rafmacalaba/datause-encoder-data huggingface dataset:
The conclusions reached in this survey are tentative. While there are many studies based on the difference-in-difference and the instrumental-variable approach that are more plausible in establishing causality, the rest of studies are based on cross-sectional correlations, making causality inference very difficult. There are also clearly alternative explanations for this body of new findings. There is always the possibility that some omitted variables accounting for the correlations of our proxies of business environment with economic outcomes. So the conclusions I present should be viewed with caution.

# **References**

- Acemouglu, Daron, and Simon Johnson. 2005. "Unbundling Institutions." _Journal of Political Economy_ 113(5), 949-995.

- Acemouglu, Daron, Simon Johnson, and James Robinson. 2001. "The Colonial Origins of Comparative Development: An Empirical Investigation.” _American Economic Review_ 91, 1369-1401.

- Adhvaryu, Achyuta, A.V. Chari, Siddharth Sharma. 2010. “Firing Costs and Flexibility: Evidence from Firms’ Employment Responses to Shocks in India.” Mimeo, World Bank, Washsington, D.C.

- Aghion, P., Bundell R, Griffith R, et al. 2009. “The Effects of Entry on Incumbent Innovation and Productivity.” _Review of Economics and Statistics_ 91 (1), 20-32.

- Aghion P, Burgess R, Redding S and Zilibotti F. 2008. “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India.” _American Economic Review_ 94 (4): 1397-1412.

- Almeida, Rita. 2005. "Enforcement of Regulation, Informal Labor and Firm Performance," IZA discussion paper no. 1759.

- Almeida, Rita, Pedro Carneiro. 2009. “Enforcement of Labor Regulation and Firm Size.” _Journal of Comparative Economics_ 37, 28-46.

- Amin, Muhammad. 2009a. “Labor Regulation and Employment in India’s Retail Stores.” _Journal of Comparative Economics_ 37, 47-61.

- Amin, Muhammad. 2009b. “Are Labor Regulations Driving Computer Usage in India’s Retail Stores?” _Economics Letter_ 102, 45-48.

- Barseghyan, Levon. 2008. “Entry Costs and Cross-Country Differences in Productivity and Output.” _Journal of Economic Growth_ 13(2), 145-167.

- Beck, Thorsten, Demirguc-Kunt, Asli, and Vojislav Maksimovic. 2005. "Financial and Legal Constraints to Growth: Does Firm Size Matter?" _Journal of Finance_ LX, 137-177.

Bertola, G. 1990. “Job Security, Employment and Wages.” _European Economic Review_ 34, 851-886.

28
the remainder of this section, we describe each of these sources in more detail.

# **2.1 International trade flows and tariffs**

Because our goal is to estimate quality and quality adjusted prices for several products, countries, and years, we cannot rely on firm level data which is only sparsely available for a few countries. We draw therefore on export and import data from the UN Comtrade Database for the period 2012- 2019. These data contain information on bilateral export (FOB) and import (CIF) values and quantities at the HS 6-digit level. The data is more disaggregated than the information used by Feenstra and Romalis (2014) who used SITC 4-digits.

We compute the bilateral f.o.b. unit values of traded manufactured goods using reports from the exporting country, which ensures that unit values do not include shipping costs. We use importers’ trade reports to calculate c.i.f. unit values using importers’ trade reports. These unit values already include the costs of shipping and hence only tariffs need to be added. To this end, we use ad valorem tariffs associated with the most favored nation status or any preferential status from TRAINS.

Following Feenstra and Romalis (2014), we correct for measurement error by dropping trade flows below $50,000 and quantities with less than 1 kg (or missing). We further omit observations in which the ratio of the c.i.f. unit value reported by the importer and the f.o.b. unit value reported by the exporter, for a given four-digit SITC product and year, was less than 0.1 or above 10. To examine the likely effects of AfCFTA, we further use tariff data from UNCTAD, which contains information at the product level on current and expected tariffs (by 2030) after the implementation of the agreement for all country members and their partners.

# **2.2 Definition of digital goods**

Many manufactured products have digital components, and the same product category used in international trade statistics can include some products that have digital components and others that do not. Digital goods are also constantly evolving from a technological point of view. Identifying digital goods in the product-level data used in international trade statistics is therefore not straightforward.

In this paper, we have adopted a broad definition of digital goods. We depart from the Bureau of Economic Analysis (BEA) Digital Economy list of Digital Inputs, which contains 52 6-digit codes of the NAICS classification (see Barefoot, Curtis, Jolliff, Nicholson, and Omohundro (2018)).<sup>6</sup> This is a broad definition of digital inputs used in national statistics, which includes

> 6The BEA reached this list in three steps. First, it developed a conceptual definition of the digital economy.

5
|from|(fal to (7b)|(fa) to fc)|(8a) ta (Bb)|(8a) to (8c)|
|---|---|---|---|---|
|;<br>Female +SameTracking x Female|-0.05<br>0.13)|-0.02<br>0.15)|0.01<br>(0.28)|-0.11<br>0.21)|
|.<br>;<br>Some Tracking +Sore Tracking x Fernale|-0.62"*<br>(0.29)|-1.418"*<br>a.44)|0.76"<br>(0135)|-0.55<br>(0.42)|
|Same Trackin<br>4|-0.516<br>(0.29)|0.57<br>(0.26)|0.44<br>(0.33)|-0.07<br>(0.26)|
|Female<br>x8<br>Tracki<br>emale x<br>Some<br>Tracking<br>Note: Cells indicate the change in the estimated coe<br>using a linear combination ofthe standard coefficien<br>errors are reported in parentheses. Significance at t<br>Source: PISA 2003 Korea, General School Strata, G|-0.46<br>0.31)<br>fficients present<br>ts estimated in t<br>he 1%, 5%, 10% <br>rade 10 Student|0.61"<br>(0.39)<br>ed in the previous<br>he previous multi<br> levels is denoted <br>s.|0.32<br>(0.48)<br> table; these are <br>nomial lagit mode<br> “*, **, and *, re|-0.49<br>37)<br> calculated by a<br>ls. Standard<br>spectively.|
uncertainty around genetic engineering’s repercussions on health, and its poor social acceptance is a real impediment to more decisive development in the near term. Nevertheless, society’s eventual familiarization with the phenomenon—by 2050, genetic engineering technologies will have been in operation for decades—and further technological advances (such as genetic erasing) pose an interesting argument for predicting a greater role for genetic technology in future food production in Asia and the rest of the world. Once again, the extent to which these advances materialize and consolidate in the region will contribute to Asia’s ability to feed its population.

The second consideration has to do with the process itself of increasing the middle class of global society, ultimately the expected key factor in the change of the relative composition of the food supply. Even though the expansion of middle classes may look like an irreversible process, evidence for Asia, particularly in China, shows that greater economic growth does not automatically lead to equalization. World Bank estimates cited at the beginning of this paper (World Bank 2012a) show that the great majority of the 649 million poor people in the world who ceased to be poor (according to the criterion of US$1.25 per day per person) between 1981 and 2008 still continue to be poor by the standards of middle- and upper-income countries. When speaking of moderate poverty, that is, persons with incomes above US$1.25 a day but less than US$2 a day, progress in the last three decades has been much more modest than in the case of extreme poverty. In fact, in absolute terms, the numbers of moderately poor increased from 648 million in 1981 to 1.18 billion in 2008. In Latin America, the region that has seen the most impressive reduction in poverty, only countries like Argentina, Costa Rica, Mexico, and Uruguay have a middle class of around 50 percent of the population—measured as those earning income between US$10 and US$100 a day—which is far above the proportions in other countries of the region (Cárdenas, Kharas, and Henao 2011). Even more demanding figures on the expected 14
Karlan, Dean and Martín Valdivia (2011) “Teaching entrepreneurship: Impact of business training on microfinance clients and institutions”, _The Review of Economics and Statistics_ 93(2): 510-527.

Karlan, Dean and Jonathan Zinman (2011) “Microcredit in theory and practice: Using randomized credit scoring for impact evaluation”, _Science_ 332(6035): 1278-1284.

Mano, Yukichi, Alhassan Iddrisu, Yukuta Yoshino and Tetsushi Sonobe (2012) “How can micro and small enterprises in Sub-Saharan Africa become more productive? The impacts of experimental basic managerial training”, _World Development_ 40(3): 458-468.

Martínez, Claudia, Esteban Puentes and Jaime Ruiz-Tagle (forthcoming) “The effects of micro entrepreneurship programs on labor market performance: Experimental evidence from Chile”, _American Economic Journal: Applied Economics._ McKenzie, David (2017) “Identifying and Spurring High-Growth Entrepreneurship: Experimental Evidence from a Business Plan Competition”, _American Economic Review_ 107(8): 2278-2307.

McKenzie, David, Nabila Assaf and Ana Paula Cusolito (2017) “The additionality impact of a matching grant programme for small firms: Experimental evidence from Yemen”, _Journal of Development Effectiveness_ 9(1): 1-14.

McKenzie, David and Christopher Woodruff (2008) “Experimental Evidence on Returns to Capital and Access to Finance in Mexico”, _The World Bank Economic Review_ 22(3): 457-482.

Premand, Patrick, Stefanie Brodmann, Rita Almeida, Rebekka Grun and Mahdi Barouni (2016) “Entrepreneurship education and entry into self-employment among university graduates”, _World Development_ 77: 311-327.

Valdivia, Martín (2015) “Business training plus for female entrepreneurship? Short and medium-term experimental evidence from Peru”, _Journal of Development Economics_ 113: 3351.

65
in export destinations lead to increased exports and sales, with the former showing stronger responses than the latter. The stronger effects on exports would be expected given the source of variation we are exploiting: unforeseen growth shocks in export destinations. We take these results as reassuring confirmation that our strategy for identifying unexpected export shocks is valid. We also observe that the magnitude of the effects of the forecast error on both exports and sales is somewhat larger than that of the forecast growth. In Table A4 of the appendix, we split firm-level export values into averages export quantities (in kilograms) and average export prices per kilogram exported. The results reveal that the effect of the demand shock on exports is entirely a quantity effect, so our estimates should be interpreted with this evidence in mind.

Columns (3) to (8) of Table 2 also show significant effects on investment (in both tangible and intangible assets), employment, value added, value added per worker, and average labor costs. For all these variables, the effects of the weighted forecast error are consistently stronger than those of the weighted forecast growth.

# [ Table 2 here ]

In Table 3, we use the employer-employee data to examine the effects on the various components of worker compensation and on the skill composition of the workforce. Regarding worker compensation, the total monthly wage consists of three components: _base wage_ , _overtime pay_ , and _other pay_ (e.g., various types of bonuses). We also have information on the number of hours worked per month, which allows us to calculate the total hourly wage. We report effects on firm-level averages of each of these variables.

# [ Table 3 here ]

The estimates in columns (1) and (2) of Table 3 indicate positive and significant effects of the forecast error on both monthly and hourly wages. Columns (3)-(4) show that this positive average effect is driven by significant increases in both base wages and overtime pay. The _other pay_ coefficient in column (5) also has a sizeable positive point estimate, though the effect on this residual wage component is not statistically significant. In line with the employment responses documented in Table 2, column (6) shows a positive impact on total hours worked.

We also investigate whether an unexpected demand shock has any effects on workers’ skill composition. We measure skill composition in two different ways: (i) the share of workers with a degree, and (ii) average person effects estimated through AKM models. The results are somewhat mixed. While the estimate in column (7) does not show any significant impact on the share of workers with a degree, the estimate in column (8) suggests a significantly negative 11
male Osow youth over time. However, a positive sign is observed for the relationship with female employment. Our interpretation is that increased female employment may have a “crowding out” effect for males in the labor market either by introducing greater competition for a limited number of jobs, or by downward pressing wage rates due to greater labor supply. That is, given that there are a limited number of jobs being created, increases in female participation lead to more limited employment prospects for males, which in turn, increases the share of Osow youth among this group.

This result is consistent with the descriptive evidence presented in Figures 4 and 5, which showed that for recent cohorts the conditions for labor market entry for males have been tighter, leading to patterns of later and lower entry, as compared to females in the same cohorts for which employment levels seem to increase earlier in the life cycle, and are of higher levels than for previous cohorts.

Moreover, these results might provide a plausible explanation for the persistent levels of overall Osow youth in Latin America documented by Cárdenas et.al. (2011) and de Hoyos et.al. (2013). Specifically, our results suggest that in the context of expanding education and low employment generation, the share of female Osow youth has been declining due to greater job market opportunities for this group –related to economic and non-economic factors- with consequent declines in employment opportunities for males, which generate persistently high shares of Osow youth for this gender group.<sup>28</sup> As for the macro variables included, there are similarities with respect to the negative and nonsignificant associations between GDP and inflation levels shown in Table 1, with the only difference being a significant effect of GDP per capita growth on the shares of male Osow youth transiting between ages 21-13 to ages 24-16. As for the trade variable, we also find negative associations as in the case of females, although the effects for males continue to be statistically significant at all ages –while they fade away for females after age 20.

Cohort size effects are also similar for females and males, although with a longer lasting significant positive effect of larger cohorts being associated with greater shares of Osow youth all the way to ages 23 for males.

As in the case of females, it is also interesting to note that the R-square for all models is quite high, and increasing along the life cycle of cohorts, reaching values of over 0.85 for the 21-23 to 24-16 age transitions.

> 28 The results and interpretation are consistent with other studies including Acemoglu, et.al. (2004) and Juhn and Kim (1999), who show that the expansion of opportunities for females had negative effects on different dimensions of male employment at different stages, in the United States.).

28
# **Table 4 Effects of Curricular** Content **and Educational Expansion** on Economic **Growth,** 1960-1985 (Full **sample)**

(Unstandardized regression coefficients are reported; dependent variable is per capita RGDP 1985)

||Equation:|(A)|(B)|(C)|
|---|---|---|---|---|
||Average Yearly<br>Instructional Time (Total)||.00010||
|Average|Mathematics|||-.00021|
|Annual|Sciences|||.00102**|
|Instructional|Languages|||.00015|
|Time in|Ars/Music|||.00064|
|Each|All Social Studies|||-.00001|
|Subject|Moral/Religion|||.00059|
|Area|Physical Ed.|||-.00027|
|(circa 1960):|Pre-Vocational Ed.|||-.00042*|
|Educational|Primary<br>Education|.0043**|.0043**|.0042**|
|Enrollnent|||||
|Rates:|Secondary<br>Education|.0034*|.0036*|.0035*|
|Control|Lagged<br>Dependent Var.|.68**|.68**|.69**|
|Variables:|Mining/Oil<br>Exporters|.08*|.08|.07|
||Export Commod.<br>Concentration|-.003**|-.003**|-.003**|
||Total Fertility<br>Rate (1965)|.015|.019|.019|
||Adjusted R<sup>2</sup>|.918|.918|.920|
||Constant|.91|.81|.74|
||Number of Cases|63|63|63|

* Unstandardized regression coefficient is at least 1.5 times its standard error

** Unstandardized regression coefficient is at least 2.0 times its standard error
**Figure 2. Food expenditure (share)**

<!-- Start of picture text --> 60<br>48<br>45<br>50 43<br>40<br>40 32<br>30<br>20<br>10<br>0<br>Bottom 20% 2nd 3rd 4th Top 20%<br><!-- End of picture text --> Source: World Bank using HIES 2018/19 for expenditure shares In response, we convert total inflation into household-specific inflation rates following Nasir et.al. (2023). The HIES 2018/19 includes information for 283 expenditure items grouped into 12 main categories based on the COICOP classification. We combine expenditures at the COICOP level with their respective inflation rates, as provided by PBS. By aggregating across COICOP categories, we estimate experienced household-specific inflation rates.<sup>9</sup> As Figure 3 shows, depending on the extent to which different COICOP categories experience differential price trends, the perceived inflation by households across the expenditure distribution will differ. For instance, in FY23, households in lower deciles of the expenditure distribution experienced inflation rates that were, on average, one percentage point higher than those experienced by the wealthiest households because of higher food price pressures. Therefore, assuming equal inflation rates across the whole expenditure distribution will potentially underestimate the projected poverty rate.

**Figure 3: Inflation Inequality across Income Deciles**

<!-- Start of picture text --> 45 Decile1 (Poorest) Decile 10 (Richest) YoY_Inflation<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Inflation<br><!-- End of picture text --> 9 Detailed descriptions of the construction of household level inflation rates for Pakistan are provided in Nasir et al. (2023).

7
¥, = fy + Gil + Gay + 8 ¥, Ws <!-- Start of picture text --> Ws<br><!-- End of picture text --> xX; es <!-- Start of picture text --> Prilt; — 1}—— 48%.)<br><!-- End of picture text --> Prilt; — 1}—— 48%.) a BX) <!-- Start of picture text --> ATE= EY,= EY, EY, 1 1<br>- ¥y] "NoesfapaWed "NoesfapaWedfapaWedpaWedWed [Ms - fo] + fo] + + y—mSmS jaepsps [fi - ¥] ¥]<br><!-- End of picture text --> ATE= EY,= EY, EY, 1 1 - ¥y] "NoesfapaWed "NoesfapaWedfapaWedpaWedWed [Ms - fo] + fo] + + y—mSmS jaepsps [fi - ¥] ¥] ATE = Ely, - Yo] = rele - fj] = Ely, - Yo] = rele - fj] Ely, - Yo] = rele - fj] - Yo] = rele - fj] Yo] = rele - fj] = rele - fj] rele - fj] - fj] fj] <!-- Start of picture text --> ATE = Ely, - Yo] = rele - fj] = Ely, - Yo] = rele - fj] Ely, - Yo] = rele - fj] - Yo] = rele - fj] Yo] = rele - fj] = rele - fj] rele - fj] - fj] fj]<br><!-- End of picture text -->
surveillance (Yoshitomi and Shirai, 2000). Nonetheless, the potential creditor nations have mdicated their preference for IMF lmkage perhaps bellevmg that even uncertam protectton is better than none at all. The ratig agency, Standard & Poor's, indicated that if the CMI does not involve IMF conditionality, credit ratmgs **in** the region could be negatively impacted **151**

- 3 _Sufficiency of support_ - Given that the aggregate official foreign reserves of the ASEAN-Plus-Three countnes was around US$900 billion **in** 2001 with US$700 billion of that amount held by the Plus-Three countnes (Cluna, Japan and Korea) alone, the potential support amount seems enormous - over 10 times that of the U S (around US$70 billion in 2001) t52 However, swap lmes to individual countries are very small (US$1-3 billion), especially when compared to amounts traded **in** the global currency market where daily volume can be as much as $1 3 tnillion **153** Even more revealmg is to look at the amount of emergency assistance committed and disbursed to the cnsis-affected countries m response to the 199798 crisis (See Table 5 8)

Table 5 8

|Co|mmitmen|tsandDisburs<br>inRespon<br>**(in**billi<br>Commitm<br>.T|ementsof<br>setothe A<br>onsof**US **<br>ents<br> f<br>Bilateral|theInternat<br>sianCrisis<br> dollars)<br>otal|ionalCo<br>Dis<br>**~(as **|mmunity<br>bursement<br>**of**53h012**0**|s<br>**DI**|
|---|---|---|---|---|---|---|---|
|**Countiy**|**IMF**|Multilateral|**Bilateral**|**-Tota**|IMF|**COther**|**Total**|
|Indonesia|150|100|24.7|497|11**6**|10.3|**21.9**|
|Korea'|211|<br>142|231|58**4**|19**8**|**106 **|**30.4**|
|Thailand|4**_0_**|<br>2_7_|105|172|3**4**|**109 **|**14.3**|
|**Total**|**40.1**|26.91|_<br>58.3|**125.3**|**34.8**|**3L8**|**66.6**|
|'WorldBankand|**ADB**|||||||
|**2**Indudesaugme|ntataonssi|nceJuly1998||||||
|3Koreahasrepai|dUSS13b|ililonofthe financi|ngprovidedb|ytheIMF**as**|ofend-May|2000||
|'Combinedmulil|ateraland|bilateraldisburse|ments|||||
|Source**IMf**(20|00cTable|1)||||||

Tis table shows that a total of US$125.3 billion was committed by IFIs and individual countnes **in** support of the three countries most affected by the cnsis - Indonesia, Korea, and Thailand When compared to this amount, the CMI's current US$18 5 billion in BSAs, the US$1 billion under its ASA, and the $7 5 **billion** under the NMI seem small mdeed The amount committed in response to the cnsis was nearly five times this amount.

There are those who are of the opinion that the Chiang Mai Initiative is not what Asia needs at this tune because circumstances have changed in the region since the 1997 cnsis For one thmg, most of the region's currencies are no longer pegged so there **is** no target for central banks to defend and thus little mcentive for speculators to attack.<sup>154</sup> 63
29 and what activities the incremental output competes with.<sup>7</sup> In the longer term, efforts to advance employment goals in lieu of productivity and efficiency goals could easily have adverse consequences for employment. For example, scarce capital may be shifted toward firms with lower potential growth rates and hence lower employment offerings in the future.

Firm Demographics. The statistical patterns of firm "birth and death" or "entry and exit" have been examined from two quite different perspectives. From one perspective, the patterns are viewed as quasi-equilibrium phenomena in the sense that net change in firm populations is assumed to be small and to have cyclical features or a modest secular trend. While it may display variation it is not expected to display significant patterns of its own. In the other perspective, changes in firm populations are seen as aspects of the dynamic development of a particular product market, industry or sector -- and the large scale patterns of change are a major focus of attention, often the major focus of attention. Interestingly, this particular

> 7 A mid-80s review of World Bank lending to small enterprises offers a confident assertion that lending to small scale industry had generated jobs at lower investment cost than larger industrial projects in the same country (Levitsky, 1986, p. 7). The subsequent discussion, however, introduces one consideration after another that would provide grounds for doubt that the overall employment consequences would necessarily be favorable. Since the bulk of the data is drawn from sub-loan applications, not checked against ex post outcomes (p. 9), a skeptic might even find reason to doubt that the policy lever that was pushed actually moved in the intended manner, much less that the consequences of its movement were the intended ones.