rafmacalaba/data-use-mentions-extended huggingface dataset:
long work hours, low pay, and the risk of harassment, particularly when jobs require regular interaction with men. Interestingly, generalized social norms (understood as empirical expectations of whether other women work or as expectations that people frown upon women working) appeared to be less of a barrier, in the FGDs, than women’s interpersonal expectations of specific male family members. Both male and female participants consistently estimated FLFP in Jordan to be above 50% (in reality, it is around 14%).

The theoretical account of social norms, described above, and the FGDs formed the basis of the quantitative survey instrument. The survey instrument examined normative influences across four related themes identified earlier: i) general views on whether women should work (which we called “working women”); ii) beliefs about gender roles as they relate to women’s ability and availability to work (“gender roles”); iii) beliefs about publicness and mixing of genders in the work environment (“public-ness and mixing”); and iv) beliefs about the link between women working and family status (“status”). All four themes were explored using a sub-group of questions, which were adapted as needed for male and female respondents (see Table 1).

**<mark>Table 1. Themes</mark>**

|**Working Women(WW)**|**Gender Roles(GR)**|
|---|---|
|_WW1. Women working from home_|_GR1. Married women working_|
|_WW2. Women working outside the home_<br>_WW3. Women working in Jordan (only SE)_<br>_WW4. Women working if husband not comfortable_<br>_(only PB/SN/IB)_<br>_WW5. Necessary for both husband and wife to_<br>_work to live comfortably (only PB)_<br>_WW6. Women can work if other women are
# **Can Labor Market Imperfections Explain Changes in the Inverse Farm Size–Productivity Relationship?**

# **Longitudinal Evidence from Rural India**<sup>**1**</sup>

# **1. Introduction**

The existence of an inverse relationship between farm size and output per unit of land is among the most well established empirical regularities in agricultural production economics. First identified descriptively in Russia (Chayanov 1926), then confirmed by Indian farm management studies (Bardhan 1973; Srinivasan 1972), and subsequently supported by a much larger set of countries (Berry and Cline 1979), this relationship proved robust to numerous challenges. These include but are not limited to better accounting for land quality and soil characteristics, use of profits instead of output, increased precision of plot area estimates, and more sophisticated panel estimation techniques.

Barring measurement error, the most likely explanation for this phenomenon is market imperfections. However, which markets are affected, how amenable they are to change, and what this implies for policy have not been established clearly in the existing literature. To take one example, the inverse relationship has been used to recommend redistributive land reform as a way to improve efficiency and equity (Lipton 2009), but how to implement this in a way that avoids negative side effects is far from clear. Recognition that rising non-agricultural wages and new technology will affect factor price ratios, supervision requirements, and the presence and extent of market imperfections that might have caused the relationship in the first place has led many to reassess such policy advice to also account for political viability.

Also, changes in factor price ratios, which increase the attractiveness of substituting capital for labor in agricultural production, together with continued subdivision in the context of generational change, may limit the scope for mechanization and thus contribute to a reversal of the inverse relationship, making some farms (or, more accurately, plots) too small for efficient cultivation (Foster and Rosenzweig 2010). Recent innovations in crop breeding
to be unrelated to any unobserved factors that might affect parental labor supply because in essence it is virtually randomly assigned by a biological natural phenomenon.

Even though the sex of a child is plausibly randomly assigned, there exist several concerns that might put this statement into doubt and threaten the identification strategy of this analysis. One important concern is the prevalence of the sex-selective abortions in Albania. In view of a manifest preference for sons, the ratio of men to women has recorded a significant rise since the mid-1990s due to the legalization of abortion and also the modernization of the available

> 15Figure 4 shows a graphical representation of Table 4 each one of the four databases used in this study. Mothers who give birth to a girl followed by another girl have a much higher fertility than those who give birth to mixed sex siblings or two boys in a row. On the contrary, mothers who give birth to _two boys_ in a row are less likely to have an additional child compared to those with an otherwise sex-mix.

> 16It is important to notice that the relationship between sibling’s sex composition and further childbearing is confirmed in each one of the databases used in this study (LSMS and DHS surveys). This is important because, unlike the 2005 and 2012 LSMS surveys where information about fertility is partly based on my own household matching between the children and their corresponding parents, the 2002 LSMS and 2008-09 DHS contain detailed fertility histories for each parent, including information on the dates of birth and sex of each child among others. This serves as a cross check for the relevance condition of the instrument. Tables A-2 and A-3 in the Appendix report estimates of the impact of child sex and sibling’s sex-composition on fertility for each one of the four surveys.

14
obese. The dotted horizontal lines separate those within each nutritional status who have healthy diets and those who do not. We will use these fractions to estimate the associated economic costs, using data on stunting in children under 5 years of age to project productivity costs across the life course, and using data on adults 18 years and over on overweight/obesity since the vast majority of productivity losses are in this age group. Hence while the FOLU (2019) study equates the combined costs of underweight and overweight/obesity on morbidity and mortality to human costs of the food system, Figure 3 shows that the situation is more complex. However, estimating the relative proportions of those with undernutrition, of healthy weight and overweight/obese with healthy diets, is not trivial.

We propose the following methods. To distinguish the share of child undernutrition associated with inadequate dietary intake and that associated with other factors such as infection, we will use the Lives Saved Tool (LiST: <u>https://www.livessavedtool.org). We can then attribute this cost share (projecting</u> forward to lifetime productivity losses) to estimate the proportion of the costs of child undernutrition attributable to unhealthy diets.

NCD costs (both treatment costs and DALY/productivity costs) among the adult population (18 years and older) are the major cost associated with unhealthy diets for those who are healthy weight and those who are overweight/obese. To estimate the proportion of NCD costs attributable to unhealthy diets we propose to use the following two equations:

- (1) O&O = f(food choice, other)

- (2) NCD risk = g(O&O, food choice, other)

10
-0.37|-0.18|
|Services|18.63|-2.58|-0.96|-0.43|-0.21|-0.13|
|**Imports**|||||||
|Agriculture|-75.55|-0.78|-0.14|-0.16|-0.14|-0.14|
|Manufacturing|-16.60|-0.70|-0.38|-0.16|-0.09|-0.06|
|Services|-32.52|-3.69|-0.86|-0.39|-0.17|-0.10|
|**Total Supply (Production + import)**|||||||
|Agriculture|-7.85|0.10|0.08|0.01|-0.02|-0.03|
|Manufacturing|-9.86|-0.18|-0.18|-0.10|-0.06|-0.05|
|Services<br>Source: CGE model simulation results|-8.46|-0.76|-0.32|-0.16|-0.09|-0.06|

Within the agricultural sector, simulation results suggest that cash crop production is expected to drop for a longer term while food crop and livestock production increase due to COVID-19 impacts. On the one hand, the driving force behind this asymmetrical impact between food and cash crops is the slowing economic activity in urban areas (e.g., productivity losses and declining export demand), which results in a significant decline in the intermediate demand for the cash crops. On the other hand, food crop and livestock production increase in response to plummeting imports.

Female-owned livestock,
_15 Why Infrastructure Financing Facilities Often Fall Short of Their Objectives_ 35. _Lessons Learned._ The analysis concludes with a review of what lessons can be drawn from the case study to determine necessary preconditions, if any, for establishing infrastructure financing facilities and to learn what factors in the design of the facility are crucial to ensure the success of a financing facility.

36. _Sources for the Analysis._ Only published sources (i. e. World Bank reports) or interviews with experts familiar with individual facilities were employed. In the future, as greater attention is focused on providing efficient government support to infrastructure, there is hope that quantitative data will be available.

37. _Selection of Cases for Detailed Analysis._ We selected three IFFs for a more detailed analysis that were at a different stage in the development of the facility to exemplify various types of policy issues that may arise in the design of the facility and may adversely affect its success. The three IFFs are (i) Colombia, (ii) India, and (iii) Pakistan. As there is limited experience with the use of contingent instruments by infrastructure financing facilities, two government sponsored facilities targeted at noninfrastructure sectors which had a track record with contingent instruments were included in the analysis. These are (iv) Argentina, and (v) Moldova.

# A. **Colombia: "Infrastructure Facility"**<sup>**23**</sup>

38. _Rationale and Objectives._ During the last several years, the Colombian govemment, as part of its "Economic Modernization Program", has attempted to improve resource allocation by attracting private sector investment in infrastructure while focusing public resources in social sub-sectors. In support of this policy a number of laws were passed in the transport, energy, telecommunications, and water sanitation sub-sectors during 1993 and 1995. Moreover, the government has been working on setting up an infrastructure facility to (i) increase private capital flows to the infrastructure sectors, (ii) improve the conditions
- The integrated estimation steps are as follows: 1. Run an unrestricted VAR and find . Implement Cholesky decomposition to extract .<sup>14</sup> 2. Draw a random orthogonal matrix uu and compute  and compute . ΣΣ<sup>�</sup> PP<sup>�</sup>

1. Run an unrestricted VAR and find 2. Draw a random orthogonal matrix uu and compute  and compute .<sup>�</sup>

3. Compute impulse responses using QQ BB<sup>�</sup> = PPQQ<sup>��</sup> 2. Draw a random orthogonal matrix and compute . 3. Compute impulse responses using calculated in the step 2. If all implied impulse response<sup>�</sup> functions satisfy the sign restrictions ( **Table 2** ), retain . Otherwise discard . BB<sup>�</sup> 4. Repeat the first two steps 50,000 times, recording each that satisfies the restrictions and BB<sup>�</sup> BB<sup>�</sup> record the corresponding impulse response functions. About one-fifth of the draws are discarded. BB<sup>�</sup> The commodity price data for aluminum, copper, lead, nickel, oil, tin, and zinc are drawn from the World Bank’s Commodities Price Data ( _Pink Sheet_ ). The price for oil is the unweighted average of the Brent, West Texas Intermediate, and Dubai oil prices. These commodity prices are deflated by the U.S. CPI from the Federal Reserve Economic Data (FRED) database. Quarterly GDP growth rates are from Haver Analytics, with forecasts based on the World Bank’s June 2022, _Global Economic Prospects_ report. Commodity production for aluminum, copper, zinc, lead, nickel, oil, tin, and zinc is drawn from the World Bureau of Metal Statistics. Quarterly averages of monthly data are available
** _hi_ +φ 2 **_Z_** _hi_ + **_λ_** 2 **_V_** + υ _hi_ , (2) where _Yhi_ is the value of crop output of household _h_ from parcel _i_ , **_Z_** _hi_ is the value of purchased inputs on each parcel, **_Q_** _hi_ is a vector denoting the pre-existing stock of different types of land-related investments (number of trees, soil and water conservation infrastructure), and υhi is an error term. All the other variables are as defined in (1). In addition to expecting positive coefficients on standard land quality and human capital characteristics, we also expect the stock of land-related improvements (visible and nonvisible) will increase the productivity capacity of land, the variables that measure tenure security may have an additional positive impact on productivity by increasing labor effort or the ability to obtain credit.

_Land values:_ As land sale values are the present value of expected profits from cultivating a plot, any factor that affects land productivity should have a similar impact in a hedonic land price regression (Rosen 1974). Regressing self-assessed land prices on the same set of right-hand side variables as was used in the productivity regression thus provides a way of checking for the robustness of our results, in addition to allowing the derivation of a monetary value for some of the tenure security variables included earlier. Presence of considerable noise in the land price data by occupants forces us to restrict the sample to owned plots for which we estimate a land value function of the form 7
_ln(disbursements)_ i,t = _α + β CPIAi,t‐1 + γ ln(populationi,t‐1) + δ ln(GNI p.C.i,t‐1) + λ’Xi,t‐k + αi + σt + εi,t_ where _i_ refers to the recipient country and _t_ to the Bank’s fiscal year. Errors _εi,t_ are robust to heteroscedasticity and clustered at the recipient country level and we include time‐fixed effects _σt_ and, in most regressions, recipient fixed effects _αi_ .

The inclusion of recipient fixed effects controls for any time‐invariant recipient characteristics that may influence aid allocations: estimates of poverty‐ and policy‐selectivity are then informed only by changes over time within countries in aid, GNI per capita, and quality of policy.<sup>22</sup> However, we also report results from pooled OLS (with standard errors clustered by recipient), in which estimates are informed primarily by cross‐country rather than over‐time variation in the data. The CPIA ratings are designed to compare the quality of policies across countries at a point in time using all relevant information at that time. Changes in ratings from one year to the next sometimes reflect newly‐available information rather than actual changes in the quality of policies and institutions, and ratings from prior years are not “corrected.” Over‐time variations in the data therefore are likely to incorporate substantially more measurement error than in the cross‐sectional variation. Reassuringly, the fixed effects estimates (which minimize omitted variable bias) reported below are largely consistent with the pooled OLS estimates (which are likely based on less noisy data).

Table 3 shows results with recipient‐ and year‐fixed effects for all IDA countries (columns 1‐ 3) and all developing countries (columns 4‐6). Trust funds and IDA provide significantly higher aid to poorer and better‐governed (i.e. higher CPIA) countries (
13 The rice NACs over the 1972-74 period fell by more than two-fifths for both highincome and developing countries. The NAC falls for wheat were not as severe as for rice, but were still substantial at more than one-quarter for high-income countries and nearly one-third for developing countries. The extent of annual decline in the NACs in the most recent price spike is slightly less than in the 1970s except for high-income wheat, and not quite as rapid: between 2005 and 2008 the NAC for rice fell 29 percent for high-income countries and 36 percent for developing countries, and for wheat it fell around 37 percent for both high-income and developing countries (Table 5). That slightly smaller and slower decline also is consistent with the fact that there were smaller and slower proportionate rises in the international prices of those cereals in 2005-08 than in the early 1970s.

Turning to all covered farm products (bottom segment of Table 5), the NAC for developing countries fell by 16 percent in the first two years before rising by half that amount in the subsequent two years of the 1970s’ spike period. The fall for high-income countries was almost the same (14 percent) but it more than recovered in the subsequent two years. As for the mid-1980s price slump period, the NAC rise for all farm products was larger for highincome countries and smaller for developing countries in1984-86 than the fall in 1972-74, consistent with the findings discussed above from Table 4(b) and Figure 3(a).

Table 6 allows an easy comparison between the mid-1980s, and the two upward price spike periods, of annual price changes in NACs. It also suggests there is little difference not only in the magnitude but also in the timing of the responses of food-importing and foodexporting countries. That is the case not only for rice and wheat (illustrated in figure 3(b)) but also for all products included in the World Bank’s database, shown at the bottom of Table 6.

In Table 7 the NRAs have been decomposed into the various border and
6

**Table 1:** _Turkey’s trade in goods and services as percent of GDP in regional perspective in 1991-98 and 1999-2004 (in percent)_

||Trade in G&S a<br>|s % of GDP<br>|Exports of G&S<br>|as % of GDP<br>|
|---|---|---|---|---|
|Country/Group|1991-98|1999-04|1991-98|1999-04|
|Turkey|42.2|57.4|19.2|27.5|
|Bulgaria|95.0|114.3|47.4|53.3|
|Croatia|105.0|99.5|49.4|46.1|
|Romania|56.3|72.3|25.1|33.1|
|**Memorandum:**|||||
|SEE-4 (simple average)|75.4|76.6|24.2|27.0|
|EU-8 (simple average)|103.6|119.0|50.7|56.9|
|of which: Poland|49.2|55.4|24.2|25.2|

SEE-4 includes Albania, Bosnia and Herzegovina, Macedonia and Serbia and Montenegro; EU-8 Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia. Source: Computations are based on World Bank WDI data base through SIMA and Turkey’s Balance of Payments statistics for 1999-2003.

Fourth, in 1985-2004 Turkey’s trade in goods and services has been persistently in the red except for three years—1988, 1994 and 2001 due to deficits in trade in goods. Revenues from exports of goods and services amounted on average to 89 percent of expenditures on imported goods and services in 1990-97 and 92 percent in 1998-2004 (Table 2). The
seasonal gaps across locations, ranging from 13.6 percent to 46.5 percent for maize and between 3.0 percent and 22.0 percent for rice (Figure 2).

Seasonality in the international prices is estimated at 10.7 percent for SAFEX white maize and 4.7 percent for Bangkok rice. These findings underline that some degree of price seasonality is to be expected, even when markets function efficiently. Second, seasonality of international maize prices is also about twice as high as this of rice, providing confidence in the domestic findings. Finally, the Tanzanian maize and rice seasonal price gaps are on average 2.5 to three times higher than those observed in the international markets, with the gap substantially above the international one in virtually all markets. This points to substantial excess seasonality in Tanzania’s staple markets.

Finally, the seasonal _R_<sup>2</sup> statistics (Table 2, column 7) show that only 13.1 percent the monthly variation in maize and rice prices is attributable to seasonality. For rice, this is about half (7.1 percent).<sup>16</sup> Seasonality does not appear to be a major contributor to staple price volatility. While there will benefits from reducing seasonal price fluctuations of staple foods to enable households to better smooth their consumption expenditures, policies aimed at this objective will not substitute for policies addressing the overall level of food price volatility.

# **4 Staple price induced seasonality affects poorer urban households and net food sellers**

The data on consumption and other household characteristics are from the three available Living Standard Measurement Study‐Integrated Surveys on Africa (LSMS‐ISA) waves for Tanzania (2008‐09, 2010‐11 and 2012‐13). These are panel datasets in which considerable effort is made to recontact households which relocate between waves as well as to track the components of the households which break up or combine, generating in effect a panel of individuals. See <u
of town in-migrants confirms the findings of other studies (Hare 1999; Zhao 1999). People in these age groups are most likely to migrate, and probability of migration decreases with age. In rural area, a piece of land that could be hold as a social security against old ages, whereas in towns, old people have to rely on some kind of social security, which has not been well established so far, preventing elderly moving into towns (Cai 2003).

Figure 1 Age differentials in migrants into town: 2000 <!-- Start of picture text --> The age distribution of in-migrants in town by<br>provinces<br>25<br>20<br>15<br>10<br>5<br>0<br>0-- 5-- 10- 15- 20- 25- 30- 35- 40- 45- 50- 55- 60- 65+<br>4 9 -14 -19 -24 -29 -34 -39 -44 -49 -54 -59 -64<br>age group<br>Zhejiang Henan Sichuan National<br>%<br><!-- End of picture text --> Source: calculated from Tabulation on the 2000 population census of the P. R. China. Table 7-5, page.1818-1827, and L705B.

9
Third, we cannot rule out that the company kept track of who we trained and kept workers because they knew they were trained, although this information was not shared with the company. While this could contribute to the employment retention effects, positive wage effects for workers who left the company would not be driven by this. This suggests that our results are not driven by the company keeping track of trained workers. Observations from the field also do not support this hypothesis. Supervisors tend to be in close contact with their workers, and closely monitor their behavior. Given how important it is for the company to be able to rely on workers to perform responsibly in these roles, it is unlikely that the company would not take into account the actual performance of workers in their performance evaluations.

Overall, the evidence is supportive of a mechanism whereby our intervention affected worker conscientiousness and this in turn affected their labor market outcomes, although more research is needed to better measure conscientiousness skills in such contexts.

# **3.3. Potential limitations**

We note two limitations to our study. First, end-line data collection took place after the onset of the COVID-19 pandemic and it is possible that our intervention interacted with the pandemic. For example, given that the training aimed to make individuals more conscientious, it is possible that they were more careful during the pandemic and lost their jobs due to wanting to stay at home rather than run the risk of exposure to the virus by going to work. On the other hand, more conscientious individuals might have felt a greater responsibility to go to work. To measure whether the intervention affected the impact of COVID-19 on the behavior of the workers, the end-line survey asked a number of questions on how the pandemic impacted their livelihoods. One set of questions asked respondents to compare current incomes and expenditures to before the start of the pandemic, specifically in February 2020. We do not find any significant differences in the extent to which treated and control individuals reported lower incomes, lower transfers and