rafmacalaba/data-use-mentions-extended huggingface dataset:
- The share of Turkish lira swaps at longer maturities increased, while the share of short-term swaps declined significantly after the adoption of the new monetary policy.

- As the strategy to discourage short-term carry trade has been quite effective, there was a significant drop in the stock of short-term speculative inflows, mainly through cross currency swaps, which can be tracked via off-balance sheet positions following the adoption of the new policy mix.

- Consequently, appreciation pressures reversed and the behavior of the Turkish lira diverged significantly from the currencies of peer emerging economies, which is estimated to have contributed to an improvement in the current account deficit (annex figure 4).

# **_Conclusions_**

As indicated in the interest rate decision summary on July 21, 2011, due to the measures taken so far via the monetary policy, the domestic demand is now under control and the current account balance will start to improve in the final quarter of the year. According to the most recent credit growth data, as of July 15, 2011, annual credit growth was 35.7 percent,<sup>11</sup> which is still above the 25 percent target for the year end, but it shows that there is a slowdown in credit growth as desired by the CBRT (annex figure 5). Although the annual credit growth rate is still not at the target level, the acceleration in credit growth came to an end in the last few months. The tightening in credit supply conditions has continued in the second quarter of 2011 thanks to the measures taken by the CBRT and the BRSA. There has been a significant increase in credit interest rates recently. Hence credit growth is expected to decelerate in the second half of the year through the lagged effect of tightening in credit supply conditions. The credit growth rate in the first half of 2011 was around 17% and if the credits grow by around 7% as a result of the expected significant deceleration, the target growth rate of 25% may be achieved.

To sum up
We are not aware of any study that provides serious estimates of TFP growth for the world as a whole, that we could use to construct estimates of China's past productivity growth performance relative to the rest of the world. All we have is the very crude development accounting exercise that we did in Section 3 of the paper. If we take this at face value, we find that productivity growth in China (measured as the residual from a simple Cobb-Douglas production function) grew at 0.7 percent per year between 1980 and 2000, while productivity growth in an aggregate of the rest of the world was zero. These are of course very naive estimates of productivity growth, but they do support the basic premise that productivity growth in China has been faster than in the rest of the world. Moreover, the estimates for China are in fact quite low, mostly reflecting the fact that we are using growth rates of per capita GDP from the Penn World Tables which makes fairly substantial -- and somewhat arbitrary -- downward adjustments to China's growth rate.

It is also informative to look at how China's productivity growth rates compare with industrial countries where we have careful estimates of TFP growth. For the United States, for example, Jorgenson and Stiroh (2000) estimate a TFP growth rate of 0.6 percent per year between 1959 and 1998, albeit with a modest acceleration to around 1 percent per year during the second half of the 1990s. Somewhat higher estimates of US TFP growth during the 1990s are provided by Basu, Fernald, and Shapiro (2001) who argue for a productivity growth rate of 2 percent per year during the 1990s (adjustment costs mean Solow residual understates TFP growth). For the UK, Basu, Fernald, and Oulton (2003) find TFP growth rates around 1 percent per year between 1980 and 2000. This suggests that reasonable estimates of TFP growth rates in China in the 1.5 to 2 percent per year range may well be somewhat higher than
|-0.006|-0.022|
||||[0.014]|||[0.014]|[0.016]|
|Index of business practices|1,592|0.502|0.054|1,613|0.545|0.007|-0.047|
||||[0.013]***|||[0.014]|[0.015]***|
|Sales in past month|1,591|17,023|4,113<br>[1,461]***|1,607|15,365|-1,136<br>[1,263]|-5,248<br>[1,439]***|
|Log sales in past month|1,372|9.19|0.112<br>[0.058]*|1,379|8.79|-0.057<br>[0.074]|-0.169<br>[0.077]**|
|Profts in past month|1,591|6,309|648<br>[506]|1,607|4,896|-512<br>[348]|-1,160<br>[502]**|

**Notes: Personal initiative** is an index of 7 questions measuring personal initiative, with a higher score denoting more initiative. It was only asked in the 2-month survey; **Score on mock test** is the proportion right on an 11-question knowledge measure, only measured at the 2-month survey; **Index of planning practices** is the proportion of 3 planning practices used; **Index of accounting practices** is the proportion of 10 accounting practices used; textbfIndex of marketing practices is the proportion of
gross income and 16% of gross agricultural income. This share only stands to increase as households get richer and urbanize, a trend already underway in many African countries.

Finally, taking a fully reduced form, we compared real per capita consumption levels (2011 PPP) among tree growing households and non-tree growing households controlling for district level effects (Table 5). As expected, tree cash crop growers were on average substantially better off (84% in Ethiopia, 19% in Nigeria, and 3% in Tanzania, though no difference was discerned in Uganda). Fruit tree growers were also better off in three of the five case countries (Ethiopia, Nigeria and Uganda), an important observation, given the potential for growth of Africa’s rising middle class. However, no positive effect was found for timber tree growing. Households with timber and fuelwood on their land appeared to be even worse off in Malawi. This result may be due to characteristics particular to the tiny (0.18%) subset of households reporting having such timber or fuelwood trees. Also a large proportion of Malawi’s territory includes areas where poverty and high forest cover overlap (Sunderlin et al., 2008).

# 3.4 Gender, Land, Proximity to Forest, and Country Factors Drive On‐Farm Tree Growing

Tables 6a, b and c present the estimated effects of different correlates on the adoption of and land allocation to trees on farms by tree type. The model had little explanatory power in explaining the adoption or land allocation to timber trees (Table 6c), but a number of clear generic patterns emerge when looking at the correlates of tree cash crops and fruit trees. First, adoption and land allocation to fruit trees and tree cash crops increased with the education level of the household head, but the adoption and land allocation to tree cash crops was about 5 percentage points less among female-headed households. This result is consistent with studies showing lower land tenure security among women in Africa (Berry, 1988
more extreme poverty line. These results imply that poorer households benefitted most from mobile broadband coverage.

The magnitude of our results can be compared with those shown by Beuermann et al. (2012) and Blumenstock et al. (2020), with the difference that they estimate the effect of access to mobile coverage rather than mobile broadband coverage. Using data from rural Peru between 2004 and 2009, Beuermann et al. (2012) find that mobile phone expansion increases real consumption by 11 percent and reduced the incidence of poverty by 8 percentage points and the incidence of extreme poverty by 5.4 percentage points. Using data from 14 rural and geographically isolated areas in the Philippines between 2016 and 2018, Blumenstock et al. (2020) find that the introduction of a new phone tower led to an increase in household income of 17 percent, and increased household expenditures by 10 percent.

# Channels of impact

Turning to the different causal channels through which access to 3G/4G translates into greater welfare, we examine if mobile broadband coverage affects labor and employment outcomes as well as prices. We find that individuals (ages 15 to 64) from households that live in areas covered by 3G/4G technologies are more likely to be in the labor force and to have wage/salaried employment. Table 8 shows a significant positive effect on labor force participation after at least two years of coverage. Similarly, those individuals who live in areas covered by 3G/4G for three years or more are more likely to work in wage/salaried jobs, but not in self-employment jobs (Table 9-11). These results are almost identical for the specifications with and without control variables. The estimates for having coverage of any length and for having it for at least one year are very close to zero and statistically insignificant. However, having coverage for at least two years increases the proportion of individuals in the labor force by 2.6 percentage points, and for at least three years, this increases to 3.3 percentage points.
needs. Notably, the Law on Land Valuation (2004) sought to standardize valuation practices; however, the reliance on "normative monetary valuation" methods persisted, often leading to discrepancies between assessed values and market realities (OECD, 2018). These normative valuations failed to reflect actual market conditions, resulting in inefficiencies and inequities in property taxation.

_2013–2020: Digitalization, Delineation, and Decentralization:_ Between 2013 and 2020, Ukraine focused on modernizing its land administration system through digitalization and the delineation of state and communal land ownership. The launch of the digital State Land Cadastre in 2013 and the State Register of Real Property Rights enhanced transparency and accessibility of land information. By 2020, over 75% of land parcels were registered electronically, streamlining transactions and reducing opportunities for corruption (Ministry of Agrarian Policy and Food of Ukraine, 2020). Decentralization of land management 9
you can sort information:

- by country (select a country and retrieve details of all commitments of that country, listed by sector)

- by mode of supply (select from commercial presence, consumption abroad, cross-border supply or presence of natural persons to retrieve a list of countries and their commitments)

- by sector and level of development (choose a sector, and within that sector choose a level of development – developed, developing, least developed, transition). To obtain a complete list of commitments in a given sector you need to compile the four resulting lists

- by region

- by level of development

You can also retrieve a complete list of horizontal commitments, or a list of MFN exemptions. Finally, a sector/country matrix report is available. This is a table which identifies instances in which a given Member has made a commitment in a given sector. Totals are supplied for each 33 ____________________________________________________________________________________________________________
<!-- Start of picture text --> Stunting prevalence (%) a ;<br>6 Poorest 20% © 3rd quintile © Richest 20%<br>60%<br>50 pS e pf e e<br>20 ° 4 o o ° i<br>10 + T<br>0<br>0 mos. 6 12 18 24 30 36 42 48 54 60<br>Age (completed months)<br>Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Income groups<br>based on the 2015 FIES matched to households in the NNS. Notes: Stunting prevalance point estimates and 95% confidence<br>intervals correspond to groups of children defined by complete age in months. This chart uses 4 month intervals where,<br>for example, 2 represents children with between 0 and 4 completed months of age. age. The line represents a non-linear<br>prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95%<br>confidence interval around the predictions from from the gam gam smoothed line is shown asaa grey area.<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Income groups based on the 2015 FIES matched to households in the NNS. Notes: Stunting prevalance point estimates and 95% confidence intervals correspond to groups of children defined by complete age in months. This chart uses 4 month intervals where, for example, 2 represents children with between 0 and 4 completed months of age. age. The line represents a non-linear prediction of the the age-conditional HAZ fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from from the gam gam smoothed line is shown asaa grey area.
110</sup> However, official retirement age is often set as the upper age limit for program eligibility, excluding older workers. Moreover, services such as job search support or training may be poorly adapted to older workers. Caseloads for employment service case workers often exceed the levels recommended by the ILO (1:100), suggesting limited capacity to provide tailored services to a group with specific training and other needs. The increase in e-government services in high- and middle-income countries needs careful design and outreach efforts to reach mature workers. A study of employment services’ self-service tools in the United States found that these features were least used by mature workers precisely because of lower digital skills.<sup>111</sup> The risk of exclusion may be even higher in L/MIC settings, given lower levels of education and skills. Digital gaps are likely to have worsened the impact of COVID-19 on mature workers’ employability as employment support often moved online.

**Lack of job and career search skills among mature workers may also be holding back matching opportunities** . Digital skills are increasingly important in identifying and applying for job opportunities (as well as increasingly important for the job itself). However, globally, digital literacy is lower in the older generation<sup>112</sup> and the gaps are likely to be higher in L/MICs, where age-related gaps in literacy and education are also higher.

> 108 Almeida et al. 2012. 109 Söderbacka et al. 2020, EU-OSHA 2016. 110 Estimates based on World Bank Unemployment Protection and Employment Services Database. 111 D’Amico et al. 2009.

> 112 ITU 2021.

21
# **III. Benchmarking Latvia**

Three recent studies – the World Economic Forum’s Global Competitiveness Report, the World Bank Knowledge Economy Index, and Porter and Stern’s analysis of national innovation capacity -- highlight some of the complex challenges that Latvia will have to surmount if it is to create a globally competitive, high value added 21<sup>st</sup> century economy.

# <u>A. Global Competitiveness Report</u>

The 2002-2003 Global Competitiveness Report seeks to understand “the microeconomic bases of a nation’s prosperity measured by its level of GDP per capita. The focus is on whether current prosperity is sustainable, and on the specific areas that must be addressed if GDP per capita is to achieve higher levels in the future.”<sup>22</sup> Microeconomic competitiveness is important, the report observes, because “sound monetary and fiscal policies and the removal of distortions in exchange rates and other prices will eliminate impediments to productivity, but microeconomic foundations must be in place if productivity is actually to increase....Without microeconomic reforms, growth will be snuffed out as exports and jobs fail to materialize, wages stagnate, and the return on investment proves disappointing.”<sup>23</sup> The Microeconomic Competitiveness Index (MICI) attempts to measure the quality of a country’s microeconomic foundations of development. The index is a weighted average of two inter-related sets of variables: (i) “the sophistication with which domestic companies or foreign subsidiaries operating in the country compete, and (ii) the quality of the microeconomic business environment.”<sup>24</sup> The first set of variables attempts to measure whether a nation’s companies have the managerial, organizational and technical capacity to shift from a competitive model based on the production of simple products with low wage labor to a competitive model based on the production of sophisticated products utilizing sophisticated production processes and high wage, skilled labor.

The
Policy Research Working Paper 5750

# **Abstract**

The authors link industry-level data on trade and offshoring with individual-level worker data from the Current Population Surveys. They find that occupational exposure to globalization is associated with larger wage effects than industry exposure. This effect has been overlooked because it operates between rather than within sectors of the economy. The authors also find that globalization is associated with a reallocation of workers across sectors and occupations. They estimate wage losses of 2 to 4 percent among workers leaving manufacturing and 4 to 11 percent among workers who also switch occupations. These effects are most pronounced for workers who perform routine tasks.

This paper is a product of the Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The corresponding author may be contacted at ann.harrison@berkeley.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
rate from the calculation, but summary statistics are also included for all choices including the implemented interest rate.

_Future-biased ratio_ is fraction of choices where allocation to sooner in near period is more than 100MK lower than allocation to sooner in far period (comparing choices in near and far periods for same interest rate). In regressions this variable excludes the implemented interest rate from the calculation.

_Fraction of decisions consistent with law of demand_ is fraction (out of 8) of pairs of adjacent choices (adjacent in interest rates) where allocation to later rises in rate of return.

_Spouse minus own allocation to sooner (MK)_ is spousal allocation to the sooner period minus corresponding allocation for respondent, for all choices excluding the randomly-chosen implemented choice.

_Implemented interest rate_ is rate of return to waiting 30 days for funds for the respondent’s randomly-selected choice (out of 10 choices made).

_HH total in bank_ is total value of balances in formal banks reported at baseline (in thousands of MK).

_HH total cash_ is total value of cash held at home reported at baseline (in thousands of MK). _HH items_ is total value of physical household items and assets owned, reported at baseline (in thousands of MK).

_HH animals_ is total value of livestock owned, reported at baseline (in thousands of MK). _Total HH wealth_ is sum of HH total in bank, HH total cash, HH items, and HH animals (in thousands of MK).

# A.2 Variables collected in revisit survey

_Indicator for death in family_ indicates death in respondent’s own household in the time since the baseline survey (reported in revisit survey).

_Shortfall in expected household income_ is expected household income minus actual household income, where expectation is reported in baseline and actual is reported in revisit survey. Both expectation and actual income reports refer to same period (time between baseline and revisit survey), reported in revisit survey.

# A.3 Variables from administrative (project) data

_Days to first disbursement
World Bank to calculate ProxyMeans Test (PMT) scores to identify poor and vulnerable households. It is used to create a proxy score of weighted variables that are highly correlated with household consumption. The PMT, like our benchmark welfare measure, is a measure of chronic poverty. This is because it uses variables that are less responsive to economic shocks than household consumption, such as assets and household composition (Lindert et al., 2018). Because the PMT variable is available in the data and was used for the UBR, it is useful to evaluate it against welfare predicted into the census extract.

## Relative Wealth Index

The Relative Wealth Index predicts the relative standard of living within countries using nontraditional data sources such as satellite imagery, cellular network data, topographic maps, and proprietary connectivity data from Meta. Using supervised machine learning models, the team predicts the relative wealth for grid cells of 2.4 km<sup>2</sup> . The estimates of wealth are relatively accurate. Depending on the method used to assess the model’s performance, the model explains 56 to 70 percent of the actual variation in household-level wealth in 56 low- and middle-income countries (Chi, et al, 2022). However, the model is trained on a wealth index, which may 13
( _N_ =18), i.e., 17 alternative destination<sup>40</sup> choices (if moved) and a choice to stay in the source province. Suppose that the utility level of choosing area _j_ for the _i_ th individual is ' _Vij_ = _zij_ γ + _eij_ where _zij_ is a vector of destination-specific attributes including expected income defined below (W�ıȷ), land availability, the likelihood of obtaining a job in the foreign sector, the prospect of obtaining a job in the state sector, and distance between origin and potential destination areas. The conditional logit model estimates the probability that an individual will choose a certain destination, conditional on choice characteristics.<sup>41</sup> Unlike the multinomial logit model, in the conditional logit model, _zij_ varies across potential destinations whereas the parameter _γ_ is constant across choices.

Since the outcome variables are not available for alternative locations except for the location where the migration actually took place, the differentials in economic opportunities are constructed based on migrants’ expectations. The expected incomes for migrants across different locations are likely to depend on both individual- and location-specific characteristics (see Table 2). Thus, predicted earning equations (Lucas, 1985)<sup>42</sup> are estimated using the data from the VHLSS 2004. The expected monthly wages for 18 alternative locations for each migrant are

> 40 Whereas the migrants who moved to HCMC, Hai Phong, and Hai Duong were interviewed only in urban areas, those who went to Dac Lac and Dac Nong were surveyed only in rural areas. This results in 17 alternative destinations.

> 42 Lucas (1985) explores empirically Todaro (1969) and Harris-Todaro (1970) hypotheses adopting micro data at the individual level in the context of Botswana. Developing prediction equations for wages and likelihood of employment, his results reveal that the higher the chance of finding employment in town and the higher the person’s expected earnings in town, the more likely it is that
3). GUS data on subgroups of the labor force add up to less than the total: the residual is in lb (employed and others).

- Sources: GUS Rocznik 1992, p.42, 203 et 1993, p. 47, 109, 111, 122, 215. 1994, p.49, 117, 227

- GUS Bulletin 1994,6: p.37, 40, 49; 1995,2: p.<sup>4 2</sup> , 51

The current recovery is expected to originate in sounder economic and market bases than the previous one. Nevertheless, at the next downturn it will be harder for the economy to "grow out" of a new 30