rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3:** Determinants of employment effect: employment protection

||||_Depend_|_ent variable:_|||
|---|---|---|---|---|---|---|
||||Emplo|yment IRF|||
||After 1 year|After 1 year|After 5 years|After 5 years|After 10 years|After 10 years|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Productivity level _log_|_−_0.229<sup>_∗∗∗_</sup>|_−_0.115<sup>_∗∗∗_</sup>|_−_0.076|_−_0.038|0.023|_−_0.003|
||(0.070)|(0.038)|(0.059)|(0.032)|(0.049)|(0.030)|
|Years Schooling|0.039|0.019|0.017|0.021|_−_0.011|0.008|
||(0.031)|(0.018)|(0.026)|(0.015)|(0.022)|(0.014)|
|WEF: Hiring and firing|_−_0.013|0.021|_−_0.035|_−_0.026|_−_0.008|_−_0.009|
||(0.073)|(0.049)|(0.062)|(0.041)|(0.052)|(0.038)|
|OECD: Collective dismissal|_−_0.007||_−_0.045||_−_0.006||
||(0.109)||(0.092)||(0.077)||
|OECD: Individual dismissal|_−_0.015||_−_0.009||_−_0.022||
||(0.044)||(0.037)||(0.031)||
|Constant|1.800<sup>_∗∗_</sup>|0.628<sup>_∗_</sup>|0.796|0.218|_−_0.091|_−_0.056|
||(0.741)|(0.366)|(0.626)|(0.307)|(0.523)|(0.286)|
|Observations<br>|56|103|55|102|56|103|
|Adjusted R<sup>2</sup>|0.188|0.105|_−_0.045|_−_0.006|_−_0.073|_−_0.022|
|Residual Std. Error|0.357|0.333|0.301|0.279|0.252|0.260|

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 Note: Regression with the scaled employment response to technology shocks as the dependent variable, and a range of variables reflecting labor market frictions as the regressors. WEF variable refers to the World Economic Forum’s survey of firm’s ability to easily hire and fire workers, ranked from 1 (difficult) to 7 (easy). OECD indicators are the OECD’s ranking of the strictness of employment legislation to protect against individual and collective dismissal, with a higher index value indicating increased strictness

**_Labor market frictions and ease of hiring and firing_** Cross-country variations in the degree of labor market flexibility and employment protections provide an alternative explanation for the variation in the persistence of employment changes following a technology shock (Rujin, 2019). The OECD’s Indicators of Employment Protection cover 73 economies, 55 of which are included in the dataset of SVAR estimations. This database includes relative measures across economies of the strictness of protections against individual and collective dismissal. A separate database produced by the World Economic Forum produces a measure of labor market flexibility on a comparative scale across all economies where SVAR estimates of the employment impact are available. While labor market structures may play an important role in governing the persistence of the employment response in theory, these variables are found to be uncorrelated with the employment impact at all horizons (Table 3).

19
Table A7: IV regression: The effect of migration on land tenure arrangements: Acreage, No Controls

||(1)<br>fxed rent|(2)<br>fxed (money)|(3)<br>fxed (crop)|(4)<br>sharecropping|(5)<br>relatives|(6)<br>contract length|(7)<br>written contract|
|---|---|---|---|---|---|---|---|
|migration|0.769*|0.778**|-0.00873|-0.735*|-0.0306|0.501|0.476*|
||(0.411)|(0.347)|(0.284)|(0.433)|(0.238)|(0.402)|(0.270)|
|2012 year|-5.788*|-1.807|-3.980*|-3.106|8.813***|19.86***|1.377|
||(3.187)|(2.863)|(2.238)|(3.155)|(1.860)|(2.979)|(2.089)|
|Constant|-2.660|-20.72|18.05|88.99***|6.824|5.606|-18.49|
||(25.68)|(21.60)|(17.64)|(27.08)|(14.90)|(25.12)|(16.84)|
|Observations|927|927|927|927|927|927|927|

Note: This table uses the shift-share instrumental variable regression to present the relation between the percentage of rented-in acreage under various terms and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). The dependent variable is as a percentage of acreage of rented in land. Robust standard errors. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.
cover ten case studies using household survey data from Bolivia (2005), Cambodia (2003), Vietnam (1998 and 2004), Malawi (2004), Nicaragua (2001), Pakistan (1999), Peru (2003), and Zambia (1998). The authors find an overall poverty-increasing impact of higher food prices because, in the sample, most poor households are net consumers of food (both in urban and rural areas).

Wodon et al. (2008) apply this method to a dozen West and Central African countries, Burkina Faso (2003), Ghana (2005-2006), Democratic Republic of Congo (2005), Gabon (2005), Guinea (2002-2003), Liberia (2007), Mali (2006), Niger (2005), Nigeria (2003-2004), Senegal (2006), Sierra Leona (2003), and Togo (2006). The authors focus on rice, wheat, maize, other cereals, milk, sugar and vegetable oils and find that a 50 percent increase in prices for selected food items would cause an average increase in the share of the population in poverty of between 2.5 and 4.4 percentage points. The average impact would be between 3.7 and 5.2 percentage points in urban areas, and between 2.2 and 4.1 points in rural areas. They also find significant heterogeneity across countries, as expected.

While the net-consumer/net-producer result is intuitive, it rests on two critical assumptions, namely that the first-order approximation is valid and that labor markets are unaffected by the price change (Deaton, 1997). The first-order approximation is analytically useful, but it is valid conceptually only for small price changes. We say “conceptually,” because in principle budget and income shares could be used to measure the distributional effects of both small and large price changes, but the approximation error will be larger when price changes are large. In these cases, it is crucial to incorporate supply and demand responses, and we discuss these below. The role of labor markets is discussed next.

# **3.3 Labor Markets**

Price changes also affect wages. The mechanisms are simple. Consider, for instance, a small open economy that takes commodity prices as given in international markets. Changes in relative product prices cause some sectors to expand and some others to contract. As a result, labor demand for different types of labor in different sectors can change, thus 11
# Table ot Contents

||Page No.|
|---|---|
|1.<br>Introduction||
|2.<br>Issues in the Design of Adjustment Programs<br>with a Poverty Focus|3|
|3.<br>The Case Studies: A Diversity of Initial Conditions<br>and Shocks|16|
|4.<br>Distributional Outcomes of Adjustment: A Synthesis|25|
|_5._<br>Lessons from the Countirfactual Simulations|34|
|6.<br>Conclusions|43|
|Notes|48|
|References|51|
|List of Figr||
|Figure 1<br>Eq. y-Efficiency Tradeoffs|6|
|Figure 2<br>Expenditure Switching, Expenditure Reduction,<br>and Structural Adjustment|9|
|Figure 3<br>Adjustment and the Factoral Distribution<br>of Income|12|
|Figure 4<br>Real Exchange Rate Depreciation, Tastes,<br>and Incowe Distribution|15|
|List of Ta&h||
|Table 1<br>Country Studies: Shocks, Adjustment, and<br>Distributional Indicators|Is|
|Table 2<br>Macroeconomic Indicators During Adjustment: A||
|Comparison with Other Countries|24|
# _Economic Crisis and Viability of Port Projects in East Asia_

The port projects in the developing economies in East Asia were economically viable until the year 2006. However, it should be reminded that benefits of expansion depend on the growth of trade, lagged construction, and resulting congestion in the ports. It is not clear that the economic viability of expansion remains in a period of economic crisis and declining trade in the region. In this section, we examine port utilization in ASEAN5 and China in 2008 and thereafter during conditions of economic crisis.

Our basic assumptions are: (i) the throughput of container terminals is proportional to trade in the real terms; (ii) port congestion is measured by the turnover ratio<sup>37</sup> , which were 82 times and 90 times, for ASEAN5 and China in 2006, respectively; and (iii) the planned rates of the expansion of ports are 18 percent in China and 2 percent in ASEAN5 per annum until 2008, i.e. the actual growth rates from 2001 to 2006. The turnover ratio in Singapore, Hong Kong and Shanghai, the most congested ports in the region, were 103, 177 and 142 in 2006, Extrapolating the provisional growth rates of real trade until 2008, the turnover ratios in ASEAN5 and China would be about 91 times and 80 times, respectively. From the data used in our regression, the sample average and standard deviation of the turnover ratio are 79 and 38. With the lower confidence boundary of one-half sigma, the effect of port congestion may emerge even at the level as low as 60 times. We may also be reminded that the perception of the congestion appeared even at the level of 80 times In 2009, real trade for ASEAN5 and China is expected to decline. While reliable estimates have not published yet, most pessimistic figures of real trade growth of ASEAN5 and China in 2009 is about a 10 percent decline in trade<sup>38</sup> . With our assumptions, the turnover ratio would be 72 to 82 times, if there is no new investment in the sector. While these ratios are considerably low, compared with the past performance, congestion in East Asian ports may still remain. As a modest implication, investment in port infrastructure may be still economically sound and effective in reducing transport costs. Port capacity investments are also, our analysis suggests,

> 37 Defined as the port throughput divided by the estimated port capacity, meaning the required turnover for the ports to process all the throughputs.

> 38 From the economic forecasts of the governments of the United States and Japan.

36
# **2.  What Do We Mean By "Governance"?**

The concept of governance is not new. Early discussions go back to at least 400 B.C. to the Arthashastra, a fascinating treatise on governance attributed to Kautilya, thought to be the chief minister to the King of India. In it, Kautilya presented key pillars of the ‘art of governance’, emphasizing justice, ethics, and anti-autocratic tendencies. He further detailed the duty of the king to protect the wealth of the State and its subjects; to enhance, maintain and also safeguard such wealth, as well as the interests of the subjects.

Despite the long provenance of the concept, there is as yet no strong consensus around a single definition of governance or institutional quality. In the spirit of this absence of consensus, throughout this paper we use interchangeably, even if somewhat imprecisely, the terms "governance", "institutions", and "institutional quality". Various authors and organizations have produced a wide array of definitions. Some are so broad that they cover almost anything, such as the definition of "rules, enforcement mechanisms, and organizations" offered by the World Bank's 2002 World Development Report "Building Institutions for Markets".<sup>3</sup> Others like the one offered by Douglass North, are not only broad, but risk making the links from good governance to development almost tautological: _“How do we account for poverty in the midst of plenty? ..... We must create incentives for people to invest in more efficient technology, increase their skills, and organize efficient markets ..... Such incentives are embodied in institutions”_<sup>_4_</sup> As we discuss further below, some of the governance indicators we survey are similarly broad in that they capture a wide range of development outcomes as well. While we recognize that it is difficult to draw a bright line between governance and ultimate development outcomes of interest, we think it is useful at both the definitional and measurement stages to emphasize concepts of governance that are at least somewhat removed from development outcomes themselves. For example, an early and narrower definition of public sector governance proposed by the World Bank in 1992 is that: _"Governance is the manner in which power is exercised in the management of a country's economic and social resources for development"_<sup>_5_</sup>

> 3 World Bank (2002), p. 6.

> 4 North (2000).

5
# Determinants of Expatriate Workers' Remittances

# In North Africa and Europe

## Ibrahim A. **Elbadawi, CECMG**

and Roberto de Rezende Rocha, **EM1TF**<sup>**4**</sup> **'**

**The** World Bank

* This paper is a product of a larger project funded by CECMG and EMTTF: "The Determinants of Expatriate Workers' Remittances".
Table 4: Heterogeneous Impacts on Fishery Incomes by Location

||Income<br>(main job)|Total<br>income|Income<br>(main job)|Total<br>income|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|**[Panel A] Geographic control **|**group**||||
|(HaTinh & QuangBinh) _×_ post|-0.251***<br>(0.079)|-0.259***<br>(0.079)|-0.413***<br>(0.067)|-0.427***<br>(0.068)|
|(QuangTri & Hue) _×_ post|-0.439***|-0.421***|-0.536***|-0.516***|
||(0.136)|(0.135)|(0.056)|(0.056)|
|Observations|2,477|2,477|872|872|
|R-squared|0.254|0.250|0.123|0.126|
|**[Panel B] Industry control gr**|**oup**||||
|(HaTinh & QuangBinh)_×_ post|-0.276***|-0.279***|-0.438***|-0.448***|
||(0.060)|(0.058)|(0.063)|(0.067)|
|(QuangTri & Hue) _×_ post|-0.429***|-0.400***|-0.574***|-0.546***|
||(0.095)|(0.092)|(0.028)|(0.029)|
|Observations|7,991|7,991|2,090|2,090|
|R-squared|0.311|0.294|0.067|0.069|
|Month FE|Yes|Yes|Yes|Yes|
|Individual FE|No|No|Yes|Yes|
|Standard errors in parenth|eses, *** _p_|_<_0_._01, ** _p_|_<_0_._05, * _p <_|0_._1|

Note: this table shows the heterogeneous impact of Formosa on fishery income (monthly;’000 VND; logtransformed) in 2016, separately for the upstream (Ha Tinh & Quang Binh) and downstream (Quang Tri & Thua Thien-Hue) Formosa-affected regions. “Post” indicates May-2016 or after. Geographic control group (Panel A) consists of all saltwater fishers living in provinces south of Phu Yen (i.e. distant from Formosa region). Industry control group (Panel B) consists of workers in unaffected industries (i.e. manufacturing, construction, and retail) living in the four Formosa-affected provinces. Columns 1-2 report results using a sample consisting of all individuals identified as saltwater fishery workers (for Panel A) and workers in the control industries (for Panel B) before May-2016. Columns 3-4 report results using a sub-sample restricting to only individuals who were surveyed twice in the Labor Force Survey 2016 – before and after May-2016. Standard errors are clustered at the district level.

34
ing is confirmed by the event study we run and show in panel (b) of the same figure. A similar conclusion can be reached if we look (again in Figure C1) at the time period from 2000 to 2016 (which is the year of the first round of data collection) or if we focus on the treatment period of 2016 to 2018.

To produce Figure C1 we focus on the 3 wards that were sampled in each LGA for this study.<sup>20</sup> In Appendix C we include graphs on different samples which summarize different ways of computing the average nighttime lights for each group (see Figure C2, Figure C3, and Figure C4). Overall, these further explorations suggest that trends are not drifting apart between the evaluation groups. At the same time, there is some indication that comparison areas might be catching up with treatment areas in terms of local economic activity over time.

# **4 Empirical Strategy**

As discussed above, our goal is to assess whether LGAs that received the project improved more between the first and second survey rounds when compared to non-project LGAs. For both the government officials and household surveys, we use responses at the individual level and, in order to formally test for different improvement rates, estimate a difference-in-differences model of the form: Yilt = α + δ1{ULGSP}ilt + γ1{SecondRound}ilt + β1{ULGSP}ilt _∗_ 1{SecondRound}il + ΠXilt + εilt (1) where Yiltis some outcome of interest of individual _i_ in LGA _l_ and period _t_ ; 1{SecondRound}ilt is an indicator that takes the value 1 if the respondent belongs to the second survey round in 2018, and 0 otherwise; 1{ULGSP}ilt is an indicator that takes value 1 if the respondent resides in a project LGA, and 0 otherwise; Xilt are individual/household characteristics used as comparisons (welfare index, household size, age, gender, marital status, and education levels) and εilt is an error term.<sup>21</sup> To evaluate the hypothesis that project LGAs were differentially affected by the program, we formally test whether β = 0; the coefficient on the interaction between the indicator for 2018 and project LGAs gives us the difference between _changes_ in project vs. non-project LGAs between 2016 and 2018 (i.e., this is the difference-in-differences coefficient). In our results, we also often present the estimates for the coefficient γ, which indicates the change in outcomes between 2016 and 2018 for the non-project LGAs.

In estimating this difference-in-differences model, we can only interpret the resulting estimates as causal if, in the absence of the intervention, the trends in outcomes for comparator and project LGAs would have been the same. In other words, for any given outcome (trust in local council, for example), our assumption is that were the project not to be implemented, the average

> 20The averages shown in the graph for each group are then the average of the wards’ nighttime lights averages.

> 21Since we estimate the model at the individual level, but the treatment is at the LGA level, our estimates of standard-errors are clustered at the LGA level.

20
_Social Capital, Household Welfare and Poverty in Indonesia_ 40

# **6.1** Asset **Accumulation**

To see whether social capital is effective in contributing to asset accumulation, we re-estimated equation (1) with an asset score variable as dependent variable. Since the data do not contain price infornation, this score was calculated using weights derived from a principal component analysis of the 15 durable goods (Filmer and Pritchett, 1998).25 The results indicate that the number of memberships is not significant, but that belonging to internally heterogeneous associations and participating actively in them is linked with higher asset ownership (Column 1, Table 10). These are, of course, the same two characteristics of associations which we found important earlier as correlates for current expenditure.

The effects are similar in magnitude as in the case of current expenditure-a 10% increase in the heterogeneity index or in the participation index increases asset ownership by 1.7-2.0%. For comparison, a 10% increase in human capital endowment corresponds to a 4.6% higher asset ownership. In other words, while social capital plays a positive role in asset accumulation by the household, its importance relative to education is less than was the case for current expenditure.

> **_25_** We also used equal weights and weights which reflected the relative scarcity of ownership of the item. This did not substantively alter the findings.
# _Implications:  The elephant in the green room_

The Green Room is the venue for key WTO negotiations. And there the great shared but unuttered fear is of competition from an increasingly dominant China.<sup>4</sup> It seems today that progress in the Doha Round hinges critically on greater market opening not in services or agriculture but in manufacturing (nonagricultural market access or NAMA in WTO-speak). Services negotiations have been given insufficient attention and are now widely regarded as too complicated to deliver significant market opening in this Round. In agriculture, with food prices high and expected to remain so, import protectionism has become less salient. Rather, it is the threat of agricultural export restrictions that is more serious, but addressing it is not on the Doha agenda anyway despite the efforts of some WTO members. So, Doha today is mostly about the negotiations on market access in manufacturing. And in manufacturing trade, as shown above, China looms large especially in the most protected sectors.

But Chinese dominance per se should not have precluded mutually beneficial bargains. The Chinese market, despite China‘s far-reaching WTO accession commitments, remains protected in a number of areas (such as fertilizers, vehicles, and certain other manufacturing items). Moreover, as Laborde, Martin, and van der Mennsbrugghe (2011) have shown, other countries would also see increased exports from the proposed Doha liberalization by WTO members. The proposals of the United States and others to move further toward free trade in selected sectors could translate into even greater export gains.

What then is stymieing the reciprocity mechanism that has delivered negotiating success in the WTO in the past? China‘s trade dominance has been achieved in large part by China‘s successful growth strategy, which has included an embrace of markets and an unusually high degree of trade openness (Subramanian, 2011). The problem, however, is the strong political perception that China‘s export success has been achieved, and continues to be sustained, in part by an undervalued exchange rate.

It seems unlikely and politically unrealistic to expect China‘s trading partners to open further their markets to China when China is perceived as de facto (via the undervalued exchange rate) imposing an import tariff and export subsidy not just in selected manufacturing sectors but across the board. The evidence on the existence and extent of undervaluation continues to be debated. On the one hand, in a survey of studies on renminbi misalignment conducted by Cline and Williamson (2008), 17 of the 18 studies concluded that the renminbi is undervalued; the average

> 4 While highlighting the difficulties for Doha posed by Chinese dominance, we do not exclude the possibility that there are other reasons for the Doha stalemate.  For instance, the United States is perceived to have progressively raised the bar – perhaps to politically impossible levels - in terms of what it seeks by way of trade concessions from other countries.

6