rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 7: Farm fixed effect regressions</u>**

|<br>|<br>**Share o**<br>**so**|<br>**f output**<br>**ld**|**Value o**<br>**per**|**f output**<br>**ha**|**Cost of**<br>**per**|**inputs**<br>**ha**|**P**<br>**p**|**rofit**<br>**er ha**|
|---|---|---|---|---|---|---|---|---|
|2022 dummy (α1)<br>|-0.296***||-0.474***||0.227***||-476.9***||
||(0.013)||(0.017)||(0.046)||(15.779)||
|2023 dummy (α2)<br>|-0.229***||-0.615***||-0.565***||-391.3***||
||(0.013)||(0.017)||(0.046)||(15.844)||
|2022*Small (β1)<br>||-0.303***||-0.453***||0.253***||-444.40***|
|||(0.016)||(0.019)||(0.054)||(18.311)|
|2022*large (β2)<br>||<br>-0.283***||<br>-0.525***||<br>0.178**||<br>-557.39***|
|||(0.022)||(0.029)||(0.078)||(28.036)|
|2023*Small (γ1)<br>||-0.243***||-0.578***||-0.441***||-369.59***|
|||(0.017)||(0.020)||(0.056)||(19.103)|
|2023*large (γ2)||<br>-0.206***||<br>-0.684***||<br>-0.788***||<br>-432.27***|
|||(0.021)||(0.027)||(0.074)||(25.809)|
|No. of obs. (farms)|3,419|3,419|2,180|2,180|1,942|1,942|2,180|2,180|
|R<sup>2</sup><br>|0.189|0.190|0.512|0.516|0.199|0.209|0.418|0.423|
|**Linear combinations**<br>|||||||||
|α2−α1<br>|0.067***||-0.141***||-0.793***||85.681***||
|<br>|(0.013)||(0.017)||(0.046)||(15.828)||
|<br>β1−β2<br>||-0.020||0.072**||0.075||112.989***|
|<br>||(0.027)||(0.034)||(0.092)||(32.601)|
|<br>γ1−γ2<br>||<br>-0.037||<br>0.106***||<br>0.347***||<br>62.681**|
|<br>||(0.026)||(0.033)||(0.090)||(31.174)|
|<br>γ1−β1<br>||<br>0.059***||<br>-0.125***||<br>-0.694***||<br>74.810***|
|<br>||(0.017)||(0.020)||(0.056)||(19.334)|
|<br>γ2−β2<br>||<br>0.077***||<br>-0.160***||<br>-0.965***||<br>125.118***|
|<br> <br>||(0.023)||(0.030)||(0.082)||(29.072)|
|<br>(γ1−γ2) −<br>||<br>-0.017||<br>0.034||<br>0.272***||<br>-50.308|
|<br>(β1 −β2)<br>_Note:_Profit is the value<br>Value of output and inp|of output n<br>ut/ha are in|(0.028)<br>et of purchase<br>logs. Year fixe|d inputs, that i<br>d effects are i|(0.037)<br>s, it includes r<br>ncluded throu|emuneration<br>ghout but not|(0.101)<br>for fixed facto<br>reported. Sta|rs and family<br>ndard errors a|(35.461)<br>labor.<br>re in|

_Note:_ Profit is the value of output net of purchased inputs, that is, it includes remuneration for fixed factors and family labor. Value of output and input/ha are in logs. Year fixed effects are included throughout but not reported. Standard errors are in parentheses, and * p < 0.10, ** p < 0.05, and *** p < 0.010. ha = hectare.

24
# **Tables**

## **Table 1**

## **Inclusion and stability variables**

|**Name of variable**|**Data source**|**Financial outcome**|**Category**|**Sub-category**|
|---|---|---|---|---|
|Borrowed from a financial institution (% age<br>15+)|Global Findex|Inclusion|Individuals|Credit|
|Credit card (% age 15+)|Global Findex|Inclusion|Individuals|Credit|
|Saved at a financial institution (% age 15+)|Global Findex|Inclusion|Individuals|Savings|
|Account at a financial institution (% age 15+)|Global Findex|Inclusion|Individuals|Account ownership|
|Purchased agriculture insurance (% working<br>in agriculture, age 15+) [w1]|Global Findex|Inclusion|Individuals|Insurance|
|Debit card (% age 15+)|Global Findex|Inclusion|Individuals|Payments|
|Used either the Internet or electronic<br>payments to make payments [w1 & w2]|Global Findex|Inclusion|Individuals|Payments|
|Percentage of firms with a checking or<br>savings account.|Enterprise Survey|Inclusion|Firms|Account ownership|
|Percent of firms using banks to finance<br>investments|Enterprise Survey|Inclusion|Firms|Credit|
|Percent of firms using banks to finance<br>working capital|Enterprise Survey|Inclusion|Firms|Credit|
|Saved to start, operate, or expand a farm or<br>business (% age 15+) [w2]|Global Findex|Inclusion|Firms|Savings|
|Used an account at a financial institution for<br>business purposes (% age15+) [w1]|Global Findex|Inclusion|Firms|Payments|
|Bank Z score|GFDD|Stability|Resilience indicators|Capital|
|Bank capital to total assets (%)|GFDD|Stability|Resilience indicators|Capital|
|Bank credit to bank deposits (%)|GFDD|Stability|Resilience indicators|Liquidity|
|Liquid assets to deposits & ST funding (%)|GFDD|Stability|Resilience indicators|Liquidity|
|Bank NPLs to gross loans (%)|GFDD|Stability|Resilience indicators|NPLs|
|Provisions to NPLs (%)|GFDD|Stability|Resilience indicators|NPLs|
|Standard deviation of the bank lending rate<br>growth|GFDD<br>|Stability|Volatility measures|Credit volatility|
|Standard deviation of the outstanding loans|Financial Access<br>Survey|Stability|Volatility measures|Credit volatility|
|Standard deviation of the bank deposit rate<br>growth|GFDD|Stability|Volatility measures|Deposit volatility|
|Standard deviation of the outstanding<br>deposits|Financial Access<br>Survey|Stability|Volatility measures|Deposit volatility|
|Cumulative loss in income relative to a<br>precrisis trend|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|
|Direct fiscal outlays due to financial sector<br>rescue packages|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|
|Peak level of NPLs|Laeven and Valen|Stability|Crisis outcomes|Milder/No crisis|

22
9

# **Table** 1: UMEA. COUNTRY-SPECIFIC PART OF INFLATION.

|Equation No<br>|1||2||3|4|5|6|
|---|---|---|---|---|---|---|---|---|
|Explanatory:<br>|Coeff<br>|<br>t<br>|Coeff|t|t|t|t|t|
|Government Consumption<br>Lagged Dependent Variable|1.072<br>0.693|(4.8)<br>(10.7)|0.893<br>0719|(3.8)<br>(112)|(3.1)<br>(99)|<br>(3.0)<br>(100)|<br>(2.8)<br> <br>99|<br>(3.0)<br>|
|DomesticCredit<br>to Covern e|<br>nt||.<br>0053|.<br>(21)|.|.|<br>(.)|(10.1)|
|Domestic CredLt|||.|.||(14)|||
|Mney (M2)||||||.|11||
|<br>Value of Exports|||||||(.)||
|<br>Private<br>Consumption|||||01|||(1.8)<br>|
|<br>DUN B376*|-0.18|(4.6)|-019|(49)|(.)<br>(42)|(0.0)<br>(44)|(0.2)<br>42|(0.7)<br>|
|DUM SE74*|<br>0.14|<br>(3.5)|.<br>014|.<br>(36)|.|.|(.)|(4.3)|
|RSQ<br>DU|0.721<br>1.656||.<br>0.736<br>1772|.|0.676<br>169|0.684<br>173|0. 681<br>|0. 689<br>|
|SER|<br>0.0394||.<br>0.0386||.|.|1.7|1.73|

**This table shows** **_orm_** regressions of a pooled cross-section end time seres of country-specLfil( lents of lnflatLon. The regressLons **are** designed to measure the lcpact of demand and policy factors on local inflation. Dependent **variable is remainder** after subtractlng first prineLpal component from (log(CPI)). EXplanatory **varLables are** country **shares in U)EDA** totals expressed **as** deviations from semple period **averages.** Cross-Sectlon and Time SerLes, Sa-ple Perlod: 1970-86.

For equations 3-6 the t-statisties only (and not the estimated coefficients) **are shown.**

*Note: Som equations also Licluded intercept dumies for two outliers, Burkina Faso 1976 end **SenJgal** 1974.
people joining and dying for the Nationalist party army, which put much less emphasis on ideology.<sup>14</sup> We obtain data on the Nationalist war casualties from the website of the National Revolutionary Martyrs’ Shrine of R.O.C in Taiwan, China, which records the names of about 140,000 people who died as members of the Nationalist forces before 1949.<sup>15</sup> We trace the places of birth of these Nationalist soldiers, and see if the number of Nationalist soldiers is related to famines in the 1920s in their home prefectures. In columns 1 and 2 of Table 9, we report the OLS and the 3SLS estimation of the effects of famines in the 1920s on Nationalist war casualties before 1949. The instrumental variables are the same as in the previous section. The number of famines during the warlord period does not have significant impact on the number of subsequent Nationalist soldiers killed in wars. This suggests that Nationalist military forces were unable to—or did not try to—draft more people from regions hit hard by prior famines. We conclude that state failure in the 1920s in China did not have a general impact on people’s choice of joining armies for livelihood, but it provoked strong support of the Communist ideology.

# **6.5. Consideration of Nationalism**

Apart from economic hardship and perceived injustice, another factor which also helps explain the ability of CCP to recruit many peasants is peasant nationalism in response to Japanese invasion (Seldon 1971). To see if our explanation remains valid, we drop casualties during the Second World War, known as the Sino-Japanese War (1937-1945) in China—the war casualties during this period could be more motivated by nationalistic sentiments. We still obtain a positive and significant relationship between famines and the Communist war

> 14 The difference between the CCP and the Nationalist ideology is evident from the official statements that a new party member of each party has to declare joining. See <u>https://en.wikipedia.org/wiki/Chinese_Communist_Party_Admission_Oath.</u> The Nationalist party emphasizes that each member has to obey traditional Chinese values such as courage, filial duty, kindness, keeping promises, peace, courtesy, obedience to supervisors, frugality, tidiness, and helping others. The CCP joining statement, in contrast, focuses on obeying the _principles_ of the party, carrying out orders, accepting disciplines, being loyal to the party, and being willing to sacrifice one’s life for the party, and never be a traitor to the cause.

> 15 The difference in the total numbers of martyrs between the CCP and the KMT largely reflects the CCP’s tradition in collecting the identity of their martyrs (see Hung, 2008). Another factor is that the KMT retreated to Taiwan, China, in 1949 and could no longer access detailed personnel data of its casualties on the mainland.

28
private inputs, the output multiplier ranges from 4 to 13 in the long run. Fishback and Kachanovskaya (2010) estimate the multiplier effects of different types of government spending during the New Deal and find that public works had the highest multiplier equal to 1.7. In general, studies of government spending multipliers in recessions and for different types of government spending are limited. Analyzing these questions could be a promising direction for future theoretical and empirical research.<sup>15</sup> 26. There exists, however, strong empirical evidence that infrastructure enhances economic growth. Exhaustive reviews of the literature show that while some authors find negative or zero returns, many find a high impact of infrastructure on growth.<sup>16</sup> Romp and de Haan (2005) undertake a comprehensive review of studies analyzing the relation between public and economic growth, many of which focused on high-income countries. They find in general an elasticity of output with respect to public capital in the order of 0.1 to 0.2. These effects differ significantly across countries, regions, and sectors, however. Using a meta-analysis based on 49 studies on OECD countries, Ligthart and Martin Suarez (2011) report an output elasticity of public capital of 0.14. There exists also some empirical evidence that returns of infrastructure investments in advanced countries tend to be particularly high if these investments complete a network that is already sufficiently developed.<sup>17</sup> 27. Critics of fiscal stimulus spending will point to the ―Ricardian trap,‖ i.e. that government spending is crowding out private spending<sup>18</sup> and argue that scarce empirical evidence supports the fact that fiscal stimulus is likely to have larger economic benefits if it is spent on projects with high economic returns that lead to permanently higher productivity than on projects that consist of ―digging a hole and filling a hole.‖ This is not to say that Ricardian equivalence is a good approximation of reality in the first place. Ricardian equivalence states that households increase savings in anticipation of future higher taxes to pay for debt-financed government spending, offsetting the short-run benefits of fiscal expansionary policies. A considerable literature

> 15 See Ramey (2011) and Parker (2011) for an insightful and comprehensive discussion.

> 16 In general, studies using physical indicators of infrastructure stocks find a positive long-run effect of infrastructure on output, productivity, and growth rates, whereas results are more mixed among studies using measures of public capital stock or infrastructure spending flows (Straub 2008, Calderón and Servén 2010).

> 17 See Roller and Waverman (2001) for a discussion on telecommunications in 21 OECD countries and Fernald (1999) for a discussion on roads in the U.S.

> 18 See, for example Barro (2009, 2010).

11
countries with poor governance systems might have high levels of pollution because of a lack of proper environmental regulations exacerbated with low compliance and, at the same time, have sizable informal sectors. One way to work around this endogeneity is to apply country and industry fixed effects as the data set has both country- and industry-level observations. It is important to acknowledge that estimations in this paper do not claim causal effects despite improving OLS coefficients by using country and industry fixed effects but only discuss correlations between emissions and informality. All these estimations are also conducted using country and sector weights, the results are not reported here as significance and sign of the coefficients remain the same. While regressions with country fixed effects have been used in other studies and produced similar results, this paper adds to the literature by including industry fixed effects and conducting industry-specific estimations.

The regressions include several control variables to take into consideration widely documented differences in capital intensity and production technologies across countries and sectors as well as to account for the relative importance of the sectors in the overall economy. At the same time, the choice of control variables is highly driven by the availability of data on a country and sector level, and the selected variables are extracted only from two data sets - GTAP and I2D2 - given the paucity of sector data in general. More specifically, the control variables include the natural logarithm of capital per worker, the share the respective industry value added contributing to the country’s total value added, and the share of imported intermediate goods in total consumption of intermediate goods in each sector. The capital intensity and share of imported intermediate goods in total aim at capturing the underlying differences in production technologies and the extent of the industry’s reliance on global value chains which can be indicative of their competitiveness and productivity. On average, capital-intensive sectors consume more electricity and, hence, are more likely to have high emissions as opposed to sectors with low capital. At the same time, sectors that rely more on imported intermediate goods might be more productive and competitive compared to those that have strong backward linkages with domestic industries only or are self-sufficient in their production processes. Hence, these sectors again might utilize more sophisticated technologies responsible for higher levels of pollution. Finally, the share of an industry’s value added in the country’s total value added accounts for the importance of the respective industry in the economy.

The first set of results for CO2 emissions per value added illustrates that there is no difference between emissions per formal workers and per informal workers based on the preferred approach of using both country and industry fixed effects in the regressions. The OLS regression in the first column of Table 1 shows a negative and significant coefficient for the impact of informal employment on CO2 emissions per 13
40 thee requires teaming how providing Ul beneflts affects consumption pattems and unemployment duration. Again, an ideal approach would Involve palmiwse comparisons between carefully chosen developing countries, with one in each pair having a Ul system and the other not. This affords a test that is Impossible in advanced countries, since no potential comparison group (that does not offer Ul) exists. While some budget studies have been conducted on households In developing countrios, none has focussed on adjustments that are made In response to unemployment (or to an Involuntary shift from modem to traditional employment) of a household member. Longitudinal diary data on the expenditures of a large sample of households can be used to Infer how consumption responds to a period of unemployment, or to a permanent shift into or out of the modem sector. Moreover, by using the same quetionnaire In otherwise similar countries that do or do not offer Ul benefits, their Impact on pattems of adjustment of consumption can be Inferred. If we find no differences In pattems of adjustment between pairs of countries, we can infer that households in the country offering Ul are sufficiently farsighted to smooth consumption in response to the expectation of unemployment. Obversely, greater smoothing in the country offering Ul provides a good justification for offering benefits as a way of easing the hardships facing myopic or liquldity-constrained households.

A similar set of sampling frames should be used to Infer how benefits affect job-finding. During the past 15 years labor economists have inreasingly relied on the distincton between incidence and duation to analyze unemployment Unfortunately, we have lIttle Information from developing economies that would enable us to apply this very fruitful dichotomy. Discovering what pattems of unemployment and nonemployment duratlon are In developing countries, and then comparing the micro data across matched Ul-non-UI countries, is essential for understanding the structure of unemployment In developing countries and how Ul programs might affect it differently from their Impacts In developed labor markets.
Notably, a number of authors starting with Neftçi (1978), have stressed that accounting for the dynamic properties of the data series, such as persistence over time, may matter to correctly understand real wage and employment cyclicality.<sup>4</sup> The dynamic properties of the data can indeed vary substantially across data series and countries, and as shown in den Haan (2000), evidence on the cyclicality of prices based on simple static measures (e.g., comovement of de-trended series) can be misleading.

We find that wages have been highly procyclical in three of the countries studied: Brazil, Colombia and Mexico. The exception is Chile, which exhibits mostly acyclical real wages in the manufacturing sector. In Brazil and Colombia there are clear signs of declining wage cyclicality over the sample period. The evidence in Mexico goes in the same direction, but differences over time are not as marked. These developments go in line with the emergence of downward real wage rigidities in the region during the macroeconomic stabilization of the late 1990s and 2000s. Although our main focus is on the study of the cyclicality of real wages, we also investigate the parallel developments of employment adjustment over the business cycle. Interestingly, declining wage cyclicality is not accompanied in all cases by an increase in the employment cyclicality, suggesting a more important role of alternative adjustment channels (e.g. hours or other labor costs) during the past decade.

An important limitation of studying the cyclical behavior of real wages with aggregated data is that we cannot control for systematic changes in the composition of employment, which are likely to occur at business cycle frequencies (Bils, 1985). Lowearning workers typically enter the labor force during upswings and exit during downturns, biasing downwardly the reaction of wages to the cycle evaluated at the macro level. Following Solon, Barsky and Parker (1994), a number of studies using micro data for developed countries have confirmed the importance of such bias. We tackle this issue in our context by looking at the Brazilian case in more detail, exploiting available micro data from the “Pesquisa Mensal de Emprego”, one of the few labor force surveys

> 4 Abraham and Haltiwanger (1995) discuss at length the different approaches taken up in the literature to measure the cyclical behavior of wages. An application using the correlation between de-trended compensation per employee and GDP in a large number of Latin American countries is Aguilera and Martínez (2009).

3
otherwise, the firm’s capital depreciates and at some point reaches _kt_ 0; given the firm’s optimality of accumulating cash reserves when _k_ = _kt_ 0 and _M_ = 0, it follows that the firm begins accumulating cash reserves at this point (note that, with _M_ = 0 as initial conditions, the firm’s problem is unaffected by changes in the initial level of _p_ ).

Let _<u>k</u>_ = _kt_ 2. We claim that the firm enters a steady state cycle in which the firm accumulates cash reserves starting from _<u>k</u>_ and then, after investing, lets capital depreciate to _<u>k</u>_ before accumulating reserves again. To see this, note that, since the firm optimally decides to start accumulating reserves at _t_ 2, it is not optimal for the firm to accumulate reserves whenever _M_ = 0 and _k > kt_ 2. Thus, in periods after the firm invests and consumes dividends, since _M_ = 0, it is not optimal for the firm to accumulate cash reserves until _k_ = _kt_ 2.

# **C Regression Tables**

29
# **Plc,Planning, and Researckn**

# **Trade Policy**

How do imports react to cyclical and secular (long-term) factors? the evidence suggests that cyclical income elasticities of import demand are generally higher than long-term elasticities - particularly for basic materials and semimanufactured goods.

Traditional models for import demand generally underestimate the cyclical respcnse in imports, and overestimate the long-term response. This has important implications for forecasting short-term import flows in developing countries. For example, estimates of income elasticity using a traditional import model developed by Pritchett and Bahmani-Oskooee average 1.4 and 1.2 respectively.

Clavijo and Faini's model suggests a cyclical elasticity averaging 2.6. Khan and Ross foun2d for a sample of 14 industrial countries that cyclical income elasticity averaged about 40 percent higher than trend income elasticity. The authors' results suggest that the two elasticities may differ by an even larger factor for developing countries.

Relative prices generally are more important in determining import demand in Latin America and Asian-Pacific countries in Clavijo and Faini's sample, but seem to have I. Ie effect in the African and (perhaps surprisingly) Mediterranean countries. In countries for which both cyclical and long-term income elasticities are significantly different from zero, relative price coefficients are also significantly different than in countries for which income parameters are not significantly different from zero. Including the cyclical component in the model seems to improve not only the fit but also the performnance of the equation.

This paper is a product of the Trade Policy Division, Country Economics Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Karla Cabana, room N8-065, extension 61539.

The PPR Working Paper Series disseminates **the** findings of work under way in the Bank's Policy, Planning, and Rcsearch Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

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