rafmacalaba/datause-encoder-data huggingface dataset:
## **2.6 Description of the index insurance products**

Table 2 describes the products offered in each study. Three products are based on rainfall, two on area-yields, and one is hybrid (that is, based both on rainfall and area-yields). In four studies, the products are explicitly targeted towards particular crops: Elabed and Carter (2014) and Stoeffler et al. (2022) target cotton production; Karlan et al. (2014) target maize production; Bulte et al. (2019) target four crops (soya bean, sorghum, sunflower, and maize). In other studies (Cole et al., 2017; Hill et al., 2019), the products are not crop-specific. Large differences exist in sale prices, both within and across studies. Within study differences are due to the price experiment design of four studies. Differences across studies reflect only to some extent differences in actuarially fair prices. Insurance purchase was generally an individual decision, except in Elabed and Carter (2014) and Stoeffler et al. (2022) where farmer groups collectively decided to purchase the insurance. In most studies, the amount of insurance that farmers could purchase depends on the amount of land they cultivate. A notable exception is the study by Cole et al. (2017) in India, which offered insurance products as stand-alone policies (treated households received 10 policies). Take-up of the different products vary from 29% in Mali to 100% for the products offered for free. Payoff triggers depend for example on the amount of dry/wet days, on the duration of dry spells, on cumulative rainfall, or on area-yields averages. While products based on rainfall typically use data from weather stations, area-yield products rely upon data from crop-cutting exercises or from purchasing companies.

# **3 Data**

## **3.1 Primary outcomes**

We focus on five outcomes: the amount of cultivated area, fertilizers, pesticides, seeds, and a risk index. This choice is guided by both theoretical and practical considerations. Conceptually, a well-established theory of change outlines that insurance provision could either increase or decrease farmer risk-taking depending on financial constraints and risk preferences.<sup>18</sup> We therefore focus on production decisions involving a certain degree of risk-taking. Practically, because our empirical framework requires outcomes to be measured in similar ways across studies, the set of outcomes is constrained by data availability. It would be interesting to analyze outcomes such as loan subscription, irrigation, and labor expenditures, however, these

> 18See Karlan et al. (2014) for a theoretical model of investment decisions under financial and risk constraints.

10
# **Table A5: Impacts of mental health on labor income – Sample of workers with positive incomes**

|Dependent variable:|Weeklylabor in|come($1,000 AUD)|
|---|---|---|
||FE|IV-FE|
||(1)|(2)|
|Standardized mental health<br>scores|-0.004*|-0.233**|
||(0.002)|(0.110)|
|_First stage of 2SLS (dependen_<br>_scores)_|_t variable is stand_|_ardized mental health_|
|Trauma experience*Year<br>after arrival||-0.064**<br>(0.028)|
|Kleibergen-Paap F stat.||55.517|
|AR 95-CIs||[-2.023, -0.112]|
|Dep. Mean|0.716|0.716|
|Other controls|Yes|Yes|
|Individual FE|Yes|Yes|
|Survey wave FE|Yes|Yes|
|Observations|1,772|1,772|
|Number of individuals|843|843|

_Notes:_ *** p<0.01, ** p<0.05, * p<0.1. Results of FE model in Column (1) and IV-FE model in Column (2). Standard errors in parentheses are clustered at the individual level. Control variables include age group dummy variables, marital status, household size, language skills (listening, speaking, reading, and writing), residence area, and home ownership. Mental health scores are the sum of the 6- item Kessler Psychological Distress Scale (K6), with a higher score indicating worse mental health. The scores are standardized to have a mean of zero and standard deviation of one. The critical value of the F-test from Stock and Yogo (2005) is 16.38.

37
# **5.  Further Robustness Checks**

I next address a variety of potential concerns about the robustness of the benchmark estimates of the multiplier presented in Table 4. Given the noisy and highly-imperfect data on government spending and output in many of the developing countries that comprise my sample, a first concern is that the results in Table 4 might be driven by a small number of influential observations. To investigate this possibility more systematically, I use a procedure suggested by Hadi (1992) to identify influential observations in the reduced-form and first-stage regressions (the ratio of the corresponding two slope coefficients being the 2SLS estimate of the multiplier). I then re-estimate the OLS, first-stage, and 2SLS regressions, excluding these influential observations. The results of this first robustness check are reported in the first three columns of Table 6. The OLS estimates of the multiplier change very little relative to the benchmark results. The 2SLS estimates of the multiplier are virtually unchanged once influential observations are removed, ranging from 0.37 to 0.39, and moreover they are slightly more precisely estimated than before. This is in part due to an even stronger first-stage relationship after removing influential observations in the IDA and high-disbursement samples, in which the first-stage F- statistics jump to 41.2 and 30.3, respectively.

A second potential concern is that I am estimating multipliers using data on total government _spending_ , whereas much of the theoretical and empirical literature on multipliers has focused on government _purchases_ , i.e. total spending less interest payments and net transfers. Unfortunately, data on the disaggregation of total government spending into purchases, interest payments, and net transfers is not available for the large set of developing country-year observations included in this paper. As a first partial step towards addressing this concern, I use readily-available data on interest payments on external public and publicly-guaranteed debt to net out this portion of interest expenditures from total government spending.<sup>21</sup> I then re-estimate the spending multipliers using this proxy for government non-interest expenditures. The results are presented in the second set of three columns in Table 6. Doing so weakens identification somewhat, particularly in the full sample, in which the firststage F-statistic now falls below the Staiger and Stock (1997) threshold of 10. However, in the remaining two samples, the first-stage F-statistics remain respectable at 19.9 and 16.0, respectively, and the point

> 21 Specifically, I use data on interest payments on public and publicly-guaranteed external debt as reported in the World Bank's Global Development Finance publication.  Aggregate interest payments from this source are based on loan-level transactions reported in the DRS database that I also use to construct my instrument.  This is only an imperfect adjustment, since subtracting interest payments on external debt from total government will not correct for interest payments on domestic debt, or net transfers.

20
# **Table 13. Standardized effects of the determinants of occupational attainment. Youths from 18-35 years of age of ENCEL 2017.**

|VARIABLES|EFF|ECTS||
|---|---|---|---|
|Social origin on occupational attainment||||
|Direct effect|-|||
|Indirect effects|0.23||***|
|Via education||0.09|***|
|Via youth’s cognitive abilities and education||0.02|**|
|Via first employment and education||0.12|***|
|Total effect (direct + indirect)|0.23||***|
|Provider’s cognitive abilities on occupational attainment||||
|Direct effect|-|||
|Indirect effects|0.03||**|
|Via youth’s cognitive abilities||0.03||
|Total effect (direct + indirect)|0.03||**|
|Youth’s cognitive abilities on occupational attainment||||
|Direct effect|0.08||***|
|Indirect effects|-|||
|Total effect (direct + indirect)|0.08||***|
|Education on occupational attainment||||
|Direct effect|0.16||***|
|Indirect effects|0.26||***|
|Via youth’s cognitive abilities||0.04|**|
|Via first employment||0.22|***|
|Total effect (direct + indirect)|0.42||***|
|First occupation to occupational attainment||||
|Direct effect|0.32||***|
|Indirect effects|-|||
|Total effect(direct + indirect)|0.32||***|

Levels of significance *** p<0.001, ** p<0.01, * p<0.05. Source: Own elaboration from ENCEL 2017 (n=3,310).

These findings lead us to conclude that social origin and the provider’s cognitive abilities are important factors that influence, through mediating factors, the youth’s occupational attainment. Nonetheless, the non-ascribed factors, on a whole, have an effect that is almost three times the effect of the ascribed-factors. Among the non-ascribed factors, the youth’s education is the factor that determines, with the greatest strength, the occupational attainment, followed by the first occupation and cognitive abilities. It is important to remember, however, that these three factors have been shaped by the two ascribed variables.

Thus, both the ascribed as the non-ascribed factors intervene in the process, with the latter having a greater effect, which is consistent with the previous studies that indicate the importance of the origin-destination association, but there is also the possibility of other factors, mainly 43
# Table 6, Cont’d: Conflict Predictions Based on Alternative Classification Algorithms

||**Classification**|**s Based on 2012-201**|**4 data (1=**|**Conflict, 0 oth**|**erwise)**|
|---|---|---|---|---|---|
||**Probit**|**Random Forest**|**Linear**|**Threshold**|**FCS List**|
|Morocco|1|1|1|1|..|
|Moldova|0|0|0|0|N|
|Madagascar|1|1|1|0|Y|
|Mexico|1|0|1|1|..|
|Macedonia, FYR|0|0|0|0|..|
|Mongolia|0|0|0|0|N|
|Mauritania|1|1|1|1|N|
|Mauritius|0|0|0|0|..|
|Malawi|1|1|1|1|N|
|Namibia|0|0|0|0|..|
|Niger|1|1|1|1|N|
|Nicaragua|0|0|0|0|N|
|Nepal|1|1|1|1|N|
|Panama|0|0|0|0|..|
|Peru|1|1|1|1|..|
|Poland|0|0|0|0|..|
|Paraguay|0|0|0|0|..|
|Romania|0|0|0|0|..|
|Senegal|1|1|1|1|N|
|Sierra Leone|1|0|1|0|Y|
|El Salvador|0|0|0|0|..|
|Swaziland|0|0|0|0|..|
|Seychelles|0|0|0|0|..|
|Chad|1|1|1|1|Y|
|Togo|0|0|0|0|Y|
|Tajikistan|1|1|1|1|N|
|Turkmenistan|1|1|1|1|..|
|Trinidad and Tobago|0|0|0|0|..|
|Tunisia|0|0|0|0|..|
|Tanzania|1|0|1|1|N|
|Uruguay|0|0|0|0|..|
|Uzbekistan|1|1|1|1|N|
|St. Vincent and the Grenadines|0|0|0|0|N|
|Venezuela, RB|1|1|1|1|..|
|Vietnam|0|0|0|0|N|
|Vanuatu|0|0|0|0|N|
|South Africa|0|0|0|0|..|
|Zambia|1|0|1|0|N|
|Zimbabwe|1|1|1|1|Y|
|Predict Conflict|35|26|43|37||
|Predict No Conflict|52|61|44|50||

Notes: This table reports predictions of conflict based on the most recently-available data 2012-2014 for each country for which complete data on all explanatory variables is available and no conflict signals are observed over this period. Predictions are based on model 3. FCS List refers to the World Bank classification of “fragile situations” as of FY2017, and is based on a country having a CPIA score below 3.2 and/or a UN Peacekeeping Operation.

42
Literature has also used panel data across the world to discuss sophisticated methodologies of verifying determinants of the property taxes and estimating their impacts on the property tax revenue. Davoodi and Grigorian (2007) attribute urbanization's importance to high demand for public services.

Using data from Office of Real Property Tax Service and Office of State Comptroller’s Office of New York State in 1992, Tae Ho Eom (2008) examines a model for the determinants of property tax assessment quality. The author uses two stage least square (2SLS) regression to solve the endogeneity of the property tax administration variables and concludes that “the median tax share, the median property value as a share of median income, the ratio of state aid to total expenditures, and the share of adults with college or higher education have significant impacts on the quality of property tax assessment”.

Sepulveda and Martinez−Vazquez (2012) estimate the determinants for property tax collection for nine Latin American countries during 1990-2007. They use fiscal decentralization, dependency of transfers, government size, income per capita, municipal cadaster, competition for public positions, and index of democracy as main independent variables. They find that fixed effect results yield statistically significant effects for fiscal decentralization, dependency on transfers, GDP per capita, competition for public positions and index of democracy. And for the random effects model, the rest of the variables are statistically significant (urbanization, municipal cadaster, government size).

Hultquist and Petras (2012) employ a panel of 635 year-location observations covering the largest city in each U.S. state from 1997-2010 to measure the impact of the various political, fiscal, administrative, demographic, and economic factors on the use of fractional assessment of local property taxation. They use panel data techniques incorporating both year and location-specific fixed effects and conclude that fractional assessment is associated with political factors, such as higher levels of Republican legislative representation and divided government, suggesting that the fractional assessment of local property taxation may be the path of least resistance for enacting effective property tax relief.

Norregaard (2013) discusses policy and administrative issues to be considered for successful reform. Based on panel data for 64 countries between 1990−2010, he uses GDP per capita, urbanization, openness of the economy, and a dummy if the country's legal heritage (is of Anglo−Saxon origin) to estimate recurrent immovable property revenue. He also estimates a Fixed Effects model to account for the influence the variations of income per capita have on property tax collection over time. He finds that a country's development over time has positive exponential effects over time.

# **III. Data**

5
revenues to finance the delivery of public services and infrastructures, overregulation and over-taxation deter investments by raising business start-up and operating costs.

In the “Law and Order” index, the law sub-component provides an “assessment of the strength and impartiality of the legal system”, while the order sub-component concerns the “popular observance of the law.” Although many aspects of the business environment affect investments, the security of property rights is the most important and the better documented issue. Because of the forward-looking nature of investment, investors need institutions that preserve the right of private property, ensure equitable and consistent rule of law in protecting this right, as well as effective incentives to respect and enforce it. A reliable judiciary, in particular, reduces transaction costs for businesses and sends positive signals to investors that rules of law will be equitably and consistently protected and enforced. On the empirical side, the issue of property rights and of rule of law has been widely covered by the literature, and the results of cross-country analysis are robust to various tests and specifications<sup>16</sup> .

# **2.2. Public Accountability (** **_PA_ )**

The second set of indicators measures “Public Accountability”. This index consists of two indicators from Freedom House (FH): “Civil Liberties” and “Political Rights”.

Public accountability is part of the investment climate of an economy. Because fixed capital investments are generally irreversible, private investment decisions are highly sensitive to the perception of the credibility and tenacity of the political regime, as well as of policies<sup>17</sup> . An open and participatory political system provides stability of social institutions and ensures a broad public support to policies, which are in this case 11
Fugazza, and Maloney (2006), which examines the reaction of formal and informal labor markets to permanent and temporary macroeconomic shocks. The paper develops a theoretical model whereby the comovement of relative earnings, sector sizes, and the real exchange rate can indicate the type of shock affecting the economy and whether labor rigidities become binding. It then studies these comovement patterns using time series for Argentina, Brazil, Colombia, and Mexico. Although Fiess, Fugazza, and Maloney study informality over the business cycle as deviation from a steady state, they do not characterize the evolution of the steady state itself. Our paper studies this evolution theoretically and uses cross-country variation to identify it. Once this is done, this paper uses the time-series variation to identify and assess the cyclical properties of informal employment.

The paper first presents a two-sector theoretical model that endogenizes the relative size of informal employment, making it a function of the productivity differential between formal and informal workers. This differential is, in turn, determined by the cost to become and remain formal and the distribution of skills in the labor force. Specifically, the productivity differential has a worker-driven component, given by workers’ individual skills, and a sector-related component, given by the relative formal-informal regulatory burden, the strength of enforcement, and the access to productive public services. The size of informal employment is then given by the proportion of workers whose skills fall below a threshold level where the worker is indifferent between the two sectors.

In the model, cycles appear as productivity shocks occur, and informal employment’s behavior over the cycle depends on how these shocks affect the productivity differential between the two sectors. The model derives its main results conducting comparative statics exercises on the cumulative function of the skill distribution (i.e,, the relative size of informal employment) and the elasticity of this cumulative function with respect to productivity shocks (i.e., the short-run response of informal employment). In the 3
Table 3: Structural Estimates for Final Goods

|VARIABLES|(1)|(2)|_M _<sup>_k_</sup><br>_j,t_<br>(3)|(4)|(5)|
|---|---|---|---|---|---|
|_σ_<sup>_k_</sup>|-0.0722|0.115|0.215|0.587|0.225|
||(0.352)|(0.359)|(0.388)|(0.407)|(0.473)|
|_σ_<sup>_k _</sup>_×_ln(_distj_)|0.133|0.0670|0.0221|-0.0829|0.0332|
||(0.106)|(0.109)|(0.118)|(0.132)|(0.152)|
|_σ_<sup>_k _</sup>_× HOMEj_|0.237***|0.230***|0.261***|0.452***|0.370***|
||(0.0112)|(0.00863)|(0.0363)|(0.129)|(0.127)|
|_V sh_<sup>_k_</sup><br>_t_||1.119***<br>(0.237)|3.282***<br>(0.329)|2.354***<br>(0.607)|2.319***<br>(0.607)|
|ln(_WV _<sup>_k_</sup>)||0.482***|0.676***|0.101|0.0658|
|||(0.0563)|(0.0598)|(0.207)|(0.209)|
|(ln(_WV _<sup>_k_</sup>))<sup>2</sup>||0.0237***|0.0538***|0.0275|0.0245|
|||(0.00441)|(0.00640)|(0.0219)|(0.0220)|
|_Ctnr_<sup>_k_</sup><br>_t_||-1.724***<br>(0.158)|-2.813***<br>(0.190)|-1.168***<br>(0.350)|-1.041***<br>(0.358)|
|_σ_<sup>_k _</sup>_× V sh_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||-0.206***<br>(0.0304)|-0.590***<br>(0.125)|-0.528***<br>(0.122)|
|_σ_<sup>_k _</sup>_×_ln(_WV _<sup>_k_</sup>)_× HOMEj_|||-0.0537***<br>(0.00910)|-0.111***<br>(0.0248)|-0.106***<br>(0.0245)|
|_σ_<sup>_k _</sup>_×_(ln(_WV _<sup>_k_</sup>))<sup>2</sup> _× HOMEj_|||-0.00522***|-0.00403|-0.00407|
||||(0.000695)|(0.00518)|(0.00467)|
|_σ_<sup>_k _</sup>_× Ctnr_<sup>_k_</sup><br>_t _<sup>_× HOMEj_</sup>|||0.162***<br>(0.0300)|0.301***<br>(0.0460)|0.281***<br>(0.0496)|
|_σ_<sup>_k _</sup>_×_(ln(_distj_))<sup>2</sup>|-0.0188**|-0.0140*|-0.0107|-0.00416|-0.0116|
||(0.00804)|(0.00813)|(0.00872)|(0.00902)|(0.0105)|
|_IHST_( <sup>�</sup><br>_X_<sup>_k_</sup><br>_jt_<sup>)</sup>|0.677***|0.612***|0.606***|0.614***|0.616***|
|_,_|(0.0333)|(0.0239)|(0.0232)|(0.0238)|(0.0242)|
|Observations|1,075,452|1,075,452|1,075,452|1,075,452|1,070,496|
|Year FE|YES|YES|YES|YES|YES|
|Flexible distance controls|NO|NO|NO|YES|YES|
|U.S. trade policy controls|NO|NO|NO|NO|YES|
|Pseudo R2|0.451|0.479|0.489|0.497|0.499|

Note: Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Estimates over the sample of HS6 products with values of the upstreamness index _<_ = 1 _._ 3. The LHS variable on all models is _M_<sup>_k_</sup> _j,t_<sup>, the total value of PR’s product</sup><sup>_k_imports of product</sup><sup>_k_from place of origin</sup><sup>_j_</sup> in year _t_ . All models are estimated using the PPML estimator on PR’s import data pooled across observations at the year, HS6 digit product, and place-of-origin level, with year fixed effects included in the estimation model. _σ_<sup>_k_</sup> estimates from Fontagné et al. (2022) are interacted with geographic frictions, and enter separately in the regression. IHST denotes the Inverse Hyperbolic Sine Transformation. Pharmaceutical products are excluded from the estimation.

28
## - 2.28 -

# V. THE CONSEQUENCES OF THE DECUNE IN COCOA PRODUCTION

> The offects of the price policy on the icomes of cocoa farmers as a whole has already been Illustrated In Flgure 3. Thabatabal (1988) argues that this must have been an kmportant factor behind the out-migration of rural labor. One could argue that the pull of the oil-rich nelghbors was too strong to resist at any sonsible domestic cocoa price, but this Is beled by the Cote d'lvolre, whose higher cocoa prices durlng this period were associated with a continued growth In cocoa output (or at least, marketings), not wlth a redirection of thelr rural labor into non-agricultural employment. Konings (1986) reported that some cocoa tenants and workers had migrated to the Coto d'lvoire intendig to farm cocoa there.

Figure 12 shows the division of the real purchasing power of cocoa revenues over two sub-periods -- the 1950s, the the of prosperity and the lead-up to the rapid expansion of output, and the last decade. The figure shows the real Income of the farmers (deflated by the Natlonal CPI), the reported real costs of the Cocoa Board, the Governmont recelpts from the cocoa export tax and the profits of the Cocoa Board (which, as a public agency may be conskiered as part of the revenue sources of the Government), and the profits on the forelgn exchange recelpts ('Forox Profit'). This last Item Is the dl'forence between the value of the forolgn exchange earnings at the PPP exchange rate and at the official rate (the earnings reported by the Cocoa Board). The are a measure of the potential surplus value available to the Government via the Bank of Ghana.

The _figur_ shows the relatively small size of the Board's costs In the earlier period, and the extent to which the prico policy stabilized the producer price, absorbing fluctuations In the world market price In fluctuathg Government revenues. During the second poriod the producer's share was squeezed by the fallig value of exports at the officlal exchange rate, the gradual increase In Board costs, and the attempt by the goverrnent to preserve Its own direct clabl on the nominal revenue (i.e., excluding Its
Table S1. Countries with deterioration in fiscal indicators (Percent of countries)

||2010|-20|2010|-19|201|9-20|
|---|---|---|---|---|---|---|
|Change (percentage points):|>0|>20|>0|>20|>0|>20|
|**Government debt increase**|||||||
|World|85|52|75|31|89|14|
|Advanced economies|76|39|55|21|97|16|
|EMDEs|87|55|80|34|88|13|
|Commodity-exporting EMDEs|92|64|85|43|89|13|
|Commodity-importing EMDEs|78|42|73|20|85|14|
|**Deterioration in primary balance**|||||||
|World|81|...|38|...|88|...|
|Advanced economies|86|...|14|...|97|...|
|EMDEs|80|...|44|...|85|...|
|Commodity-exporting EMDEs|85|...|54|...|85|...|
|Commodity-importing EMDEs|74|...|28|...|86|...|

Note: Table shows the share of countries in respective country groups with an increase in government debt or the deterioration in primary balance over the denoted period. The classification of countries follows World Bank (2021).

33