rafmacalaba/datause-encoder-data huggingface dataset:
This paper is primarily concerned with gender differences in plot management. In the ECVMA, respondents were asked who managed each plot operated by the household. However, respondents were allowed to either identify a specific person in the household who managed the plot or to simply state the plot was managed by several household members. In the latter case, the specific household members were not listed. In our final sample there were 2,288 plots managed by a male, 613 managed by a female, and 1,913 managed by several members (henceforth collectively managed plots).

The large share of collectively managed plots poses a problem since the analysis requires at most two groups to compare. There are several potential approaches that could be taken. First, we could attempt to group the collectively managed plots into male and female using alternative information. For example, we could assume the head of the household is the de facto manager of these plots. However, that is a rather strong assumption that cannot be verified. In addition, collectively managed plots may in fact be quite different from those where a single manager is specified. If this is the case, it would be inappropriate to group collective plots with male and female plots. We could instead do pairwise comparisons of the three groups to assess differences not only between the male and female plots but also the collectively managed plots. While this kind of analysis may be informative, it would complicate our results and would be a departure from our primary goal of examining _gender_ differences in productivity. We therefore opt to exclude the collectively managed plots from the analysis entirely. This requires dropping nearly 40 percent of the sample, but seems the most palatable solution given the drawbacks of the alternatives. As a robustness check we shall also examine the alternative approaches mentioned above.

Table 1 presents mean estimates for various plot characteristics for the total sample and by the gender of the plot manager. The average agricultural production per plot is estimated at 35,571 FCFA. The average production of male managed plots is 39,934 FCFA, nearly twice the production of plots managed by a woman, which is 19,417 FCFA. Likewise, male managed plots have significantly higher yields (50,369 FCFA per hectare) than those managed by females (35,885 FCFA per hectare). The difference is also apparent when comparing the two distributions in Figure 1; female managed productivity distribution is predominately to the left of the male distribution.

# <<<<<< TABLE 1 AROUND HERE >>>>>>

The main features of Nigerien agriculture are common to both male and female managed plots, especially regarding poor access to inputs. Despite many similarities, there are also some striking differences between these two types of plots. Considering demographic characteristics, the plot-manager is usually the head of 6
therefore, jobseekers plan to migrate to megacities. Lastly, 1.7% of the job postings are in foreign countries.<sup>11</sup>

**Figure 1. Geographical distribution of jobseekers, employers, and job postings**

_Source_ : Authors’ calculation using the data from Rozee.pk. _Note_ : (1) We refer to Lahore, Karachi, and Islamabad/Rawalpindi as first-tier cities and Faisalabad, Gujranwala, Hyderabad, Multan, Peshawar, and Quetta as second-tier cities. (2) Job locations are multiple for a substantial number of job postings. Thus, the sum of the percentages for job postings is greater than 100%.

Comparing Rozee.pk users and the entire labor market participants based on the Labor Force – Survey 2014 15 and Enterprise Survey 2013 shows notable differences between them. First, the proportion of female workers is smaller in the online job portal than the entire labor market. Women constitute 21% of jobseekers in the portal but 25% of the entire labor force (Table 1). This may be because many female workers are agricultural and household workers, who mostly do not use Rozee.pk. Second, jobseekers in the online job portal are much younger (mean age is 26) than the entire labor force in Pakistan (mean age is 35). In the portal, 83% of all the jobseekers are aged 30 and below while only 44% of the entire labor force in the economy is aged 30 and below. The age distribution of the online job market is concentrated between 20 and 30 years old: the proportion of the 20- to 30-year-old labor force is 77% in the online job market but only 33% in the entire labor market. The composition of jobseekers by education in Rozee.pk is significantly different from the entire labor market of Pakistan. Of jobseekers at Rozee.pk, 86% have postsecondary degrees, and 32% have master’s degrees or a Ph.D. By contrast, among the entire labor force, only 9% have a postsecondary degree.

> 11 Since foreign jobs do not reflect labor markets in Pakistan, we exclude job postings in foreign countries in the analysis.

4
Some studies show that women are less likely to remarry than men, though most focus on widows rather than divorcees. One study in Uganda showed that 65.1% of widowers remarried compared to 27.3% of widows but that both widows and widowers whose spouse died of HIV were more likely to remarry than if the spouse had died of other diseases (Ntozi 1997). However, preliminary evidence from a more recent study in Tanzania indicates remarriage is significantly less likely for HIV positive women. It is also less likely for HIV positive men but the difference is not significant (Gregory et al. 2007). Another study gave evidence that more women than men were currently widowed or divorced, most notably among the oldest age group where 22% of women had not remarried but only 5% of men had not remarried (Munguti et al. 1997). One possible reason for fewer women remarrying is that cultural norms making remarriage likely for widows have been changing. 37.8% of respondents in a qualitative study in Uganda said that the tradition of widow inheritance by relatives of the deceased husband is no longer practiced due to fear of HIV (Mukiza-Gapere and Ntozi 1995).

In remarrying, these widowed and divorced who are more likely to be HIV positive also could infect their new spouse. Within marriage, intercourse is generally more frequent than outside it so there are more opportunities to infect the new spouse with HIV (Bracher et al. 2003; Clark 2004). One way to prevent these new spouses from becoming HIV positive is to use a condom. However, based on one nationally representative sample in Uganda, condom use within marriage is uncommon. This study showed that condom use increased when the subjects knew one partner was HIV positive, but most individuals in this study did not know that they were infected (Bunnell et al. 2008). Other studies support the finding that condom use within marriage is uncommon, even within discordant (when one spouse is HIV positive and the other is not) couples (Biraro et al. 2009; Allen et al. 2003).

Because of this possibility of spreading HIV to new spouses, it is all the more important to better understand this remarried segment of the population. Section 3 of this paper will explain 5
While suggestive, these correlations are not necessarily informative on the role of human capital for economic development. On one hand, the relationship might not be causal in nature, as third factors might explain cross-country differences in both human capital and GDP per worker, and the latter might also explain the accumulation of the former. Moreover, several dimensions of human skills are inherently difficult to measure, and the proxies considered here might miss an important part of the picture. Finally, even a positive and causal relationship between average human capital and development would not be fully informative on the potentially complex role of human skills in the production process.

The approaches reviewed in this paper attempt to overcome these problems through different strategies. As a background for the discussion of these methodologies, I start by sketching in Section 2 a simple production function framework which highlights how human capital can affect aggregate economic performance.

Section 3 reviews results from the cross-country regressions approach, which consists of relating measures of economic performance and human capital in a regression model. Section 4 discusses development accounting, a methodology based on the direct calibration of an aggregate production function (rather than its estimation). Here I also replicate some of the key results in this literature using data from 2010. Section 5 reviews some of the key insights from quantitative models on the accumulation and allocation of human capital. Finally, the last Section concludes by drawing general lessons and highlighting avenues for future work.

# **2 A General Framework**

The starting point for most macroeconomic analyses on the effects of human capital is the postulation of an aggregate production function, The aggregate production function relates GDP ( _Y_ ) to the amounts of physical capital ( _K_ ), human capital ( _H_ ) and technology ( _A_ ) available in the economy. In the simplest specification, aggregate human capital is the sum of the human capital of all workers, that is _H_ “ _hL_ where _h_ is the human capital of a representative worker and _L_ is the number of workers. Alternative specifications assume that _H_ is a more complex aggregator of different types of workers.

A common choice for the _F_ aggregator is the Cobb-Douglas function, 3
# Table 10: lnfluence Of CoUntry-Specif ic Social Clans ksasures on PLPII Achlevewet In Urban and Rural Schools In alawl

||TOTAL<br>TE<br>Urban|<br>ST SCORE<br>Rural|CHICH<br>Urban|ENA<br>TEST SCOR<br>Rural|E<br>MATH<br>T<br>Urban|<br>EST SCORE<br> <br>Rural|
|---|---|---|---|---|---|---|
|RegIon's wealth|||.09<br>(8.00)"||||
|Sex (girl-1)||||-.59<br>(-5.37)0|||
|Grade<br>level|||||||
|Mother<br>tongue<br>Chichewa||1.22<br>(7.90)*||.56<br>(5.19)'||.65<br>- (6.06)-|
|father's<br>work|||||||
|Mother's school Ing||||.55<br>(4.30)'|||
|Mother's readIng<br>practloes|||||||
|Pupil's<br>work tasks||||||-. 004|
|xthatch roof||||||( 5.57)*|
|House w/electrIcity|1.21<br>(8.28)"||.52<br>(4.16)'|1.06<br>(5.74)'|||
|Radio In house||||.52<br>(4.23)*|||
|H's estlaato fathers<br>farmlng|.02<br>(6.15)'||||.013<br>(5.21)'||
|H's estIlate ho es|||-. 01|||.04|
|w/thatch<br>roofs|||(5.95)|'||( 5.49)*|
|Intercept|-2.40|1.80|-3.54|-2.82|-0.23|-2.03|
|Of|2,55|1,42<br>|3,58<br>|5,38<br>|1,56<br>|2,42<br>|
|R-sqyare|.16|.16|.26|.35|.09|.22|

# Note: Lhstandardized regresslon coeffIcIents and f-values reported.

PN.05 used as crIterIon for varlable to remain In equatlon.

-

p<.05 " pc.01 *- P<.C.O1

- 33 -
and on ports and ship datasets from open-source datasets and a database made available through the International Maritime Organization (IMO). Fields available in the port calls dataset include:

- Type of the event: arrival or departure

- UN/LOCODE 7 Port Name

- <mark>Date / Time in UTC</mark>

- Vessel particulars:

- <mark>IMO number</mark>

- <mark>MMSI (Maritime Mobile service Identity) number</mark>

- <mark>Vessel name</mark>

- <mark>Vessel type</mark>

- <mark>Draught at the time of the recording</mark>

- Length, width, and max draught

The dataset was prepared using MarineTraffic port call data covering over 5,000 container ships calling at over 800 ports during the first two quarters of 2022. Based on estimated time differences between recorded arrivals and departures to port facilities, an indicator of turnaround time per port was constructed. In addition, ship data, including capacity in twenty-foot equivalent units (TEU) is available. Ship types range from small feeders with capacity of up to 1,000 TEU to ultra large container vessels with capacity starting at 14,501 TEU. Additional details regarding this data source are in Appendix 4.

# **3. Definition and processing of KPIs**

From tracking data to country indicators The micro-logistics datasets used for the LPI 2023 consist of large numbers of voyages of individual shipments (e.g., container or parcels). For shipping data, they contain the port call sequence. The raw tracking data consists of the following fields:

- ID of the voyage

- Origin, destination

- Coded location of the event: tracking data localize the event by port, airport, or more generally UN/LOCODE.

- Time of the event

- Type of the event (structured categories, specific to the dataset): e.g., container loaded on ship, parcels arriving at the bureau of destination, port arrival etc.

The individual voyage statistics are sliced to create lead time between two consecutive events either at the same location (e.g., container stay or dwell time at a port) or at two different locations. The data is then combined across voyages going through this location (if the consecutive events are at the same place), or through a pair of locations (if the consecutive locations are different). The data is processed according to a growing level of aggregation. First the information is aggregated by trade lane: Statistics of lead time of a shipment or cargo unit from origin to destination or key logistics steps, for instance dwell time at gateways (ports, airports, postal bureau) at origin or destination. The indicators in the LPI 2023 have been decided with the data partners depending on the ease of interpretation for final users.

12
Distributed Lag model (ARDL) for the period 1994-2018. Results using annual data are in line with those using quarterly data, although the RER coefficient is poorly determined due to the small sample size (see Table A.1 in the Appendix).

Table 1: Long-run relationships for Pakistani exports

|VARIABLES<br><br>|Quarterly<br>(1)|
|---|---|
|lll<br>t<br>|-0.492***<br>(0.165)|
|llt<br>∗|1.046***<br>(0.076)|
|Quarterly dummies|Yes|
|Observations<br>|95|
|R<sup>2</sup>|0.881|

***, **, * imply significance at the 1%, 5% and 10% respectively. Newey-West SE are reported in parenthesis.

In column 1 of Table 2, we present the results for the error correction model. The estimated speed of adjustment is significant and negative for both quarterly and annual data. For quarterly data, about 28 percent of the disequilibrium from the long-term relationship is corrected in one quarter. Annual data estimates show that 73.5 percent is corrected within the year, in line with quarterly estimates (see Table A.2 in the Appendix). The significance of the coefficient implies that, as expected, deviations from equilibrium conditions are corrected, and its absolute value below one shows corrections are gradual. This provides further evidence for the finding of cointegration when using the two-step Engle-Granger test.<sup>18</sup> In Column 2 we present results when we separate deviations from the long-run equilibrium into positive and negative, allowing for different speed of adjustments to new equilibria when exports are above or below the long-run equilibrium modeled by equation (1). This parameterization of the ECM model is analogous to a Threshold

> 18 For robustness, we test cointegration of exports, real exchange rates, and world income using a VECM, finding evidence aligned with that reported here. Results from the VECM are presented in Table A.7 in the Appendix.

22
generally have higher net revenues, but in the particular year of the sample, small farms with cooler long run temperatures had higher annual revenue. In practice, the weather during the sample year for farms near the equator was normal but the weather in the more southern hemisphere was cooler than normal. This difference can explain some of this variation between the land value and net revenue results.

Figure 3a shows the response of land value to rainfall for small and large farms and Figure 3b shows the net revenue response. The small and large farm responses to precipitation are similar. Land values tend to increase with more annual precipitation but net revenues decrease with precipitation for both small and large farms.

We also conduct a Ricardian analysis on subsamples of rainfed farms and irrigated farms following Schlenker et al 2005. Table 4 shows the results of these two regressions on land value. There is no question that the rainfed and irrigated models are different. They respond differently to many soil types, altitude, mixed soil texture, electricity, computers, and the age of the head of household. Although some of the differences are hard to explain, generally irrigation is more sensitive than rainfed to other factors that affect net revenue. The high cost of installing irrigation is profitable only if the farm is otherwise highly productive. The dummy variable for the Andes region is highly negative for rainfed but zero for irrigated land implying the problems with farms in the Andes region applies strictly to rainfed farmers.

However, the key comparison in Table 4 concerns climate. Both the rainfed and irrigated farm models have many significant temperature coefficients but few significant precipitation coefficients. The individual interaction terms between climate and large farms suggests that large and small farms of each type have similar climate response 13
easily be assumed, and reductionism simplifies communication in useful ways. But in a field that depends on collaboration among people with different backgrounds and priorities, common understanding should not be assumed, and ‘short-hand’ communication can impede comprehension and backfire. One need not look far to find well-designed programs that failed during implementation because too little attention was paid to the socio-psychological mechanisms behind behavior change. A rule of thumb is a simple tool that is normally easy to teach; and sometimes understanding is impeded by a simple knowledge ‘constraint’ ( _e.g.,_ the fact that water can be contaminated after it is chlorinated). In contrast, mental models are often deeply intertwined with cultural values and expectations regarding the actions and judgments of others ( _e.g.,_ the social roles of men and women). Reducing the latter (cultural values and expectations) to the former (a simple knowledge constraint) belies what it takes to change mental models.

Akerlof has noted that ideas that fall outside the typical province of economics are generally regarded as “taboo” within the discipline. He argued that although rational choice theory is probably adequate for analyzing most types of economic behavior, a new field of “psycho-socio-anthropo-economics” is “probably applicable to the big problems, to the really big things that we don’t yet understand in economics...it is probably applicable to the question of why underdeveloped countries are so poor” (interview in Swedberg, 1990, p. 68). Studies associated with 21<sup>st</sup> century behavioral economics borrow ideas from these disciplines, but sometimes screen out useful interpretations and causal connections (Davis, 2013). Successfully addressing dysfunctional organizations and institutions around the world requires recognizing a quasi-rational, enculturated actor. In many ways, the interventions at the frontier of behavioral 28
17

# Scenario **1.** Grdually restoring **early-9Os** replacement ratios with constant share of informal sector

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.04|9.36|9.51|9.64|9.88|10.12|10.65|10.76|
|Total expenditure|8.49|9.61|10.79|11.63|13.02|16.25|17.31|18.46|19.04|
|Fund balance|-0.06|-0.57|-1.43|-2.12|-3.37|-6.37|-7.18|-7.81|-8.28|
|Average replacement ratio|29%|29%|31%|33%|37%|46%|47%|46%|49%|
|Dependency ratio|111%|120%|120%|119%|118%|117%|118%|121%|117%|
|Share of formal sector employment|59%|59%|59%|59%|59%|59%|59%|59%|59%|

# Scenario 2. Gradually restoring early-90s replacement ratios with declining informal sector

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.02|9.95|10.52|11.25|12.91|14.03|15.80|16.43|
|Totalexpenditure|8.48|9.62|10.46|11.19|12.24|14.51|15.14|15.95|16.44|
|Fund balance|-0.06|-0.59|-0.52|-0.67|-0.99|-1.60|-1.10|-0.15|-0.01|
|Average replacement ratio|28.6%|29%|31%|34%|38%|47%|48%|48%|50%|
|Dependency ratio|110.8%|120%|108%|101%|92%|78%|72%|68%|65%|
|Share of formal sector employment|59%|59%|66%|70%|75%|88%|96%|99%|99%|

Note: This is a 'what-if' scenario In the sense that there is no economic rationale for an increasing formal sector. The share of informal sector Is set arbitrarily so that the Pension Fund will remain roughly balanced.

|Scenario 3. Maintaining the "status quo" - rep|lacement|ratio prac|tically co|nstant|||||
|---|---|---|---|---|---|---|---|---|
|1995|1996|1997|1998|2000|2005|2010|2020|2030|
|PAYG Pension Fund (% of formal GDP)|||||||||
|Payroll contributions<br>8.43|9.04|9.36|9.51|9.64|9.88|10.12|10.65|10.76|
|Total expenditure<br>8.49|9.61|10.03|10.49|10.77|10.89|11.18|11.94|12.03|
|Fund balance<br>-0.06|-0.57|-0.67|-0.99|-1.12|-1.00|-1.05|-1.28|-1.27|
|Average replacement ratio<br>29%|29%|29%|30%|30%|30%|30%|30%|31%|
|Dependency ratio<br>110.8%|120%|120%|119%|118%|117%|118%|121%|117%|
|Share of formal sector employment<br>59%|59%|59%|59%|59%|59%|59%|59%|59%|
new quantity control, SPS and TBT measures, the positive interaction coefficients for GVC firms in Columns (2) suggest these effects are muted for them. Both effects are statistically significant and of economic relevance.

GVC firms are also more likely to export as market access conditions affect them less than non-GVC firms. Column (4) shows that tariffs, pre-shipment and quantity control measures undermine the likelihood of non-GVC firms participating in exports. In contrast, these measures have a muted impact on GVC firms’ likelihood to participate in exports as their positive and significant interaction effect attests to.

Intriguingly, GVC firms are more likely to leave markets when new quantity control measures are introduced. This is reflected in the positive and statistically significant interaction effect Quantity Control _i_ , _j_ , _p_ , _t_ × GVC Firm _f t_ in in Column (6), indicating a higher exit probability for GVC firms. Conversely, non-GVC firms are less likely to exit markets when quantity controls are introduced. A potential explanation is that new quantity measures are more binding for GVC firms than non-GVC firms due to their elevated exports in the first place.

# **6 Conclusion**

This paper studies how NTMs, the dominant instrument of today’s trade policy, challenge firm-level export decisions to access foreign markets. Our panel analysis of Colombian firms exporting to Latin America reveals that both TBT and quantity control measures decrease their exports on average. We rationalize this trade-deterring effect through an increase in firms’ compliance costs relative to consumer benefits. Other non-tariff measures and tariffs play a minor role.

These average effects of trade regulations mask significant heterogeneities. TBT and quantity measures reallocate trade from small to big firms and those participating in global value chains. However, quantity control measures exert progressive effects under specific conditions, making it more likely that big firms will leave export markets, with the opposite effect occurring for small firms. We argue that the quantity controls are 31