rafmacalaba/datause-encoder-data huggingface dataset:
<u>Table 4 Average working hours and median earnings of teachers and other wage workers</u>

||<br>Monthl<br>earnings|<br>y median<br>in PPP($)|<br>Monthly m<br>relative to|<br>edian earnings<br>GDPper capita|<br>Hourly<br>earnings|<br>median<br>in PPP($)|<br>Hours wor|ked weekly|Proportio<br>seco|n of having a<br>ndjob|
|---|---|---|---|---|---|---|---|---|---|---|
|||Other||Other||Other||Other||Other|
||Teachers|<br>workers|Teachers|<br>workers|Teachers|<br>workers|Teachers|<br>workers|Teachers|<br>workers|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Burkina Faso|650|232|4.8|1.7|4.0|1.3|39.2|52.8|22.3%|25.9%|
|Côte d'Ivoire|871|339|3.5|1.4|6.6|2.1|31.1|47.0|15.9%|13.2%|
|DR Congo|100|114|1.6|1.8|0.7|0.7|34.3|45.4|46.9%|22.9%|
|The Gambia|265|235|2.0|1.8|N.|A.|N.|A.|N|.A.|
|Ghana|656|389|2.0|1.2|4.4|2.1|34.3|50.7|26.9%|14.9%|
|Liberia|220|278|3.1|3.9|1.7|1.5|32.7|51.7|5.8%|3.8%|
|Malawi|337|257|4.0|3.0|2.6|1.8|33.2|42.0|3.3%|1.2%|
|Namibia|2,306|633|2.9|0.8|14.7|3.4|39.3|50.5|6.5%|3.3%|
|Niger|401|533|5.1|6.8|2.8|3.0|34.3|45.4|15.4%|8.9%|
|Nigeria|460|460|0.9|0.9|2.9|2.8|38.8|46.3|2.1%|1.1%|
|Senegal|819|647|4.6|3.6|5.6|3.2|35.1|48.4|14.8%|8.7%|
|Sierra Leone|334|353|2.3|2.4|2.8|1.8|31.3|50.4|21.2%|8.1%|
|Tanzania|805|773|3.8|3.7|5.0|3.9|42.4|53.2|22.5%|7.6%|
|Uganda|321|296|2.3|2.1|2.1|1.5|39.5|50.0|47.1%|20.9%|
|Zambia|1,639|465|5.1|1.5|10.7|2.3|39.2|52.1|10.1%|5.2%|
|**Average **|**679**|**400**|**3.2**|**2.4**|**4.4**|**2.1**|**36.1**|**49.0**|**18.6%**|**10.4%**|

Notes: All the earnings are in PPP ($,2011). Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. Other wage workers include respondents who self-identified as a wage worker in other occupations; university faculty, special education teachers and other teachers were excluded from the group of other wage workers. Both the teacher sample and other wage worker sample only include workers with secondary or post-secondary education and those who reported both earnings and hours worked weekly. The top and bottom 1% earnings are trimmed. The survey in The Gambia did not report weekly working hours. Source: Surveys listed in Table 1.

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formal transaction was about $100 in 1995 and 1996, while the median informal transaction was about $50 in the same years.<sup>10</sup> The Banking Law of 1999, dollarization in 2001, and sharp decreases in transfer costs, have surely led to a further decrease in informal flows. Most likely reflecting this shift, remittance growth in El Salvador has been explosive in recent years, with recorded remittances surging by 53 percent (in current USD) from 1999 to 2003.

# _Philippines_

According to national statistics, the Philippines is the third largest developing country recipient of remittances (and sixteenth largest recipient of remittances/GDP), receiving approximately $6.4 billion per year. Stratified surveys are available for 19922000; they are administered to households reporting having any members overseas in the last five years, from a nationally representative sample of roughly 40,000 households.

About 17-19 percent of households report receiving remittances from overseas. The survey indicates that about 6 percent of remittances went through informal channels (friends and co-workers) in 1992, and that the share fell to 1 percent in 2000 (Table 8). The change was due to a shift away from sending through friends and co-workers and towards using banks. Door-to-door (a common name for couriers/offices which personally deliver packages including cash to the doorstep of the household) and agencies decreased their share over the period. Remittances carried by migrants may be official or unofficial, as by far the largest share of remittances in the BOP in the Philippines comes through migrant transfers. Some are transferred using bank services, as opposed to cash

> 10 In 1995 and 1996 amounts were listed in Colones, converted using market exchange rate.  In 1997, amounts were given in U.S. dollars.  Transaction size in 1997 were larger on average in dollars (about $150), but formal transactions were still about 20 percent larger on average than informal.

26
Each bar is the mean value of employment shares from the overlap sample of 80 villages which were surveyed in all three survey rounds.

In the pre-tsunami period, fishing was the dominant industry in the coastal villages in Aceh.<sup>iv</sup> The impact of the tsunami on fishing community was substantial: the underemployment rate spiked up to 46% from the pre-tsunami level of 12%, and the fishing reefs and mangroves were devastated. During the first six months after the tsunami, the majority of households worked for cash-for-work programs or made a life by receiving living cost allowances from local governments, which explains the largest share of workers in the “others” job category in 2005 (29%) in Figure 1. Fishing sector temporarily recovered in 2007, but by 2009 the share of fishing industry had declined again.

On the other hand, the non-fishing industry continued to develop due to an increased labor demand in the construction sector (called “spending effect” by Corden (1984)) for rebuilding private houses and public buildings (e.g. hospitals, schools, and mosques). While aid activities were heavily clustered in the north-west part of mainland Aceh, there were only a few agencies which provided assistance in remote areas (further down the south part in Aceh Jaya) and in the isolated island in Pulo Aceh. Except for construction business, n <mark>on-fishing sector in Aceh was mainly trading (e.g., fish-related retail business and transportation) which relies on fishing activity growth. This is household production on a very small scale, producing simple goods, unlike small modern factories which produce exportable goods in India (Foster and Rosenzweig, 2004). As most aid efforts came close to the end in 2009, public investments wind down, increasing underemployment rate.</mark> Table 2 comes from the household survey and shows a big shift of fishing status from being a captain to crew from 2007 and 2009. This shift indicates limited fishing opportunities for crew in 2007. The fall in the number of boat owners and captains in 2009 also implies that some household-specific shocks occurred in later years. The distribution of workers also became somewhat diversified: many former fishermen, especially those with higher education and with trauma (psychological and physical reactions) from the tsunami, started working in non-fishing jobs.

4
restricted tariffs tend to be reduced significantly faster, though this effect is weaker in customs unions. One possible explanation is that the MFN tariffs in those sectors are on average very high (36%), and high tariffs may be easier to reduce. Furthermore, some applied tariffs are actually higher than their ceilings, indicating that some countries may have lowered tariffs overtime to comply with their WTO commitments.<sup>24</sup> While magnitudes are smaller, lagged changes in preferential tariffs again induce reductions in external tariffs in FTAs, but not in CUs. In column (2) we include the import share variables. As in Table IV, we find that more important products tend to be liberalized faster in FTAs. The other results remain qualitatively similar.

Column (3) includes an interaction between _bind_ and the change in the preferential tariff, with and without the CU dummy, both lagged one period. Interestingly, we find that constrained MFN tariffs fall by more when FTA tariffs come down. This indicates that GATT/WTO bindings tend to be more effective in bringing applied tariffs down when a country offers preferences in the context of FTAs, suggesting a distinct way in which FTAs can help the multilateral system. Intuitively, it is possible that tariff complementarity forces predominate in some sectors while tariff substitutability forces prevail in others. However, when the tariff overhang is small, tariff substitutability forces are neutralized, as the MFN tariff cannot be raised. As a result, the ‘net’ tariff complementarity effect becomes stronger in bound sectors. This effect is reversed in CUs, however. In column (4) we repeat the exercise using a stricter cutoff in which the bind variable is unity only when there is no tariff overhang—i.e., MFN tariffs are at or above their bindings. The results are roughly unchanged. In column (5) we use a sample that excludes the restricted sectors; basic results change very little. Column (6) reports results

> 24 In fact, applied tariffs above ceilings are observed mainly in the pre-WTO (i.e. before 1995) period. After 1995, it happens only in 1996 in Brazil (motor vehicles) and in three sectors in Mexico until the end of our sample (slaughtering, preparing and preserving meat; manufactures of dairy products; malt liquors and malt). Incidence of applied tariffs above WTO ceiling has been observed also by Bchir and Laborde (2005), the only empirical study of tariff overhangs that we aware of.

31
explained by the advent of the financial crisis and food price hikes in 2008 whose effects may have started to appear after 2009. The decline of food consumption levels while non‐food consumption remained stable lends support to this presumption.

# ii)

ii) There is a decline in overall consumption between NPS 2010/11 and NPS 2012/13 (using the survey inter‐year deflators) for the three poorest quintiles of the population, with the decline being more substantial for the poorest quintile. This decline seems to be driven by a reduction of food consumption accompanied by an even greater reduction of non‐food consumption. However, this decline vanishes when the CPI for inter‐year price adjustment is used, as we observe an increase in consumption levels of all population groups including for the poorest quintiles even though the improvements remain more substantial for the better‐off groups. iii) In contrast with NPS, the HBS data show a significant increase of the food and total consumption levels of the poorest segments of the population and a slight reduction of the food consumption levels for the richest group.

iv) The comparison (between HBS and NPS) of the distribution of consumption across the different quintiles, adjusted by the CPI and taking HBS 2011/12 as base year, shows that total consumption and food consumption are significantly higher in NPS 2008/09 than in HBS 2007 for all population groups, particularly the poorest ones for whom aggregate and food consumption levels are respectively 35 percent and 70 percent higher in NPS 2008/09 than in HBS 2007. Non‐food consumption is however around 30 percent higher in the HBS. This could be explained by higher underreporting and fatigue due the 28‐day diary in the HBS, and thus underestimation of consumption in the 2007 round. This can be supported by the pattern of respondent fatigue in Figures 7 and 8. The problem seems to be mainly due to underreporting by the poor, as average household sizes and number of dependents in HBS 2007 are slightly lower than in the other HBS and NPS rounds.<sup>9</sup> The data quality and underreporting problem seems to have improved with the higher supervision in HBS 2011/12. Table 5 shows close mean household consumption levels for HBS 2011/12 and NPS 2010/11 (when adjusted by the CPI and taking HBS 2011/12 as base year), but the mean consumption level in NPS 2012/13 is over 10 percent higher. The difference is driven by higher levels of aggregate and food consumption levels by the upper quintiles. While the consumption levels of the poorest groups are slightly higher in HBS 2011/12 than in the last two rounds of NPS, the aggregate and food consumption levels of the richest groups are

> 9 This is based on the assumption that poor households have larger family size and higher number of dependents.

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# <u>Table 6. Schooling, skills and labor force participation</u>

||||**Men**<br>|||<br>|**Women**<br>||
|---|---|---|---|---|---|---|---|---|
||(1)<br>Working|(2)<br>Working|(3)<br>Working|(4)<br>Working|(5)<br>Working|(6)<br>Working|(7)<br>Working|(8)<br>Working|
|Years of schooling|-0.022***|-0.024***|-0.017***|-0.0076**|0.012***|0.012***|0.0097***|0.0099***|
||(0.0018)|(0.0020)|(0.0031)|(0.0032)|(0.0012)|(0.0013)|(0.0021)|(0.0022)|
|Cognitive skills|||-0.046***|-0.016|||0.016|0.013|
||||(0.013)|(0.014)|||(0.011)|(0.012)|
|SEM skills|||0.054***|0.056***|||0.0016|0.00068|
||||(0.0097)|(0.0098)|||(0.0062)|(0.0059)|
|Constant|0.17***|0.20***|0.18***|1.05***|-0.058***|-0.062***|-0.048***|-0.070***|
||(0.024)|(0.028)|(0.033)|(0.032)|(0.0062)|(0.0067)|(0.012)|(0.022)|
|Observations|2595|2217|2217|1978|2360|2174|2174|1925|
|Adjusted R-squared|0.14|0.15|0.16|0.060|0.060|0.060|0.060|0.060|
|Mean dependent|0.79|0.76|0.76|0.83|0.06|0.07|0.07|0.06|
|Sample|All|Skills measures|Skills measures|Skills measures,<br>no students|All|Skills measures|Skills measures|Skills measures,<br>no students|
|District FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

* p<.10  ** p<.05  *** p<.01

_Notes_ . This table reports estimates of the relationship between schooling, skills, and labor force participation for men and women in Pakistan. All the columns report mean regressions estimates. “Working” is an indicator variable equal to 1 if the respondent is currently employed. The cognitive skills index is the mean of the Urdu, Mathematics, and English scores computed using Item Response Theory with a two-parameter logistic (2PL) model. The socioemotional (SEM) skills index is computed using principal-component factor analysis. Column (1) includes all men in the sample. Among them, one has missing information for years of schooling. Column (5) includes all women in the sample. For columns (2), (3), (6), and (7), the sample includes men/women who answered the direct version of the questionnaire. Columns (4) and (8) exclude any currently enrolled individuals. All regressions include age and district fixed effects. Standard errors are in parentheses (clustered at the village level).

47
more ordinary. This variable controls for the thickness of food markets (and thus accounts for food risk). In fact, in Uganda, food markets are fairly common whenever there are paved or tarred roads. Food market density is a good aggregate indicator of district infrastructure and development and could capture additional unobserved district characteristics.

The main results are shown in Table 4. In the first panel, we report estimates from linear IV regressions. The first column corresponds to a simple linear model that only includes households characteristics **x** _hc_ as controls. We find that market density, _Mc_ , is negatively � and significantly associated with poverty ( _α_ 1 = _−_ 0 _._ 56).<sup>16</sup> In column (2) of Table 4, we add district characteristics and infrastructure variables, **z** _c_ , and the export crop price index, _prc_ (Table A1 in Appendix B reports these estimates). The negative association between export market density and poverty is robust to the inclusion of these variables. Notice, however, that the addition of district variables has a sizeable impact on the coefficient of market density, which drops to _−_ 0 _._ 27. Export prices do not seem to have a significant impact on poverty (conditional on all the other controls).<sup>17</sup> In column (3), we include food market density as an additional district variable to control for further unobserved district effects. We find that poverty is negatively associated with the presence of food markets, although the relationship is not as strong as expected. For our purposes, however, the critical finding is that the negative association between export market density and poverty still shows up strongly in the regressions; further, the magnitude of the coefficient, _−_ 0 _._ 28, does not change much with respect to Model 2 (which suggests that there are no additional unobserved district effects in the model). Our findings support the hypothesis that households residing in districts endowed with more agriculture export markets are, on average, less likely to be poor: increasing the density of export markets by 10 percentage points (from an average market density of 0 _._ 37—see Table 3) would cause poverty to decline by 2.8 percentage points.

The validity of these results depends to a larger extent on the quality of the instrument. We can assess how good our instrument is by looking at the first stage regression, whereby we regress the endogenous variable, market density, on the out-of-district transportation

> 16Since _Mc_ varies at the district level, the estimation of the variance is corrected for clustering effects.

> 17See the discussion at the end of the section.

17
Production 1976-1998” (Nicita and Olarreaga, 2001). For each industry and year, the shares are measured as the ratio of industry-specific imports over value added. Trade data were collected at the 4-digit level and input-output data at the 3-digit level for the period 1981-98 (information on earlier years is available for Korea but not for the other countries), and both were aggregated to 2-digit and 3-digit levels for consistency with the R&D data (16 industries).

Table 1 shows that each developing country imports more from its Northern neighbor than from more distant OECD regions. On average over the period 1981-1998, out of its total imports from the three OECD regions, Mexico imported 82% from North America, 13% from the EU and 5% from Japan; Korea imported about 43% from Japan, 36% from North America and 21% from Europe; and Jordan imported 50% from the EU, 31% from North America and 19% from Japan.

Thus, both in terms of trade volume and in terms of distance, Korea (Mexico) (Jordan) is a “natural trading partner” of Japan (North America) (the EU). As for traderelated R&D stocks, each developing country gets access to more of the technology from its Northern neighbor than from more distant OECD regions.

# 4. <u>Empirical Results</u>

We now proceed with the estimation of the impact of trade-related technology diffusion from each OECD group on the TFP of each of the three developing countries and examine whether these impacts vary by country and by OECD group. Estimation is carried out in first differences and with the White heteroskedasticity-consistent estimator.

Equipment; (14) 384 Transportation Equipment; (15) 385 Professional Goods; and (16) 39 Other 6
Table 2: R&D/GDP, Decade Average

||**1963-1969**|**1970-1979**|**1980-1989**|**1990-1999**|**2000-2004**|
|---|---|---|---|---|---|
|**Japan**|1.95%|2.45%|3.31%|3.72%|3.31%|
|**USA**|2.79%|2.25%|2.64%|2.58%|2.70%|
|**Israel**|1.03%|1.78%|3.08%|3.04%|4.71%|
|**Singapore**|0.08%|0.28%|0.79%|1.84%|2.24%|
|**Australia**|–|1.17%|1.42%|1.92%|1.83%|
|**Republic of Korea**|0.45%|0.64%|1.74%|3.07%|3.54%|
|**New Zealand**|–|1.14%|1.26%|1.38%|1.15%|
|**China**|–|–|0.71%|0.67%|1.22%|
|**Scandinavia**|1.43%|1.67%|2.43%|3.44%|3.16%|
|**OECD**|2.30%|2.19%|2.71%|2.74%|2.58%|
|EAP|0.44%|0.49%|1.01%|1.41%|1.27%|
|**ECA**|1.73%|1.44%|1.79%|1.16%|1.03%|
|**MENA**|0.04%|1.05%|0.40%|1.18%|0.90%|
|**SHA**|0.24%|0.27%|0.78%|0.82%|0.63%|
|SA|0.28%|0.42%|0.82%|0.69%|0.17%|
|**LAC**|**0.47%**|**0.59%**|**0.35%**|**0.59%**|**0.65%**|

_Source:_ Author’s calculations based on data from Lederman y Saenz (2005) and UNESCO **Note:** LAC: Latin America and the Caribbean; Scand.: Scandinavia, EAP: East Asia and the Pacific; ECA: Europe and Central Asia ; MENA: Middle East and Noth Africa (excluding Israel); SA: South Asia; SHA: Sub-Saharian Africa . All previous groups exclude high income countries..

Table 3: Per Capita R&D Expenditure (2000 US$), Decade Average

||**1963-1969**|**1970-1979**|**1980-1989**|**1990-1999**|**2000-2004**|
|---|---|---|---|---|---|
|**Japan**|255.0|500.3|896.5|1319.7|1239.5|
|**USA**|467.8|458.1|655.3|778.6|949.0|
|**Israel**|71.6|191.1|401.9|493.0|836.1|
|**Singapore**|2.1|21.7|84.2|333.0|508.7|
|**Australia**|–|150.5|213.5|333.7|378.4|
|**Republic of Korea**|6.4|16.6|76.2|264.7|385.6|
|**New Zealand**|–|122.0|145.9|165.4|164.3|
|**China**|–|–|2.7|4.2|13.7|
|**Scand.**|194.7|276.7|527.4|835.0|941.0|
|**OECD**|220.0|288.8|426.1|445.6|533.5|
|**EAP**|13.0|14.1|38.8|94.8|49.3|
|**ECA**|32.6|35.5|62.7|31.1|40.7|
|**MENA s/ISR**|0.3|40.7|23.5|65.0|66.2|
|**SHA**|0.7|1.1|10.0|5.9|14.2|
|**SA**|0.6|1.1|2.8|2.9|1.0|
|**L.A.C.**|**15.3**|**18.9**|**11.6**|**22.4**|**22.2**|

_Source: Author’s calculations based on_ Lederman y Saenz (2005) and UNESCO **Note:** LAC: Latin America and the Caribbean; Scand.: Scandinavia, EAP: East Asia and the Pacific; ECA: Europe and Central Asia ; MENA: Middle East and Noth Africa (excluding Israel); SA: South Asia; SHA: Sub-Saharian Africa . All previous groups exclude high income countries..

25
As shown in Table 9, the _average relative budgetary gains_<sup>_N_</sup> _GI_<sup>_<u>CIB</u>_</sup> at the commune level is more than two times larger than that at the provincial level. When compared with ecozone-level or stratum-level targeting, the improvement is even more dramatic.<sup>19</sup> Second, on average, there remains large room for improvement even if targeting is carried out at the level of very small geographic areas such as the village level. Hence, it is very important for policy-makers to combine geographic targeting with other forms of targeting whenever feasible. Third, the equivalence gains vary substantially depending on the proportion of targeted people. This point can be seen more clearly in Figure 11(a), which shows the equivalence gains for different levels of geographic targeting. We see that the equivalence gain first goes up and then goes down. By definition, equivalence gains take zero when the proportion of targeted population is zero or one. The equivalence gains for underweight is given in 11(b).

Fourth, as with the equivalence gains, budgetary gains change with the goal of FGT reduction. Relative budgetary gains are suitable for evaluating how well particular targeting scheme is for a given goal of FGT reduction. Figure 12(a) shows the relative budgetary gains for stunting. Clearly, commune-level targeting is more efficient if the goal of FGT reduction is lower, capturing most of the potential gains from targeting. When the goal is low, commune-level targeting can be as high as five times more efficient than stratum-level targeting. This is an important point, because realistic values of FGT reduction in the short run will not be extremely high. For example, a Cambodia Millennium Development Goal is to reduce the prevalence of stunted children by 35 percent by 2010 and 50 percent by 2015 (MoP, 2003). Hence, the _average_ relative budgetary gains somewhat understate the efficiency gains of commune-level targeting in the short run. The relative budgetary gains

> 19To make the graphs readable, we omit stratum-level targeting in the graphs. Stratum-level targeting is close to provincial-level targeting but slightly less efficient because it is done at a more aggregated level. We prefer to present provincial-level estimates because they are practically more important. Also, while we report the results for village-level targeting, we take the commune-level targeting as a benchmark for disaggregated geographic targeting. This is because commune-level targeting is more realistic than village-level targeting as communes have some degrees of autonomy whereas villages do very little.

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