rafmacalaba/datause-encoder-data huggingface dataset:
zero elsewhere. In the empirical estimations, we make use of district data: the district maize balance establishes whether a district is a buyer of seller district (see below).

To capture developments in road infrastructure and road quality, developments in transportation technology, changes in the structure of the trading sector, and developments in telecommunication infrastructure, a general trend variable is included. The trend variable takes a value of one at the start of the sample period and increases by one each period. Partial derivatives with respect to trends are expected to be negative, reflecting cost reductions caused by structural developments, such as the roll-out of the mobile phone infrastructure, which are shown to reduce the information and search costs of trade (e.g., Aker 2008; Jensen 2007; Muto and Yamano 2009).

There is a distinct seasonality in maize prices. Because the domestic maize trade is determined by maize prices in different locations, it is likely that seasonality in maize prices also translates into seasonality in transaction costs. Therefore, a set of monthly dummy variables— binary variables with a value of one for a specific month and zero elsewhere—is included to account for seasonality in transaction costs.

To further generalize the specification, we allow for seller-specific and buyer-specific seasonal patterns by interacting monthly dummies with seller and buyer dummies. The estimated equation is summarized as follows:ln (tctkj) = β0 + β1ln (diskj) + β2ln (pf,t) + β3trendt + where _tc_<sup>_kj_</sup> is the transaction cost of trading goods from _k_ to _j_ ; _dis_<sup>_kj_</sup> is the distance between location _j_ and _k_ ; _pf_ is the transport fuel price; trend is a trend variable; _μk, νj_ , and _δm_ are seller, buyer, and monthly fixed effects; _λkm,_ and τ _jm_ are seller-monthly and buyer-monthly fixed effects; and _ε_<sup>_kj_</sup> is an error term with a zero mean and a constant variance ( _ε_<sup>_kj_</sup> ∼(0,σ<sup>2</sup> ))<sup>7</sup> .

_<<B>>Selecting price differences for estimation of unit transaction costs_ 12
||All Small<br>States|Sample<br>Group|
|---|---|---|
|Average GNI per capita US$|4759|4329|
|Classified as:|||
|Low income|9|18|
|Lower middle income|48|47|
|Uppper middle income|43|35|
|Average Ratio of Public Debt to GDP|52|62|
|External public debt/GDP|34|40|
|Domestic public debt/GDP|18|22|
|With an IMF program|43|59|
|Rescheduled with external creditors|43|65|

Source: World Bank

# **Findings from the DeMPA in Small States**

The DeMPA findings confirm that most of the 17 small states assessed have rudimentary debt management functions, practices and systems. No small state in the sample group met the minimum requirement for more than twenty of the thirty five DPIs assessed and more than half of them (53 percent) met the minimum requirement for less than ten DPIs. These scores are well below those of the other 42 developing countries for which DeMPAs have been conducted (Figure 2).

**Figure 2: Debt Management Capacity in Small States and Other Developing Countries Compared**

_Percent_ <!-- Start of picture text --> % of Countries in Each Category<br>60 52.94 52.38<br>47.06<br>50<br>40 33.33<br>30<br>20 14.29<br>10<br>0.00 0.00 0.00<br>0<br> <10 >=10 & <20 >=20 & <30 >=30<br>No. of indicators out of 35 indicators that meet minimum<br>requirements<br>Non-small states Small states<br>Source: DeMPA reports<br><!-- End of picture text --> Taken as a group the small states in the sample met the minimum requirement for, on average, 32 percent of dimensions assessed, while the non-small states met the minimum requirement for 41 percent. Strengths in debt management practices, indicated by a score of A and B were 12
2% in Jordan, and only negligibly in Turkey and Egypt (Table 4). The embargo on trade with Syria has been a major factor behind the deterioration in Syria’s per capita welfare, reducing it by more than 15%, while capital destruction and loss of workers are responsible for declines of more than 5% and 7%, respectively. In Iraq, the per capita welfare losses are associated with the deteriorating environment and the resulting decline in productivity. In Lebanon, the main effect comes from the massive inflow of Syrian refugees.

**Table 4 Welfare effects of war and trade disintegration in the Levant (%)**

||Turkey|Egypt|Jordan|Lebanon|Syria|Iraq|
|---|---|---|---|---|---|---|
|_Direct per capita effects of war_|_-0.5_|_-0.1_|_-1.4_|_-10.6_|_-14.0_|_-16.1_|
|Output effects|0.0|0.0|0.0|-0.1|-6.3|-12.6|
|Capital destruction|0.0|0.0|0.0|0.0|-5.4|-0.1|
|Trade cost escalation|0.0|0.0|0.0|-0.1|-0.1|-0.2|
|Trade embargo on Syria|0.0|0.0|0.1|0.0|-15.4|0.0|
|Labor force effects of refugees|0.4|0.0|1.0|6.0|-7.5|2.8|
|Population effects of refugee movements|-0.9|-0.2|-2.5|-16.4|20.8|-6.1|
|_Per capita effects of trade disintegration_|_-1.4_|_-9.0_|_-5.8_|_-2.2_|_-8.6_|_-12.0_|
|Foregone agricultural liberalization|0.0|-0.1|0.0|0.0|0.0|0.0|
|Foregone transport logistics reform|0.0|-0.1|0.0|-0.2|0.1|-1.0|
|Foregone services liberalization|-1.4|-8.8|-5.7|-2.1|-8.7|-11.0|
|_Per capita cumulative effects_|_-2.0_|_-9.1_|_-7.2_|_-12.8_|_-22.6_|_-28.1_|
|_Aggregate cumulative effects_|_-1.1_|_-8.8_|_-4.7_|_3.9_|_-38.3_|_-23.4_|
|Direct aggregate effects of war|0.3|0.1|1.0|6.4|-30.7|-10.7|
|Trade disintegration effects|-1.4|-8.9|-5.7|-2.5|-7.5|-12.7|
|_Cumulative effects in US$ 2007_|_-6,510_|_-10,483_|_-834_|_912_|_-12,280_|_-3,997_|

Syria’s direct aggregate welfare decline is much larger than its per capita welfare loss (Table 4). Syria’s economy shrinks by almost a third due to the massive outflow of Syrian refugees and war casualties. By contrast, Iraq’s aggregate welfare loss of 11% is smaller than its per capita welfare 19
<!-- Start of picture text --> Fig 2 Share of total owned area by holding<br>size: all India, 1953-54 to 1982<br>* 100%<br>180%.<br>6> 6 ha<br>o 60%.<br>a 40% ^! r| m C] -2 - hha<br>0 31 -2ha<br>e! 20% ^0.4-1 ha<br>0% E<0.4ha<br>1953-54 1961-62 1971-72 1982<br><!-- End of picture text -->

One would expect the distribution of operational holdings to be less skewed than ownership holdings, on the assumption that larger owners generally rent land to smaller ones. While this is generally true, ground reality is more complex, and landlords and tenants cannot be divided into mutually exclusive classes (cf. Neale 1990). Many with relatively large operational holdings lease-in land from smaller owners (the phenomenon of so-called 'reverse tenancy'). Where there has been rapid technological change (Haryana, Punjab) operational holdings have become more concentrated owing to the displacement of tenants, increase in hired labor, and diversification of assets away from land, especially in irrigated areas. At least until the early 1980s, the following trends could be observed from all-India data. Although operational land remains very unequally distributed, inequality has lessened over time. The number of small and marginal operational holdings has increased owing to population growth and subdivision, particularly in the northern (former _zamindari)_ states of Uttar Pradesh, Bihar, Orissa and West Bengal, but the average size of marginal holdings also increased. Larger holdings fell proportionately in number and area but their average size did not decline. The most significant net gains overall were for medium holdings, except in Kerala. Figures 3 and 4 illustrate the changing structure of operational holdings and share of total area operated by size of holding since independence.

<!-- Start of picture text --> Fig 3 Distribution of household operational<br>holdings: all India, 1953-54 to 1982<br>100%<br>(U<br>80%<br>0 >6ha<br>@, 60% -2 - 6 ha<br>°~ 40% E31 - 2 ha<br>e 20% - X - 0 04-1 ha<br>X 2 0% _<br>1953-54 1961-62 1971-72 1982 * Landless<br><!-- End of picture text --> _15_
Table A2: ESS3 <u>weights — Impacts of hosting refugees on household welfare and poverty</u>

|||All househol<br>|ds<br>||
|---|---|---|---|---|
||Consumption|Consumption poverty|Wealth|Wealth poverty|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.175**|0.167***|0.047|-0.007|
||(0.071)|(0.051)|(0.044)|(0.043)|
|Constant|9.599***|-0.814**|0.478|0.623**|
||(0.417)|(0.337)|(0.318)|(0.311)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.068|0.042|0.185|0.112|
|# of EAs|432|432|432|432|
|N|12711|12711|13274|13274|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS3 applied. All columns control for household (HH) FEs, round FEs, and 12 timevarying characteristics. The coefficient estimates for these 12 characteristics are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5%, and 1% levels, respectively.

52
<!-- Start of picture text --> 7<br>oSac $iY<br>o<br>5<br>&<br>a<br>9Q<br>ao sere, - a<br>aus<br>S 1g<br>= \/<br>OL'<br>1990 1995 2000 2005 2010<br>Year<br>——— Change inFDIIGDP = ~*~ =» Change in M&AVGDP<br>vrvrrereeee Change in GF/GDP<br><!-- End of picture text --> <!-- Start of picture text --> a”<br>a3c<br>5 F \<br>& con i ‘<br>a < Newesaglher”<br>fa9g ’ oT<br>ac = ea ““<br>u ee =<br>s sary f<br>8 Ne ~"- _-_<br>A pee?<br>oT' wae¥<br>1990 1995 2000 2005 2010<br>Year<br>ss--*ee-* 1 SD above Rolling Mean —~~-~—-~- 1 SD below Rolling Mean<br>——— Change in FDI/GDP<br><!-- End of picture text -->
Our data<sup>8</sup> on the cases is quite revealing in some respects. **First** , there is not one single occasion where a developing country (non-OECD member) imposed countermeasures to induce compliance even when faced with non-implementation. **Second** , non-OECD Members when faced with non-compliance, with two exceptions (Brazil and Ecuador) do not even enter into the process of calculating their damage, that is, the first step towards requesting authorization to impose countermeasures. **Third** , even OECD Members when facing a recalcitrant opponent which happens to be a “larger” market (Mexico vs. EC in _Bananas_ ) have sometimes refrained from requesting the authorization to impose countermeasures. **Fourth** , the opposite is true when OECD Members face non-implementation: all countermeasures have been imposed by OECD Members. It is difficult to pronounce on the effectiveness of countermeasures, but it is certainly true that OECD Members (namely, the US and Canada) have used this instrument. The two cases when no use was made are quite odd: Canada did not impose countermeasures against Brazil most likely because Brazil could do the same in the same dispute (export subsidies in regional aircrafts where Bombardier and Embraer, Canadian and Brazilian producer respectively hold a joint dominant position in the world market); the EC did not impose countermeasures against the US in the _FSC_ dispute, probably in light of the possible repercussions (the EC was granted the right to block trade worth approximately 4billion $/ year).

Our data suggest even more: there is not one single case with uncertainty as to its outcome when an OECD Member is complainant and a non-OECD Member defendant. There are six cases in the opposite scenario and still no request to impose countermeasures. Thus, the data suggest that countermeasures are a more or less

> 8 At the moment of writing, the EC had announced its intention to request authorization to adopt countermeasures against the US (as a result of the latter's unwillingness to implement the AB rulings in

14
99 percent level and the R-square of the regressions is 0.67. In column (2), we compute the share of employment at risk of automation based on educational attainments. Also in this case, the coefficient is positive and significant at the 1 percent level and the R-square is 0.65.

The main data source for the labor market analysis is the Indonesia’s labor force survey (Survei Tenaga Kerja Nasional - Sakernas), which is published by Indonesia National Statistics Bureau (BPS). The Sakernas is a cross-sectional dataset with wide national representation undertaken twice a year. The August waves of the survey since 2008 are representative at the regency level, which allows us to construct regency-level labor market measures.<sup>34</sup> Besides information on wages, employment status, sector and work location, the survey also includes information on the occupation of the worker identified according to the Indonesian classification (Klasifikasi Baku Jenis Pekerjaan Indonesia - KBJI). This is compatible with the International Standard Classification of Occupations (ISCO), allowing us to construct exposure to robots measures at the regency level as explained above. We focus on wage employees as those are the workers more directly affected by firms’ adoption of robots. This focus is also relevant from a policy perspective, as wage employment typically provides a more reliable and higher incomes than self-employment in a developing country like Indonesia.

Summary statistics for the labor market data are presented in Table 11. To facilitate the interpretation of the results, we normalize the change of regency ETR to have zero mean and unitary standard deviation in the sample.

Employment increased substantially during the period with a 12 percent mean increase across regencies. Employment growth has been particularly high in services, construction and utility sectors, which were the sectors where most jobs were created as a result of the commodity-driven growth of the 2000s in Indonesia (World Bank (2015)). At the same time, wages doubled in the average regency, partly driven by minimum wage growth, particularly since 2012.

Table 12 presents the baseline results on total and manufacturing employment and

> 34Since we do not need 2006 census year information in the regency-level analysis, in this part of the analysis we take 2007 as the base year.

32
We also analyze wage inequality using quarterly household survey data. Mexican household data are from the Encuesta Nacional de Empleo Urbanao (ENEU) and the Encuesta Nacional de Empleo (ENE) over the period 1988-2004 and from the Encuesta Nacional de Ocupacion y Empleo (ENOE) over the period 2005-2011. Working-age adults who have zero or unreported earnings are excluded, and we further restrict the sample to adult males between 18 and 65 years of age. The Mexican data report monthly earnings; we base our analysis on monthly earnings throughout the paper. The ENEU/ENE/ENOE surveys expanded to cover more rural areas over the last two decades. To maintain a constant sample, we restrict the sample to workers from major metropolitan areas that have consistently been included (the same group that comprise the urban sample for the census data described earlier): Mexico City, the State of Mexico, San Luis Potosí, Leon, Guadalajara, Chihuahua, Monterrey, Tampico, Torreon, Durango, Puebla, Tlaxcala, Veracruz, Merida, Orizaba, Guanajuato, Tijuana, Ciudad Juarez, Matamoros, and Nuevo Laredo.

To compare the relative importance of between and within inequality in the household surveys, we estimated standard Mincerian log-wage equations with education, age, age squared, city, and industry on the right hand side and capture the coefficients of the observed variables, the R<sup>2</sup> , and the residuals. The captured coefficients tell us how returns to observable characteristics change over time (e.g. the returns to education as a proxy for the demand for skills). The R<sup>2</sup> and wage residuals indicate the importance of within-industry heterogeneity holding observable worker characteristics constant.

Figure 11 shows the R<sup>2</sup> values for the quarterly Mincerian wage equations. The R<sup>2</sup> rises from 1988 until around 1997 and then falls, which indicates that the change in the wage structure in the 1988-1997 period is increasingly explained by observable characteristics, such as the returns to skill. Indeed, the coefficient on education follows the same path as the R<sup>2</sup> values. After 1997, however, the observable characteristics explain less and less of the changes in wages. The fall in the R<sup>2</sup> values is consistent with the idea that within-industry heterogeneity (holding observable characteristics constant) is increasingly important.

The main lessons from the decomposition of wage inequality are that wage inequality fell between 2000 and 2010 and much of the decline (perhaps as much as half) is due to factors other than returns to age, 19
Levine, Ross, Norman Loayza, and Thorsten Beck. 2000. "Financial Intermediation and Growth: Causality and Causes." Journal of Monetary Economics 46: 31-77.

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18
21

# **Table 1 (con’t): Summary statistics**

||Sa|lary|Non-p|oor bonus|Equal|bonus|Poor|bonus|Joint F-<br>test|
|---|---|---|---|---|---|---|---|---|---|
|Observations||96||99|7|9|1|05||
|Current state of personal finances (4 =<br>Excellent)|1.86|(0.61)|1.76|(0.59)|1.76|(0.56)|1.77|(0.67)|0.574|
|Interest in task (5 = Very interesting)|4.21|(0.48)|4.13|(0.62)|4.27|(0.59)|4.14|(0.75)|0.446|
|Confidence in payment to schools (5 =<br>Strongly agree)|3.61|(1.28)|3.72|(1.25)|3.57|(1.32)|3.65|(1.16)|0.879|
|Schools are effective (5 = Strongly agree)|3.39|(1.28)|3.54|(1.21)|3.51|(1.39)|3.76|(1.10)|0.184|
|Clarity of instructions (5 = Always)|3.54|(1.18)|3.26|(1.18)|3.49|(1.10)|3.66|(1.21)|0.111|
|Questions about cases were fair? (5 =<br>Very fair)|3.85|(0.62)|3.87|(0.55)|3.81|(0.60)|3.83|(0.60)|0.912|
|Are the cases common? (5 = Strongly<br>agree)|3.48|(1.44)|3.58|(1.31)|3.46|(1.52)|3.70|(1.32)|0.590|
|Qualifications||||||||||
|Nurses (=1)|0.54|(0.50)|0.56|(0.50)|0.43|(0.50)|0.53|(0.50)|0.350|
|Midwife (=1)|0.08|(0.28)|0.10|(0.30)|0.08|(0.27)|0.11|(0.32)|0.808|
|Doctor (=1)|0.00|0.00|0.03|(0.17)|0.04|(0.19)|0.02|(0.14)|0.312|
|Other (=1)|0.38|(0.49)|0.31|(0.47)|0.46|(0.50)|0.33|(0.47)|0.218|

Note: Standard deviations in parentheses.
62

- idle CO emissions limit for all 4-wheel gasoline-driven vehicles shall not exceed 3% by volume;

- idle CO emissions limit for all 2- and 3-wheel gasoline-driven vehicles shall not exceed 4.5 % by volume;

- smoke density for diesel vehicles shall be restricted to 75 HSU at full load, 60 and 70% of maximum rated engine speed, for type approval; on the road, the limit on the free acceleration test is 65 HSU or equivalent.

The above standards went into effect on October 1, 1989. By April 1, 1991, automobile standards roughly equivalent to the EEC R 15-04, but tailored to Indian driving conditions will go into effect. Similarly, diesel vehicle requirements will be tightened at that time [Walsh and Karlsson 1990].

Thailand has a very modest vehicle pollution control program, including roadside checks of smoking diesel vehicles by the police. No I/M requirements exist for automobiles at present but plans exist to introduce idle test standards in the future.

In an effort to address the diesel particulate problem in Hong Kong, smoke observer teams issue citations to smoking vehicles, requiring such vehicles to report to a central testing station for instrumented (Hartridge) tests. Vehicles which fail must obtain necessary repairs or face fines. In addition, a large automated inspection facility is expected to open by September 1990. At that time, annual inspections will be required for all goods vehicles (currently only those over 11 years old are tested), taxis, and buses. Private cars at present are only tested for roadworthiness in private garages if they are six years old or more but this has very little if any impact on emissions.

In Philippines, the diesel smoke control has three key elements: information, education, communication -- coupled with strong enforcement. Teams operate on key routes in Metro Manila and on the basis of Ringlemann chart readings issue citations and strip offending vehicles of their registration plates. To reclaim their plates, vehicles must be presented to the central testing facility for a more technically sound Hartridge instrument test. This test must be passed (67 HSU or less) to avoid a fine (200 pesos for the first failure, 500 for the second and 1000 for the third). The citation rate has risen to 5000 vehicles per month. Vehicles passing this test still emit visible smoke because the limits are so lenient [Walsh and Karlsson 1990].

In many developing countries, weak administrative and regulatory arrangements could result in massive evasion of I/M programs or worse lead to corrupt practices on part of I/M officials and inspectors. In general, the experience with enforcement of traffic and safety regulations in developing countries is not encouraging and I/M programs for vehicle emission control may prove equally ineffective [Harral 1990].