rafmacalaba/datause-encoder-data huggingface dataset:
**<mark>Table III.8: Export starters and non-starters three years after the start</mark>**

____________________________________________________________________________________

|Country<br>____________________|Year of<br>start<br>_____________|Labor prod<br>of export-s<br>(percent)<br>____________|uctivity premia<br>tarters<br>[p-value]<br>________________|No. of<br>observ.<br>______________|No. of<br>starters<br>__________________|
|---|---|---|---|---|---|
|Belgium|1999|9.248|[0.654]|231|8|
||2000|1.447|[0.852]|245|10|
||2001|7.415|[0.212]|294|22|
||2002|12.928|[0.098]|274|18|
|Chile|1993|1.758|[0.600]|2,055|51|
||1994<br>|1.910<br>|[0.675]<br>|2,034<br>|50<br>|
||1995|1.763|[0.585]|1,895|23|
||1996|2.660|[0.576]|1,768|37|
|China|2001|-4.607|[0.002]|25,180|475|
||2002|-4.383|[0.001]|25,137|603|
|Colombia|1984|-10.191|[0.020]|2,914|27|
||1985|-0.856|[0.876]|3,062|36|
||1986|-2.238|[0.735]|3,199|31|
||1987|5.466|[0.219]|3,265|45|
||1988|3.106|[0.495]|3,280|48|
|Denmark|1998|NA||3|2|
||1999|NA||2|1|
|Republic of Ireland|1994|-4.619|[0.213]|442|30|
||1995|4.691|[0.087]|447|31|
||1996|-0.963|[0.799]|448|32|
||1997|3.308|[0.345]|421|33|
||1998|6.502|[0.162]|409|25|
||1999|0.977|[0.777]|439|29|
||2000|-2.116|[0.271]|447|32|
||2001|-2.665|[0.461]|428|30|
|Slovenia|1997|1.489|[0.939]|82|8|
||1998|6.168|[0.717]|89|10|
||1999|21.813|[0.176]|95|13|
|Spain|1993|-1.673|[0.631]|320|23|
||1994|2.217|[0.528]|316|27|
||1995|3.081|<br>[0.429]|293|20|
||1996|-4.607|[0.174]|275|20|
|Sweden|2000|-0.001|[0.950]|5,214|119|
||2001|0.016|[0.257]|5,201|107|
|UK|1998|-2.928|[0.594]|403|22|
||1999|-15.397|[0.275]|461|12|
||2000|16.024|[0.045]|489|18|
|____________________|2001<br>_____________|10.327<br>____________|[0.141]<br>________________|441<br>______________|24<br>_____|

Note: Results are for firms from ISIC industries 15 – 36 with at least 10 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of labour productivity (defined as total sales per employee) in a year are excluded from all computations. The labour productivity growth premia are estimated in an OLS-regression of the growth rate of labour productivity (computed as the difference of the log of labour productivity in t+3 and t+1) on a dummy variable for export starters controlling for the log of number of employees and its squared value, log wages and salaries per employee, and dummy variables for 4-digit-industries, all measured at the start year t. To facilitate interpretation the estimated coefficients for the exporter dummy variable have been transformed by 100(exp(ß)-1).

80
Child Development Workers educate and talk to parents or children, 57 percent conduct infant feedings, and nearly 80 percent engage in home visits and growth monitoring. These results show an intensification of the provision of a range of crucial health and ECDrelated services for both young children and parents in program areas.

**Table 1. Functions of Service Providers by Type of Provider and ECD Program Area,** **<u>Philippines ECD Study (Round 3)</u>**

|Provider Type:|Rural<br>Mid|Health<br>wife|Bar<br>Health|angay<br>Worker|Day<br>Wo|Care<br>rker|Child<br>Development<br>Worker<sup>a</sup>|
|---|---|---|---|---|---|---|---|
|Program (P) or non-<br>program (NP) area:<br>_Functions:_|P|NP|P|NP|P|NP|P|
|Examine patients|95.5|81.7*|3.1|6.1|n.a.|n.a.|n.a.|
|Provide diagnosis|30.5|12.0*|0.24|0|n.a.|n.a.|n.a.|
|Dispense medicines|88.7|91.7|15.4|16.5|0|0|2.8|
|Make referrals|57.1|36.7*|14.2|10.8|7.7|0*|11.1|
|Educate or talk to<br>clients|67.2|36.7*|19.5|15.4|54.1|35.0*|48.2|
|Teach parents|n.a.|n.a.|n.a.|n.a.|32.8|2.0*|42.5|
|Monitor new cases|43.5|19.3*|3.9|4.7|n.a.|n.a.|n.a.|
|Home visits and|69.0|52.3*|86.1|78.5*|71.4|37.0*|78.7|
|follow-up||||||||
|Take weight and height<br>measurements|93.8|94.5|93.8|93.2|47.3|4.0*|77.8|
|Nutrition counseling|68.4|35.8*|16.8|11.8|n.a.|n.a.|n.a.|
|Dispense supplements|91.5|86.2|64.2|42.0*|2.9|1.0|5.6|
|Conduct infant feedings|n.a.|n.a.|n.a.|n.a.|44.4|19.0*|56.5|
|N|176|109|416|279|207|100|108|

a. Child Development Workers are found in ECD program areas only (see text). n.a. not applicable to this provider type. * Mean is significantly different between program and non-program children at the .05 level. _Source_ : 2003/2004 Early Childhood Development Provider Survey.

Table 2 shows that by the third survey round, midwives and day care providers in the program areas are substantially more likely than providers in non-program regions to have received training in topics such as parent education, management of child illness, and growth monitoring; two thirds of child development workers received training on educating parents about child health. Although barangay health workers were less likely to receive training than other types of providers, they are more likely to receive health and ECD-related training if they work in a program barangay than in a non-program barangay.

6
household consumption per capita together, using both the cross sections and the balanced panel in Figure 1.3 (Appendix 1). This figure shows similar V-shaped trends over time for both variables, although the income line is somewhat lower than the consumption line in earlier years (up to 2001).<sup>17</sup> We further plot in Figure 1.4 (Appendix 1) other variables including household labor income per adult, household pension per capita, and individuals’ labor earnings; these different definitions of incomes show qualitatively similar trends over time.<sup>18</sup> These results are consistent with estimates for the Gini coefficient using labor income by Calvo _et al._ (2015), who also found it to decrease by 18 percent between 2002 and 2012. Finally, we also re-estimate the mobility regressions using individuals’ labor earnings and plot these results in Figures 1.6 and 1.7 (Appendix 1), which show qualitatively similar trends to Figures 7 and 8. But the magnitudes of the estimated coefficients are unsurprisingly slightly larger; for example, moving from a part-time job to a full-time job was associated with a 12-percent increase in one’s labor income in the 199498 period.

# _Top Incomes_

A typical issue with household survey data, including the RLMS, is that such data may not capture individuals with the top incomes. In that case, the survey data may offer a downward biased estimate of income inequality due to the survey underreporting the higher end of the income distribution (see, for example, Picketty _et al._ (2018) and Novokmet _et al._ (2018)). Yet, researchers

> 17 A recent study by Kim, Gibson, and Chung (2017) suggests that income might be underreported during this period because of the higher share of informal economy. However, we also calculated the Gini coefficient using the per capita consumption variable, which displays a similar downward trend over the period 1994-2015 (Figure 1.1, Appendix 1). This result is consistent with the finding in a recent study which shows that income inequality is similar to consumption inequality for the US (Aguiar and Bils, 2015).

> 18 For example, when we re-plot Figure 1 with household income net of pension, estimation results, shown in Figure 1.5 (Appendix 1), indicate similar trends.

24
<!-- Start of picture text --> (a) Rural EAs (b) Urban and rural EAs with N<br>probability of selection rN<br>Probability of<br>Population selection<br>MM o0-25 Hl 0.00 - 0.04<br>MM 26 - 81 MM 0.05<br>MM 82 - 157 MM 0.15--0.140.25<br>158 - 254 0.26 - 0.39<br>255 - 367 0.40 - 0.54<br>ll 368 - 489 lll 0.55 - 0.70<br>MM 490 - 618 ll 0.71 - 0.87<br>MM 619 - 750 01 440iKi Ml 0.88 - 1.00 0 440 Kim<br>Ax ft SAPS i<br>2ws,> of 3 MiataBt enemearWh&oy“7te 1SA<br>war" » a y<br>BN, = > \@ $ lesen es<br>G sa¥ { “es ap XGKe Se] esone, 2 oea: ‘<br>* os s ig ie ha oy oy. e<br>x Phe<br>~~ » > 4<br>0 3Km 0 3 Km 0 3 Km 0 3Km<br>EE —— | —— | us<br><!-- End of picture text -->
**Figure 1: Interest rates in the United States**

A. Government bond yields B. Forecast error variance decomposition of 2- year bond yields, 1982-2022 <!-- Start of picture text --> Yield Percent<br>14 100 Reaction shocks<br>10-year bond 90 Real shocks<br>12 2-year bond 80 Inflation shocks<br>10 70<br>60<br>8<br>50<br>6 40<br>30<br>4<br>20<br>2 10<br>0<br>0<br>1-month 10-months 20-months<br>ahead ahead ahead<br>Sources: Federal Reserve Bank of St Louis; World Bank.<br>A. Last observation is September 2022.<br>B. Based on a sign-restricted Bayesian VAR model with stochastic volatility. The real shock is identified as one that raises<br>both 2-year and 10-year interest rates, inflation expectations and equity prices. The inflation shock raises interest rates and<br>inflation expectations but lowers equity prices. The reaction shock raises interest rates but lowers inflation expectations and<br>equity prices. See section 2.1 for more details.<br>1984 1986 1989 1991 1994 1996 1999 2002 2004 2007 2009 2012 2015 2017 2020<br><!-- End of picture text --> 22
**Table 13: Composition analysis – Kremer, Brannen, & Glennerster’s (2013) sample of studies on pedagogical interventions to match teaching to students’ learning**

||**Conn 2014**|**Glewwe et al. 2014**|**Krishnaratne, White,**<br>**& Carpenter 2013**|**McEwan 2014**|**Murnane & Ganimian**<br>**2014**|
|---|---|---|---|---|---|
|Banerjee et al. (2007) –<br>India|-|Computers &<br>electronic games|Materials|Instructional materials/<br>Teacher training/Class<br>size or<br>composition/Contract or<br>volunteer teachers|Computer-assisted<br>learning|
|Kremer, Duflo, & Dupas<br>(2011) - Kenya|Class size & composition|-|-|Class size or composition|Class size|
|Percentage of studies<br>included in review|50%|50%|50%|100%|50%|
|Percentage of RCT<br>studies included in<br>review|50%|50%|50%|100%|50%|

34
``` .di"aggregateForeignValue-Added"%14.2fsum_fva[1,1] aggregateForeignValue-Added380473.47 ``` ``` .
.quiicio,exporter(chn,all)output(gvc) ``` ``` .mata:st_matrix("sum_gvc",colsum(st_matrix("r(gvc)"))) ``` ``` .di%11.0g"aggregateGVC-relatedtrade"%14.2fsum_gvc[1,1] aggregateGVC-relatedtrade781287.59 ``` We now select a different trade flow, moving to bilateral sectoral exports. In particular, we consider the Chinese exports of computer, electronic and optical products to the United States. The default assessment of the extent of GVC participation, as well as a breakdown of the flow in terms of value-added components consistent both with the aggregate Chinese exports and with total Chinese exports to the United States, is obtained using the _default_ “exporting country” perspective as: ``` .icio,exporter(chn,17)importer(usa) ``` ``` Decompositionofgrossexports: Perspective:exporter Exporter:CHN Importer:USA Sectorofexport:17 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`Gross exports (GEXP)`|`107292.76`|_`100.00`_|
|`Domestic content (DC)`|`79824.00`|_`74.40`_|
|`Domestic Value-Added (DVA)`|`77386.31`|_`72.13`_|
|`VAX -> DVA absorbed abroad`|`76957.76`|_`71.73`_|
|`DAVAX`|`72570.84`|_`67.64`_|
|`Reflection`|`428.55`|_`0.40`_|
|`Domestic double counting`|`2437.68`|_`2.27`_|
|`Foreign content (FC)`|`27468.76`|_`25.60`_|
|`Foreign Value-Added (FVA)`|`26827.72`|_`25.00`_|
|`Foreign double counting`|`641.04`|_`0.60`_|
|`GVC-related trade (GVC)`|`34721.92`|_`32.36`_|
|`GVC-backward (GVCB)`|`29906.44`|_`27.87`_|
|`GVC-forward (GVCF)`|`4815.48`|_`4.49`_|

``` DAVAX:Value-Addeddirectlyabsorbedbytheimporter ``` Again, we can select a perspective in line with the level of aggregation of the chosen trade flow, i.e. a sectoral-bilateral perspective - `perspective(sectbil)` , for instance to measure to what extent the Chinese GDP could be exposed to a tariff imposed by the United States on the imports of computer, electronic and optical products: ``` .icio,exporter(chn,17)importer(usa)perspective(sectbil) ``` 21
The poorest regions experienced a higher population growth rate than the average of Argentina as a whole during 1991–2001. The Northwest and Northeast regions reached a population growth rate of 21 and 19 percent, respectively. This compares to the Cuyo region where the population only expanded by 15 percent and the city of Buenos Aires which lost 6 percent of its population during 1991–2001.

The rural population, defined for census purposes as people living in communities with populations under 2,000 or in the open countryside, represented 11 percent of total population in 2001, down from 13 percent in 1991 and 28 percent in 1960. Hence, currently rural Argentina is home to around 3.9 million rural dwellers, although the population was reduced by 8.4 percent during 1991–2001 (Figure 2.1 and Table 2.1). Moreover, demographic developments in rural areas have not been homogeneous in the last decade. The rural Northeast region experienced a net out-migration (12.1 percent) while the rural Northwest experienced population growth and some in-migration (1.4 percent). Some provinces, such as Mendoza, Catamarca, and Tierra del Fuego, experienced positive rural population growth rates of 4.5, 8.9, and 43.7 percent, respectively. This compares to Chaco and Santa Cruz which experienced negative rural population growth rates of 24.3 and 44.8 percent, respectively.

<!-- Start of picture text --> Figure 2.1: Trend in Rural and Urban Population Share in Argentina<br>Selected years during 1960–2001<br>Urban population<br>Rural population<br>Share rural population of total<br>35 30<br>30 25<br>25<br>20<br>20<br>15<br>15<br>10<br>10<br>5 5<br>0 0<br>1960 1970 1980 1990 2001<br>Source: INDEC.<br>Population (millions)<br>Rural population share (%)<br><!-- End of picture text --> 6
that many of the stories of large disruptions in rural labor markets were not unfounded. It is perhaps because of this, and the potential social unrest that might have occurred in its wake, that the government launched such a robust stimulus package in such a rapid fashion.

Although the initial fall in rural employment was striking, it is even more remarkable that the decline in rural off-farm employment was arrested quite quickly. It is true that the gap was still large in the first four months of 2009 (described above), but it was already beginning to narrow in percentage term. By April 2009 the gap between the BAU estimated share and the actual off-farm employment share was 6.8 percentage points, which means it was affecting only 11.7 percent of those that would have been employed under the BAU scenario. In the same way that China’s second quarter GDP figures showed that the decline in growth had stopped falling and that growth was picking up again, rural off-farm employment also was showing the initial signs of recovery. In other words, China’s economy was already showing signs of recovery as early as the first and second quarters of 2009, less than six months into the global financial crisis.

# **(b) Financial Crisis and Wages**

The effect of the financial crisis extended beyond those who lost their off-farm jobs (both re-hirees and long-term laid-offs) and those that were unable to find one (delayed entrants). Many of those that did not lose their job also found that their earnings fell. After rising in real terms between 2000 and 2008 (Park et al., forthcoming), monthly 13
Bolivian data, but they at are not statistically significant. In the Mexican data, female-owned firms at the 75<sup>th</sup> percentile of sales are 7.3 percent less productive than male-owned firms. The regressions for the Peruvian data are the only ones that show a large difference in productivity for firms at the 75<sup>th</sup> percentile of sales. However, the results for this dataset are likely to be biased due to the lack of information on machinery values.

**<u>Table 3b: Quantile Productivity Regressions</u>**

||Mexico|Bolivia|Peru|Enterprise<br>Surveys|
|---|---|---|---|---|
|25<sup>th</sup>percentile|-0.131***|-0.203|-0.119*|-0.064***|
||(0.046)|(0.197)|(0.065)|(0.023)|
|50<sup>th</sup>percentile|-0.087**|-0.169|-0.105|-0.018|
||(0.035)|(0.212)|(0.064)|(0.032)|
|75<sup>th</sup>percentile|-0.073*|0.185|-0.204***|0.020|
||(0.044)|(0.151)|(0.071)|(0.043)|
|Numberof firms|8,293|265|582|2,175|

_Note_ : The table displays the coefficients on the female dummy in separate productivity regressions that are based on a Cobb-Douglas production function of the form Y = (AL)<sup>1-α-β</sup> H<sup>α</sup> K<sup>β</sup> , where A stands for technology, L stands for labor, H for human capital, and K for physical capital. The outcome variable is ln sales. Control variables and the respective notes are the same as for Table 3a. Robust standard errors in parentheses. Significance levels: *** 1 percent, ** 5 percent, * 10 percent.

_Source_ : Encuesta Nacional de Micronegocios (ENAMIN), 2002, Bolivian World Bank Micro Enterprises Survey, 2007, Peruvian World Bank Micro Enterprises Survey, 2008, World Bank Enterprise Surveys for Brazil, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua, 2003.

Taken together, the results in Table 3a and Table 3b suggest that only micro and small femaleowned firms are less productive than male-owned firms. This is true for the firms in the Mexican data, which are predominantly micro firms and for the firms at the 25<sup>th</sup> percentile of sales in the Bolivian data and the Enterprise Surveys, as well as for firms at the 50<sup>th</sup> percentile of sales in the Bolivian data. The larger female-owned firms (in terms of sales) in the Bolivian data and in the Enterprise Surveys are equally productive as male-owned firms.

# **4. Possible Reasons for Differences across Female- and Male-Owned Firms**

This section examines a number of factors that could be causing the differences in firm size and other characteristics across female- and male-owned firms. It first examines difference in external financing and then considers other obstacles to firm operation and growth.

# **4.a. Differences in External Financing**

A possible reason why female-owned firms are smaller in terms of employees, property, machinery, and sales than male-owned firms could be that female-owned firms have less access 13
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32
interest expenditure. In addition, business climates and policy frameworks improved in most of the 2001 LICs.

Rapid economic growth in LICs has contributed to poverty reduction. The share of extreme poor in the population of 2001 LICs has fallen by 16 percentage points, on average, and this has contributed 20 percentage points—about one-third—to the decline in the global poverty headcount between 2001 and 2015. This contribution, however, mostly reflects sharp declines in the poverty headcount of the 2001 LICs that reached middle-income levels, while masking broadly unchanged poverty headcounts among the countries that have remained, or became, LICs (Figure 2). Today’s LICs account for less than one-tenth of the global population (one-quarter their share in 2001). However, they are home to around 40 percent of the world’s extreme poor. This share is expected to remain elevated amid continued fragility in many of these economies, while the poverty headcount is expected to continue to decline elsewhere.

# **Figure 2. LIC poverty**

## **A. LIC poverty rates and headcounts**

<!-- Start of picture text --> Millions Percent<br>Poverty headcounts<br>800 80<br>Poverty rate (RHS)<br>600 60<br>400 40<br>200 20<br>0 0<br>2001 Latest 2001 Latest<br>LICs turned MICs Continued LICs<br><!-- End of picture text -->

## **B. World poverty and LIC share of world population**

<!-- Start of picture text --> Percent of world population<br>45<br>Other countries<br>2001 LICs turned MICs<br>30<br>Today's LICs<br>LIC population<br>15<br>0<br>2001 Latest<br><!-- End of picture text --> Source: United Nations, World Bank.

A. Latest reflects 2015 data. Due to data limitations, poverty share for “LICs turned MICs” includes 28 of 35 countries and reflects 94 percent of the sample’s total population number in 2015; “Continued LICs” includes 24 of 29 countries and reflects 84 percent of the sample’s total population number.

B. Latest reflects 2015 data. Due to data limitations, poverty share for “LICs turned MICs” includes 28 of 35 countries and reflects 94 percent of the sample population in 2015; “Today’s LICs” includes 31 of 31 countries.

Today’s LICs face severe challenges that threaten to hold back convergence of their per capita incomes with those in MICs. Today’s LICs also face significantly larger spending needs than nonLIC EMDEs to meet the Sustainable Development Goals by the end of next decade, underscoring the need for sustained and robust growth (Gaspar et al. 2019).

Against this backdrop, this paper examines three specific questions. First, what has driven and supported growth in LICs since 2001? Second, how have these factors affected LIC progression to MIC status? Third, what are the prospects for future progression among today’s LICs? The following findings are presented. First, growth in low-income countries—and, especially those that have progressed to middle-income status—has benefited from a confluence of favorable developments since 2001. Second, prospects for further progress by today’s LICs toward middleincome status are challenging. Compared to the LICs of 2001 that became MICs, today’s LICs 3