rafmacalaba/datause-encoder-data huggingface dataset:
lowest paid occupations. Autor and Dorn (2013) hypothesize that polarization stems from the interaction between consumer preferences, which favor variety over specialization, and the falling cost of automating routine, codifiable job tasks. They find that local labor markets that specialized in routine tasks differentially adopted information technology, reallocated low‐skill labor into service occupations (employment polarization), experienced earnings growth at the tails of the distribution (wage polarization), and received inflows of skilled labor.

The papers discussed in this section suggest that for our empirical analysis we should expect positive and heterogeneous effects of ICT adoption on labor productivity, and ambiguous effects of ICT adoption on labor demand depending on worker and occupation types.

Focusing on Latin America, Grazzi and Jung (2016) explore the determinants of broadband adoption in a large sample of countries in 2010, and study their relationship with innovation and productivity. They show that using broadband internet is positively and significantly correlated with the probability of product and process innovation in firms. Nonetheless, simple access to ICTs is not enough to foster firm innovation as technology needs to be used adequately to exploit its full potential. The use of broadband also has a positive effect on labor productivity, and this result is robust when controlling for endogeneity.

In Argentina, a series of studies based on different firm‐level technology surveys show that during the 2000s, improvements in innovation resulted in an increased demand for labor, productivity and wages. Novick, Rojo, Rotondo and Yoguel (2010) analyze the relationship between innovation activities and employment dynamics during the period 2004‐2007. Their results show that the efforts of innovation (training, presence of an R&D team, or IT specialized employees) are positively correlated with firm‐level employment growth and workers’ earnings, holding age, size, industry sector, and labor productivity constant. In another study, Molina, Rotondo and Yoguel (2013) present descriptive evidence of the importance of ICT on firm productivity. Their results suggest that what matters is how the firm integrates ICT into the organization. While the adoption of ICT cannot explain the change in the productivity of the firms, the effort of innovation (number of workers in R&D teams) is a significant variable. Hence, the adoption of ICT per se does not lead to higher levels of productivity. In a related 6
multinationals increased investment in the US during the Covid-19 pandemic, with further increases following Russia’s invasion of Ukraine.

# _3.2. Evolution of the determinants of US investment flows: Triple difference_

One potential issue with the event study approach is that the results could be driven by unobservable characteristics such as global trends or country-specific shocks. For instance, nearshoring of production may be happening, but given that future economic prospects in Asia may be improving, an analysis for the US only would not capture this trend. Also, the reshoring results could be driven by exceptionally favorable investment conditions in the US. Thus, we employ a triple-difference approach where we compare US investment to EU27 investment over time. This specification allows us to control both for source- and destination-time fixed effects as well as county-pair fixed effects.

Table 1 reports the estimates from equation (2). Results point to strong evidence of nearshoring for the US FDI compared to the EU. The border dummy increased both in terms of magnitude and statistical significance following the outset of the Covid-19 pandemic (column 2). Estimates from column (3) show that the border coefficient decreased slightly after Russia’s invasion of Ukraine from 0.804 to 0.643. This translates to investment being 90 percent higher in neighboring countries compared to others.<sup>10</sup> This large effect and absence of significance during the trade war period, can be explained by the uncertainty related to the North American Free Trade Agreement (NAFTA) and subsequent ratification of the United States–Mexico–Canada Agreement (USMCA), which came into effect on July 1, 2020, and stimulated US investment in Canada and Mexico.

The triple-difference approach confirms the intensification of reshoring activity by US companies which intensified over time. The coefficient of the same country dummy variables for the US in the post-invasion of Ukraine period is 0.795. This implies that,

> 10 exp(0.643) −1 = 0.902.

12
It is this reliance on female teachers that enables the private sector to oer aordable education.<sup>6</sup> In the context of a highly patriarchal society, limited geographical and occupational mobility for women implies that locally resident women oer a cheaper (captured) supply of teachers. Female wages are indeed 30 percent lower than male wages after controlling for educational qualications and experience (World Bank 2005). More than 70 percent of all women live in the village where they were born; less than 3 percent are engaged in o-farm work; and among those with secondary education and a wage-earning job, 87 percent are teachers or health workers. Safety concerns and a patriarchal society restrict the ability of women to nd wage work outside the village where they live or in occupations other than teaching and publicly provided health care (World Bank 2005).

The presence of locally resident women can thus reduce the overall cost of wages for schools, but an assured supply in the local vicinity is critical. However, the supply of potential female teachers is low and varies across villages based on the availability of nearby schooling options. In 1981, there were four literate (adult) women (out of 242) in the median village in Punjab, the largest and most dynamic province in the country. Over 60 percent of villages in the province had three or less secondary-school educated women, and 41 percent had none. This was driven in part by a shortage of local secondary schooling options for rural women. A simple correlation in our regression sample between the availability of GSS and secondary educated women (in 1998) suggests that the presence of a GSS is associated with an increase of over 50 percent (compared to the median village without a GSS) in the (1998) percentage of women with a secondary education (from 3 to 4.6 percent).

These two features of the market for female skilled laborlow wages and limited supply combined with the unrestricted and unsubsidized market for private schooling inform our empirical strategy. The presence of a GSS should generate cross-sectional variation in the availability of locally resident women with secondary education. If teacher supply constrains

> 6In comparison, wages for public sector teachers are ve times higher for both men and women. As a result, per-child spending in rural private schools (Rs. 1012 annually) is half of that in rural public schools (Rs. 2039 annually), although available facilities are comparable across the two.

8
perceptions of corruption.<sup>13</sup> Given that there is no agreed way to determine to what extent the substantive focus of the CC and the CPI exists, this is advantageous because it suggests that alternating between the two should not affect the results.

Furthermore, all three subjective indicators predict changes in the objective measure of corruption in the manner anticipated. Namely, higher scores on the subjective indicators are associated with a statistically significant lower incidence of issued parking tickets. This relationship is most robust when using the CPI (significant at the 1% interval) but also remains significant for the other two subjective measures of corruption (albeit at the 5% level). However, while the associations are robust, the magnitude of the relationship is not as strong (ranging from 0.19-0.30). This suggests that when using the pooled data, the relationship between the objective and subjective measures is noisy.

Focusing on the OECD sub-sample (Table 8) the results are largely similar. The subjective indicators are highly and significantly correlated with each other, with the only change being that the CC and the GE are now slightly more closely correlated (0.95 versus 0.93). Interestingly, the association between the subjective and the objective indicators is now considerably stronger. Specifically, the inverse relationship between the CPI and the per capita number of tickets issued is now significant at the 1% level, and twice as strong (0.60 versus 0.30). The relationships between the number of tickets issued and the other two subjective indicators is also stronger (0.58 versus 0.19 for the CC, and 0.62 versus 0.19 for the GE) and in the case of the CC, more robust (significant at the 1% confidence interval). Of course, it is not possible to deduce whether the imperfect correlation between the indicators is due to the fact that the tickets are measuring only a narrow type of corruption, or the subjective indicators are not capturing objective assessments. However, the fact that the associations are robust provides a basis for using these measures, even if they have to be subjected to exhaustive robustness tests.

_Table 7 Correlation between Measures of Corruption (2000s data‐Pooled)_

|**Measure**|**CPI**|**CC**|**GE**|
|---|---|---|---|
|**CPI**|-|||
|**CC**|0.98***|-||
|**GE**|0.93***|0.93***|-|

13 Although their validity cannot be verified by these correlations.

25
||**Country Name (survey**<br>**year)**|**Number**<br>**of Firms**|**Percent of Global**<br>**Sample**|**Percent of**<br>**Firms with**<br>**New Product**|**Predicted Percent**<br>**of Firms with**<br>**New Product****|
|---|---|---|---|---|---|
||2005)|||||
|37|Madagascar (2005)|269|1.04|38.66|39.46|
|38|<br>Malawi(2005)|153|0.59|53.59|53.51|
|39|Mali(2003)|70|0.27|37.14|37.13|
|40|Mauritius (2005)|160|0.62|48.75|49.29|
|41|Mexico (2006)|1,117|4.33|34.38|34.28|
|42|Moldova (2002, 2003,<br>2005)|347|1.34|59.37|59.28|
|43|Nepal(2000)|207|0.80|6.76|6.82|
|44|Nicaragua (2003)|448|1.74|47.54|47.49|
|45|<br>Oman*(2003)|91|0.35|36.26|36.18|
|46|Panama (2006)|236|0.91|55.93|55.95|
|47|Paraguay (2006)|375|1.45|67.73|67.86|
|48|Peru (2006)|360|1.40|77.78|77.94|
|49|Philippines (2003)|633|2.45|48.34|48.28|
|50|Poland (2002, 2003,<br>2005)|742|2.88|44.88|44.86|
|51|Portugal*(2005)|130|0.50|27.69|27.75|
|52|Romania (2002,2005)|468|1.81|37.18|37.09|
|53|Russian Federation<br>(2002,2005)|272|1.05|54.41|54.34|
|54|Serbia and Montenegro*<br>(2002,2005)|149|0.58|51.01|50.99|
|55|Slovak Republic (2002,<br>2005)|65|0.25|53.85|53.85|
|56|Slovenia*(2002,2005)|104|0.40|38.46|38.48|
|57|South Africa (2003)|562|2.18|68.51|68.55|
|58|Spain*(2005)|134|0.52|42.54|42.36|
|59|Syrian Arab Republic<br>(2003)|507|1.96|41.22|41.14|
|60|Tajikistan (2002, 2003,<br>2005)|195|0.76|47.18|47.16|
|61|Tanzania (2003)|172|0.67|35.47|35.25|
|62|<br>Thailand (2004)|1,377|5.34|50.40|50.35|
|63|Turkey (2002,2005)|1,572|6.09|36.32|36.34|
|64|Ukraine (2002,2005)|316|1.22|64.87|64.90|
|65|Uruguay(2006)|351|1.36|66.95|67.05|
|66|<br>Uzbekistan (2002, 2003,<br>2005)|220|0.85|31.82|31.91|
|67|Vietnam (2005)|1,396|5.41|42.12|42.07|
|68|<br>Zambia (2002)|84|0.33|55.95|55.70|
||Totals|25,806|100.00|48.84|48.81|

Source: Author’s calculations based on data from the World Bank’s _Enterprise Surveys_ database, http://www.enterprisesurveys.org/

* Countries with real GDP per capita > $14,000 (PPP), which are not in sample of model (5) in Table 2.

** Predictions from model 4 in Table 2, which are used as the dependent variable in models of Table 3.

22
Figure 2. Survey-based and market-based measures of inflation expectations: Country evidence A. 5-year-ahead inflation expectations, selected advanced economies, average <!-- Start of picture text --> Percent<br>4 Professional Market-based measure<br>3<br>2<br>1<br>0<br>United Japan United<br>States Kingdom<br><!-- End of picture text -->

# C. Volatility of inflation expectations, selected advanced economies

B. 5-year-ahead inflation expectations, selected EMDEs, average <!-- Start of picture text --> Percent<br>Professional<br>7<br>Market-based measure<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>Brazil Chile Mexico Turkey South<br>Africa<br><!-- End of picture text --> D. Volatility of inflation expectations, selected EMDEs <!-- Start of picture text --> 0.6 Professional 1.2 Professional<br>Market-based measure Market-based measure<br>1.0<br>0.8<br>0.4<br>0.6<br>0.4<br>0.2<br>0.2<br>0.0<br>0.0 Brazil Chile Mexico Turkey South<br>United Japan United Africa<br>States Kingdom<br><!-- End of picture text --> Source: Bloomberg; Consensus Economics; International Monetary Fund; World Bank.

Note: EMDEs = emerging market and developing economies.

A.-D. Market-based inflation expectations are inflation swap rates (five-year, five-year forward) for advanced economies and break-even inflation rates (five-year-ahead) for EMDEs.

A.C. The sample period is 2007H1-2018H1.

B.D. The sample period is 2012H2-2018H1.

C.D. Volatility is measured by standard deviation.

19
<!-- Start of picture text --> on A All<br>5 6,000<br>= 5.000<br>= 4.000<br>E 5 000<br>= ,<br>s 2,000<br>E “000<br>5 4,000<br>+ 2,000<br>fOLEREGE”—Ey eS & e Se ee<br><!-- End of picture text --> <!-- Start of picture text --> C. OBC<br>6,000<br>5,000<br>4.000<br>3,000<br>1,000<br>2,000 :<br>-1,000 U<br>-2,000<br>eeeSP“SPF.COO ee eee-°ES<br><!-- End of picture text --> <!-- Start of picture text --> B. Upper castes<br>6,000<br>5,000<br>4,000<br>3,000<br>> 000<br>1,000 |<br>1,000<br>2 000<br>fOGUBELESEy LE OS Se # Ef ee<br><!-- End of picture text -->

<!-- Start of picture text --> D. SC<br>6,000<br>5,000<br>4.000<br>3,000<br>1,000<br>2,000<br>-1,000 UJ<br>-2,000<br>wees 2 teeSESwe SESSeeCE E CE°EFA<br><!-- End of picture text -->
currencies that reinforce the dollarization process (Kem, 2001; Ra, 2008; Samreth, 2010, 2011; Siregar and Chan, 2014). Other studies discuss and analyze the negative impacts of dollarization by focusing on its negative effects on seigniorage (Zamaróczy and Sa, 2002; Kang, 2005; Samreth, 2010). However, other dimensions of costs and benefits of dollarization in Cambodia have not been addressed sufficiently.

This paper aims to assess a consequence of high dollarization that is often overlooked by the literature: how US interest rate hikes (i.e. Fed decisions) might not only affect financial and capital flows but also indirectly hinder the competitiveness of the export sector of a highly dollarized country like Cambodia vis-à-vis third countries (in the EU, in particular) as the USD appreciates. To our knowledge, the closest study to this paper is Duma (2011), which assessed the impact of US interest rate increases on macroeconomic variables in Cambodia.

Drawing from Duma (2011), this paper contributes to the literature in several ways. First, through a longer sample period, the vector autoregression (VAR) estimation is expected to provide more rigorous results. Second, it examines the impact of US interest rate increases not only on Cambodia’s total trade balance but also on its trade balances with its main trading partners (the EU and US). As a robustness check, it estimates Cambodia’s export function with the EU using a cointegration approach. Third, an extension of the analysis incorporating international reserves is introduced. Furthermore, to ensure the robustness of results, Bayesian VAR estimation is also conducted.

# **3. Analytical approach**

# **3.1 Theoretical framework**

The Mundell-Fleming model, at the core of the “Macroeconomic Trilemma,” was widely applied in the analysis of transmission impacts of economic policies in an open economic framework. It was developed by Fleming (1962) and Mundell (1963, 1964) as an extension of the standard Keynesian IS-LM framework. The model was then extended by Dornbusch (1976) for the analysis of the exchange rate movement in the context of perfect foresight. Since then, this Mundell-Fleming-Dornbusch model has replaced the conventional Mundell-Fleming model as a tool for analyzing the effects of policy transmission. The model has been revisited by Obstfeld and Rogoff (1995) and Obstfeld and Rogoff (1996), who incorporated microfoundations to capture intertemporal behaviors of economic agents.<sup>2</sup> Our theoretical framework is essentially based on the standard Mundell-Fleming-Dornbusch model, given that the welfare effect examination

> 2 Duma (2011) also provides a brief background discussion of the Mundell-Fleming-Dornbusch model.

6
produces especially large variations in the PLs in the South, with Maputo City and the other urban areas of Maputo Province recording the highest differentials, and urban areas nationwide exhibiting far greater variation than their rural counterparts. With the notable exception of urban Gaza and Inhambane, where the baseline values differ from the official more than other PLs, the direction of change in the areas most affected by underestimation (i.e. the Northern and Central regions) is towards lower PLs and thus towards lower poverty figures.

**Table 3: Sensitivity of Official and Revised Poverty Lines to Underestimated Observations (Set A)**

|13 regions|**Se**<br>|**t A**<br>|**Nati**<br>Full|**onal baske**<br>Without|**t P.L**<br>|**Locat**<br>Full|**ion sp. bask**<br>Without|**et P. L**<br>|
|---|---|---|---|---|---|---|---|---|
||Obs.|Share|<br>sample|<br>set A|**Diff.**|<br>sample|<br>Set A|**Diff.**|
|Niassa & C. Delgado-<br>rural|2|0.00|18.79|18.11|**0.68**|18.13|18.14|**-0.01**|
|Niassa & C. Delgado-<br>urban|39|0.09|20.61|19.87|**0.74**|22.89|22.16|**0.73**|
|Nampula-rural|8|0.02|14.57|14.05|**0.52**|10.36|10.30|**0.06**|
|Nampula-urban|56|0.12|16.55|15.96|**0.59**|13.16|12.50|**0.66**|
|Sofala & Zambezia-rural|17|0.02|15.81|15.25|**0.56**|13.36|13.21|**0.15**|
|Sofala & Zambezia-<br>urban|168|0.25|17.62|16.99|**0.63**|19.13|17.68|**1.45**|
|Manica & Tete-rural|21|0.02|20.87|20.12|**0.75**|19.11|19.05|**0.06**|
|Manica & Tete-urban|95|0.21|20.19|19.46|**0.73**|21.40|20.00|**1.40**|
|Gaza & Inhambane-<br>rural|126|0.19|15.99|15.42|**0.57**|19.18|18.77|**0.41**|
|Gaza & Inhambane-<br>urban|151|0.27|17.04|16.43|**0.61**|15.87|16.52|**-0.65**|
|Maputo Province-rural|53|0.43|18.74|18.07|**0.67**|28.37|27.75|**0.62**|
|Maputo Province-urban|332|0.57|22.59|21.78|**0.81**|32.07|28.02|**4.05**|
|Maputo City|313|0.35|22.31|21.51|**0.80**|33.78|28.79|**4.99**|
|**National**|**1381**|**0.12**|**17.80**|**17.20**|**0.60**|**19.20**|**18.40**|**0.80**|

**National:** single national PL and 13 provincial price deflators; **Location specific** : 13 PLs based on local food basket, without entropy correction.

In conclusion, underestimated observations tend to bias the location-specific PLs through both their food and non-food components. By contrast, a single national PL seems to be far less affected by consumption underestimation. The sensitivity of PLs to measurement error seems to be directly correlated to the number of excluded observation sets: the larger the sample of consumption observations, the lower the effect of underreporting. In results not reported even the national-level urban and rural PLs were more affected by underestimation than the single, national PL.

18
Table 2. Bridge to link Consumption Expenditures (urban) to Input-Output accounts

|||Food,|Dining||Applian-|Health-|
|---|---|---|---|---|---|---|
|||Tobacco|Out|Clothes|ces|care|
|||1|2|3<br>...|<br>11|24<br>...|
|Agri|1|0.141|0.280|0.000|0.000|0.000|
|Coal|2|0.000|0.000|0.000|0.000|0.000|
|Crude|3|0.000|0.000|0.000|0.000|0.000|
|natgas|4|0.000|0.000|0.000|0.000|0.000|
|nonenergy|5|0.000|0.000|0.000|0.000|0.000|
|Food|6|0.781|0.100|0.000|0.000|0.000|
|textile|7|0.000|0.000|0.030|0.000|0.000|
|apparel|8|0.000|0.000|0.776|0.000|0.000|
|lumber|9|0.000|0.000|0.000|0.000|0.000|
|paper|10|0.000|0.000|0.000|0.000|0.000|
|Refine|11|0.000|0.000|0.000|0.000|0.000|
|Chem|12|0.002|0.000|0.000|0.011|0.130|
|Build|13|0.000|0.000|0.000|0.000|0.000|
|pmetal|14|0.000|0.000|0.000|0.001|0.000|
|metal|15|0.000|0.000|0.000|0.000|0.000|
|machin|16|0.000|0.000|0.000|0.020|0.000|
|tequip|17|0.000|0.000|0.000|0.030|0.000|
|emachin|18|0.000|0.000|0.000|0.638|0.088|
|electro|19|0.000|0.000|0.000|0.162|0.000|
|Instru|20|0.000|0.000|0.000|0.000|0.000|
|Other|21|0.000|0.000|0.006|0.010|0.074|
|Elect|22|0.000|0.000|0.000|0.000|0.000|
|gasprod|23|0.000|0.000|0.000|0.000|0.000|
|constr|24|0.000|0.000|0.000|0.000|0.000|
|transp|25|0.006|0.000|0.026|0.023|0.000|
|commun|26|0.000|0.000|0.000|0.000|0.000|
|commerc|27|0.069|0.000|0.156|0.106|0.083|
|Hotel|28|0.000|0.620|0.000|0.000|0.000|
|finance|29|0.000|0.000|0.000|0.000|0.079|
|realest|30|0.000|0.000|0.000|0.000|0.000|
|business|31|0.000|0.000|0.000|0.000|0.000|
|service|32|0.000|0.000|0.006|0.000|0.546|
|admin|33|0.000|0.000|0.000|0.000|0.000|
|Sum of share||1.000|1.000|1.000|1.000|1.000|

‐57‐
where the theory predicts that an increase in money supply, expected inflation, the exchange rate and foreign prices will all drive prices up, while an increase in real income will lead to a decline in the inflation rate. The effect of sluggish adjustment due to rigidities and inertia can be captured by adding the effect of lagged prices to the equation.

Tables 5, 6, and 7 present the results of the empirical counterpart to our theoretically derived equation by using monthly data for the period 1993:01-1997:09. Our empirical approach is divided into two parts. First, we employ Engle-Granger (1987) and Johansen and Julelius (1990) co-integration analyses in order to determine the existence of the long-run relationship between, P, e, y, and M.Y2 It is widely believed that variables hypothesized to be linked by some theoretical economic relationship should not diverge from each other in the long-run. Although such variables may drift apart in the short-run, they converge toward an equilibrium in the long-run, thanks to disequlibrium forces. Co-integration is viewed as the statistical expression of the nature of such longrun equilibrium relationships.

The empirical results indicate that there is a long-run equilibrium relationship between P and e, y, and M. The findings also show that, in the long run, inflation in Albania is positively related to money supply, and the exchange rate (USD), while it is negatively related to real income, which is proxied by the power consumption (PC)."<sup>3</sup> "<sup>2</sup> We have employed both the ADF test and Phillips-Perron test to investigate the time series properties of the variables involved. Considering the length of our sample, unit root testing with standard critical values could be misleading. Nonetheless, based on visual inspection and the results of these tests, it is safe to conclude that all the variables are integrated order of 1, 1(1).

'3 As widely acknowledged, there is a close link between aggregate macroeconomic activity and power consumption in market economies (with an electricity-GDP elasticity close to unity). Dobozi and Pohl 23