.

Alongside the rise in unconditional mobility, Table 3 also reveals that there was an increase in conditional mobility. Just under two-thirds of the poor in 1993/94 remained poor in 2004/05 (compared with three-quarters in the preceding interval), and 51–61 percent of the vulnerable (in panels B and A respectively) remained vulnerable (down from 64 percent). Downward mobility among the secure also declined, from about 45 percent in the 1987/88–1993/94 interval to less than a third in 1993/94–2004/05.

If we consider the 2004/05–2011/12 interval—a somewhat shorter period—we can see in Table 4 that mobility increased further. Alongside a striking reduction in poverty, from 35.5 percent to 24.8 percent, consumption mobility rose, with off-diagonal entries in panels A and B now representing 43–46 percent of the population. More than half of the poor in 2004/05 (about 54 percent) were no longer poor in 2011/12. Interestingly, however, although (conditional) mobility by the poor into the category of the secure did increase compared with the earlier intervals, it remained a rather rare event: only about 5 to 10 percent of the poor were able to make this transition across two welfare classes between 2004/05 and 2010/11. The picture of poverty decline emerging from this assessment is that although the poor did see improvements in living standards during the 2000s, they generally continued to face a heightened risk of falling back into poverty.<sup>17</sup>

> 17 As discussed earlier, the IHDS panel does not provide the perfect benchmark panel data to compare with the NSS given its much less detailed consumption aggregate and considerable attrition rate. Still, it could be useful to qualitatively gauge the mobility patterns if we are to employ the value of rho that is estimated from the IHDS panel, but treating it as repeated cross sections. Table A.2 in Appendix A replicates the estimates for Table 4 above, using the estimated ρ value of 0.39 based on the IHDS. Estimation results remain qualitatively similar. Off-diagonal entries in panels A and B represent 42–49 percent of the population, and more than half of the poor in 2004/05 (about 58 percent) were no longer poor in 2011/12. These estimates provide supportive evidence for the estimation results offered in Table 4.

21
Even when controlling for the presence of children, less educated women have a higher propensity for home-based work in Brazil and Mexico (12 and six percent for _nosch_ and _primsch_ only) while the education coefficient for men are very small. The strong effects for women and absent effects for men suggest that education alone does not lead to home-based work. Instead, perhaps the combination of being female and uneducated leads to a higher value for her domestic work since uneducated families tend to be poor (Wodon 2000) and poor families have fewer labor saving devices and more children (Wodon 2000).

Older individuals are more likely to be home-based workers, thus supporting the qualitative findings that those with obsolete market skills and limited mobility tend to work at home. The probability of being a home-based worker is monotonically increasing with age for men and women in all three countries, with young women being particularly unlikely to accept this type of work in Brazil and Ecuador. This reflects the propensity for the informal self-employed, who are a sizeable portion of the home-based worker in the sample, to be older workers (Cunningham and Maloney 2001).

There is less evidence that those with limited access to the labor market, whether due to demand or supply constraints, leads people to home-based work. The regression using the Brazilian data shows that non-white individuals are neither more nor less likely to be home-based workers compared to the (favored) white workers.

# **5.2 Hourly remuneration**

Both men and women who are home-based workers earn lower hourly wages than do non-home-based workers in all three countries. When controlling for human capital differences, Brazilian home-based workers earn 22 (21), 26 (20), and 19 (21) percent less 20
Notice that according to these estimates, the total effect on exports of benefiting from the Special Rule four years is 242%, a figure larger than the average effect of 168% estimated in column 5 of table 2, as only three exporters in our sample make it to the fourth year.

Columns 2a and 2b show the differential effect of the single-transformation rule across the 7 exporters. The effect for all countries is positive. The effect of the Special Rule on exports from Kenya, Madagascar, and Swaziland are found to be the largest. The different performance among receivers of the Special Rule begs the question: Why were some African countries so much more successful at taking up preferences and at experiencing higher export growth in apparel? Among others, a possible explanation lies in the business environment of a country that may be more conducive to attract foreign investment in apparel plants and to diminish trading and other fixed costs which can be proxied by a country’s rank in the World Bank “Doing Business” indicator.<sup>17</sup> Figure 2 confronts the Ease of Doing Business (DB)<sup>18</sup> ranking of African countries benefiting from the Special Rule against their apparel export growth during AGOA (measured by the difference of exports (in logs) at 2004 and at the beginning of AGOA). Indeed, on average countries best ranked along the DB indicator experienced higher growth in apparel exports during AGOA, and the correlation coefficient, ( ρ =-0.55) is highly significant.

Figure 2 here: Export Growth and the Business Environment Recall that the SR dummy is set equal to one if a country benefits from the rule for at least 9 months. As Botswana and Malawi were eligible to the Special Rule from August 2001, whereas Swaziland was eligible from July 2001, the effect may already be reflected in the exports data from 2001 onwards. Column 3 reports the export elasticity estimate at an average preferential margin when the dummy, _RA_<sup>_j,k_</sup> _i,t_ , is equal to one from 2001 onwards for Botswana, Malawi and

> 17  The indicator, available in the form of a ranking for 178 countries, is a simple average of the regulations affecting ten stages of a business’ life: starting a business, dealing with licenses, employing workers, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts and closing a business. Being quantitative rather than subjective, the indicator is less subject to bias than the more-widely used indicators of governance.

> 18 We use Doing Business (DB) data for 2008 released on June 1, 2008, as it includes more African countries in the sample than reports in previous years.  Indeed, for instance, DB 2006 coverage is limited to 6 out of the 22 African countries.  Yet, for these six countries, the relative ranking of DB 2008 does not change significantly with respect to DB 2006.

17
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find out if economic shocks were the only determinants of renegotiations, or if there were other flaws, in contract or regulatory framework design, that were pivotal in explaining the high incidence of renegotiation.

Table 4: Renegotiation by Type of Initiator and Year

||**1989**|**1990**|**1991**|**1992**|**1993**|**1994**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**Total**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**All countries**<br>outstanding<br>concessions|10|38|38|50|78|103|123|132|156|187|187|165||
|Number of||||||||||||||
|renegotiations|0|13|3|9|12|14|23|15|15|11|27|20|162|
|Firm-led reneg.|0|12|2|2|0|1|3|3|11|4|1|14|53|
|Govt-led reneg.|0|0|0|0|10|13|19|11|3|7|25|6|94|
|Joint-ledreneg.|0|1|1|7|2|0|1|1|1|0|1|0|15|

Source: Guasch, Laffont and Straub (2005) Guasch, Laffont and Straub (2003, 2005) results are based on a sample comprising 307 projects in the water and transport sectors, in five countries (Argentina, Brazil, Chile, Colombia and Mexico), across 12 years, for a total of 1287 observations (see Guasch, 2004). For each contract, there is information on the general characteristics of the projects (sector, year of award, duration), on the award process, the investment and financing conditions, the institutional and regulatory context and the type of price regulation in place (price cap versus rate of return), and other contract clauses (arbitration, income guarantees, take-over clauses, etc.). These are completed by macroeconomic data (growth rate, exchange rate evolution), dummies for national and local elections and a full set of institutional indicators (corruption, quality of the bureaucracy, rule of law).

15
# **<u>Table 2: Tax rates required to attain various levels of a basic income</u>**

||1|2|3|4|5|
|---|---|---|---|---|---|
|**Parameters**||||||
|**Basic income guarantee ($/year)**|19,759|20,340|21,900|22,920|24,100|
|Extra cost of living in U.S. (% of earnings in the US)|10|10|10|10|10|
|Nonpecuniary premium (%)|20|20|20|20|20|
|**Simulation results**||||||
|Tax on purchase of Work Permit (%)|0|13|46|91|141|
|Average earnings of renters ($)|10,300|9,800|8,400|6,600|5,400|
|Expected earnings of buyers in U.S. ($)|50,100|50,700|54,600|59,800|63,900|
|Number of renters(buyers) (M)|33.2|31.6|23.5|18.1|14.6|
|Total earnings of migrants ($B)|1,666|1,612|1,322|1,088|932|
|Earnings of migrants net of foregone earnings of U.S. workers as<br>% of U.S. GDP|6.7|6.6|5.6|4.8|4.2|
|Tax revenue from migrants’ earnings ($B)|327|319|272|234|204|
|Revenue from taxes on WPs ($B)|0|74|162|197|206|
|Net gains for renters ($B)|262|215|145|90|62|
|**The U.S. poverty rate (excluding migrants); base=12.3%**|9.3|8.1|6.0|4.7|2.8|
|Note:_Basic income guarantee_($/year) is the desired amount of BIG;|see Notes to Ta|ble 1.||||
**Table T2.IU: Intergenerational Persistence in Schooling in Urban India**

|||**LINEAR CEF**||**QU**|**ADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|**0.46*****|**0.52*****|**0.066*****|**0.55*****|**0.65*****|**0.094*****|
||(0.0074)|(0.0080)|(0.011)|(0.023)|(0.025)|(0.034)|
|Quadratic Coefft.||||**-0.0070*****|**-0.0093*****|**-0.0022**|
|||||(0.0014)|(0.0016)|(0.0022)|
|Intercept|**6.96*****|**5.97*****|**-0.99*****|**6.81*****|**5.80*****|**-1.00*****|
||(0.071)|(0.075)|(0.10)|(0.085)|(0.086)|(0.12)|
|No. Observations|9449|9070|18519|9449|9070|18519|
|||**Estimates**|**of Mobility**|**fromQuadr**|**atic CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|6.81|5.80||**_IGME0 _**|0.55|0.65|
|**_ES5 _**|9.39|8.82||**_IGME5 _**|0.485|0.556|
|**_ES10_**|11.61|11.37||**_IGME10_**|0.414|0.464|
|**_ES16_**|13.82|13.82||**_IGME16_**|0.330|0.353|

Notes: (1) The data used are IHDS 2012 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling. (3) ESK is expected schooling when father has K years of schooling. K=0,5,10,16. (4) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
# **Figure 1. Emerging Economy Imports Affected by TTBs through 2011 (cont.)**

<!-- Start of picture text --> a. By policy<br><!-- End of picture text --> <!-- Start of picture text --> b. By category of affected trading partner<br><!-- End of picture text --> <!-- Start of picture text --> percent Peru<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Philippines<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>1997 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Thailand<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text -->

**Stock: imports subject to any imposed TTB in effect Stock: imports subject to AD only in effect Flow: imports subject to any newly initiated TTB investigation Flow: imports subject to newly initiated AD investigation only**

<!-- Start of picture text --> Peru<br>percent<br>25<br>20<br>15<br>10<br>5<br>0<br>1993 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>percent Philippines<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>1997 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br>Thailand<br>percent<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>1990 91 92 93 94 95 96 97 98 99 2000 01 02 03 04 05 06 07 08 09 10 11<br><!-- End of picture text --> <!-- Start of picture text --> All trading partners' exports under any TTB in effect<br>China's exports under any TTB in effect<br>Other emerging economies' (non-China) exports under any TTB in effect<br>High income countries' exports under any TTB in effect<br><!-- End of picture text --> Notes: Shares of nonoil imports, constructed by the author with policy data from Bown (2012) and trade-weighting with HS-06 import data from UN Comtrade via WITS, following Appendix equation (A2).

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**Table 4.1  Distribution of farmers,  model sample  2007**

|**Farmers**|**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Overall**|
|---|---|---|---|---|---|---|
|Java-Bali (%)|89|79|76|68|36|70|
|Off Java-Bali (%)|11|21|24|32|64|30|
|Total (No of farmers)|1,575|1,576|1,577|1,577|1,578|7,883|

_Source_ : World Bank staff calculations.

The results below suggest there is a positive impact on rice yields when using urea at adequate levels, but there is a threshold that, if exceeded, the relationship reverses and fertilizer starts having a negative impact (Table 4.2 (1)).

**Table 4.2  Estimation results (2SLS second stage regression) in 2007**

|**_Dependent variable:_**<br>**_Rice yields_**|Overall|Quintile 1|Quintile<br>2|Quintile<br>3|Quintile 4|Quintile<br>5|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Urea use|4.2098|0.4997|0.4269|0.3091|0.3506|0.1552|
||(2.26)*|(7.54)**|(6.43)**|(4.28)**|(4.77)**|(2.31)*|
|Urea use squared|-0.3882||||||
||(-2.08)*||||||
|Land size|-0.0959|-0.0685|-0.0472|-0.1029|-0.1383|-0.2004|
||(-5.86)**|(-3.27)**|(-2.25)*|(-4.56)**|(-4.90)**|(-7.59)**|
|Labor cost|0.0431|0.0561|0.0540|0.0740|0.0958|0.0760|
||(3.08)**|(5.50)**|(4.84)**|(5.84)**|(6.84)**|(7.24)**|
|Dummy irrigation|0.0339|0.0216|0.0089|0.0383|0.0403|0.1017|
||(2.76)**|(1.18)|(0.53)|(2.03)*|(1.74)+|(3.25)**|
|Education|-0.0006|0.0034|0.0042|0.0026|-0.0012|0.0003|
||(-0.32)|(1.32)|(1.64)|(1.01)|(-0.42)|(0.09)|
|Age|-0.001|-0.0007|0.0013|-0.0019|-0.0006|-0.0014|
||(-1.82)+|(-1.04)|(0.34)|(-2.43)*|(-0.68)|(-1.22)|
|Dummy for Java-Bali|-0.0937|-0.1625|-0.1384|-0.096|-0.1148|-0.0021|
||(-4.49)**|(-4.39)**|(-4.69)**|(-2.86)**|(-3.28)**|(-0.06)|
|Constant|-2.3646|5.8908|6.0816|7.1419|6.9928|8.5999|
||(-0.52)|(17.11)**|(15.57)**|(15.84)**|(13.80)**|(17.43)**|
|R-squared|.|.|0.09|0.25|0.32|0.30|

z statistics in parentheses. + significant at 10%; * significant at 5%; ** significant at 1%. All variables are estimated in natural logarithm The impact of fertilizer use on yields is not uniform, and smaller in magnitude and significance for larger farms off-Java. When looking at the relationship between fertilizer use and yields by quintile, a linear model seems to fit the relationship better (the urea squared term is not significant), which differs from the non-linear relationship found for the overall sample. The fact that the relationship is different when breaking down the sample by land size suggests that smaller farmers have a different production function than larger farmers. Also, the analysis by plot size captures geographical differences in rice farming that could lead to different production functions; for example, in Java, farms are not only smaller or sowed more

**14**
of instructional quality.22 The EDURURAL project in North East Brazil providec evidence of the cost-effectiveness of this strategy.

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with less corrupt governments and governments that better protect private property rights.<sup>30</sup> So how do we reconcile our results with the inefficiencies in the formal financial system described in Section 2? The formal financial system does serve a small sector of the economy and both the aggregate statistics and firm responses suggest that there are imperfections in the allocation of capital. However, our results show that despite these weaknesses, at the margin, private sector firms that have loans from the formal sector do better than other firms and having a better developed financial system will only bring more benefits.

We have no evidence that the firms depending on external financing of their investment through informal channels are the fastest growing firms. Rather, the evidence suggests that, if anything, retained earnings are associated with better performance than external informal financing, and that firms that have bank loans grow faster than firms relying on informal or other sources. However, even though in the aggregate informal financing sources are not associated with high growth, the low reliance of very small firms on bank financing suggests that self financing is important for small entrepreneurial firms that depend on it for their growth and survival. For some of these firms, informal financing may play a role akin to the critical role played by angel finance, in the financing and creation of rapid-growth start-ups in developed economies. Thus, while the

> 30 Fan et al. (2007) estimate a cross-country (without China) FDI model explaining inward FDI using different measures of institutions and make out-of-sample predictions from their model for China. They find that China is no exception since Chinese FDI inflows are in line with what the model predicts for a country at China’s level of institutional development as measured by general government quality and rule of law.  However, when they measure institutions by “strength of executive constraints”, they find that China receives more FDI than predicted by their model. But they argue that this could be because of other reasons including underestimation of the strength of checks on executive power in China or foreign firms enjoying better protections than identical Chinese firms.

43