rafmacalaba/datause-encoder-data huggingface dataset:
that cultivate only one maize plot. These robustness checks, which are available upon request, work to ensure that the results (1) hold across a greater geographic area in the Eastern region; (2) are not being driven by outliers in yield data; and (3) in part rule out the theory that errors in selfreported production are driven by difficulty in reporting production at the plot level, rather than the (maize) farm level.

Further, as documented in the Appendix Table A3, while IR persists at the mean and throughout the distribution of plot-level maize yields anchored in farmer-reported production, CRS is observed throughout the distribution of objective yield measures. This finding is in stark contrast with the new evidence put forth by Savastano and Scadizzo (2017), who, based on farmer-reported panel survey data, provide support for the IR among the sample of farmers above the median land productivity measure.

Contrary also to the findings by Bevis and Barrett (2017), who use the perimeter-to-area ratio as a proxy for edge effect and find plot edges to be more productive than the interior, the results summarized in Table 4, and reported in full in the Appendix Tables A4.1-A4.3, suggest that plot edges are generally not more or less productive than the interior of the plot, and when significant, the direction of the coefficient suggests the plot edges are less productive. This finding is robust to an alternative definition of the edge effect, namely a 1m buffer along the plot edge as opposed to the 4m buffer presented here. The support for CRS, as opposed to the IR, in the MAPS II estimations that use the full-plot crop cut yield as the dependent variable raises further questions on the viability of the edge effect hypothesis in our context.

Finally, to illustrate the difference in plot area coefficients across specifications, Figures 3, 4, and 5 present the plot area coefficients with 95 percent confidence intervals for the overall, pure stand, and intercropped sample, respectively. The confidence intervals around the estimated coefficients originating from regressions that use sub-plot crop cutting-based yield estimates are considerably tighter than the comparable confidence intervals associated with the plot area coefficients estimated from regressions that use self-reported production-based yield estimates. The following section examines the farmer-reported maize production data more closely and seeks to identify the correlates of farmer over-estimation.

# **6.2. Drivers of Farmer Over-Estimation**

Before addressing the correlates of farmer over-estimation and potential sources of systematic error, we first address the feasibility of correcting this error with the use of correction factors. In order for correction factors to be an appropriate course of action to improve the quality of selfreported production data, the error must be consistent across time and within household. Figure 6, however, illustrates that this is not the case.

22
26 Ministry of Finance, accounts for about 70 percent of permanent employment and 80 percent of value-added in all modem sector industrial firms. It includes virtually all larpe firms and a great number of the small ones. It is hence designed to capture the entire size distnbution of modem sector firms, but it naturally undersamples the numerous small enterprises. In terms of sectoral coverage, the sample includes 24 firms in the food industry,' 18 firms in the chemical and extraction industries, 13 firms in the mechanical (machinery and equipment) industry, 11 firms in the textile, clothing and leather industry, 10 firms in the paper and wood industry, and 4 firms in other industrial groups TABLE 4.3 Senea Toal Empkymt of Pinnmnent Wodm by Inbntry

||1977|1982|1984|
|---|---|---|---|
|Food|13,188|13,722|13,961|
|Tobacco and Matches|546|466|518|
|Textiles Clothing6,1<br>andLeather|613|5.0517<br>S,OOO|S,157|
|Wood|936|268|304|
|Paper|275|404|206|
|Printing|402|371|496|
|Chemicals|1,69S|2,796|3,138|
|Construaion|829|753|544|
|Mechanical Industries|2,313|2,942|_4,55S_|
|TOTAL|26,697|26,722|28,879|

Source: Direction de la Statistique for 1977, 1982 and 1984.

1. The food industy is composed of the following sub-industries: sugar, oil, fish, flour, and other food.
# **5.1 Intensive Margin**

Here, I examine the effects of outages on firm performance and consequently firms’ demand for labor and labor cost as potential channels underlying the outage-employment nexus. Using firm-level data from the WBES, I estimate the following IV specification: IV first-stage: IV second-stage: where _Yikct_ is the outcome of firm _i_ in city _k_ , country _c_ , and year _t_ . _Outagesikct_ is the outage intensity experienced by firm _i_ . Two measures of outage intensity are explored here: the number (frequency) of outages a firm experiences in a typical month, and the total number of outage hours experienced by the firm in a typical month. _θc_ and _δd×t ′_ represent country and industry _×_ year fixed effects respectively. **Xikct**<sup>isavectoroffirm,</sup> climate, and city controls including mobile phone coverage rate as a proxy for the rate of technology diffusion at the city level. _Lightningkct_ is a proxy of the average lightning intensity in city _k_ at time _t_ . Again as highlighted in section 3.4, I use CAPE _×_ P as a proxy for lighting intensity. Standard errors are clustered at the city level.

## **5.1.1 Firm Performance and Labor Demand**

Table 5 presents the OLS, IV, and reduced-form estimates of the effect of outages on firm performance.<sup>47</sup> The results show a negative and statistically significant impact of outages on sales, sales per worker, and value-added per worker. The findings are consistent with

> 47Table A11 in the online appendix presents the first-stage results

33
resolution of 15-arc seconds – approximately 500m x 500m tiles near the equator. For this paper, the raw data are aggregated to the district level, on a quarterly basis. Nighttime light is measured in Nanowatts/cm<sup>2</sup> /steradian.

_Formal economic activity_ Formal sector activity is proxied through the number of active formal firms. Admittedly formal firms have different sizes, and the ones for which data are available are likely to be among the largest ones. YY<sup>FF</sup> However, previous research suggests that larger formal firms are more likely to be impacted by conflict (Blumenstock et al. 2018). It follows that the observed change in the number of active formal firms overestimates the actual change in the overall economic activity. The net effect of these size and impact biases is unclear. In the absence of more information, it is assumed here that the relative change in formal economic activity is proportional to the relative change in the number of formal firms for which data are available. yy<sup>FF</sup> The number of active formal firms is computed from a list of organizations that signed up for plans with multiple phones belonging to the same account. Public and non-profit organizations are excluded from this list, whereas multiple accounts from the same organization are consolidated into one. This results in a sample of 2,300 private firms covering 125,000 subscribers. Whether a firm is active is inferred from call detail records from all the phones registered under the corresponding corporate subscriber account. Call records contain the identity of the parties involved in the communication, as well as the timing of the communication and the location of the antenna where the call was initiated. The raw data were provided by a major mobile phone operator in Afghanistan and processed by Blumenstock et al. (2018). For this paper, the data were made available at the district level, on a monthly basis, for the period from April 2013 to April 2016. Formal firms with active cell phone traffic are measured per 1,000 inhabitants.

_Illicit economic activity_ The land surface devoted to opium poppy cultivation is used to estimate Illicit economic activity . It is recognized that poppy yields per hectare may vary across different parts of the country and over time. YY<sup>OO</sup> Yields could also decline as the cultivated surface increases, and more marginal land is used. However, the relevant margin in this case is not between cultivated and uncultivated land, but between land devoted to poppies and land devoted to wheat, with both being similar in terms of their agricultural potential (Keohler and Zuercher 2007). Given the absence of detailed information on poppy yields across 12
A Longitudinal Cross-Country Dataset on Agricultural Productivity and Welfare in Sub-Saharan Africa

# **Thomas Bentze**<sup>**1**</sup> **, Philip Wollburg**<sup>**1,2**</sup>

**JEL codes** : O12, I32, P36

**Keywords:** Agricultural Productivity; Household Welfare; Panel Data; Household surveys; Economic Development;

> 1 <mark>The World Bank, Living Standards Measurement Study, Development Economics Data Group, Rome, 00184, Italy.</mark> 2 <mark>Wageningen University, Development Economics Group, Wageningen, 6706KN, The Netherlands.</mark>

> **Disclaimer:** The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
34 On the other hand, single-sex education appears to have less positive effects on Nigerian adolescent males, where the students experience lower mathematics achievement and hold more stereotypic views of mathematics. These findings about single-sex education, therefore, resemble those reported in the Thailand study more than the findings of the study of American Catholic 4 high schoolsl. . Although many of studies which examine the effects of single-sex education have found more positive effects for girls' than boys' schools (especially in developed countries), only two studies in developing countries (this study and the Thai study) have found that boys' schools had negative effects on their students. Therefore, it should not be concluded from this study that single-sex schooling is more beneficial to students than coed.ication; rather, it seems to be particularly effective for females.

Possible explanations for findings. Why might this be the case? Although this study was designed to evaluate the relative effectiveness of the two types of schools for male and female adolescent students in Nigeria, rather than to describe the internal workings of schools, we can hypothesize possible explanations for such findings from the school descriptors included here. The relative sizes of the schools is suggestive. Girls' schools have the smallest, and boys' schools the largest enrollments. As mentioned above, larger school size may be see as either an advantage or a disadvantage. It may be that the more intimate environment of smaller schools overshadows the disadvantage accruing from fewer resources, producing an environment where student learning is fostered. This may be also reflected in smaller student/teacher ratios in girls schools (although those in the larger boys' schools are not corresponding larger). Time u:;age is also suggestive, since girls' schools report spending most time on lecturing and least on evaiuation,
7 Table 1: Parts and products: selected product networks identifiable in SITC Rev. 2.

|PARTS||<br>COMPONENTS|FINAL PRODUCTS|
|---|---|---|
|**_1. O_**<br>|**_ffice_**_machines and automatic data processing machin_<br>|_es_**_(SITC_**_Division_**_75)_**<br>|
|75911||Typewriters (7511)|
|75915||Duplicating machines (75181), other office machines<br>(75188) and check-writing machines (75118)|
|75919||Photocopying machines (75188)|
|7599||Calculating/accounting<br>machines (7512)|
|**_2._**_Te_<br>|_lecommunications and sound recording and reproduci_<br>|_ng apparatus and_**_equipment (SITC 76)_**<br> <br>|
|764||Telecommunication<br>and sound recording/reproducing<br>equipment (76)|
|76491|Electrica**l**ine telephonic and telegraphic apparatus (7641)|<br>|
|76492|Microphones, loud speakers, electric amplifiers (7642)||
|76493||TV receivers (761), radiobroadcast<br>receivers (762),<br>transmitters and receivers (7643) telecommunication<br>,equipment,n.e.s. (7648)|
|76499||<br>Sound recorders and reproducers (tape decks), television<br>imaqe (763)|
|77689|TV tubes, electronic microcircuits, diodes, transistors<br>(7761-64 and 7768)||
||<br>_3._**_Motor vehicle_**<br>|**_s (SITC 78)_**<br>|
|7139|Piston engines (7132)|Road vehicles (78), tractors (722), works trucks (74411)<br>and tanks (95101)|
|784||Tractors (722), automobiles (781), trucks (782), other road<br>vehicles (783)|
||Electrical starting and ignition equipment for internal<br>combustion engines (77831)||
||<br>Electrica**l**ightning, signaling, vipers, defrosters (77832)||
||Generating sets with internal combustion engines (71623)||
|7441|9|Works trucks for handling<br>of goods (74411)|
||<br>**_4._**_Furniture_**_(S_**|<br> <br>**_ITC 82)_**|
|82119||Chairs and other seats (82111)|
||Mattress support, etc. (82122)<br>|Furniture for medical, surgical or veterinary practice<br> <br>(82121|
|82199|_________________________________________<br>|____________<br><br>Metal furniture (82191), wood furniture (82192)|

The SITC Rev. 2 system falls well short of allowing precise estimates of foreign trade activity within a network. Leaving aside standard weaknesses of foreign trade statistics, the problem is that it does not allow distinguish among various uses of a part or component. For instance, piston engines (7132) include a whole array of engines for cars, trucks, tractors, works trucks and armored vehicles. Parts (7139) include not only parts for piston engines (7132) but also parts used in production of engines for boats (7133). In consequence, one may overestimate the trade generated by a given production network.

The analysis of trade developments within networks supplements that of developments in trade in parts. While the latter may be regarded as a first step in identifying country's involvement in intra-product specialization, the former sheds extra light on assembly operations carried out in a country. In order to identify a country's strong and weak sectors we calculate indices of RCA (revealed comparative advantage).
**The Heterogeneous Growth Effects of the Business Environment: Firm-Level Evidence for a Global Sample of Cities**<sup>**1**</sup>

José-Daniel Reyes Mark Roberts Lixin Colin Xu

**JEL codes** : D9, G3, H1, H2, H7, L5, O1.

1 This paper has been prepared, in part, as input into the LCR “Cities and Productivity” Flagship Report (P158837), which is a product of the World Bank’s LCR Chief Economist’s Office working in collaboration with its Social, Urban, Rural and Resilience Global Practice (GSURR). The authors would like to thank Gilles Duranton, Carlos Vegh, and Daniel Xu for comments/discussions on an earlier draft of this paper, as well as participants at Authors’ workshops for the LCR “Cities and Productivity” Flagship Report that took place in July 2016 and May 2017. They would also like to thank Daniel Lederman and Christine Qiang for encouragement in preparing this paper. Financial support from the World Bank’s “Global Research Program on Spatial Development” (P143985), which is funded by UK DFID and the World Bank’s Multi-Donor Trust Fund, is also gratefully acknowledged. The views expressed in this paper are the authors own, and do not necessarily reflect those of the World Bank or its member countries. All three authors are affiliated with the World Bank. Corresponding author: L. Colin Xu (lxu1@worldbank.org).
planning and harnessing the potential for property tax collection—thus setting in motion a virtuous cycle of rising land prices and better and more effective provision of local public goods that can enhance accountability.

Because registration fees are a central policy variable, it will be of interest to characterize conceptually and assess empirically what fee levels would maximize the direct and indirect benefits of coverage and operation of property registries in settings with heterogeneous agents. Exploring these issues in more detail will be important for follow-up research that could have considerable policy relevance.

19
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The paper is organized as follows. Section 2 provide some background, in which we highlight the unique setting of a quasi-experimental design in Korea. Section 3 introduces the sample data for our main analysis. Section 4 presents the main empirical finding and the estimation models. Section 5 presents evidence on the task reallocation mechanism. Section 6 examines potentially alternative drivers of the main empirical finding. Section 7 concludes.

# **2 Background**

## **2.1 An Unforeseen Change in Labor Market Policy**

Stringent labor market regulations had long been pointed to as a major factor hindering the competitiveness of the Korean economy. According to the Global Competitiveness Report (Schwab and Sala-i Martin, 2015), Korea was once ranked 115 out of 140 countries for the efficiency of its hiring and firing practices. Against that background, President Park Geun-hye’s administration pursued increasing labor market flexibility after taking office in 2013.

Unexpectedly, however, the impeachment of President Park from December 2016 to March 2017 due to a series of scandals, followed by the regime change in two months, led to more rigid labor market policies in the coming years.<sup>9</sup> Elected as Park’s successor on May 9, 2017, and inaugurated just one day later, President Moon Jae-in took a distinctly different labor policy stance from that of the previous government. The new administration quickly proposed guidelines for converting irregular workers to regular workers in July 2017 and an annual conversion plan three months later.<sup>10</sup> The government also pushed for a revision of the Labor Standards Act, which includes reducing maximum working hours from 68 hours to 52 hours a week. The amendment was passed in Febru2018 and for with 300 or more in of the same ary implemented firms employees July year.<sup>11</sup> Yet another, and arguably much more impactful, labor policy came into force in 2017,

> in low-wage locations: Slaughter (2000) in US industries and Konings and Murphy (2006) using European multinationals. Although these earlier studies explored the intensive margin channel of MNCs, the unit of analysis is broad (i.e., industry) and the estimation strategy does not fully address endogeneity issues.

> 9 The Encyclopedia Britannica (www.britannica.com/biography/Park-Geun-Hye) provides a detailed description about the impeachment and related events.

> 10Although the plan was supposed to apply only to workers in the public sector, moral suasion encouraged the private sector to follow the same practice voluntarily.

> 11Since its first implementation for large firms, the 52 working hours restriction has been sequentially activated for firms that hire 50~299 workers in January 2020 and for the rest of small firms in July 2021.

5
A variety of household-level variables are also included. The probability of enrollment increases with per capita household income. Enrollment is significantly higher among people living in a household where the head of household had completed at least lower secondary education. Enrollment increases with grade completed above this level.

Rather than including community-level variables _per se_ , the enrollment equation includes fixed effects measured at the province level. This gets round two problems associated with the VLSS community-level data. First, they were collected only in rural and peri-urban areas. Second, like all community-level data, they may well miss out some potentially important community-level influences on the enrollment decision. For example, Behrman and Knowles (1999) find the premium to be a factor influencing enrollment by students in VHI. It is known that the premium for voluntary enrollment varies geographically in Vietnam (World Bank et al. 2001), but the VLSS provides no information on this variation. Similarly, Jowett (2003) finds that informal risk-sharing networks influence the uptake of VHI, but the VLSS provides no information on their existence. The province-level fixed effects vary considerably, the null hypothesis of common effects being decisively rejected.

_Table 3: Enrollment propensity score estimation results_

|Covariate|Mean|Std Dev.|Coef.|z-statistic|
|---|---|---|---|---|
|Primary school|0.234|0.423|0.0441|0.84|
|Lower secondary school|0.472|0.499|0.2442|5.00|
|Upper seconday school|0.089|0.284|0.3209|5.42|
|Post-secondary school|0.094|0.292|0.7058|12.21|
|Age in years|25.373|19.905|0.0008|0.96|
|Currently in school|0.254|0.435|0.4098|12.89|
|Employer in main job is communist party, government or army|0.016|0.126|1.7935|19.55|
|Employer in main job is social organization|0.002|0.044|1.7481|7.04|
|Employer in main job is state-owned company|0.013|0.113|1.3885|14.82|
|Employer in main job is joint venture with foreign partner|0.000|0.021|0.2520|0.42|
|Employer in main job is cooperative|0.006|0.075|0.4152|2.98|
|Employer in main job is private company or household|0.049|0.215|-0.4517|-5.90|
|Employer in second job is communist party, government or army|0.002|0.043|0.8068|3.12|
|Employer in second job is social organization|0.000|0.011|-0.3994|-0.27|
|Employer in second job is state-owned company|0.000|0.018|0.3002|0.49|
|Employer in second job is cooperative|0.002|0.050|0.0043|0.02|
|Employer in second job is private company or household|0.009|0.096|-0.5056|-3.11|
|Pensioner|0.032|0.176|0.5197|7.50|
|Per capita household income|1914|2725|0.0000|3.14|

Note: Chi squared for province fixed effects: 1140 with 72 df.

Figure 1 and Table 4 provide some descriptive statistics of the propensity scores. Prior to matching the distributions of the insured and uninsured differed markedly with respect to the estimated propensity score.<sup>9</sup> Table 4 shows that contrary to the impression given by Figure 1,

> 9  The distributions shown here are for the sample used in the analysis of the impact of VHI on the probability of being inpatient during the previous 12 months.

9