rafmacalaba/datause-encoder-data huggingface dataset:
� _σ_<sup>_k_</sup> ’s from Fontagné et al. (2022), and appear in Table 3.

The column 1 results offer a simple example of our method. Pre-multiplying _HOMEj_ by _σ_<sup>_k_</sup> prior to estimation allows the associated regression coefficient to be interpreted as a measure of structural trade costs. Accounting for functional � form, the estimate implies a tariff-equivalent trade cost of _τHB_ = 0 _._ 267. In other words, the estimate implies that the commonalities that PR shares with the U.S. mainland (common legal system, currency, etc.) is equivalent to a tariff of 26.7 percent on foreign imports.<sup>34</sup> This estimate is biased downward because it ignores the counteracting effects of the JA on sea-shipped imports.

� When we add<sup>_−→_</sup> _Z_<sup>_k_</sup> to the regression in Column 2, _γ_ is largely unchanged. The estimate grows slightly in column 3, where we include the interactions of _HOMEj_ � with<sup>_−→_</sup> _Z_<sup>_k_</sup> . The inclusion of interaction terms in the regression means that _γ_ no longer captures a cross-product average trade cost, it now represents an estimated trade cost for a product with particular characteristics. In this case, � _γ_ measures an implied tariff equivalent of home bias for a product that is always air-shipped, is not typically containerized, and has a weight-to-value ratio of 1 (i.e. ln( _WV_<sup>_k_</sup> ) = 0). This estimate of � _γ_ is somewhat larger than those in Columns 1 and 2.

The coefficients on the interaction terms are of greatest interest. They retain the same intuitive sign pattern as in the reduced form regression. The coefficient estimate on _σ_<sup>_k_</sup> _× V sh_<sup>_k_</sup> _y_<sup>_× HOMEj_implies that shipping the same product exclusively</sup> by vessel rather than exclusively by air implies an increase in<sup>�</sup> _t_<sup>_k_</sup> _JA_<sup>of</sup><sup>_e_0</sup><sup>_._206.Heavier</sup> products have larger implied _τ_ � _JA_<sup>_k_’s,whilecontainerizedshipmentsfacesubstan-</sup> tially lower tariff-equivalent JA costs. The magnitudes of the estimates suggest that containerization largely offsets the additional costs of sea shipping when a product’s weight-to-value ratio is low.

> 34As a point of reference, Anderson and van Wincoop (2004) estimate that the U.S.-Canada border imposes a tariff-equivalent border cost of 47 percent.

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assets, and household characteristics that may affect crop output, and φ s are the parameters to be estimated. Assuming that the disturbance term is composed of two additive components where _vi_ is pure _2_ white noise, and _ui~ N_<sup>_+_</sup> _(0,_ δ _u )_ is a term that follows a truncated normal distribution with zero mean that can be interpreted as producer’s level of technical inefficiency (Coelli 1995). A measure of ability or technical efficiency _TEi = exp(-ui)_ can then be predicted directly.

To compare effects of market transactions to those of non-market transactions (i.e., inheritance, gift, dowry, etc), we run an identical ordered probit model to identify key determinants for non-market land transfers with some modifications of the variables to be included in the ordered probit model. For example, the entire argument of transaction costs associated with land sale and land purchase will not be relevant to inheritance and gift exchange. Correspondingly, we treat the two thresholds in the ordered probit model as constant rather than variable. As discussed in the estimation strategy section, we treated the lower and upper bounds of the ordered probit model as constant because the transaction costs are unlikely to be relevant to non-market transactions. To allow direct comparability for robustness check, we also estimate the ordered probit model with variable upper and lower bounds for the non-market land transaction (see appendix table 2), and results are similar to those reported earlier.

# **3. Data sources and descriptive evidence**

The main source of data used in our analysis is two rounds of NCAER’s ARIS/REDS survey that were conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India’s major states. Even though the first round sample, which is stratified by farm size and wealth class, was limited to project areas, the survey was significantly expanded in 1982 to make it more representative at the national level, covering slightly less than 5,000 households (Foster and Rosenzweig 1996). The 1999 sample contains all of the households included in 1982 as well as replacements for those who were no longer present. If the original household had split, all of the households belonging to the same dynasty in the original village plus a sub-sample of successor households outside the village were interviewed, bringing the total to about 7,500 households (Foster and Rosenzweig 2004).

Data from household and community surveys are complemented by information on income and land ownership by all villagers from the survey listing. Moreover, to assess the impact of climatic shocks, we complement household data with information on rainfall for each month in the 1970-2004 period at the regional level. We use these data, together with the long-run average for each month, to defined a climatic shock as rainfall being more than 50% below or above the long-term mean for two consecutive growing seasons and will use this as one of the right hand side variables in the regressions.

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Andrée, B.P.J., Chamorro, E., Andres, F., Kraay, A., Spencer, P., & Wang, D. (2020). Predicting Food Crises (English). Policy Research working paper, no. WPS 9412. Washington D.C., Washington D.C., United States. Retrieved 02 01, 2024, from <u>http://documents.worldbank.org/curated/en/304451600783424495/Predicting-Food-Crises</u> Arbia, G., Solano-Hermosilla, G., Nardelli, V., Micale, F., Genovese, G., Amerise, I., & Adewopo, J. (2023). From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling. 10(446). doi:10.1038/s41597-023-02211-1 Baquedano, F. (2015). Developing a price warning indicator as an early warning tool - a compound growth approach. GIEWS – Global Information and Early Warning System on Food and Agriculture. Rome, Italy. Retrieved from - <u>https://www.fao.org/fileadmin/user_upload/foodprice/docs/resources/a i7550e.pdf</u> BudgIT. (2022). State of States 2022 Edition. [Okeowo, Gabriel; Fatoba, Iyanuoluwa, eds]. - BudgIT. Retrieved 01 02, 2024, from https://yourbudgit.com/wp <u>content/uploads/2022/10/2022-State-of-states_Official.pdf</u> Dastjerdy, B., Saeidi, A., & Heidarzadeh, S. (2023). Review of Applicable Outlier Detection Methods to Treat Geomechanical Data. Geotechnics, 3, 375-396. doi:10.3390/geotechnics3020022 Dou, Z., Stefanovski, D., David, G., Lindem, M., Rozin, P., Chen, T., & Chao, A. M. (2020). Household Food Dynamics and Food System Resilience Amid the COVID-19 Pandemic: A Cross-National Comparison of China and the United States. Frontier in Sustainable Food System, 4. doi:10.3389/fsufs.2020.577153 Easterly, W., & Fischer, S. (2021). Inflation and the Poor. Journal of Money, Credit and Banking, 33(2), 160.

EC-JRC. (2022). Food Price Crowdsourcing in Africa - FPCA. Retrieved 01/03/2024, from European Commission Joint Research Center Data Portal: https://datam.jrc.ec.europa.eu/datam/mashup/FP_NGA/index.html; Last accessed: 01/05/2024 FAO. (2017). Building Agricultural Market Information Systems: A literature review. Rome, - Italy. Retrieved 01 07, 2024, from http://www.fao.org/3/a <u>i7151e.pdf</u> FAO, IFAD, UNICEF, WFP and WHO. (2023). The State of Food Security and Nutrition in the World 2023. Urbanization, agrifood systems transformation and healthy diets across the rural–urban continuum. Rome, Italy. doi:10.4060/cc3017en FSIN and Global Network Against Food Crises. (2023). Global Report on Food Crises 2023 - GRFC2023. Rome. Retrieved 03 01, 2024, from - <u>https://www.fsinplatform.org/sites/default/files/resources/files/GRFC2023 compressed.pdf</u> Galtier, F., David-Benz, H., Subervie, J., & Egg, J. (2014). Agricultural market information systems in developing countries: New models, new impacts. Cahiers Agricutures, 23, 232244. doi:10.1684/agr.2014.0716 Gatti, R., Lederman, D., Islam, A., Bennet, F., & Andrée, B. (2023). Altered Destinies: The Long-Term Effects of Rising Prices and Food Insecurity in the Middle East and North Africa.

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First, the structure of Slovak imports contains a larger share of goods expected to have higher import elasticities. Generally, import demand elasticities for manufactured goods tend to be higher compared to those of raw materials and food,<sup>12</sup> which can explain the lower responsiveness of imports to REER development in Macedonia. Machinery and equipment account for the largest share of Slovak imports (see Table 4). On the other hand, Macedonian imports are dominated by goods imported for processing and re-exported (textiles and iron and steel; the bulk of imports classified as other manufactured products) as well as a sizable share of basic consumption goods (food and oil). Imports for processing are more sensitive to wage pressures (rather than REER developments) which have been modest due to the high unemployment in Macedonia.

**Table 4: Imports and exports structures, SITC classification, 1999-2006, in % of** **<u>total</u>**

||Impo|rts Struc|ture|Expo|rts Struc|ture|
|---|---|---|---|---|---|---|
|**Slovakia**|1999|2003|2006|1999|2003|2006|
|Food, drinks and tobacco|6.2|4.3|4.8|3.5|2.8|3.8|
|Raw materials|4.0|3.4|3.3|4.0|2.5|2.4|
|Fuels, lubricants and related materials|13.0|12.1|14.1|4.7|5.1|6.4|
|Chemicals and related products|11.3|9.8|8.8|7.8|5.0|5.5|
|Other manufactured products|27.8|29.1|29.9|40.4|36.4|32.8|
|Machinery and transport equipment|37.7|40.7|38.8|39.4|47.4|48.5|
|**Macedonia**|||||||
|Food, drinks and tobacco|13.8|12.8|10.7|19.0|16.8|16.0|
|Raw materials|3.2|2.6|3.6|4.3|2.9|4.7|
|Fuels, lubricants and related materials|9.1|14.0|20.2|1.9|5.4|9.4|
|Chemicals and related products|10.4|11.1|9.7|4.6|5.2|4.2|
|Other manufactured products|20.8|20.0|36.7|61.0|63.6|60.7|
|Machinery and transport equipment|20.0|18.8|18.3|7.0|5.9|4.9|

_Source_ : EUROSTAT for Slovakia and State Statistics Office for Macedonia Second, Macedonian exports are concentrated in only few sectors (firms) with low excess capacity and growing competition. Almost 50% of Macedonian exports are re-exports of iron and steel, and textiles, or, in recent years, fuel where the corresponding firms operate close to full capacity utilization. As a result, Macedonian reliance on few export companies has grown over time resulting in growing export concentration. On the other hand, there are relatively more exporting Slovak firms, and Slovakia’s exports

> 12 Among the first who made this observation was Kreinin (1973).

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(paddy, maize and wheat) when jatropha yield is 10 tonnes per hectare (see left pane in Figure 4). However, a much higher jatropha price would be needed to cover its production costs when jatropha yield is low, such as 3 or 5 tonnes per hectare. If, for example, the price of jatropha seeds is US$0.21 per kg (i.e., price to cover production costs under the 10 tonnes per hectare yield case), there would be a net loss of US$683 and US$410 if jatropha yields are 3 and 5 tonnes per hectare respectively. This analysis suggests that the plantation of jatropha would not be economically attractive to farmers if jatropha yield is low, because farmers gets higher rents from their lands through traditional crops than that from jatropha. If only two cereals are grown in the land under the alternative use, plantation cultivation of jatropha is more economically favorable. Note here that we have assumed a market for jatropha seeds is available. Since biodiesel is a new commodity, farmers and investors might perceive much higher risk on jatropha compared to traditional crops.

**Figure 4: Gross margin from agricultural land (US$/ha/year)**

<!-- Start of picture text --> 272<br>196 218 222<br>90<br>-410<br>-683<br>3t/ha) 5t/ha)<br>5t/ha) 3t/ha)<br>10t/ha)<br>Jatropha ($0.5/kg & Jatropha ($0.3/kg & Jatropha (0.2/kg & Paddy+Wheat+Maize Wheat+Maize Jatropha (0.2/kg & Jatropha (0.2/kg &<br><!-- End of picture text --> Note: The required data for cereal crops (crop yield, production cost, and producer price) were obtained from the field survey (household interview) in Chitwan district. Appendix 3 presents the overview of the data used for comparisons of the gross margins from cereals and gross margin from jatropha seed production.

# **_4.2. Economic Analysis of Biodiesel Production (Biodiesel Plant Owner’s Perspective)_**

The production costs of biodiesel under various levels of jatropha yield are compared with the price of diesel in Figure 5. The comparison is made on the energy equivalent basis accounting for the fact that a liter of biodiesel contains 12% less calorific value compared to diesel. The wholesale price 16
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The dataset used for the analysis is mostly cross-sectional. Hence it is quite possible that a firm’s hiring/firing decision and exporting decision can be simultaneously determined and the inference drawn can be biased. For example, efficient firms can complain less about labor regulations. These firms can attract workers with higher skill levels and utilize their workers in a more productive way. Thus the additional burden of hiring an employee might be less for them. At the same time as modeled in Melitz (2003), efficient firms are more likely to export. On the other hand, less efficient firms might feel more constrained by the EPL which might create the negative relation between stringent labor laws and exporting.

To alleviate this problem and capture the effects of the restrictiveness of regulations on exporting, I try several methods. The dataset from Enterprise Surveys cover all major sectors like manufacturing, wholesale, retail, construction and others according to 2-digit ISIC classification. In the first method, I analyze the relation between labor regulations and exporting at sector level. The averages for sectors are likely to be exogenous to firm’s own decisions. Moreover, I can perform a comprehensive analysis of exporting incorporating export of services and not just focus on export of products. In this method, I analyze whether variation in the restrictiveness of EPL across sectors explain the variation in participation in export markets. In the second method, I use a sample of panel firms which is constructed from the original dataset. The use of the panel data mitigates the endogeneity problem that exists in the cross-sectional data. As a third method, I restrict the sample to manufacturing firms and analyze how much of the variation in EPL across 2-digit industries in this sector explain the variation in the decision to export.

To find how constraining EPL are in each sector, I compute job creation (JC) and destruction (JD) rates as weighted average of firms’ growth rates in each country. I use firms’ employment levels as weight. Defining _L jc_   _lic_ as the total employment in sector _j_ in country _i_  _j_ _c_ , I get Then net job creation ���� in sector _j_ is computed as the difference between job creation and job destruction rates _JN jc_  _JC jc_  _JDjc_ . Higher the values of net job creation _,_ more constrained firms 7
Policy Research Working Paper 4999

# **Abstract**

There is increasing evidence that conditional cash transfer programs can have large impacts on school enrollment, including in very poor countries. However, little is known about which features of program design— including the amount of the cash that is transferred, how frequently conditions are monitored, whether noncomplying households are penalized, and the identity or gender of the cash recipients—account for the observed outcomes. This paper analyzes the impact of one feature of program design—namely, the magnitude of the transfer. The analysis uses data from a program in Cambodia that deliberately altered the transfer amounts received by otherwise comparable households. The findings show clear evidence of diminishing marginal returns to transfer size despite the fact that even the larger transfers represented on average only 3 percent of the consumption of the median recipient households. If applicable to other settings, these results have important implications for other programs that transfer cash with the explicit aim of increasing school enrollment levels in developing countries.

This paper—a product of the Human Development and Public Services Team, Development Research Group—is part of a larger effort in the department to study the impact of social programs and their role in promoting human development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org . The authors may be contacted at dfilmer@worldbank.org and nschady@worldbank.org.

_The Impact Evaluation Series has been established in recognition of the importance of impact evaluation studies for World Bank operations and for development in general. The series serves as a vehicle for the dissemination of findings of those studies. Papers in this series are part of the Bank’s Policy Research Working Paper Series. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
One possible explanation for the lack of conclusive results may depend on how productivity is measured. The empirical research on industrial productivity has suffered from two major shortcomings. First, a large number of studies' were based on the traditional measure of total factor productivity, pioneered by Solow (1957). The consistency of this measure depends on the validity of the assumptions it makes, namely perfect competition, constant returns to scale, and perfect mobility of all inputs. **Yet,** although the potential biases of the productivity estimates which take place when these assumpdons are violated have long been recognized<sup>2</sup> , litde was actually done to correct for these errors. Second, even when the problems of scale economies, quasi-fixed factors, and non-competitive pricing are successfilly dealt with, the problem of aggregation remains. Most studies which attempted to estimate productivity have used macro or sectoral data, implicitly assuming that a well defined prciuction technology describes all plants within the industry, sector or country of analysis. Tybout (1991) points out that "if technological innovation takes place through a gradual process of efficient plants displacing inefficient ones, and/or through the diffusion of new knowledge, the approaches to productivity measurement based on 'representative plant' behavior are at best misleading. At worst, they fail to capture what is important about productivity growth altogether, as Nelson (e.g. 1981) has long argued".

In this study, we will first attempt to get a consistent estimate of productivity by using industrial census data and taking into account the heterogeneity across firms. Second, we will ask the question: Does trade liberalization actually increase firm-level productivity? In section 11, the production model and estimation techniques will be discussed. In section m, recent changes in the Moroccan trade policy will be reviewed and evaluated. Section IV describes the estimated TFP. In section V, the estimation results of the link between productivity and trade are presented. The conclusion is given in section VI.

'See for example Nishimizu and Robinson (1984) or Krueger and Tuncer (1982). 2See for example Nishimizu (1979) or Kim and Kwon (1977).

2
with the lowest incidence of financial burden and vulnerability, while Cyprus and Greece have the highest incidence of financial vulnerability.

Interestingly, there is a negative and often significant correlation between the size of mortgage markets and the share of households that are in default on at least one loan product as well as with the financial burden as reported by households. We find this negative correlation both across the sample of all countries, as well as within the subsample of new EU countries. On the aggregate level, a deeper mortgage market does therefore not seem to be associated with higher financial vulnerability of households.

How do financial burden and arrears vary across income and age groups? Figure 8 shows a significant negative correlation of income with financial burden across all countries in 2007. Here we show the average weighted survey response across income deciles for all surveys in 2007; the share of households reporting that housing costs are a heavy financial burden decreases with income in most countries, although the increase is not always linear. There do not seem to be significant differences across countries. Similarly, Figure 9 shows that, on average, arrears in payments decrease with the income deciles, though not monotonically, most likely due to the small number of households with arrears. Figure 10 shows that the financial burden is, on average, not correlated with the age of the household age, with the exception of Denmark, Great Britain, Ireland, Netherlands, Norway, Sweden and the UK, where we see a decline in self-reported financial burden with the age of the household head. In none of the new EU countries can we see a clear relationship between self-reported financial burden and age. On the other hand, Figure 11 shows that with few exceptions – most prominently Greece – there is a negative relationship between the age of the household head and the likelihood that the household is in arrears. While these graphs show us the distribution of financial burden and 26
# **<u>Table 4: Rayon level farm structure in 2020</u>**

||**Total**|**Crop d**<br>**No**|**amage**<br>**Yes**|
|---|---|---|---|
|Number of operational farms|72.73|71.27|95.66|
|Number of farms with 500-2500 ha|12.50|12.30|15.69|
|Number of farms greater than 2500 ha|6.06|6.00|6.94|
|Total cultivated land in ha|37,735|37,137|47,120|
|Share with 500-2500 ha|0.330|0.326|0.385|
|Share with greater than 2500 ha|0.455|0.455|0.447|
|Number of Rayons|535|503|32|

_Source:_ Own computation based on SSSU form29.

18