rafmacalaba/datause-encoder-data huggingface dataset:
percentage (see Table 10). Manufacturing firms tend to use more the internet for managing inventory compared to service firms. The percentage of small and medium firms managing their inventory is very close to the average.

_More than 70% of firms with internet connection in Uganda, Zambia and Kenya do marketing on the internet._ Table 11 shows the percentage of firms with internet connection that do marketing on the internet. While manufacturing firms tend to use more internet for this purpose, it is not standard for all countries. Manufacturing firms in Kenya and Zambia, for instance, tend to do less marking online compared to firms in the service sector.

_<u>Table 11 Firms Doing Marketing on the Internet (Percentage in terms of Firms with Internet Connection)</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|39.8|40.8|64.4|72.7|72.9|70.6|60.2|
|**Main Sectors**||||||||
|Manufacturing|45.6|59.2|69.7|76.4|70.3|64.5|64.3|
|Service|34.0|31.6|62.2|71.9|73.9|73.5|57.8|
|**Size**||||||||
|Small|37.0|30.4|62.4|68.5|66.2|59.9|54.1|
|Medium|41.4|49.2|70.1|81.7|84.3|81.6|68.1|
|Large|47.8|80.4|54.1|85.5|84.0|81.0|72.1|

Source: Author’s own elaboration from enterprise surveys _Kenya is by far the country with larger internet intensity use from the six African countries._ Looking at the internet index described above for all African countries, Kenya’s internet intensity is higher than other African countries (see Table 12). At the sector level, manufacturing firms uses more internet than firms in the service sector. Moreover, huge differences emerge while looking at the firms’ size. On average, the internet index is nearly three times higher in large firms than small firms.

_<u>Table 12 Internet Index by Country, Sector and Size</u>_

|Categories|Ghana|DRC|Tanzania|Uganda|Zambia|Kenya|Average|
|---|---|---|---|---|---|---|---|
|**Aggregate**|91.26|62.33|59.78|66.04|105.45|152.08|100.00|
|**Main Sectors**||||||||
|Manufacturing|94.07|70.50|63.69|59.66|138.19|149.85|104.63|
|Service|88.33|59.64|58.32|68.13|96.37|153.07|95.44|
|**Size**||||||||
|Small|66.96|49.92|50.82|60.04|93.79|127.72|69.15|
|Medium|131.40|109.98|88.33|61.97|118.59|179.86|137.33|
|Large|173.20|190.94|98.35|218.55|211.61|204.18|187.76|

Source: Author’s own elaboration from enterprise surveys

# **5.4 Overall ICT intensity**

The overall ICT index is a combination of the internet index and the computer and software index. Table 13 shows the ICT index by country, sector and firm size. Kenya shows the highest index followed by Zambia, Ghana, Uganda and Tanzania. Although the manufacturing sector shows a higher index, the difference is not substantial. On the other hand, larger firms have ICT index values that more than double those of small firms.

19
# **Figure 9. Foreign Equity Holdings**

This figure shows the evolution of foreign equity holdings in China. Panel A shows the outstanding value of foreign equity holdings bought through the QFII and RQFII programs (combined) and the quota limits of each program (abolished in 2020). Panel B shows the outstanding value of foreign equity holdings bought through the Stock Connect program and the quota limit of this program (abolished in 2016). Values are in billions of current U.S. dollars (USD). Source: Wind.

## **A.  Foreign Equity Holdings and Quota Limits of the QFII and RQFII Programs**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>QFII and RQFII Holdings QFII Quota Limit RQFII Quota Limit<br>Billions of USD<br><!-- End of picture text -->

## **B.  Foreign Equity Holdings and Quota Limit of the Stock Connect Program**

<!-- Start of picture text --> 350<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>2014 2015 2016 2017 2018 2019 2020<br>Stock Connect Quota Limit Stock Connect Holdings<br>Billions of USD<br><!-- End of picture text -->
Figure 2.3: Net Interest Income for Retail Business Line (BRL million) Figure 2.4: Net Interest Income for Corporate Business Line (BRL million) <!-- Start of picture text --> Interest Interest<br>Revenues* 151.9 Revenues* 73.1<br>Interest Interest<br>Expenses* 57.2 Expenses* 44.9<br>Interest Interest<br>94.7 28.2<br>Margin Margin<br><!-- End of picture text --> _* Interest revenues include interest received from performing and non-performing loans, remunerated reserve requirements, and inter-business line transfers. Interest expenses include interest paid on various types of liabilities (deposits, acceptances, reverse repos, borrowings used to fund directed lending) where applicable. Interest margin is the difference between interest revenues and expenses (i.e. net interest income). Source: Study calculations based on Bacen data._ 13. **Most of the Retail business line’s net interest income is actually derived from lending rather than deposit-taking, while the situation is more balanced for Corporate** . Using a transfer pricing methodology that is common in financial analysis, one can actually decompose the interest margin by business line into two parts: margin from lending versus margin from deposit-gathering activities. The lending margin consists of the loan spread over-and-above the relevant funding rate, while the borrowing margin is essentially the interest benefit derived from sourcing deposits at a lower cost than the interbank market – see Appendix I for a detailed explanation. According to this decomposition, around 90 percent of Retail’s margin comes from lending activities, while for Corporate the figure is more balanced (Figures 2.5 and 2.6). It is worth noting that, even though the benefit of deposit gathering is quite significant (i.e., the difference between the interbank rate and the average deposit rate is large), it is partly negated by the fact that around 40 percent of total deposits are ‘tied up’ in low-yielding reserve requirements and directed lending (e.g., rural lending and housing finance) obligations. Brazil has unusually high reserve requirements that have proven a good cushion in times of crisis, although they also have an important effect on the system’s income structure.

12
firm size in our equations (Table 6).<sup>22</sup> We chose to use size categories (discrete variables) because our goal is quantifying the differences in export performance of medium and large firms with respect to small firms, given that several services available for Ecuadorian firms frequently depend on their size category.<sup>23</sup>

**Table 6: Ecuadorian firms: classification per size category**

|**Category**|**Number of employees**|**F**|**irms**|
|---|---|---|---|
|||**Number**|**Share (percentage)**|
|Small|1 to 19|145|33.6|
|Medium|20 to 54|146|33.8|
|Large|55to500|141|32.6|
|Total|1 to 500|432|100.0|

Source: ICS-Ecuador.

Out of the 432 firms kept after cleaning up the data, 126 firms (29.2 percent) exported in 2002.<sup>24</sup> In the group of exporters, the share of a firm’s total sales exported (export intensity) in 2002 was identified.

Table 7 lists the independent variables we considered to be the core variables in explaining export propensity and export intensity of Ecuadorian firms, using a sample of 432 firms surveyed by the ICS in 2003. A correlation matrix for the core variables and the variables to be added later on to our basic model is included in the Appendix (Table A2).

**Table 7: List of core variables**

|**Set**|**Variable**|**Unit**|**Expected**<br>**sign **|**Measure of**|
|---|---|---|---|---|
|Firm|_Age_|Years of operation|+|Experience|
|Idiosyncratic|_Medium_|Dummy for employees [20, 55[|+|Efficiency|
|Characteristics|_Large_|Dummyforemployees [55, 500]|+|Efficiency|
|Technology|_R&D_|Dummy for in-house R&D since 2000|+|Technology|
||_Innovation_|Dummy for process innovation since<br>2000|+|Technology|
||_Quality certification_|Dummy for quality certificate|+|Technology<br>absorption|
||_Web-use_<br>|Dummy for web-use|+|Technology and<br>globalization|
||_Imported inputs_<sup>1</sup>|Share of inputs that were acquired<br>throughdirectimportsin 2002|+|Technology<br>absorption|
|Business|_Listed company_|Dummy for publicly listed company|+|Solvency|
|Environment|||||
||_Loan_|Dummy for loan|+|Investment|
||_Foreign ownership_<br>|Dummy for 50 percent or more of<br>firm’s capital is foreign|+|Foreign<br>networks|
||_Capacity utilization_<sup>2</sup>|Capacityutilization in 2002(percent)|+|Business cycle|

Notes: 1Used only as an explanatory variable for export propensity. 2Used only as an explanatory variable for export intensity.

> 22 The firm size “small” was omitted in our equations. Please, refer to section 4.3.

> 23 For example, the _Banco Nacional de Fomento_ (National Development Bank) offers special credit lines to be used in the acquisition of fixed capital for firms with less than 20 employees. 24 Exporter was defined as a firm that exported any positive quantity in 2002.

16
where the indicator functions are constructed as dummies using data for each country on Y,, **G,.,** and **G,.** (An exponential formulation for the costs (i.e. Xexp(Y, - **G,);** Mexp(G,, - G,), does not require the use of indicators (dummies) and is such that the costs are non zero for all observations.) Under these assumptions, the unconstrained Euler equation becomes (for each country, K and G cancel out): _p (2+r) (K1_ **_-_** _Kt_.) +4 (Gt_ - **_Gt+)_** _+ca_ (l+r) **_(Gt_ -** _G- ) +A1 r.t_ **_-_** _Gt.,I_ **_W(Yt+l_** _GC+I) -A1Irtao)( 1+1 +r) ( Y,-G,)_ +lla(Ye a;o _(I+r)_ Y---) **C 18)** _+11 1tar.1-Occal (GC-Gt-l)_ l ,so _( 1 + 1 +Z ) ( Ge-I-Gt )_ +>lo I s-L S _(1+.r)_ **_(Gt-2-Gt-,)_** O~~~~~~2
of public schools in order to obtain the effect of public school supply on the coefficient of intergenerational persistence of educational attainment.<sup>2</sup> However, one fundamental limitation of the empirical strategy is that, unlike in Duflo (2001) or Meghir and Palme (2005), the growth of the local supply of public schools in Jordan was gradual rather than an abrupt change due to a specific reform. It is thus likely to have been affected by both supply-side and demand-side factors, making it challenging to disentangle the causal impact of local school supply. While it is difficult to rule out this limitation, the results are likely causal for three reasons. First, the allocation of public schools in Jordan is highly centralized and was essentially aimed at reducing initial inequality in education across subdistricts. This argument is supported by analyzing the association between the growth in the supply of schooling at the subdistrict level with initial local educational and occupational characteristics. Second, the analysis includes a full set of interactions of governorate-of-birth and year-of-birth fixed effects, which control for time-varying characteristics of governorates that may drive differences between subdistricts in the growth of local school supply and educational attainment. This should arguably account for most of the time-varying spatial heterogeneity in Jordan. Third, since one may be concerned about the selected migration of parents across subdistricts, which is potentially induced by the differential growth of the local supply of schools, a robustness check is added where the analysis is re-estimated excluding movers, i.e., children who changed their subdistrict of residence between age zero and 15 (it is not possible though to identify children whose parents moved before they were born). The results remain unchanged.

> 2. Potential endogeneity of parent’s schooling is not a concern here because the objective of the empirical analysis is not to identify the causal impact of parent’s schooling on child’s schooling but rather the effect of the expansion of public schooling on the correlation between parent’s schooling and child’s schooling.

5
<!-- Start of picture text --> Change in Youth Attending School<br>: se<br>£ScS so ° dykes weed!<br>2 |e, °<br>3<br>C) . 2000 4000 6000<br>Per Capita GDP(USS)<br><!-- End of picture text -->

<!-- Start of picture text --> Change in Youth Working Unpaid<br>gs °<br>; 8 ° e<br>§ |£ecew<br>5 lew Geet e<br>42 3ee e*e @? ° °<br>& .<br>C) 2000 4000 6000<br>Per Capita GDP(USS)<br><!-- End of picture text -->

<!-- Start of picture text --> Change in Wage-Employed Youth<br>|"ES<br>aa= o e Pe 3bd7 e ee<br>3 oon of.<br>& 8 C) 2000 4000 6000<br>Per Capita GDP(USS)<br><!-- End of picture text -->

<!-- Start of picture text --> Change in Youth Working in Agriculture<br>2 8 °<br>Eo2 3 3°. e .<br>5 hd hE ;<br>S $<br>5 8e :<br>C) 2000 4000 6000<br>Per Capita GDP(US$)<br><!-- End of picture text -->
Policy Research Working Paper 7302

# **Abstract**

This paper analyzes smoking prevalence and smoking behaviors in Papua New Guinea. Using the 2009–10 Papua New Guinea Household Income and Expenditure Survey, the paper analyzes the determinants of tobacco use and tobacco choices in Papua New Guinea. The results show that adults (18 years and above) in the poorest quartile are more likely to smoke. Tobacco consumption imposes a large financial burden to poor households.

Tobacco consumption accounts for about 23 percent of total household food expenditure for households in the poorest quartile, compared with 15 percent for the entire sample. However, most of these households consume nonprocessed tobacco. The study reveals the urgency to control tobacco consumption in Papua New Guinea and considers some practical challenges that the country may face.

This paper is a product of the Health Nutrition and Population Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at xhou@worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 4: Global analysis at the pixel level

||Panel A: <br>(1)|ln(SO2) an<br>(2)|d ln(GDP p<br>(3)|er capita)<br>(4)|
|---|---|---|---|---|
|ln(GDP per capita)|0.190<sup>_∗∗∗_</sup>|0.618|-6.126<sup>_∗∗_</sup>|-7.206<sup>_∗∗_</sup>|
||(0.066)|(0.791)|(3.007)|(2.969)|
|ln(GDP per capita)<sup>2</sup>||-0.024|0.735<sup>_∗∗_</sup>|0.823<sup>_∗∗_</sup>|
|||(0.046)|(0.342)|(0.330)|
|ln(GDP per capita)<sup>3</sup>|||-0.028<sup>_∗∗_</sup><br>(0.013)|-0.030<sup>_∗∗_</sup><br>(0.012)|
|Observations|11163331|11163331|11163331|11163331|
|R-square|0.016|0.017|0.020|0.145|
|Y-mean|0.493|0.493|0.493|0.493|
|Y-sd|1.790|1.790|1.790|1.790|
||Pan<br>|el B: ln(SO<br>|2) and ln(G<br>|DP)<br>|
|ln(GDP)|0.164<sup>_∗∗∗_</sup>|-0.731<sup>_∗∗∗_</sup>|0.106|0.380|
||(0.040)|(0.174)|(0.454)|(0.338)|
|ln(GDP)<sup>2</sup>||0.030<sup>_∗∗∗_</sup>|-0.027|-0.043<sup>_∗_</sup>|
|||(0.007)|(0.035)|(0.026)|
|ln(GDP)<sup>3</sup>|||0.001<br>(0.001)|0.001<sup>_∗∗_</sup><br>(0.001)|
|ln(Population)|0.048|0.012|0.015|0.111<sup>_∗∗∗_</sup>|
||(0.042)|(0.036)|(0.037)|(0.029)|
|R-square|0.083|0.108|0.109|0.197|
||Pa|nel C: ln(SO|2) and ln(H|DI)|
|ln(HDI)|2.786<sup>_∗∗∗_</sup>|-1.213|9.022|-102.199|
||(0.753)|(9.863)|(50.113)|(63.811)|
|ln(HDI)<sup>2</sup>||3.987|-16.853|211.375|
|||(9.928)|(109.894)|(129.588)|
|ln(HDI)<sup>3</sup>|||13.788|-138.517|
||||(77.635)|(83.864)|
|R-square|0.020|0.021|0.021|0.147|
|Country FEs||||Y|

_Notes:_ Unit of SO2: nano gram/m<sup>3</sup> , unit of GDP and GDP per capita: constant 2011 international US dollar. Standard errors are clustered at the country level.

24
with developing countries.<sup>43</sup> Moreover, Schiff et al. (2002) show that developing country trade with technologically advanced countries is very important in technology intensive sectors, but trade with developing countries can be important for productivity spillovers in less technologically complex products in which developing countries have comparative advantage. So on low R&D products like footwear and textiles and apparel, trade with China and Indonesia could be as important for technology diffusion as trade with the EU and the US.

In summary, this literature shows that the purchase of intermediate inputs and FDI from industrialized countries is an important mechanism for the transmission of R&D and productivity growth in developing countries. For small developing countries, trading with large technologically advanced countries is crucial for TFP growth. But for products in which developing countries have a comparative advantage, developing country trade may be important for spillovers.

In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of new technologies is the elasticity of varieties with respect to the price. Schiff _et al_ ., (2002, table 1) have shown that for R&D intensive sectors, trade with industrialized countries contributes significantly to total factor productivity in developing countries, but trade with developing countries does not. Averaging over the industries in Schiff _et al_ .,( 2002, table 3) yields that trade with industrialized countries in R&D intensive products is about eight times more valuable for developing country TFP increases. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries.

Based on these considerations, we first classify the increasing returns to scale sectors of our model into low, medium and high technology sectors. The classification is defined by the share of R&D expenditures in total sales, based on U.S. data. For low R&D intensive sectors, we assume that the elasticity of firms with respect to price is the same for the CIS region as for the EU, but the elasticity is only one-third of Rest of the World elasticity (trade with the CIS or EU regions misses out on trade with China or the U.S.). For medium and high R&D intensive sectors, we assume that trade and FDI with the CIS region is only one-eigth as valuable as trade with the Rest of the World (as discussed above), while trade with the EU is two-thirds as valuable as trade with the Rest of the World. Finally, we allow the elasticity of the Rest of the World to vary depending on the R&D intensity of the sector, where we allow for more technology diffusion in more R&D intensive sectors. The results of these assumptions are in table 6b.

> 43 Schiff and Wang do not compare lagged industrialized trade to lagged developing country trade, which may bias the results against the relative benefits of industrialized trade.

114
# **b. Changing specialization**

# **i. Goods trade**

The specialization patterns within goods trade differ across countries (Figure 3). China, Malaysia, the Philippines, Thailand and Vietnam experienced increasing specialization in manufacturing – relative to the rest of the world ‐ over the previous decades. Mongolia and Myanmar continued to focus mostly on mining exports. For Indonesia, the share of manufacturing in total merchandise exports has increased from less than 10 percent in 1980 to more than 40 percent in 2015, yet mining products still dominate the country’s export basket. As indicated in Table A.5, manufacturing as a share of goods trade grew significantly during the 1980s and 1990s and then stayed broadly at the same level in countries like China, Indonesia, Malaysia, Philippines, and Thailand. In contrast, Vietnam experienced an increase of about 30 percentage points, between 2005 and 2015.

Figure 3. Share of manufacturing in goods exports (percent) <!-- Start of picture text --> IDN<br><!-- End of picture text --> <!-- Start of picture text --> MMR<br><!-- End of picture text --> <!-- Start of picture text --> KHM<br><!-- End of picture text --> Sources: WTO Note: ISO3 country codes are indicated in labels. See Table 1 for country names.

Within manufacturing, there is evidence of significant heterogeneity in exporting patterns across countries and over time (Table 1). Some countries have been and still are focused on light manufacturing (Cambodia) while others have started upgrading to more sophisticated manufacturing (Vietnam). Countries that began industrialization earlier are currently trading heavily in electronics and transport industries (Malaysia, Thailand, Philippines). China has a 8
**Table 4.** **<u>5</u>**<sup>**th**</sup> **<u>Grade Students Share by Mathematics Test Scores Quintiles within Stratum</u>**

|**Stratum **|**Quintile 1**|**Quintile 2**|**Quintile 3**|**Quintile 4**|**Quintile **|**5**<br>**Total**|
|---|---|---|---|---|---|---|
|Community School|26.0|23.4|20.2|18.2|12.2|100.0|
|Indigenous School|33.2|26.9|20.1|13.4|6.4|100.0|
|Public rural school|22.5|21.4|20.1|19.1|16.9|100.0|
|Public urban school|15.7|18.5|20.6|23.9|21.3|100.0|
|Private urban school|6.4|10.2|13.6|24.4|45.3|100.0|

Source: Primary Education Assessment Survey, second round 1997.

**Table** **<u>5. 5</u>**<sup>**th**</sup> **<u>Grade Students Share by Spanish Scores Quintile within Stratum</u>**

|**Stratum**|**Quintile 1**|**Quintile 2**|**Quintile 3**|**Quintile 4**|**Quintile 5**|**Total**|
|---|---|---|---|---|---|---|
|Community School|30.7|30.5|17.7|15.2|5.9|100.0|
|Indigenous School|34.8|28.8|16.7|15.7|4.0|100.0|
|Public rural school|22.5|24.9|18.9|20.8|12.9|100.0|
|Public urban school|15.4|20.8|17.5|25.6|20.8|100.0|
|Private urban school|4.9|6.9|6.9|19.9|61.4|100.0|

Source: Primary Education Assessment Survey, second round 1997.

Tables 3, 4a, and 4b in the Appendix show the distribution of the school variables across learning achievement quintiles. An exploratory analysis identifies different factors. Quintile 1 represents ineffective schools, while quintile 5 includes effective schools. For all the school strata considered, the results indicate a strong correlation between school effectiveness and teachers' pedagogical effort, family income and level of schooling. In public rural schools, parents’ expectation of a child’s scholastic achievement is positively and highly correlated with learning achievement. For all public schools, the quality of educational services as perceived by the parents has a positively strong effect on achievement. Other relevant variables in public schools were teaching experience, teachers' residence in the community, teachers' training, number of students in the classroom, enrollment in _Carrera Magisterial,_ parents' participation in the learning process; didactic material available to the teacher and supervision. All other variables show a weak correlation with school effectiveness.

# **5. TEACHERS’ INCENTIVES AT THE SCHOOL LEVEL IN MULTIVARIATE MODELS** .

As discussed above in the overall learning levels, the quality of basic Mexican education is low. According to the EEEP, 52.8 percent of 5<sup>th</sup> grade students are below the Spanish mean of 54.9. Just over half are also below the Mathematics mean of 48.7. Lopez-Acevedo and Salinas (2000b) show that these average 11