rafmacalaba/datause-encoder-data huggingface dataset:
responses by plants? To answer the question, we notice that the two trade policy instruments generate different cost structures. From an importer’s perspective, PSI generates both a monetary and a likely larger non-monetary fixed cost per shipment, due to the increased risk of shipping delay. Rather than switching to domestic inputs, the uncertainty cost can be “smoothed” by an alternative strategy, i.e. importing less frequently larger quantities.

This strategy on the other hand, is not effective in reducing the cost increase of tariffs, as they generate monetary costs proportional to the quantity imported. The absence of an available smoothing strategy may make switching to domestic inputs a more likely choice, even at the expense of inputs’ quality.

To test this hypothesis, we turn to monthly customs’ data (see Appendix B.4 for details on the data). For each importer and year, we regress the (log) number of annual shipments and their (log) value on the share of imports exposed to PSI.<sup>26</sup> Customs data only provide information on the values of traded goods, but not additional information on the importer.<sup>27</sup> Therefore, we can only control for importer fixed effect and year fixed effect. The results - presented in Table 7 - are consistent with our hypothesis. In particular we find strong evidence that within a year, the application of PSI is associated with a lower number of shipments (column 1), but with shipment of higher value on average (column 2). For comparison, we report the impact of import tariffs as well. Unlike PSI, import tariffs on do not appear to significantly affect the frequency and value of shipment, although both coefficients are large and negative.

The strategy of importing higher value shipments less frequently should impact plants’ inventories of raw inputs, a hypothesis supported by the results in Table 8. The coefficient in column (1) suggests that PSI on inputs increases inventories.<sup>28</sup> This is consistent with the notion that higher inventories help buffering against the greater uncertainty of input supply generated by PSI.<sup>29</sup> Inventory costs increase with the length of time the input lies in stock. Therefore, plants can lower markup to increase sales and reduce inventories. Consistent with this view, the coefficient of input PSI on sales in

> 26We express the shares as a fraction of the total value of imports in the first available observation. We then drop the first observation to avoid issues of endogeneity.

> 27Due to such data limitations we cannot link customs and SI data.

> 28The lower number of observations relative to Table 5 is due to missing data on inventories.

> 29Inventories are expressed as (log) averages of beginning- and end-of-the-year values of raw inputs, deflated by the plant-level input price index.

26
<!-- Start of picture text --> Sub-Saharan Africa po<br>nonfragile low-income countries po<br>COMESA Pe<br>SADC {| sd<br>middle-income countries {_<br>fragile low-income countries po<br>resourch-rich countries [{<br>Lesotho [TCCid<br>Mozambique Pe<br>Zambia [id<br>Namibia | Cid<br>Malawi [sd<br>South Africa pd<br>0 2 4 6 8 10 12<br>% of GDP<br>capital expenditure Ooperations and maintenance<br><!-- End of picture text -->
to expect measurement error in crop cuts to be related to the overall size of the plot from which the subplot is selected.<sup>4</sup> The key finding in this paper is that when we use crop cuts to measure yields, the plot-size IR disappears. In contrast, and consistent with previous studies, when we use self-reported yield, the relationship is strongly negative. This finding suggests that production is systematically over-reported on small plots and under-reported on larger ones.

In addition to advancing the IR literature, this paper contributes to the less voluminous literature on how systematic measurement error in household surveys can affect statistical inference. This paper adds another example to the literature demonstrating that not all measurement error is white noise (Aguiar and Bils, 2015; Beegle et al., 2012; Gibson and Kim, 2007). As is well-known, but perhaps insufficiently emphasized, this illustrates that the ‘Iron Law of Econometrics’ – as Hausman (2001) called the observation that measurement error in the independent variables results in attenuation bias in regression coefficients – critically depends on the assumption that measurement error occurs randomly. If measurement error is systematic, the data may produce spurious correlations. Most previous studies focused on systematic measurement error in the independent variables, but this study illustrates that nonrandom measurement error in the dependent variable can also induce spurious correlations.

In the next section, we discuss the data and provide some descriptive statistics. We then show the conditions in which measurement errors in self-reported production can generate the plot-size IR and discuss the estimation strategy. In the results section, we show that the existence of the plot-size IR depends on the method used to measure yields. In conclusion, section 5 discusses the implications of our findings for the farm-size IR.

# **2. Data and descriptive statistics**

We use data from the first two waves of the Ethiopia Socioeconomic Survey (ESS), which is representative of all rural areas of Ethiopia.<sup>5</sup> This survey is a continuing project to collect panel data in Ethiopia on both household wellbeing and agricultural activities. It is implemented by the Central Statistical Agency of Ethiopia in close collaboration with the LSMS-ISA<sup>6</sup> team of

> 4 A caveat to this assertion is the possibility of an ‘edge effect’. The concern here is that crop cuts are more likely to be done in a plot’s interior, which introduces measurement error correlated with the size of the plot if yields along the edges differ from yields in the interior. This concern is discussed in detail later in the paper.

> 5 All data and relevant documentation are available at <u>go.worldbank.org/HWKE6FXHJ0. This includes</u> a manual detailing the crop-cutting procedures the enumerators followed.

> 6 LSMS-ISA: Living Standards Measurement Study – Integrated Surveys on Agriculture.

5
Table D2: Effect of random assignment on participation

||Dependent variable: Accepted task<br>(1)<br>(2)<br>Rider-ride<br>Rider-day|
|---|---|
|Assigned to reserved space|-0.029*<br>-0.022<br>(0.015)<br>(0.014)|
|Constant|0.397***<br>0.320***|
||(0.008)<br>(0.007)|
|Observations|5056<br>4517|
|Riders|244<br>244|

_Note:_ Unit of observation in column (1) is one rider. Unit of observation in column (2) is one rider-day, and the outcome variable indicates whether the user accepted at least one ride in the day. Standard errors in parentheses, clustered at participant level. All specifications include rider fixed effects.
jump to as high as 28 fold in two generations. On the other hand, populations of Heliothis collected from the fields subjected to two SP sprays (one of deltamethrin and one of fenvalerate) in a single insect generation, showed a RF of 5.2. On the basis of these findings it is concluded that Heliothis should not be subject to more than two pyrethroid treatment in a single generation. This finding has to be contrasted with the fact that farmers in this area have been applying several rounds of SPs in succession. Clearly, given the technical "optimum", farmers are overusing SPs and it is to be expected that resistant strains will develop rapidly. An important policy implication of this finding is that alternating the use of SPs with the traditional pesticides is crucial to the management of Heliothis resistance (also see Forrester, 1990).

In addition, often poor coverage due to faulty equipment, bad timing and sublethal doses by farmers, all of which are observed to be occurring in the Guntur area, exacerbate the problem of the survival of pests after spraying.

The cumulative impact of these local selection pressures was reflected in changes in the RF values for cypermethrin and fenvalerate which rose from 0.6 and 0.8 in July 1986 to 40 and 120 in November 1987. By March 1988, they were as high as 750 and 287 respectively. These values were recorded at ICRISAT. Data from Juzzuru, a town close to Guntur recorded a value of 325 in October 1987, a value which vastly exceeded the requirement for field 47
kilometer distance between country pairs) and other common gravity measures. The final dataset has just over one million observations in which about half are considered to be true zeros.

# _Tariffs_

One of the main problems with including labor clauses is that they may be correlated with the tariff changes that are part of the trade agreement. One solution is to incorporate the tariff data included in the HDTA (Mattoo et al. 2020). The HDTA includes three tariff measures at the six-digit Harmonized System (HS) level: the Most-Favored Nation (MFN) tariff level, the preferential tariff, and the tariff preference margin (the difference between the MFN tariff and the preferential tariff). The tariff preference margin (TPM) is a measure of the degree to which the preferential agreement reduces tariffs.

One of the concerns is that tariff levels, or TPMs, are correlated with labor clauses. Some industries might be more sensitive than others. For example, it seems likely that the more labor-intensive products (apparel, electronics, appliances, automobiles) might be more likely to be paired with more restrictive labor clauses. To get a heuristic sense of these relationships, Table 3 includes the unweighted and unconditional pairwise correlations between the Categorylevel labor clauses measures and the unweighted arithmetic average of all HS6 TPMs for sixteen industry groups (as well as the mean across all HS6 groups). The TPMs are all positive, so larger values indicate lower tariff levels (relative to the MFN tariff levels).

Positive correlations in Table 3 indicate that labor clauses would be more likely to appear when tariffs were lower and negative correlations would imply that labor clauses would appear when tariffs were higher. While it would be helpful to interpret these correlations as representing relationships that are either substitutes or complements, unfortunately it is not that straightforward. Lower tariffs might appear with labor clauses meant to reinforce and encourage trade. At the same time, lower tariffs might appear with labor clauses meant to offset the tradecreating effects of tariffs.

The second goal of Table 3 is to present some evidence about the econometric implications of omitting tariffs when estimating the relationship between labor clauses and trade flows. Thus, in addition to the sign of the correlations, the absolute magnitude can provide information about the potential omitted variable bias.

pg. 9
economies our instrument is the average unemployment rate of OECD countries, which is available from an on-line OECD database<sup>6</sup> . The citizens of these countries also have options to go to other countries such as the US, UK or Australia if unemployment rates remain high. But, countries such as the US, UK and Australia have quotas on foreign talent while Singapore does not. Hence, it is appropriate to use unemployment rates in OECD countries, and China and the Philippines as instrumental variables. The Chinese unemployment rate is available from the People’s Republic of China _Statistical Yearbook_ and the Philippines unemployment rate is available from the Philippines _Yearbook of Statistics_ . _3d. Regional FDI Activities_ One notable feature that helps explain the structure of foreign investment is that MNEs often establish an affiliate in Singapore and then use it as a base to invest in neighboring developing countries (Low, Ramstetter and Yeung, 1998). Examples of such corporations are presented in the Annex. Outward investment by foreign-controlled firms was around 60 percent of total outward investment during 1995, declining to 40 percent by 1998.7 It declined further to 35 percent by 2003 due to waves of mergers and consolidations in which Singapore based corporations purchased majority stakes in foreign firms operating in Singapore<sup>8</sup> . To check the robustness of the results using Singapore GDP, and to account for the ASEAN market size, we also use ASEAN GDP to account for regional investment activities for both manufacturing and aggregate investment. Specifically, the variables 6 OECD. http://www.oecd.org/topicstatsportal/0,2647,en_2825_495670_1_1_1_1_1,00.html Paris: OECD.

> 7 Department of Statistics, Government of Singapore

> 8 Department of Statistics, Government of Singapore

19
investments in land and buildings. This size cut-off was chosen in order that our treatments (described below) would be a large shock to business capital.

We began by using the 2001 Sri Lankan Census to select 25 Grama Niladhari divisions (GNs) in these three districts. A GN is an administrative unit containing on average around 400 households. We used the Census to select GNs with a high percentage of own-account workers and modest education levels, since these were most likely to yield enterprises with invested capital below the threshold we had set. The GNs were stratified according to whether the area was directly affected, indirectly affected, or unaffected by the tsunami. A door-to-door screening survey was then carried out among households in each of the selected GNs. This survey was given to 3361 households, with less than 1 percent of households refusing to be listed. The screening survey identified self employed workers outside of agriculture, transportation, fishing, and professional services who were between the ages of 20 and 65 and had no paid employees.

The full survey was given to 659 enterprises meeting these criteria. After reviewing the baseline survey data, we eliminated 41 enterprises either because they exceeded the 100,000 rupee maximum size or because a follow-up visit could not verify the existence of an enterprise. The remaining 618 firms constitute the baseline sample. For the purposes of this paper we exclude the firms which were directly affected by the tsunami, since the recovery of assets damaged by the tsunami might affect returns to capital. This gives a baseline sample of 405 firms which were not directly affected by the tsunami. The remainder of the paper will focus only on these firms.

The 405 firms are almost evenly split across two broad industry categories, with 203 firms in retail sales and 202 in manufacturing/services. Firms in retail sales are typically small grocery stores. The manufacturing/services firms cover a range of common occupations of microenterprises in Sri Lanka, including sewing clothing, making lace products, making bamboo products, repairing bicycles, and making food products such as hoppers and string hoppers.

# _The Experiment_

The aim of our experiment was to randomly provide firms with a positive shock to their capital stock, and measure the impact of this on business profits. Firms were told 6
Table 7a: HRS on Ability/Effort by Head Teacher Owns at Least Part of the School

|||_Agree/Strongly Agree_<br>Including Control|
|---|---|---|
|_Ability/Effort (by Head teacher owns at_<br>_least part of the school)_||<br>(1)|
|**Able to teach all topics to even the most**<br>|Treatment|-0.07|
|**problematic students**||(0.07)|
||Treatment*Head teacher owns part of school<br>|0.04|
||interaction term||
|||(0.13)|
||Head teacher owns at least part of the school|-0.05<br>|
|||(0.09)|
|**Confident in teaching all topics in**|Treatment|-0.08**|
|**subject**||(0.03)|
||Treatment*Head teacher owns part of school|0.01|
||interaction term||
|||(0.06)|
||Head teacher owns at least part of the school|-0.00|
|||(0.01)|
|**Changes teaching method at least once**<br>|Treatment|-0.21***|
|**a month**||(0.07)|
||Treatment*Head teacher owns part of school|0.30**|
||interaction term|(0.13)|
||Head teacher owns at least part of the school|-0.23**|
|||(0.11)|
|**Maintain composure when student**|Treatment|-0.08|
|**becomes disruptive**||(0.07)|
||Treatment*Head teacher owns part of school<br>ii|-0.29**|
||nteracton term|(0.12)|
||Head teacher owns at least part of the school|0.10|
|||(0.12)|
|**Believes absence from school**<br>|Treatment|-0.18**|
|**acceptable**|||
|||(0.07)|
||Treatment*Head teacher owns part of school<br>|0.01|
||interaction term|(0.24)|
||Head teacher owns at least part of the school|0.08|
|||(0.14)|
|**Student's learning achievement**<br>|Treatment|-0.12*|
|**motivates teacher**|||
|||(0.06)|
||Treatment*Head teacher owns part of school<br>|0.16|
||interaction term|(011)|
||Head teacher owns at least part of the school|.<br>-0.14|
|||<br>(0.10)|
|**Domain 2 Index**|Treatment|-0.59**|
|||(0.24)|
||Treatment*Head teacher owns part of school<br>itti t|0.44|
||neracon erm|(0.35)|
||Head teacher owns at least part of the school|-0.46|
|||(0.35)|

* _p_ <0.1; ** _p_ <0.05; *** _p_ <0.01

26
There are two more time paths that are needed to run the growth simulations – investment share of GDP �� and participation rate ���� . Both these drivers of growth are ~~�~~ �� ~~�~~ expected to play a big role going forward and have been identified as such by the policy makers. For this reason, I consider different scenarios for their time paths that help in identifying their relative importance.

- ��

- **Investment share of GDP: .** Bangladesh has realized continued increase in the ��� ~~�~~

- investment share of GDP during the last 35 years. The investment share of GDP has almost doubled from 1980 to 2015 with most of the increases coming from private investment. Needless to say, capital accumulation has been an important driver of growth in the country. This point is illustrated in the graph below which shows the average GDP per capita growth rate during 2011 – 2015 against the average investment share during the same period for more than 200 countries for which data is available from the World Development Indicators database.<sup>6</sup> The relationship between the two variables is positive and highly significant. However, a more important

<!-- Start of picture text --> Correlation = 0.37***<br>CHN<br>BGD LKA IND<br>NPL<br>PAK<br>0 20 40 60<br>Average Investment Share of GDP, 2011 - 2015 (%)<br>Source: World Development Indicators (***Significant at 1%)<br>10<br>5<br>0<br>-5<br>Average GDP per Capita Growth., 2011 - 2015 (%)<br>-10<br><!-- End of picture text --> question is whether capital accumulation can deliver a sustainable high level of growth going into the future.

Average investment as a share of GDP for the country during 2011 – 2015 was upwards of 28 percent and the country delivered higher growth than what is expected at that level of investment. The country has a higher investment rate compared to

> 6 Note that for some countries data for 2015 are not available, in which case the average reflects the average during 2011 – 2014.

11
|P076795|2005|2006|2/23/2006|4 Closed|St. Lucia|
|---|---|---|---|---|---|
|P076795|2005|2007|10/27/2006|5 Closed|St. Lucia|
|P076795|2005|2007|5/24/2007|6 Closed|St. Lucia|
|P076795|2005|2008|11/17/2007|7 Closed|St. Lucia|
|P076795|2005|2008|6/10/2008|8 Closed|St. Lucia|
|P076795|2005|2009|10/20/2008|9 Closed|St. Lucia|
|P076795|2005|2009|3/19/2009|10 Closed|St. Lucia|
|P076795|2005|2010|8/25/2009|11 Closed|St. Lucia|
|P076795|2005|2010|3/16/2010|12 Closed|St. Lucia|
|P076795|2005|2010|6/18/2010|13 Closed|St. Lucia|
|P076798|2003|2003|6/17/2003|1 Closed|St. Kitts and Nevis|
|P076798|2003|2004|6/3/2004|3 Closed|St. Kitts and Nevis|
|P076798|2003|2005|3/26/2005|5 Closed|St. Kitts and Nevis|
|P076798|2003|2006|7/1/2005|6 Closed|St. Kitts and Nevis|
|P076798|2003|2006|12/28/2005|7 Closed|St. Kitts and Nevis|
|P076798|2003|2007|10/17/2006|8 Closed|St. Kitts and Nevis|
|P076798|2003|2007|12/21/2006|9 Closed|St. Kitts and Nevis|
|P076798|2003|2007|6/14/2007|10 Closed|St. Kitts and Nevis|
|P076798|2003|2008|10/30/2007|11 Closed|St. Kitts and Nevis|
|P076798|2003|2008|1/9/2008|12 Closed|St. Kitts and Nevis|
|P076798|2003|2008|6/7/2008|13 Closed|St. Kitts and Nevis|
|P076798|2003|2009|7/22/2008|14 Closed|St. Kitts and Nevis|
|P076798|2003|2009|12/17/2008|15 Closed|St. Kitts and Nevis|
|P076798|2003|2009|6/24/2009|16 Closed|St. Kitts and Nevis|
|P076799|2005|2005|3/25/2005|2 Closed|St. Vincent and the Gr|
|P076799|2005|2006|8/8/2005|3 Closed|St. Vincent and the Gr|
|P076799|2005|2006|6/9/2006|4 Closed|St. Vincent and the Gr|
|P076799|2005|2007|12/21/2006|5 Closed|St. Vincent and the Gr|
|P076799|2005|2007|3/22/2007|6 Closed|St. Vincent and the Gr|
|P076799|2005|2008|8/14/2007|7 Closed|St. Vincent and the Gr|
|P076799|2005|2008|4/19/2008|8 Closed|St. Vincent and the Gr|
|P076799|2005|2009|10/16/2008|9 Closed|St. Vincent and the Gr|
|P076799|2005|2009|4/3/2009|10 Closed|St. Vincent and the Gr|
|P076799|2005|2010|8/28/2009|11 Closed|St. Vincent and the Gr|
|P076799|2005|2010|12/11/2009|12 Closed|St. Vincent and the Gr|
|P076799|2005|2010|6/23/2010|13 Closed|St. Vincent and the Gr|
|P076799|2005|2011|2/23/2011|14 Closed|St. Vincent and the Gr|
|P076799|2005|2012|9/7/2011|15 Closed|St. Vincent and the Gr|
|P076802|2003|2003||0 Closed|Dominican Republic|
|P076802|2003|2004|6/3/2004|2 Closed|Dominican Republic|
|P076802|2003|2005|3/3/2005|4 Closed|Dominican Republic|
|P076802|2003|2005|6/21/2005|5 Closed|Dominican Republic|
|P076802|2003|2005|6/23/2005|6 Closed|Dominican Republic|
|P076802|2003|2006|12/30/2005|7 Closed|Dominican Republic|
|P076802|2003|2007|10/27/2006|8 Closed|Dominican Republic|
|P076802|2003|2007|12/21/2006|9 Closed|Dominican Republic|
|P076802|2003|2007|6/13/2007|10 Closed|Dominican Republic|
**A4 India**

**Table A4: Regional disparity trends** in **India**

|Year|Max/Min<br>**(MMR)**|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>(Gu)|d Weighted<br>gini index<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1980|2.848|0.312|0.290|0.221|0.175|0.152|0.039|
|1981|2.903|0.318|0.289|0.216|0.178|0.152|0.039|
|1982|2.867|0.327|0.291|0.211|0.182|0.152|0.039|
|1983|2.808|0.308|0.286|0.219|0.172|0.151|0.038|
|1984|2.702|0.323|0.292|0.235|0.182|0.154|0.040|
|1985|2.804|0.345|0.305|0.239|0.191|0.159|0.043|
|1986|2.712|0.342|0.302|0.239|0.190|0.157|0.042|
|1987|2.945|0.343|0.306|0.238|0.191|0.161|0.043|
|1988|2.825|0.333|0.302|0.227|0.186|0.159|0.043|
|1989|3.196|0.350|0.333|0.248|0.196|0.175|0.052|
|1990|3.034|0.345|0.323|0.240|0.195|0.171|0.049|
|1991|3.271|0.349|0.328|0.255|0.198|0.176|0.051|
|1992|3.464|0.369|0.370|0.286|0.212|0.199|0.065|
|1993|3.521|0.373|0.385|0.296|0.215|0.207|0.070|
|1994|3.576|0.375|0.393|0.312|0.219|0.215|0.074|
|1995|4.051|0.386|0.414|0.323|0.224|0.225|0.081|
|1996|3.838|0.398|0.419|0.327|0.232|0.228|0.084|
|1997|3.811|0.387|0.414|0.334|0.226|0.227|0.082|

**Figure A4: Regional disparity trends in India**

<!-- Start of picture text --> 0.450<br>0.400 2..-<br>0.350<br>2 0.300 - '<br>E 0,250 . .<br>rn 0200 -,,<br>0*~~~~~~~~~~~~~~~~~_<br>-0.150<br>0.100.<br>0.050<br>0.000<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997<br>Year<br><!-- End of picture text --> Simple coefficient of variation (CVu) **-** Weighted coefficient of variation (CVw) - Relative mean Deviation (Rw) ~ Unweighted gini index (Gu) - Weighted gini index (Gw) -Theil index **(T)** 38