rafmacalaba/datause-encoder-data huggingface dataset:
specialization does not tend to increase over time. Wacziarg and Wallack (2003) find little evidence that labor turnover increases following trade liberalization, using data from 25 episodes of trade liberalization. The countries in their sample, however, tend to be ones with excessive regulations, such as Argentina and Bolivia, and they do find increases when reform is broader and includes deregulation and privatization. Other related work also offers only modest evidence of significant labor reallocation as openness increases in developing countries (see, for example, Papageorgiou et. al. 1991, Revenga 1997, Currie and Harrison 1997, Roberts and Tybout 1996). The fact that trade reform does not affect employment in developing countries is often attributed to excessive labor regulations. Indeed, Blanchard and Portugal (2001) show that employment protection has strong negative effects on the reallocation of labor. This suggests that trade will have a lower effect on growth in economies with rigid labor laws.

The micro evidence that trade promotes an intra-sectoral reallocation of resources is stronger. Firm heterogeneity and a wide dispersion of productivity levels are well documented within industries; as a result, exit is necessary to rid the sector of low productivity firms and entry is important to enable the arrival of new industry leaders. Roberts and Tybout (1996) find that about 80 percent of employment reallocation in a handful of semi-industrialized countries occurs within rather than across four-digit ISIC industries. They also find that productivity dispersion tends to be relatively greater in industries not exposed to foreign competition, and that productivity growth is higher in tradable products. This implies that foreign competition serves as a disciplining device within industries. Bernard and Jensen (1999) use plant level data from the United States and show that the effect of reallocation on productivity is quite large, making up over 40% of total factor productivity growth in the manufacturing sector. They also find that reallocation to exporting firms is important in generating growth effects. Pavcnik (2002) finds 5
10

## Improving Efficiency and Reducing Waste in Drug Supply

# _.Selecdon and Quantification of Dtgs_

The staring point for an esential dus polity is the careful sekction and quantfication of drug needs acrd1ng to the ealth needs of the population to be served. Selection invohl **balancing** considertions of com with those of efficay, safety, aem of administration, and other lcal coiderationo An cumple of the range of costs of treatment with various compsrble dru is quito usde to aid in the section process. In 1975, an anicle appeared which used Tanzanian data to compare the prices of treatments with various drugs The author dIstIguisbhd betwen utype A drus, wc are older, poen drugs, and often available in generic form; and 'ype BO drugs, invented more recently and still under patent and avaiable only under brand nams and usually much more expensivem Th ngeS betwen the che t (uswually an older drug avilable in generic form) **and** the most expensie brand-name drug were quite wide; for mxapb, anpalc treatment with a brnd of ibuprofen was 150 times the urt of treatment with aspirin; the range for tranquilizers wa 50.60 times for hookm m, 130 times, and for oral antibiotica, 10.50 times. He made the argument tha unnecsay use of more expensive drugs exhausts the drug budget and deprives tillow citizens of needed treatment. (Speight, _1975)_

**A** more detailed treatment of the subject was provided in 1980 with the publication by AMREF ot a manual on therapeutic guidelines which was later reorinted under the tite, Guidelines to Drmg Usage. (Upunda et al, 1983) in it the authors compared the various drugs used for the major diseases. Although the coSts are now out of date, the relative comparisons stil remain vaLid. A few of the cost comparisons are pres..nid in Table 4.

Clearly the costs of different drugs need to be taken into account in selecting drugs Expenditures on antibiotics are particularly important in Table 4. Cost Comparisons of Alternative Drugs for Common Conditions

|Condition/courws|Lowest cost|USS|Highest cost|rerceni<br>USS|Diflerence|
|---|---|---|---|---|---|
|arhrtis(Wi)<br>**ascrsis**<br>(dose)|aspirin<br>piperazine|O.Z1<br>0.08|lbuprolen<br>mebendazole|1.8<br>0.60|750|
|hypenension (yr)|reserpine|24.50|methyldopa|141.25|576|
|infammtion<br>(wk)|aspirin|0.21|naproxen|2.67|1271|
|epiepsy (yr)|pbenyto.in|3.56|carbamazepine|219.83|6209|
|urnay" tract in (wk)|nitrofurantoin|0.46|ampkilin|2.80|609|
|otitis media (wk)<br>NB: Prices in19e2 USS.|phenoxym.pen.|0.84|amodcillin|3.50|417|

Source: Upunda, Yudkln, and Brown, Guidelines to Drug Usage, 1983.
Kose, M. A., G. Meredith, and C. Towe. 2005. "How Has NAFTA Affected the Mexican Economy? Review and Evidence." In Monetary Policy and Macroeconomic Stabilization in Latin America, edited by R. Langhammer and L. de Souza, 35-81. Berlin, Heidelberg: Springer-Verlag.

Kose, M. A., A. Rebucci, and A. Schipke. 2005. "Macroeconomic Implications of CAFTADR." in Markus Rodlauer and Alfred Schipke ed., Central America: Global Integration and Regional Cooperation. IMF Occasional Paper 243, 7-40. International Monetary Fund, Washington, DC.

Kose, M. A., C. Otrok, and C. Whiteman. 2008. "Understanding the Evolution of World Business Cycles." _Journal of International Economics_ 75 (1): 110–130.

Kose, M. A., and M. E. Terrones. 2015. Collapse and Revival: Understanding Global Recessions and Recoveries. Washington, DC: International Monetary Fund. Laforte, J. P. and J. Roberts. 2014. "November 2014 Update of the FRB/U.S. Model.", Board of Governors of the Federal Reserve System FEDS Notes.

Lane, P. R., and G. M. Milesi-Ferretti. 2007. "The External Wealth of Nations Mark II: Revised and Extended Estimates of Foreign Assets and Liabilities, 1970–2004." _Journal of International Economics_ 73: 223-250.

Lederman, D., W. F. Maloney, and L. Serven. 2005. Lessons from NAFTA for Latin America and the Caribbean. Palo Alto, CA: Stanford University Press.

Levchenko, A.A., and N. Pandalai-Nayar. 2015. _TFP, News, and "Sentiments": The International Transmission of Business Cycles_ . NBER Working Paper 21010, National Bureau of Economic Research, Cambridge, MA.

Ljungqvist, A., and M. Smolyansky. 2016. "To Cut or Not to Cut? On the Impact of Corporate Taxes on Employment and Income." Finance and Economics Discussion Series 2016-006. Board of Governors of the Federal Reserve System, Washington: DC.

Mattoo, A., D. Roy, and A. Subramanian. 2003. "The Africa Growth and Opportunity Act and its Rules of Origin: Generosity Undermined?" _The World Economy_ 26 (6): 829-851.

McCauley, R. N., P. McGuire, and V. Sushko, 2015. "Dollar Credit to Emerging Market Economies." _BIS Quarterly Review_ 2015: 27-41.

Metiu, N., B. Hilberg, and M. Grill. 2015. "Financial Frictions and Global Spillovers." Discussion Paper 04/2015, Deutsche Bundesbank, Frankfurt.

McKinsey Global Institute. 2010. "Growth and Competitiveness in the United States: the Role of its Multinational Companies." McKinsey & Company.

31
# **_Table A4.5e Relationship between country-level characteristics and IEG outcome rating, results for IBRD/IDA_**

|||**De**|**pendent vari**<br>**By**|**able: IEG 1-6**<br>**lending sour**|**outcome rati**<br>**ce**|**ng**|||
|---|---|---|---|---|---|---|---|---|
||**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|**IBRD**|
|Prearation TTL|0.158***|||0.151***||0.146*||0.166**|
|p|(0.041)|||(0.047)||(0.079)||(0.081)|
|1<sup>st</sup>half supervision||0.187***||-0.033||-0.086||-0.080|
|TTL||(0.046)||(0.063)||(0.089)||(0.098)|
|2<sup>nd</sup>half supervision|||0.191***|0.152***||0.177**||0.170*|
|TTL|||(0.036)|(0.051)||(0.083)||(0.089)|
|Practice Manaer<sup>1</sup>|||||-0.025|-0.052||-0.017|
|g|||||(0.082)|(0.094)||(0.121)|
|Country Director<sup>1</sup>|||||||-0.003|-0.099|
||||||||(0.072)|(0.122)|
|Constant|3.198***|3.431***|3.315***|3.276***|2.269***|1.959**|2.906***|1.750**|
||(0.512)|(0.473)|(0.468)|(0.509)|(0.767)|(0.807)|(0.415)|(0.857)|
|Observations|942|1,144|1,129|904|644|470|1,137|432|
|Adjusted R-squared|0.158|0.142|0.135|0.172|0.178|0.236|0.105|0.220|

|||**De**|**pendent vari**<br>**By**|**able: IEG 1-6**<br>**lending sour**|**outcome rati**<br>**ce**|**ng**|||
|---|---|---|---|---|---|---|---|---|
||**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|**IDA**|
|Prearation TTL|-0.007|||-0.060||-0.064||-0.045|
|p|(0.036)|||(0.042)||(0.048)||(0.055)|
|1<sup>st</sup>half supervision||0.088**||0.073||0.043||0.068|
|TTL||(0.041)||(0.056)||(0.070)||(0.070)|
|2<sup>nd</sup>half supervision|||0.127**|0.101||0.102||0.080|
|TTL|||(0.049)|(0.063)||(0.068)||(0.074)|
|Practice Manager<sup>1</sup>|||||0.083|0.099||0.114|
||||||(0.053)|(0.069)||(0.070)|
|Country Director<sup>1</sup>|||||||0.036<br>(0.050)|-0.044<br>(0.075)|
|Constant|1.853***|1.877***|1.953***|2.130***|1.903***|2.710***|1.797***|2.580***|
||(0.516)|(0.399)|(0.407)|(0.552)|(0.497)|(0.706)|(0.394)|(0.740)|
|Observations|1,096|1,416|1,384|1,042|1,079|752|1,403|697|
|Adjusted R-squared|0.114|0.098|0.097|0.119|0.100|0.101|0.096|0.126|

Note: Robust standard errors clustered at the country level are reported in parentheses (***p<0.01, **p<0.05, *p<0.10). All regressions are estimated using OLS, and controlling for region, sector, original commitment amount of project, original length of project and IEG evaluator fixed effects.

53
them except one, the force that was applied in subsequent years following the crisis in 2008 propelled their momentum in 2011 back to around the same levels or higher than prior to the crisis.

**Figure 24: Moldova’s largest Force items and their momentum’s** <u>(percentage points)</u>

<!-- Start of picture text --> Top 5 Force items between 2009-11 and their momentum<br>4% 2008 2009 2010 2011 2009-11 Momentum<br>3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>291 Capital transfers to 242 Purchase of fixed 241 Capital investments 132 Transfers for 111 Remuneration of<br>implement the assets production purposes work<br>externally-funded<br>projects<br><!-- End of picture text --> Notes: Disaggregation used in BOOST database is by Economic Classification at the 3 digit level Source: Force toolkit using Moldova BOOST dataset As capital expenditures were two of the top five highest force items between 2009 and 2011 (Figure 24) we can use the toolkit to take a closer look to see what the underlying drivers of momentum and force were within capital expenditures. At the aggregate level Figure 24 highlights that in the years following the crisis the strong force applied to capital transfers to implement externally funded projects built momentum back up to positive and even higher than before the crisis. The category of capital investments had its momentum returned back to pre-crisis levels. A third category of investments in the budget described as capital transfers within country also made the top 10 highest force items in 2011 and is of similar weight in the budget as capital transfer from externally funded projects (Table 10). Combining these three categories of capital expenditures we can see that they fell by 32 percent in 2009 but recovered strongly to grow by 14 percent and 28 percent in 2010 and 2011 respectively. Through the toolkit we can decompose at greater detail the underlying sources of capital expenditures growth in the budget. Table 11 shows the top 10 levels of force within capital expenditures by project and six of them came from external funds. The largest force came from the Social Investment Fund II Project that was only one percent of all capital expenditures in 2011 but with a growth of 350 percent contributed a momentum of 3.6 percentage points and a force of 4.9 percentage points towards capital expenditures in 2012. In terms of contributions to growth, the Competitiveness Enhancement Project had a larger momentum as it had a
_Low-wage earners represent a significant proportion of wage workers, especially affecting women_<sup>_11_</sup> Low-wage earners represented 8.6 percent of wage earners in 2008; women were more affected than men by low-wage (11.8 percent versus 6.4 percent). Women are overrepresented in low-wage employment; this result is consistent with worldwide results (ILO, 2010b). Compared with 2006, there was an increase in the share of low wage earners (from 6.5 percent in 2006). This increase in low-wage earners occurred for both men and especially women (from 5.2 and 8.5 percent for men and women respectively).

Low wage workers are found mostly in the agriculture sector. In the latter sector, 22.4 percent of wage workers were in low-wage employment, while only 7.1 percent of manufacturing wage workers, and 5.8 percent of service workers were in low-wage employment. Interestingly, the difference in proportion of low-wage workers in the manufacturing and service sectors is not statistically significant. Low-wage female workers are about as likely as male low-wage workers to be in the agricultural sector, but they are slightly more likely to be in the industrial sector and less likely to be in the services sector (Table 19).

**Table 19: Low-wage workers by sector and gender (2008)**

||Women|Men|All|
|---|---|---|---|
|Agriculture|35.9|32.1|34.2|
|Industry|41.1|35.3|38.5|
|Service|23.0|32.6|27.3|
|Total|100.0|100.0|100.0|

_Source: (VHLSS, 2008)_ When looking at disaggregated sectors, three sectors (electricity, gas; real estate, renting and business activities; and extraterritorial) have no low-wage workers, for the rest, there is too much variability to find statistically significant differences (Table A 3).

The large majority (about 91.5 percent in 2008 and 94 percent in 2006) of low-wage earners are underemployed. Low hours seem therefore to be the main reason for earning wages below the poverty line, although it is less the case in 2008 than in 2006. In this

> 11 Low wage earners are defined as earning less than the per capita income poverty line in their main job. We use the national income poverty line: (2006: 200 thousand VND for rural areas and 260 thousand VND for urban areas; 2008: 290 thousand VND for rural areas and 370 thousand VND for urban areas )  (see GSO, 2009).

24
<!-- Start of picture text --> 0.300 200%<br>0.250 \ a - oo _<br>0.200 140% 5<br>=2B5 120% 5><br>5 0.150 100% ©)<br>= =<br>A iz)<br>\<br>0.100 80% g<br>; — — Es<br>0.050 : \ := ,rail = a [h - / _40%<br>4 N<br>0.000 0%<br>10th 20th «= 30th 40th«=50th«= 60th «= 70th «= 80th «= (90th<br>mmm Gender Differential =—s=Endownment Share of the Gender Differential<br><!-- End of picture text -->
Controlling for cyclical factors, most regions appear to grow at a pretty common rate, with poorer ones growing faster during expansions and slower during recessions. Factors specific to individual countries play a major role. Southern Italian regions grew more slowly than the Italian average, whereas Spain's poorest regions grew slightly faster than the national average. This is, indeed, less than we bargained for. Our analysis of income per capita denies that fast convergence in levels is taking place. It suggests that, on average, uniform long-run growth rates are to be expected and relative differences will not disappear.” Cappelen et al. (2003) also find more convergence at the national level than the regional level for 1980-1997. Cuadrado-Roura (2001) finds evidence for σ convergence from the 1960 to the mid 1970s, and no convergence thereafter. Similarly, Fagerberg and Verspagen (1996) find substantial convergence across 70 regions in six member states from 195-1970, slowing of convergence from 1970-1990, with absence of convergence from 1980-1990. Gardiner et al. (2004) find slow σ and β convergence in productivity as measured by LFS hours-worked of barely 1% per annum during 1980-2001.

The second key strand is that while there has been convergence, it has often been conditional and to respective steady states. However, different steady states themselves have continued to persist, showing, at the minimum, that EU regional policy has not been effective in helping poorer regions leap to higher levels of growth. Canova and Marcet (1995) question Barro and Sala-iMartin (1991) and using a Bayesian model find that their average estimates of convergence rates are higher than that found in the literature, about 23 % for the regions, with each unit converging to its own steady state. However, they reject the hypothesis that the steady state is the same across regions and find that poor regions stay poor and over time the differences are reduced only by a small amount. They also claim EU cohesion policy to be without much impact and that structural changes in economic environment are needed for catch-up.

Canova (2004) presents evidence for club convergence among EU regions. He finds that there is a natural clustering of units in four groups of regional income per capita. Further he finds that within groups, poor units converge faster to their steady state than rich ones and that they tend to cluster around a pole of attraction that is substantially below the average. He shows that there is 21
preferences, and when countries discriminate by raising protection through the adoption of temporary trade barriers. These results demonstrate the empirical importance of specific channels through which global supply chains shape governments’ trade policy choices.

We conclude with a few thoughts about future work in this area. First, we have focused on how governments set protection on final goods, setting aside the issue of optimal input tariffs. In future work, we plan to tackle the more complex problem of how governments could jointly set tariffs on final goods and intermediate inputs to protect and promote domestic value added. The analysis we have conducted here is a key input into that general problem, describing how optimal final goods tariffs depend on value-added content. Because input tariffs can change value-added contents, they are then naturally tied to final goods tariffs.

Second, in our empirical analysis, we have focused on _bilateral_ tariff preferences and TTB coverage ratios. This empirical setting distinguishes our work from the bulk of the empirical trade policy literature, which focuses primarily on _multilateral_ tariffs and non-tariff barriers. We have demonstrated that bilateral protection is a fertile testing ground for the theory of trade protection; future work is also likely to benefit from this empirically rich bilateral context to test alternative theories of trade policy formation.

# **References**

- **Antr`as, Pol, and Robert W. Staiger.** 2012. “Offshoring and the Role of Trade Agreements.” _American Economic Review_ , 102(7): 3140–83.

- **Bagwell, Kyle, and Robert W. Staiger.** 1990. “A Theory of Managed Trade.” _American Economic Review_ , 80(4): 779–95.

- **Bagwell, Kyle, and Robert W. Staiger.** 1997. “Multilateral Tariff Cooperation during the Formation of Free Trade Areas.” _International Economic Review_ , 38(2): 291–319.

- **Bagwell, Kyle, and Robert W. Staiger.** 1999. “An Economic Theory of the GATT.” _American Economic Review_ , 89(1): 215–48.

- **Bagwell, Kyle, and Robert W. Staiger.** 2011. “What Do Trade Negotiators Negotiate About? Empirical Evidence from the World Trade Organizaion.” _American Economic Review_ , 101(4): 1238–1273.

- **Baier, Scott L., and Jeffrey H. Bergstrand.** 2007. “Do Free Trade Agreements Actually Increase Members’ International Trade?” _Journal of International Economics_ , 71(1): 72– 95.

- **Baldwin, Richard.** 2012. “WTO 2.0: Global Governance of Supply-Chain Trade.” Centre for Economic Policy Research Policy Insight No. 64.

33
stratum fixed effects.<sup>21</sup> Panel A of Table 6 reports the coefficient estimates. In column (1), treatment effects are captured by indicators for receiving biweekly CHW visits and for receiving the subsidy, and their interaction. In column (2), these treatment indicator variables are additionally interacted with an indicator for “care required” in order to understand whether parents behave differently on days on which care is or is not required. This is in particular important for the effect of information on care-seeking; if CHW visits reduce both overuse and underuse, the effect should be negative when care is not required, but positive on care-required days.

Panel B reports the hazard ratios for each subgroup formed by the dummy variables above, relative to the control group in (1), and the control group on days when care is not required in (2). For example, the row “Full program, care not req.” shows that the daily probability of care seeking on days when care is not required is 3.583 times higher in the group that receives both subsidy and CHW visits than in the control group. Appendix Table C.7 replicates the estimates with left-censored spells included as a robustness check (treating these spells as uncensored); the results are similar.

**Results.** We begin by observing that the illness profiles and pre-existing patterns of demand in our sample show that there is considerable scope for both underuse and overuse (IMCI based): out of 955 spells in the control, 50.2% never required care. These are unlikely to simply be illness spells that were about to become serious but received treatment early, since only 3.8% of care never-required spells received formal care. Conversely, in only 11% of the 476 spells where care was required, the child (eventually) saw a doctor.

The spell data also confirm that the parents’ decision is a dynamic problem. 210 out of 476 “care ever required” spells entered care-required status only later on. Put another way, out of 689 spells that initially did not indicate a need for care, 30.5% eventually did require care (not shown). Yet in most cases, even without treatment the spell did end eventually, 21Of these controls, only assets affect health care demand positively and significantly, while the distance to the closest provider as a measure of non-monetary costs has the expected (negative) sign, but is not significant.

27