rafmacalaba/data-use-mentions-extended huggingface dataset:
men. Similarly, we also find larger effects on the number of female-owned firms, compared to male-owned firms. Further, we find that the effects are only significant for small nontradable service firms. Our evidence also suggests that this positive impact is likely due to an increase in consumer demand for local non-tradable services that results from the growth in tradable services employment.

Our paper makes an important contribution to the literature by showing that international trade can benefit non-tradable services. This is enabled through increased household demand for non-tradable services, resulting from the growth of tradable services. The result does not preclude other ways in which non-tradable services can benefit from international trade. For instance, employment in non-tradable services can benefit from increased household demand resulting from the growth of other traded sectors, such as manufacturing or agriculture. Non-tradable services can also be indirectly exported through forward linkages with these goods-producing sectors. Future research on the role of the services sector in India’s structural transformation can assess these relationships.

An avenue for future research can be to also examine the impact of growth in tradable services on non-tradable services in terms of output and productivity. However, this would require moving beyond the Economic Census data that only contain information on the number of workers. Services firms, however, are not covered adequately in India’s official statistics. The absence of good and comprehensive data for services firms, especially in a panel format, poses difficulties for estimating the technical efficiency or total factor productivity. The absence of regular data on informal firms is also particularly problematic for the services sector. Informal firms are, by definition, excluded from administrative data sources, such as tax records or business registers. Further, the informal sector surveys conducted by the National Sample Survey Organization are few and far between. These issues are symptomatic of gaps in the coverage and reporting of data on services firms in other countries too. Better and more complete data are crucial to fully grasp the growing contribution of the services sector to growth and structural transformation.

20
thing, the association is negative for the case of individuals living in municipalities with stronger provision of basic public goods (column 2 in Panel C). Perhaps unsurprisingly, individuals living in municipalities experiencing improving economic conditions do consider that the economic situation of the country at the moment of the interview was better than a year before (column 3). This association is exclusively explained by responses from individuals living in municipalities characterized as “strong state capacity” (column 3 in Panel C).

I next analyze how individuals’ attitudes toward democracy respond to variation in income at the municipality level. Higher income levels in the municipality are associated with preferences for democracy over other types of government (column 4). This effect is essentially explained by individuals living strong state capacity (albeit standard errors for the implied effect are large, see column 4 in Panel C). Further, income does not appear to shape people’s satisfaction with democracy (column 5).

# **5 Conclusion**

Trust and confidence in most Salvadoran institutions have declined over time. This decline started in the aftermath of the 2008 global financial crisis and has particularly accelerated over recent years, coinciding with a path of low economic growth. In this paper, I study whether individuals’ attitudes toward Salvadoran institutions are shaped by respondents’ municipality-specific business cycles. Exploiting multiple waves of two opinion surveys and a two-way fixed effect model, I find that individuals’ trust towards both the president and the municipal government are positively associated with a higher level of income at the municipality level. These documented empirical relationships mask relevant heterogeneity from a rural-urban divide and differences in municipal state capacity. Most of the positive association between income and trust in the Salvadoran president is explained by individuals living in rural areas and municipalities with low levels of state capacity, whereas most of the positive association between income and trust in the municipal government comes from individuals living in urban areas.

I also find that municipality-specific business cycles strongly predicts trust in mass media and, to a lesser extent
**North**<br>**Central**|**North**<br>**West**|**North**<br>**East**|**variation across**<br>**states**|
|Multidimensionally|||||||||
|<br>poor|51.9%|23.3%|36.1%|45.1%|54.2%|71.7%|80.5%|0.44|
|Monetary poor|40.5%|9.7%|20.8%|38.2%|43.1%|61.8%|69.9%|0.61|
|Other- dimensions<br>poor|27.9%|16.2%|20.9%|15.4%|30.0%|37.0%|45.6%|0.47|
|Omittedpoor|11.4%|13.5%|15.3%|6.9%|11.1%|10.0%|10.6%|0.52|
|Consistently poor|16.5%|2.6%|5.6%|8.4%|18.9%|27.0%|34.9%|0.79|
|Coefficient of overlap|2.1|1.8|1.4|2.0|2.2|1.7|1.4|0.30|
|Life expectancyat birth|49|52|50|50|49|49|45|0.06|

_Source:_ Authors’ estimates based on data from NLSS 2019 _Note_ : The table shows different poverty rates: multidimensional poverty, monetary poverty, and non-monetary poverty at the national level and
of households or people are being examined. The only exception is when the sample size is discussed, primarily to point out that the number of households being interviewed was too small for meaningful results (for example, the number of rural households in the bottom 40 percent connected to the grid). In each country, people are divided into quintiles based on per capita expenditures, with quintile 1 being the poorest and quintile 5 being the richest. Per capita expenditures are derived from consumption aggregates used to construct official poverty measures.<sup>5</sup> Poverty measurements are based on real expenditures, either per capita or per adult equivalent, except in Botswana where nominal values are used. Each quintile has the same number of people, not households. Because household size typically declines with increasing wealth, and because the rich tend to be concentrated in urban areas, upper quintiles have more households than lower quintiles and are more urban than rural. The results are analyzed by consumption quintile, households’ poverty status (poor or non‐ poor based on the official poverty measures), location (urban or rural), and gender of the head of household. Where official poverty is based on per capita expenditure, the bottom quintile is always poor, but where poverty is based on expenditure per adult equivalent, the overlap between the poor and the bottom quintile

> 4 The 39 countries are not identical. Non‐overlapping countries between the components are Angola, Chad, and Namibia in the tariff analysis and the Central Africa Republic, the Republic of Congo, and Sudan in the analysis of quasi‐ fiscal deficits. Of the 22 countries with household surveys, all but Angola were analyzed for quasi‐fiscal deficits. 5 The methodology for consumption aggregation for the purpose of defining poverty varies from government to government, and therefore quintiles are not strictly comparable across countries because what is included in consumption aggregates is not the same.
||period for the Lower house of the country and 0 otherwise._Source:_<br>Inter-Parliamentary Union (IPU) and website searches|
|---|---|
|GovExp|General government expenditure (% of GDP)._Source_: International<br>Monetary Fund, World Economic Outlook Database, October 2017.|
|Mortality|Mortality rate, adult, female (per 1000 female adults)._Source:_WDI,<br>World Bank|
|PopGrowth|Population growth (annual %)._Source:_WDI, World Bank|
|Inflation|Inflation, GDP deflator (annual %)_Source:_WDI, World Bank|
|_Regions_||
|East Asia and Pacific (EAP)|Dummy indicating a country in East Asia or Pacific region._Source:_WDI,<br>World Bank|
|Europe and Central Asia (ECA)|Dummy indicating a country in Europe or Central Asia region._Source:_<br>WDI, World Bank|
|OECD high income|Dummy indicating a country in OECD high income group._Source:_WDI,<br>World Bank|
|Latin America and Caribbean (LAC)|Dummy indicating a country in Latin America or Caribbean region.<br>_Source:_WDI, World Bank|
|Middle East and North Africa||
|(MENA)|Dummy indicating a country in Middle East or North Africa region.<br>_Source:_WDI, World Bank|
|South Asia (SA)|Dummy indicating a country in South Asia region._Source:_WDI, World<br>Bank|
|Sub-Saharan Africa (SSA)|Dummy indicating a country in Sub-Saharan Africa region._Source:_<br>WDI, World Bank|

# **3. Findings**

We begin by controlling for cross-country differences in income levels using GDP per capita, a proxy for political change, and year and country fixed-effects. We find that richer countries have less incidence of poverty, and
for downgrading of skills, for density e¤ects and adjusting for employment rates and for the short-run capital adjustment. Section 6 concludes the paper.

# 2 Model

We construct an aggregate model of the economy to examine the long-run wage e¤ects using data from 1990 and 2000 national censuses on migration and native non-migrant labor force by education level. The analysis builds on two di¤erent strands of the literature. The ...rst strand aims at identifying the impact of immigration on national labor markets while the second analyzes the external e¤ects of individual schooling on overall productivity. In this context, international migrations have two essential long-run e¤ects. The ...rst is to change the schooling composition of the sending and receiving economy. The second is to introduce in the host countries workers with a di¤erent skill sets from natives and, hence, even for given education, not perfect substitutes with them. These two aspects imply that the size and the educational composition of immigrants and emigrants relative to the non-migrants are the crucial factors in determining the long-run domestic wage e¤ects of international migration patterns.

## 2.1 Aggregate production function

The prevalent model adopted in this literature is based on a production function where the labor aggregate is represented as a nested constant elasticity of substitution (CES) function of di¤erent types of workers. In the production function (we omit country subscripts for simplicity), we assume that at time t output (Yt) is produced in a country according to a constant-returns-to-scale Cobb-Douglas production function with two factors, physical capital (Kt), and labor in e¢ ciency units (Qt): The term A<sup>e</sup> t represents the total factor productivity (TFP), and � is the income share of labor.

Assuming that physical capital is internationally mobile and that each single country is too small to a¤ect the global capital markets, the returns to physical capital are equalized across countries. If R<
0.3 percent. (The trend rate of economic growth is defined here as the regression coefficient of the log mean on time.) However, as was seen in the NAS data, there was a marked break in the trajectory around the turn of the century. The trend growth rate prior to 1999 was 0.9 percent per annum (standard error = 0.1) while it was 4.3 percent (0.2 percent) from 1999 onward.<sup>5</sup> (The difference in trends is statistically significant: _t_ = 3.32, _p_ = 0.009.) It is especially notable that this break reflects the higher economic growth rate of the developing world outside China, which rose from 0.6 percent (0.1 percent) prior to 1999 to 3.8 percent (0.3 percent) from 1999 onward.

The key point to emerge from these observations—drawing on both national accounts and surveys—is that the output and living standards of the developing world as a whole have been growing at faster rates than for the rich world since the turn of the century, reversing a prior pattern of little or no progress toward economic convergence. How this happened is not yet clear. Possibly the (private and public) capital flows to developing countries since 2000 have been sufficient to get out of past poverty traps. Or possibly the conditions required for the existence of poverty traps did not in fact hold in practice (as argued by Kraay and Raddatz, 2007), or they only hold for certain countries, or for pockets of poverty within countries, but not in the aggregate. Then it was just a matter of time, aided by more supportive policy environments (including political stability), before the catching up process would be revealed. Less optimistically, given the uncertainties about the dynamics of the processes involved, one cannot yet rule out the possibility that the forces creating the earlier traps are still there, and will reemerge in
coordination problems) and, hence, foster structural change and spur growth. Aizenman and Lee (2007), on the other hand, suggest that RER undervaluations may be used to internalize a learning-by-doing (LBD) externality in the traded sector if the LBD calls for subsidies to labor in tradables. This debate has led to a heated argument about the desirability of undervaluations and the likelihood to support them through economic policies.

Official intervention on the foreign exchange market is one of the crucial issues in the subject of academics and policy-related literature. It has been suggested that intervention may tend to introduce a deviation of the exchange rate relative to its long run equilibrium. An abundant body of research has been conducted on the effectiveness of FOREX market intervention in stabilizing exchange rates. For instance, Taylor (2004) estimated a Markov-switching model to examine the effectiveness of intervention on the US$-DM exchange rate (from 1985 to 1998) and found that intervention increased the likelihood of stability when the real exchange rate is misaligned, and that this influence grew with the degree of misalignment. However, intervention can also generate greater instability. According to Sarno and Taylor (2001) overall, the evidence on the effectiveness of official intervention, through either the portfolio balance channel or the signaling channel, is still mixed on balance, although the more recent literature does suggest a significant effect of official intervention on both the level and the change of exchange rates.

Doroodian and Caporale (2001) support the view of Friedman and Schwartz that exchange rate intervention destabilizes the foreign exchange market by introducing additional level of uncertainty. They test the effectiveness and the impact of Federal Reserve intervention on US dollar against German mark and Japanese yen of daily data from January 3, 1985 to March 19, 1997. Their results from GARCH suggest that the intervention causes significant increase in the conditional variance of spot exchange rates.

Why is this study of real exchange rate misalignments so relevant? Real exchange rate misalignments help to signal distortions in relative prices. Measuring the misaligned currencies (
**Figure 10: Nominal Exchange Rates of Currencies Against the Dollar, 2005-2011 (monthly)**

<!-- Start of picture text -->
1.4<br>1.3<br>1.2<br>1.1<br>SwissFranc/USD (actual e-<br>1 rate)<br>0.9 Euro/USD (actual e-rate)<br>0.8<br>Pound/USD (actual e-rate)<br>0.7<br>0.6 Yen/USD (Index Jan '05 =1)<br>0.5<br>Renminbi/USD (Index Jan<br>0.4<br>2005... 2005... 2005... 2006... 2006... 2007... 2007... 2007... 2008... 2008... 2009... 2009... 2010... 2010... 2010... 2011... 2011... '05=1)<br><!-- End of picture text -->

Source: Data from World Bank Global Economic Monitor database.

Note that Euro and Pound data are actual exchange rates per dollar (i.e., 2 dollars per pound = 0.5 pounds per dollar). Yen and Renminbi are indices set to January 2005 = 1 in order to fit in a similar scale. The key point here is not the level, but rather the trends and the volatility.

In globalized financial markets, the international dimension of credit goes well beyond monetary policy. For some key currencies, particularly the US dollar (and, to a lesser extent, the euro), the domain of use extends well beyond the issuing country.<sup>85</sup> In larger countries, the stock of credit in foreign currency tends to be modest in relation to
|
||1986:4|15.72|211.42|1989:3|
|Philippines|1982:2|1.66|7.56|1984:2|
||1986:2|0.27|3.82|1990:1|
|South Africa|1983:1|2.85|4.21|1986:1|
|Thailand|1985:1|0.27|1.53|1990:2|
|Turkey|1982:2|6.68|10.58|1984:4|
||1986:3|7.88|14.52|1988:4|
||1989:3|12.04|17.23|1996:4|
||2000:1|11.12|12.21|2000:4|
|Venezuela|1983:1|1.80|12.94|1989:3|
||1991:4|7.28|16.61|1995:3|

a As was explained in Section 3, the inflation figures presented in the table are the filtered values of actual quarterly consumer price inflation using a nine-quarter centered moving average process.

24 There were inflation episodes in Argentina, Colombia, Israel, Korea, Malaysia, Mexico, South Africa, and Thailand whose start date turned out to be before 1980 and, as a result, could not be included in the investigation. The lightly shaded areas in Figure 2 present some of these episodes, which commenced before 1980 and ended in the early 1980s.

25 We have also considered including Chile in our study. However, the evolution of the inflation rate in this country displays a significantly different pattern since Chile has reduced its trend inflation noticeably during the late 1970s and the early 1980s compared to the countries involved. On this ground, we have not included Chile in our analysis.

22
The results illustrate what poverty trends would have looked like had the underlying relationship between consumption and its correlates remained constant from 1996/97 to 2008/09. Coefficients’ time invariance is arguably a significant assumption.<sup>14</sup> However, bearing in mind the evidence that the model based on the IAF 1996/97 is less affected by measurement error, the out-of-sample predictions for 2002/03 and 2008/09 produce revised consumption estimates that are demonstrably less biased than the original figures. Furthermore, using the 1996/97 parameters, variations in consumption figures over time are fully explained by variation in the independent variables (see Appendix).

Figure 3, below, shows median consumption per capita in 2002/03 and 2008/09 using both the official and revised consumption data. Whereas in 2002/03 the difference between the original and revised figures seems to be remarkable only in the South, the 2008/09 model estimate shows both higher median consumption in the Southern provinces and urban areas and much lower consumption in the Northern and Central provinces and rural areas. For example, in the revised estimates for the urban South median consumption is only slightly higher than in the official figures for 2002/03, but the gap widens dramatically in 2008/09. By contrast, in rural Nampula and Niassa-Cabo Delgado the revised estimates for median consumption are significantly lower than the official figures for both periods.

> 14 An alternative could be to re-estimate the inaccurately measured observations using a so-called ‘hybrid model’ (see Appendix). The model is estimated by using a sample of arguably reliable observations, and the estimated _β_ coefficients are then applied to the whole sample; the fitted consumption estimate is assigned only to the biased observations, and errors are estimated by the random assignment of normally-distributed errors from the equation. Both methodologies come with their drawbacks; estimates based on a reduced sample are not regionally representative (due to the regional pattern of dropped observations), while estimates based on 1996/97 coefficients assume
074)|(0.065)|
|Lagged Dependent Variable (Baseline)|||0.203***<br>(0.023)||0.363***<br>(0.032)|||||
|Malawi Developmental Assessment Tool: Fine|||||||0.373***||0.384***|
|Motor/Perception Skills(Baseline)|||||||(0.038)||(0.038)|
||T2=T3|0.377|0.333|0.276|0.383|0.648|0.887|0.337|0.443|
|F-test for Equality of Parameters - p-value|T2=T4|0.686|0.556|0.692|0.815|0.690|0.553|0.405|0.366|
||T3=T4|0.635|0.697|0.521|0.536|0.956|0.653|0.868|0.883|
|District-bin Fixed Effects?||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Baseline Control Variable and Age Dummies?||No|Yes|No|Yes|No|Yes|No|Yes|
|Number of observations||2,009|2,009|2,029|2,029|2,027|2,027|2,027|2,027|

Notes: .01 - ***; .05 - **; .1 - *; OLS regressions at child level using baseline and 36-month data on scores. Standard errors in parentheses are clustered at the CBCC level. We use all children for whom the relevant variables are available. We weight observations using sampling weights. For each
, where households with the lowest income and the highest income express the highest levels of trust in institutions. The reason for this non-linearity is not instantly clear. Also, it appears that improved access to services sometimes generates a lower trust dividend than could be expected. Trust in government is indeed below the levels that could be expected given the objective improvements in infrastructures and access to services over the past two decades.

Additional data collection and experimental studies could help further understand the drivers of trust in public institutions and address concerns related to endogeneity and reverse causality. For example, future research could investigate how trust in public institutions evolves with election cycles and major policy or reform decisions. Additional survey waves would also have the significant advantage to enable time-series analysis

> 21This finding echoes empirical evidence that while welfare inequalities are declining in Morocco, vulnerable and lower middle-class households have a relatively worse perception of the evolution of their living standards than better-o↵households. See Clementi et al. 2019.

24
applications received to<br>total applications|% of approvals secured to total<br>approvals|
|---|---|---|
|**Healthcare**|36|35|
|**Agriculture**|19|18|
|**Clinical trials**|14|18|
|**Industrial products &**<br>**processes**|9.5|5|
|**Bio-medical devices &**<br>**instruments**|8|5|
|**Infrastructure**|7|3|
|**Bio-energy &**<br>**environmental biotech**|5|8|
|**Field trials**|1.5|8|
|Total|100|100|

Source: DBT BIPP database.

> 24 The reported figures are based on published figures and information from companies on unpublished revenues. Unpublished figures do not factor group revenues and stand-alone revenues of companies.

31
||(in|millionof|US dolla|rs)|||2000=100|
||Radiotelegraphic &||||||||||
|7643|radiotelephonic|0.02|0.08|1.12|1.19|0.66|0.89|1.9|17.8|2708|
|7648|Telecommunications equipment|0.17|0.32|0.14|0.68|0.28|0.87|6.6|6.8|2467|
|7758|Electro-thermic appliances|0.21|0.17|0.40|0.64|0.56|0.54|11.3|24.7|4430|
|7752|Household type refrigerators|3.81|2.23|5.49|4.76|1.42|12.2|19.2|30.3|2134|
||TOTAL ABOVE|4.21|2.80|7.16|7.27|2.91|14.5|38.9|79.6|2735|
|Memor|andum:||||(inper|cent)|||||
||Share of the above in EU-||||||||||
|7|destined exports of machinery<br>Share of the above in EU|5.8|6.9|13.6|10.7|3.8|14.1|23.2|31.8|834|
||imports|0.06|0.06|0.12|0.10|0.03|0.14|0.31|0.47|1650|

Source: Based on EU Data from UN COMTRADE Statistics.

Exports of household type refrigerators provides an interesting illustration of sectoral “hick-ups’