rafmacalaba/datause-encoder-data huggingface dataset:
price shocks, among others, threaten to dampen already-low rates of fertilizer use with potentially significant impacts on agricultural production and productivity.

In this paper, we leverage high-frequency phone survey data to shed light on dynamics in African smallholder agriculture against the backdrop of these crises, particularly as they relate to the availability, use, and price of inorganic fertilizer. Our data come from the longitudinal highfrequency phone surveys (HFPS) implemented since April 2020. The LSMS-HFPS regularly fielded a rotating set of questions on agricultural practices and outcomes, which form the basis for this paper. This phone survey data is also complemented with data from the face-to-face longitudinal household surveys that have been conducted under the LSMS-Integrated Surveys on Agriculture (LSMS-ISA) initiative and that have served as the sampling frames for the LSMSHFPS. Together, the data span the period from 2018 to 2024. In total, we rely on 34 waves of survey data across six countries, Burkina Faso, Ethiopia, Malawi, Nigeria, Tanzania and Uganda.

Using these data, we unpack the farmer experience with inorganic fertilizer over time, including not only incidence of use, but also the strategies employed by households in the face of price shocks and other constraints. We find that while the incidence of application remains relatively stable over most of the crisis period, a significant share of households reported using less than the amount that they needed, driving many households to apply fertilizer to a smaller share of their cultivated area or apply at a lower intensity. Some farmers tried to minimize further reduction by borrowing money or selling assets to buy the input, increasing their vulnerability to future shocks.

The remainder of the paper is organized as follows. Section 2 discusses the data utilized and the methodology deployed. Section 3 presents the results, and Section 4 discusses the importance and policy implications of these results. Section 5 concludes.

# **2. Data and Methods**

# _2.1. High-Frequency Phone Surveys_

This paper uses data from a series of high-frequency phone surveys (HFPS) conducted in six SSA countries. The HFPS data used here have been collected primarily by national statistics offices,<sup>1</sup>

> 1 The Ethiopia HFPS was implemented by a private survey firm, not the national statistics office.

3
selection on financial ability), but that individuals who receive unsolicited advice do not. These results suggest that more motivated individuals are likelier to respond to advice, and that compulsory advice-giving may be ineffective. Their experiment complements non-experimental analysis of data on actual 401(k) plan participants, in which they do not find that financial advice has any effect on plan-related outcomes.

# **_Promoting savings among low-income individuals and minorities_**

A number of programs are targeted towards increasing savings and usage of financial products specifically among low-income individuals and minorities. As survey evidence indicates, individuals in such communities tend to have lower levels of financial literacy and less access to financial products. The efficacy of financial education seems to vary depending upon the program. Servon and Kaestner (2008), perhaps the only randomized study of a full-fledged program, analyzes the impact of financial literacy and Internet-use training on the financial behavior of urban low- and moderate-income individuals. They find little quantitative evidence of an increase in online banking, the main objective of the program, as well as relatively few other positive effects on behavior, although implementation issues may have affected the results.

Clancy et al. (2001) examine the relationship between the amount of financial education received and savings behavior among participants in Individual Development Accounts (IDAs), which are subsidized savings accounts targeted to the poor. They find large positive effects on savings for the first zero to twelve hours of financial education, after which the effect levels off.

Such programs have also experimented with using different instructional formats and innovative media. For instance, Spader et al. (2009) provides some non-randomized experimental evidence on the impact of a Spanish-language ―telenovela‖ designed to impart financial education to Latino immigrants in the United States. They employ propensity score matching to reduce bias in the selection of the treatment and control groups, which were recruited through different channels. The results show that the television show positively affects viewer attitudes with regard to bank account usage and preparation for homeownership, which are also corroborated by a qualitative analysis.

Tufano et al. (2010) presents preliminary results from a unique program to improve financial decision-making skills among low-income and minority adults using video games 24
33

## RAdams

L:\ADAMS\Guatemala. pov paper combine. doc.doc September 24, 2004 1:41 PM

# <u>Notes</u>

1 While no estimates are available regarding the size of internal remittances, Ratha (2004) has recently estimated that official international remittances to the developing world now total $93 billion per year, making them the second most important source of external funding in developing countries.

2 For estimates of the economic costs of international migration, see Stahl (1982) and Adams (1991, 1998).

3 While Stark, Taylor and Yitzhaki (1986) and Adams (1992, 1995) examine the impact of internal and international remittances on income inequality in Mexico and Pakistan, respectively, they do not analyze the effect of these financial transfers on poverty.

4 This 2000 Guatemala household survey was implemented as part of the “Program for the Improvement of Surveys and Measurement of Living Conditions in Latin America and the Caribbean” (ENCOVI), which was sponsored by the Inter-American Development Bank (IDB), the World Bank and the Economic Committee for Latin America and the Caribbean (CEPAL).

5 For more details on this 2000 Guatemala ENCOVI household survey, see World Bank (2004).

6 The five possible responses to this “where do these people sending (your household) remittances live” question were: (1) Guatemala; (2) United States; (3) Mexico; (4) Central America; and (5) other countries.

7 As explained on page 8, the focus here is on predicting per capita household expenditure (excluding remittances), rather than income. See page 8 of text.

8 This method of predicting incomes (expenditures) assumes that the only way in which households receiving no remittances differ from households receiving internal and international remittances is that the latter group of households receive remittances. Non-remittance receiving and remittance receiving households are not assumed to differ in any entrepreneurial or other way which might affect their expenditure in a manner apart from the relationships captured by the variables used in the predicted income equations.

9 Results are similar when equation (2) is estimated by a log-linear model.

10 The expenditure variable in this data set is quite comprehensive and includes household expenditures on the following categories of goods: food (purchased and home produced), consumer goods, household services (energy, water), housing (annual use value), durable goods, education, health, transport and communication.

11 For more information on this expenditure-based poverty line for Guatemala, see page 12 of text.

12 For more on the life-cycle effects of household expenditure, see Deaton (1992, 1997).

13 See, for example, Psacharopoulos (1985, 1994) and Glewwe and Jacoby (2004).
**Figure 3. The Export Response to Antidumping Imposition and Removals: Share of the Export Market**

<!-- Start of picture text --> percent All Exporters percent China as Exporter<br>35 40<br>AD imposed AD removed AD imposed AD removed<br>30 35<br>30<br>25<br>25<br>20<br>20<br>15<br>15<br>10<br>10<br>AD on<br>5 AD on<br>5<br>0<br>0<br>t-2 t-1 t t+1 t+2 T-2 T-1 T T+1 T+2<br>t-2 t-1 t t+1 t+2 T-2 T-1 T T+1 T+2<br><!-- End of picture text --> <!-- Start of picture text --> percent High Income Exporters percent Other Emerging (non-China) Exporters<br>45 20<br>AD imposed AD removed AD imposed AD removed<br>40 18<br>35 16<br>14<br>30<br>12<br>25<br>10<br>20<br>8<br>15<br>6<br>10<br>AD on 4 AD on<br>5 2<br>0 0<br>t-2 t-1 t t+1 t+2 T-2 T-1 T T+1 T+2 t-2 t-1 t t+1 t+2 T-2 T-1 T T+1 T+2<br>Exports to all G20 economies Exports to G20 Emerging economies Exports to G20 High Income economies<br><!-- End of picture text --> Notes: _Exports hijk_ defined here as country _j_ ’s share of total world exports of product _h_ to policy-imposing country _i_ at year _k_ , for years _k_ around the imposition (year _t_ ) and removal (year _T_ ) of country _i_ ’s antidumping import restriction. Constructed by the author with policy data from Bown (2012) and HS-06 import data from UN Comtrade via WITS.

25
when NAFTA was at work in the absence of a devaluation effect.<sup>7</sup> Between 1993 and 1994, the average size of integrated firms remained constant, while the proportion of integrated firms increased. Therefore, NAFTA (in the absence of a devaluation) helped relatively small firms to incorporate into the global markets.<sup>8</sup> After 1995, when the devaluation effect was very strong, even smaller firms where pushed into the global markets, hence explaining the increase in the proportion of integrated firms and the reduction in their average size. After 1995, the changes in the distribution of size among integrated firms in the market can be attributed to a combination of NAFTA and the peso devaluation. The simultaneity of these two events resulted in an expansion of integrated firms but this time the small ones (many of the exporters and to a lesser extent the importers) were not able to survive the crises. Therefore, the average size of the integrated firms increased after 1995. This increase in the average size among integrated firms after the trade reforms is consistent with trade models `a la Melitz (Melitz 2003, Bernard, Redding, and Schott 2007, Melitz and Ottaviano 2008).

Figure 4: Patterns of integration and firm size <!-- Start of picture text --> 1992 1994 1996 1998 2000 2002<br>year<br>% of Integrated Firms Mean Size Integrated Firms<br>Source: EIA, INEGI<br>58 360<br>340<br>56<br>320<br>54 300<br>% of Integrated Firms Number of Employees<br>280<br>52<br>260<br><!-- End of picture text --> Figure 5 shows the performance in value added labor productivity per hour by integration status. Between 1993 and 1994 (the period of NAFTA without a peso devaluation), average productivity in all integration groups rose, with the fully integrated firms benefiting most. After the peso devaluation and until 1996, labor productivity of integrated and non-integrated firms decreased with the non-integrated firms experiencing

> 7Given that the peso crisis took place on the 20 December 1994, the effect of the devaluation is not captured by the data from year 1994.

8Yet another way of interpreting the increase in small integrated firms between 1993 and 1994 is by assuming that larger firms had a better chance of anticipating NAFTA, therefore integrating before the agreement was enacted.

11
# **Table 4. Summary Statistics for Respondents** _<u>Panel (a): Basic Demographics</u>_

|||<br>U|nweighted|Weighted|
|---|---|---|---|---|
||||(%)|(%)|
|Female|||35.3|18.0|
|Age<br>0-25|||25.0|21.9|
|26-35|||35.5|35.6|
|36-45|||21.8|22.8|
|46-55|||11.6|12.8|
|56+|||6.1|6.9|
|Education<br>Less than primary|||18.2|16.5|
|Some primary|||23.4|22.8|
|Some secondary|||19.0|19.6|
|Some high school|||14.6|16.0|
|<br>High school degree|||9.0|9.5|
|Bachelors degree or|higher||15.5|15.4|
|Missing|||0.3|0.3|
|Industry<br>Garments|||25.4|20.4|
|Other Manufacturin|g||13.5|15.5|
|Trade/Transportatio|n||22.9|27.8|
|Other services|||38.3|36.3|
|_Panel (b): Typ_<br>|_e of Employ_<br>|_ment_<br>|||
|**Male**||**Female**|**Tot**<br>**%**|**al**<br>|
|Unweighted|Weighted||Unweighted|Weighted|
|Paid employee in government<br>4.2|4.3|5.5|4.7|4.5|
|Paid employee in semi<br>1.6|1.7|2.7|2.0|1.9|
|government entity|||||
|Paid employee in a private<br>38.0|38.3|49.9|42.2|40.4|
|entity|||||
|Apprentice/Intern/Trainee<br>0.2|0.1|0.3|0.2|0.1|
|Seasonal worker<br>0.6|0.2|0.7|0.6|0.3|
|Day laborer / casual worker<br>10.3|10.4|3.2|7.8|9.1|
|Domestic worker in a private<br>0.8|0.3|10.5|4.2|2.1|
|household|||||
|Self-employed / business owner<br>19.5|19.7|16.9|18.6|19.2|
|with no employees|||||
|Self-employed / business owner<br>6.9|7.0|4.5|6.1|6.5|
|employing only paid or unpaid|||||
|family members|||||
|<br>Self-employed / business owner<br>14.9|15.0|1.9|10.3|12.7|
|employing some non-family<br>members|||||
|Paid or unpaid family member<br>working in a household<br>2.9|2.9|4.0|3.3|3.1|
|business|||||
|Total<br>100|100|100|100|100|

42
The weight uncertainty results produced for Bangladesh show marked differences from the weights adopted in the previous study conducted by IMC Worldwide and CEGIS. These differences are because our analysis used a statistical approach while the previous study used a qualitative approach based on expert judgment. This difference highlights a potential bias from solely relying on expert judgment in weight estimation. Ideally, a weight estimation procedure combining statistical and qualitative approaches, whereby weight uncertainty is determined using the statistical approach (i.e., machine learning and large-scale sensitivity analysis), then the qualitative approach (i.e., expert judgment) is used to select a weight combination (or multiple combinations) from within the uncertainty range. Such a combination of statistical and qualitative approaches could further reduce the uncertainty range and provide more guidance to decision-making on road investment.

The next step is to translate the uncertainty quantification and transform it to the decisionmaking support tool. Although accounting for different uncertainty sources can help make robust investment decisions, understanding and interpreting large-scale sensitivity analysis results can be challenging for decision-makers who need to choose an acceptable level of uncertainty tolerance. New data visualization techniques such as parallel coordinates plots (Inselberg, 2009) can help decision-makers and stakeholders sift through the multi-dimensional uncertainties related to landslide investments. Online data visualization tools such as Polyvis <u>(https://www.polyvis.org/) can support inter-institutional and multi-stakeholder deliberation on</u> the uncertainties related to landslide susceptibility and help zero in on an acceptable level of uncertainty tolerance.<sup>*</sup>

> * An interactive parallel coordinates plot of the sensitivity analysis results shown in Fig 6 can be accessed on Polyvis via the following link, where different weight combinations can be filtered, and their susceptibility implications can be explored: https://www.polyvis.org/sheet/pvphofbsoj8f.

28
percent of the households in Finland have ever held a short position during the 1995-2003 time period.<sup>27</sup> Mean portfolio concentration is 0.20, roughly corresponding to holding five stocks with equal value weights of 20%.

We use a framework similar to the one we utilize in the US, in order to test the validity of hypotheses (1) and (2.a) for Finnish investors. We run a hazard model estimating the conditional probability that a stock is sold controlling for stock illiquidity, firm characteristics, investors’ demographic information as well as investor sophistication. Consistent with our US analysis and following standard reporting convention, we report hazard ratios instead of estimated coefficients.

Panel A of Table 8 reports results for the hazard model run on the transaction-level Finnish data set. Results are remarkably similar to the US. Hazard ratio coefficient on the adjusted Amihud illiquidity measure (AdjIlliq) in column (1) is less than one (0.984) and statistically significant. Using either Roll’s C (in column 2) or Zerofreq (in column 3) instead of AdjIIliq yields similar results. Our basic analysis suggests that the average investor in Finland holds stocks with higher transaction costs for a longer period of time, consistent with our findings in the United States and as predicted by Amihud and Mendelson (1986). When we control for household and firm specific effects using stratification in column (4) we find that the hazard ratio coefficient on the adjusted Amihud illiquidity measure (AdjIlliq) is still less than one (0.976) and statistically significant.

Similarly, further controlling for stock characteristics in addition to household specific effects in column (5) also yields a hazard ratio coefficient on the adjusted Amihud illiquidity measure (AdjIlliq) that is less than one (0.982) and statistically significant. In column (6) we control for household and stock characteristics together and once again find a hazard ratio of 0.982 on the

> 27 This is not entirely surprising since Bris, Goetzman, and Zhu (2007) suggest that short selling became legal in Finland in 1998 but that tax laws inhibit would-be short sellers.

33
is given by: Similarly, _Language Overlapij_ measures the degree of overlap in languages spoken at any pair of districts. min _{s_<sup>_l_</sup> _i_<sup>_, sl_</sup> _j_<sup>_}_is the intersection of people from each district who</sup> speak the same language _l_ . Since each person has only one mother tongue, summing over all possible mother tongues, we have the overlap of people from two districts that can understand each other. This is calculated as: Our linguistic proximity measures do not take into account the genealogical relations (linguistic distance) between languages,<sup>13</sup> and thus can be considered a lower bound of the linguistic proximity across districts.

Table 3 summarizes the language and distance measures by contiguity of districtpairs. Overall, neighboring districts are closer to each other in terms of distance and linguistic proximity relative to non-neighboring districts. The average district-todistrict log distance is 6.8. For neighbors, regardless of whether they are in the same state or not, the log distance is 4.3, which is 12 times smaller. Districts that are in the same state have greater linguistic proximity than district-pairs from two different states. This confirms that language was an important consideration in the drawing of state borders. Consistent with this, even though neighboring districts in different states have higher linguistic overlap than non-neighboring districts in different states, this overlap is lower than that among districts in the same state.

13Several studies use language trees from Ethnologue and use number of shared nodes between two languages to construct a linguistic proximity measure. Such studies include Adsera and Pytlikova (2015); Belot and Hatton (2012); Desmet, Weber, and Ortuño-Ortín (2009) and Desmet, OrtuñoOrtín, and Wacziarg (2012).

14
Table 15: Worker individual-level results: IV Estimation

||(1)<br>M|(2)<br>onthly|(3)<br>|(4)<br>Hourly|
|---|---|---|---|---|
||Wage|Wage|Wage|Wage|
|Schooling x Regency ETR|0.442***|1.180**|0.390***|1.024***|
||(0.179)|(0.523)|(0.0945)|(0.335)|
|Observations|458,684|52,798|450,722|51,962|
|R-squared|0.208|0.195|0.177|0.144|
|Worker Controls|Yes|Yes|Yes|Yes|
|District-Year FE|Yes|Yes|Yes|Yes|
|Sample|All|Manufacturing|All|Manufacturing|

The table presents 2SLS estimates of the relationship between industry exposure to robots and worker’s income. Exposure to robots is constructed as the share of workers susceptible to automation (Graetz and Michaels (2018)) at the regency-level in a year. The dependent variables are, respectively, log of real monthly wage and log of real hourly wage. Standard errors are clustered at the regency-year level. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **6 Conclusions**

This paper has provided novel evidence that robots increase plant level efficiency, value added and employment in a large developing country experiencing a robust increase in imports of robots. Moreover, we find evidence of positive employment spillovers from downstream automation, within manufacturing and across other sectors.

The positive employment impact of automation in Indonesia contrasts with the evidence not only from advanced economies but also from emerging ones, such as Mexico (Artuc et al., 2019) and China (Giuntella and Wang, 2019). In these countries the increase in labor demand driven by higher productivity is not sufficient to offset the labor displacement effect of robots. While a systematic investigation of the reasons behind this difference is beyond the scope of this paper, we discuss two candidate explanations.

The first is related to the evidence that the productivity impact of robots is subject to diminishing returns to scale (Graetz and Michaels, 2018). In a country with a low initial density of robots, their adoption may result in particularly large productivity increases that could offset the labor displacement effect. This low initial density seems to apply to Indonesia, which in 2011 - the first year we have the data for the 3 countries - recorded 0.018 robots per thousand workers, versus 0.04 in Mexico and 0.1 in China.<sup>37</sup> This

> 37These numbers are obtained by combining IFR data on robots’ stock with ILO data on total employment across all sectors.

38
_Railway Reform in the Central and Eastern European (CEE) Economies_ _9_

**Figure** 14

<!-- Start of picture text --> Shares of West European<br>Surface Passenger Market<br>Bheare<br>100<br>so -<br>40<br>20 g,<br>1965 1970 1975 1980 1985 1989<br>Year<br>Rai Cas & Ta8ds Busas &Coaches<br><!-- End of picture text --> 8. This rapid and wrenching change in the railway role means that there must be a corresponding change in the organization of the railway and in its relationship with the government and the transport sector. W!'en the government commanded the economy, the railway role was simple: just as all other sectors of the economy were required to do, the railway served the "Plan." As competitive, market-oriented structures begin to drive the economy, CEE and CIS railways must make the same transition in organization and mentality as has been, or is being, made in the market economies. The railway can no longer focus on the operation of trains and the repair of track (a production-driven role); it must instead focus on the needs of the customer.

9. This will not be a simple transition. Nor will it take place on the same time scale in all the CEE economies because some are already further ahead than others in the transition. This restructuring of the railway may also not include exactly the same aspects or facets in all of the different countries because the size and function of the railway varies considerably among the economies (see Figure 6). This being said, there are a series of initiatives which, with an appropriate adjustment for the particular characteristics of each economy, should probably form an underlying, comprehensive agenda for use in the restructuring process. This agenda is described below.

# AN AGENDA FOR CHANGE

10. As noted in paragraph 7, railways in market economies have faced a steadily declining role in the transport market. Typically, rail management and governments dealt with the emerging problems by "tinkering at the margin" -- for example, by debt write-offs -- only to find that the problem reappeared within a few years. Following a series of such failures to attack the underlying cause of the malaise, governments in the market economies are increasingly coming to the conclusion
# Average Road Density in Low, Middle, and High Income Economies

<!-- Start of picture text --> GNP/capita, $ Density, km/mil inhab<br>100000-<br>-100000<br>17420 10110 8660 10000<br>10000<br>1660<br>1000<br>1720 470<br>1000 170<br>320 4010<br>100<br>10<br>PGNP < $545 $545-$6,000 PGNP > $6,000<br>GNP/Capita Paved Roads<br><!-- End of picture text --> GNP/Capita Emi Good Paved Roads FIGURE 6