rafmacalaba/datause-encoder-data huggingface dataset:
nancial hedges are complements rather than substitutes. If that is the case, sectors with natural hedge would be the ones better able to use nancial instruments and would be stronger in countries where these instruments are available. Disentangling these competing explanations is an interesting topic for future research.

# 5 Conclusion

This paper has shown evidence of an indirect causal eect of exchange rate volatility on trade by looking at the dierential eect of this volatility on the export share of sectors producing commodities whose price correlation with the home country's exchange rate oers dierent degrees of natural hedge against exchange rate uctuations. The results indicate that sectors that oer natural hedge (i.e. those whose international price comoves negatively with the country's nominal exchange rate) are indeed larger relative to others, even after controlling for standard determinants of trade composition, in countries with more volatile exchange rates. Thus, exchange rate volatility tilts the structure of trade towards sectors that produce goods that oer a natural hedge against this volatility. This dierential eect comes mainly from countries with a oating exchange rate regime. Contrary to expectations, there is only weak evidence that the relation between natural hedge, exchange rate volatility, and export composition is stronger in countries where rms have no access to nancial instruments to hedge currency risk, such as exchange rate derivatives.

Overall, the results provide evidence that exchange rate volatility has real consequences for trade and that the structure of trade responds not only to standard factors, such as endowments, but also to other more institutionally driven constraints. If what you export matters, as suggested by Hausmann et al. (2007), addressing these sources of ineciencies would be a better way of reaching a trade pattern that is not driven by risk hedging than engaging in some sort of industrial policy.

The analysis of this paper can be extended along several dimensions. First, using more disaggregated data may permit obtaining better proxies of international price uctuations and, therefore, better pinpoint the relevance of the natural hedge mechanism. This can be done by using other new sources of data, such as the Gaulier et al. (2007), BACI database, which has a shorter time span but covers trade at 6-digits of the Harmonized System. Second, the hedge provided by an industry depends not only on the commodity it produces, but also on how the price of their imported inputs comoves with the nominal exchange rate. This paper has taken the stance that that source of hedging operates largely independently of the one on the product side, but of course this does not need to be the case. Addressing this issue faces some serious problems of measuring that would require further assumptions, but it is an interesting area of future research.

25
household contributions unchanged in the short run. These differences lead to a testable prediction: If household and school inputs are (technical) substitutes, unanticipated inputs will have a larger impact on test scores than anticipated inputs. We test this using data on educational spending for largely substitutable school inputs, such as books and writing materials in both Zambia and India.

Our data from Zambia allow us to distinguish between two different types of school spending: a predictable and fixed rule-based school block grant and an unpredictable districtlevel source of funds that varied widely across schools. The cross-sectional variation in the _perstudent_ rule-based grant comes from variation in school enrollment, which is instrumented for with the size of the catchment area (Case and Deaton 1999, and Urquiola 2006 use a similar instrumental variable strategy). We find that household spending substantially offsets variations in predicted per-student school grants. Evaluated at the mean, for each dollar spent on schools via the predictable grants, household spending on education reduces by a similar amount. In contrast, unpredictable grants have no impact on household spending. We also find that student test scores respond positively to the unanticipated sources of funds (test scores in schools receiving these funds are 0.10 standard deviations (SD) higher for both the English and mathematics tests for a mean transfer of just under $3 per pupil), but that they do not vary with variations in anticipated funds. This evidence is strongly suggestive that the two main predictions of the model are correct and is robust to several checks. However, we cannot fully rule out all identification concerns, and therefore test the model again using experimental variation induced by a randomly-assigned school grant program in the Indian state of Andhra Pradesh.

The Andhra Pradesh (AP) school block grant experiment was conducted across a representative sample of 200 government-run schools in rural AP with 100 schools selected by lottery to receive a school grant (also around $3 per pupil) over and above their regular allocation of teacher and non-teacher inputs. The conditions of the grant specified that the funds were to be spent on inputs used directly by students and not on any infrastructure or construction projects. The program was implemented for two years. In the first year, the grant was exogenously assigned and a surprise for recipient schools, while in the second year, the grant continued to be exogenous (relative to the comparison schools), but was now anticipated by the parents and teachers of program schools.

2
implications of related theoretical models. In addition, we find that firms employing labourintensive technologies are more likely to have rigid wages.

Finally, there seems to be a positive (although non-monotonous) relationship between product market competition and downward nominal wage rigidity. A possible cause of this empirical result is that in highly competitive industries rents should be low, and therefore so should wages. This leaves smaller margins to reduce wages, because firms paying low wages that are closer to a collectively agreed or legislative minimum level have less flexibility than firms having a so-called wage cushion between the minimum and the actual wage level. We find that the positive relationship between competition and wage rigidity is more significant in non-euro area countries, which lends further support to the above-described cause of this finding, since it is more likely that in these countries severe competition is associated with low wage levels. However, this positive significant relationship is not present in all the regression specifications, indicating that the results are dependent on the way competition is measured.

Our findings of the patterns and determinants of wage rigidities in 15 European Union countries contribute to the discussion of the role of monetary policy and its effects. The analysis of the monetary policy implications of wage rigidities was motivated by the conclusions of the Eurosystem Inflation Persistence Network (IPN). One of the key results reported by the IPN was that there is a substantial degree of persistence in inflation, which needs to be taken into account when implementing common monetary policy. It was further suggested that in the current monetary policy regime inflation persistence may originate from wage rigidities.<sup>23</sup> Similarly to the IPN’s finding of heterogeneity in inflation persistence across European countries, our results indicate the presence of country-specific patterns of downward nominal and real wage rigidities. To the extent that rigidities and their variation across regions of a monetary union complicate the design of optimal monetary policy (Carlsson and Westermark, 2008; Fahr and Smets, 2008), policies that facilitate adjustment in the monetary union in the presence of imbalances may need to be considered.

> 23 See Altissimo, Ehrmann and Smets (2006) for a summary of the IPN’s findings.

32
salaried workers who do not receive social security in non-agricultural sectors (ILO, 2010a).<sup>5</sup> This means that some workers may be informally employed in the formal sector.

The results from the LFS suggest that about 47 percent of workers working in the formal sector were informally employed (i.e. are not covered by social insurance) in 2009. Being informally employed in the formal sector concerns all age groups, and men and women alike. About 15 percent of men were informally employed in the formal sector against 11 percent of women. The likelihood of being informally employed in the formal sector decreases by age group for both men and women. At all age groups, women are less likely than men to be informally employed in the formal sector.

The results also suggest that women are less likely than men to be informally employed in the non-agricultural sector. While about 58 percent of non-agricultural female workers were formally employed in 2009, only 49 percent of their male counterparts were formally employed. Informal employment in Vietnam represented about 37 percent of total employment in 2009 (up from 36 percent in 2007). Men were at a disadvantage compared with women: 40 percent of male workers, 34.5 percent of female workers were informally employed (compared with 38 percent and 34 percent respectively in 2007). The small increase in informal employment was mostly due to the increase of informal employment among men.

Younger cohorts of workers are at somewhat of an advantage. The share of informal employment in total employment increases with age up to the 35-44 age group and then decreases with age (Table 18). The increase in informal employment that is observed over the period 2007-2009 concerns mostly workers aged 35 to 64. Although the youth labor force participation and employment-to-population ratio increased (Table 2) and the share of youth working in wage employment increased (Table 15), the share of youth who are informally employed did not increase. Given that in the meantime the share of youth working in the agricultural sector decreased, there was an improvement in the quality of employment of youth (measured as working informally or not). The relative

> 5 With the LFS dataset, this means that informal employment is constructed in the following way: are considered informally employed in the non-agricultural sector all unpaid family workers and all others (employers, self-account workers, members of cooperatives) whose establishment does not provide social insurance.

20
poorly on tests designed by the Department of Education, Culture and Sports which are intended to measure minimum acceptable levels of achievement. The quality of education provided by the private sector appears to be noticeably higher (Jimenez et. al. 1995).6 There are numerous studies about the relationship between schooling and labor market performance in the Philippines (for example, Hossain and Psacharopolous 1994; Lanzona 1997; Maluccio 1998; Tan and Paqueo 1989). None of these studies focus on the issue of possible non-linearities in the earnings function per se, and those which use national-level data are by now outdated. The Lanzona (1997) and Maluccio (1998) papers are based on a panel of households interviewed in 1978, 1983, and 1994. Because the households selected are all in a single province of the 78 which exist in the Philippines (the province of Camarines Sur, in the Bicol region), it is not clear whether the results in these papers are applicable to the country at large.

# 3. **The data set**

This paper is largely based on the 1998 Annual Poverty Indicator Survey (APIS). The APIS covered 38,710 households, including 195,791 individuals. It is the first nationwide multipurpose survey conducted in the Philippines, and includes a basic consumption and income module, as well as modules addressing the educational

> 6 The low quality of education may be one of the factors contributing to the poor economic performance of the Philippines. Since 1984, real GDP per capita has essentially been stagnant: The total change in real GDP per capita between 1984 and 1999 was 2.6%, a very small amount indeed in comparison with many of the Philippines' East Asian neighbors, which posted _annual_ growth rates above 5% for much of the period. Productivity in the Philippines declined for the economy as a whole (by about 0.9% over the fifteen-year period), and fell dramatically in industry (declining by 17.0% during the period).

6
**Table** 7: **Additional transaction costs in the land market**

|**Stage**|**Step**|**Person**<br>**involved**|**Approx.**<br>**time taken**|**Ad**|**ditional costs**|
|---|---|---|---|---|---|
|||.<br>.<br>:||~~~~~~~<br>**~~~~~~am**|**~~~Approx.**<br>**Purpose**<br>**ount**|
|REGISTRATION|||j||<br>|
|REGISTRATION|s<br>:<br>Execute<br>transaction on<br>stamp paper|<br>.<br>r S \<br>=<br>Licensed<br>Vendor|.........<br>.<br>Neg.|,<br>.<br>.<br>7<br>~<br>Rs. 50|~Toundervalue<br>the<br>price of land and bring<br>it in line with the<br>prevailing government<br>rate|
||Te<br>i<br> <br> <br>|hs**i** <br>v<br> <br> <br>|ld<br>tes<br>|r<br>||
|M|<br>objections to the<br>sal<br>on<br>re<br><br>Fo<br>N<br>3 f <br>UT<br>the<br>re<br>office|<br> <br>e<br> <br>ce<br>of<br>**r**m<br>o.<br> o<br>T<br>AT<br> <br>gis|<br> <br>pt<br> <br>m<br>ehs<br>2 w<br>IO<br>tra|<br> <br>ild<br>ee<br>N<br>r's|ar<br>ks<br>|
||_-Patia_issued if<br>no objections|Revenue<br>inspector|4 weeks|Rs. 400-500|For speedy issuance of<br>the_patta_|
||-If objections|Tehsildar|5 years*|-||

* If there are objections from, say, joint owners, a case is registered in the tehsildar's revenue court, in the first instance, for hearing and disposal.

Shaded rows indicate activities where computerisation might benefit the transacting agents by reducing their transaction costs.

Source: Compiled from field notes.

35
after controlling for period effects, location, and industry, older firms are larger than firms younger than five years old (reference sample).

In columns 1 and 2, we are comparing the mean size of firms that are 40+ years old in the pooled sample with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. Figure 5 plots the mean employment and the confidence intervals for firms younger than five years old in each census year and shows that the average size of the youngest firms almost doubles between 1983 and 1995<sup>18</sup> and then reduces.

In order to obtain true lifecycle effects and to see if firms indeed grow as they age over their lifecycle, we would want to compare the size of older firms at any given point in time with their size when they were born. In the absence of panel data we are unable to look at the employment of the exact firm when it was born and hence rely on the mean employment in its birth cohort. Thus we construct Firm Size Ratio, which is the size of firms in a particular age bin scaled by the mean size of the firms in their birth cohort. Since our sample period extends from 1983 to 2005, the oldest firms in our sample for which we can obtain this comparison are firms that are 20-24 years old in 2005 with their birth cohort being firms that were <5 years old in 1983. Columns 3 and 4 in Table 3 present regressions with the Firm Size Ratio. Col. 3 shows that firms that are 20-24 years old are 1.419 times the size of average firms in their birth cohort. The constant in col. 3 is again the reference category of firms younger than five years old and since we are scaling each firms employment by the mean employment of their birth cohort (firms younger than 5 years old when the firms was born) and using OLS we would expect it to be 1 as it is. Col. 4 shows that after controlling for industry, location, and year dummies, firms that are

> 18 Hsieh and Klenow (2012) use data from 1995 for their main analysis thus scaling the employment of all firms with that of the youngest firms in 1995, which is also the year when the youngest firms were the largest in our sample period.

19
**Figure 1: Share of Export Prices Received by Producers (percent)**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003<br>Source: Bibangambah (1996), Akiyama (2001), and Uganda Coffee Development Authority<br><!-- End of picture text -->

**Figure 2: Robusta Export Unit Value as Share of ICO Indicator**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Source: Authorʹs Calculations from World Bank and Uganda Coffee Development Authority data<br><!-- End of picture text -->

**_— 22 —_**
Policy **Roeearch** Working Paper **Series**

|**Title**|Author|Date|Contact<br>for**paper**|
|---|---|---|---|
|WPS**1**78 Proeuctivity<br>of Public Spending,<br>Sectoral Allocation Choices, and<br>Economic<br>Growth|John Baffes<br>Anwar Shah|Septem..er<br>1993|C.**Jones**<br>37699|
|WPS**1**79 How the Market Transition Affected<br>Export Performance<br>In the Central<br>European Economies|Bartlomlej<br>Kaminski|September<br>1993|P. Kokila<br>33716|
|WPS**1**80 The Financing<br>and Taxation<br>of U.S.<br>Direct Investment<br>Abroad|Harry Huizinga|September<br>1993|R. Vo<br>31047|
|WPS**1**81 Reforming<br>Health Care: A Case for<br>Stay-Well Hoalth Insurance|Zeliko Bogetic<br>Dennis Heffley|September<br>1993|F. Smith<br>36072|
|WPSI 182 Corporate<br>Governanco<br>in Central<br>and Eastern Europe: Lessons from<br>Advanced<br>Market Economies|Cheryl W. Gray<br>Rebecca J. Hanson|September<br>1993|M. Berg<br>31450|
|WPSI 183 Who Would Vote for Inflation**..i**<br>Brazil? An Integrated<br>Framework<br>Approach<br>to Inflation and Income<br>Distribution|Cheikh Kane<br>Jacques Morisett|September<br>1993|T. Hollestelle<br>30968|
|WPSI 184 Providing Social Benefits in Russia:<br>Redefining<br>the Roles of Firms and<br>and Government|Simon Commander<br>Richard Jackman|September<br>1993|0. del Cid<br>35195|
|WPS1 185 Reforming<br>Hungarian<br>Agricuftural<br>Trade Policy: A Quantitative<br>Evaluation|Morris E. Morkre<br>David G. Tarr|September<br>1993|N. Artis<br>37947|
|WPS1 86 Recent Estimates<br>of Capital Flight|Stijn Claessens<br>David Naud6|September<br>1993|R. Vo<br>31047|
|WPS**1**87 How Should Sovereign<br>Debtors<br>Restructure<br>Their Debts? Fixed<br>Interest Rates, F exible Interest<br>Rates, or Inflation-indexed|Andrew Warner|September<br>1993|J. Queen<br>33740|
|WPS1 188 Developmentalism,<br>Socialism,<br>and<br>Free Market Reform: Three Decade<br>of Income Distribution<br>in Chile|Mario Marcel<br>s Andr6s Solimano|September<br>1993|S. Florez<br>39075|
|WPS1189 Can Communist<br>Economies<br>Transform Incrementally?<br>China's<br>Experience|Alan Gelb<br>Gary Jefferson<br>Inderjit Singh|September<br>1993|PRDTM<br>37471|
|WPS**1**90 The Govemment's<br>Role in Japanes<br>and Korean Credit Markets:<br>A New<br>instiutional Economics Perspectiv|e Yoon Je Cho<br>Thomas Hellmann<br>e|September<br>1993|T. lshibe<br>37665|
# Table (F8) Impacts of the Program on Child Outcomes

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Child SDQ score, std|0.56***|(0.17)|[0.01]|0.01|(1.00)|859|
|Count of non-adult dependents (under 18)|-0.22|(0.20)|[0.37]|3.44|(2.02)|1,321|
|Share of non-adults attended school all of last week|-0.02|(0.07)|[0.55]|0.78|(0.35)|981|
|Days child slept hungry last week|0.32|(0.23)|[0.31]|0.89|(1.50)|1,118|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Child SDQ score, std reports the standardized SDQ score where higher scores reports worse difficulties. Strengths and Difficulties Questionnaire (SDQ) is a screening questionnaire administered to a randomly selected child aged three to eight years old and covers emotional, conduct, hyperactivity, peer, and prosocial problems. Count of non-adult dependents under 18 reports household members under 18. Share of non-adults attended school all of last week reports the share of children aged 6-17 who attended 5 days of school last week. Days child slept hungry last week reports the number of days any child under 18 went to bed hungry because there wasn’t enough food to eat.

- The regressions also have assessment month-by-year fixed effects, enumerator fixed effects, community-level controls (Irbid/Mafraq governorate and population quartile), and household-level controls (vulnerability-assessment quartile, shelter program, baseline number of children, baseline number of children plus adults, respondent gender, and respondent age). In the parentheses are robust standard errors clustered at the locality level. Regressions are weighted by the number of people interviewed in each household. Statistical significance represented by * (10%), ** (5%), and *** (1%). Q-values are calculated per Anderson (2008) and correspond to Family xxx in the preanalysis plan.

36
large exporters. di Giovanni & Levchenko (2009) find a similar result in a different context, namely that more openness tends to lead to greater product concentration.

_Ex ante_ , we expect density to be positively related to export diversification, since higher values should imply that a sector has a higher export potential. At the crosscountry level this was shown by Hidalgo _et al._ (2007). The coefficients reported in the first row of Table 8 confirm that this is likely to be the case. The results suggest that a 1 percent increase in sectoral density in 2002 is associated with an increase in the number of products exported until 2007 of between 1.2 and 1.6 percent.

Having in mind the caveat that trade liberalization may not be independent of structural change, we now consider the relationship between sectoral export diversification and trade liberalization. Although tariffs operate by restricting _imports_ , there are _a priori_ reasons why lower tariffs at a 2-digit sectoral level may be associated with more diversification at the 6-digit level. Reductions in tariffs in a given sector could reduce the costs of complementary inputs located within the sector; this is relatively common among manufactured products. For example, reductions in tariffs in cotton (HS 52) would lead to lower costs for raw cotton (HS 520100), which would in make it more likely that a textile producer would choose to produce and export plain woven cotton fabrics (HS 521111). Similarly, a reduction in tariffs in motor vehicles (HS 87) would allow exporters of small motor cars (HS 870321) to benefit from a lower import price of engine-fitted chassis (HS 870600). Of course, this measure would be imperfect, since it would not capture reductions in the cost of other critical inputs, such as capital goods, nor would it include inputs that are closely related by tied to a different 2-digit HS sector.

We see this ambiguity reflected in the coefficient estimates for tariffs. Throughout the regressions, the tariff variable typically enters with a coefficient that is not statistically different from zero—regardless of whether we use the logged average tariff or the change in average tariffs over the period (not reported) as an explanatory variable. However, Table 8 also shows that the tariff variable carries the expected sign throughout all regressions, and that the fit of the model improves dramatically with the inclusion of the variable. This suggests that we cannot rule out a role for tariffs in determining export diversification—as shown in Goldberg _et al._ (2008)—and that the problem in our regressions may ultimately be due to both endogeneity in the specifications as well as tariffs being important in some sectors but not in others.

Finally, while we would expect a more sophisticated export basket to be conducive to diversification, the PRODY measure does not enter the regressions with coefficients significantly different from zero, and their signs are unstable. We therefore refrain from drawing any inference from these coefficient estimates.

26