rafmacalaba/datause-encoder-data huggingface dataset:
6 us what causes what. More plausibly, they are simultaneously and jointly determined by a number of common exogenous factors influencing farm household's production and consumption decisions. This paper also, unlike other studies (e.g., Binswanger et al., 1987), does not treat population density as an exogenous variable determining output supply.

The agroclimatic endowments and infrastructure which affect agricultural output also influence agricultural employment, rural real wages and nonfarm employment. For policy purposes, it is important to know whether the policies that have fostered agricultural growth have had any powerful effect on rural wages and employment. Because the causal factors need not influence the observed outcomes in the same direction, the objective of this paper is to differentiate the factors that promote simultaneous expansion in agricultural production, real wage, farm and nonfarm employment from the factors that exert opposing effects.

A central problem of estimating the causal relationships is that public programs and infrastructural investment respond to agricultural opportunities implied by the agroclimatic potentials of an area. Government invests more in a better agroclimatic area where the return to public investment is high. The rural households also respond to better agroclimates by increasing output and thus government programs cannot be considered exogenous to the household's output decisions. Labor demand is determined by agroclimate, infrastructure and the level of output and this makes it difficult to estimate the causal effect of public programs on outcomes such as rural wages and employment. However, these problems can be circumvented if we use a panel dataset. In this paper we use a district-level panel data from 85 districts in India.

The paper is structured in the following order. An analytical model based on the theory of farm household production with estimation
# <u>Informal Shipping and Routes</u>

The informal sector, active mostly in the Leeward and Windward islands, is characterized by locally owned operators operating small ships of various types and capacities. They use mostly converted fishing boats or small ro-ro ships<sup>21</sup> with stern ramps – these latter vessels, however, are usually not used in the traditional ro-ro manner carrying trucks, trailers, private vehicles and other wheeled cargo. Instead, cargo is moved on board via stern ramp by forklift trucks and small trailers and then stowed on deck conventionally.

These informal vessels provide almost all the inter-island small-parcel size, break-bulk cargo movements (Table 35). Despite its niche-market domination of inter-island break-bulk movements, the informal shipping industry is largely in decline, although it is surviving surprisingly well in St Vincent.

There are about 16 vessels operating out of Dominica: most of these are based at Portsmouth in the North. They carry local produce, personal effects, and smaller scale-industrial products, with empty containers as additional outgoing cargo and construction material and manufactured goods as additional incoming cargo.

There are five informal vessels operating out of Grenada. Three of the vessels are based in Grenville on the Windward coast, and two in St Georges. The three boats based in St Georges serve mainly Trinidad, bringing in building materials, wholesale goods for retailers, and other general cargo, and taking out agricultural produce only occasionally. The ratio of imports to exports is reported to be 10:1. Almost all of the vessels operating out of Grenada make a single weekly trip to Trinidad, leaving on a Tuesday and returning on a Friday.

There are 21 informal vessels operating out of St Vincent, mostly based in Kingstown. As in Grenada, almost all make a single weekly trip to Trinidad, leaving on a Tuesday and returning on a Friday, with cargo being off-loaded on Friday afternoons and Saturday mornings. Imports include building materials, wholesale goods for retailers, and other general cargo. Exports are mainly agricultural products.

**Table** **<u>35: Regional Break-Bulk Informal Traffic within the Small Island States and Barbados (metric tonnes)</u>**

||**Barbados**|**Dominica**|**Grenada**|**St Lucia**|**St Vincent &**<br>**Grenadines**|**Trinidad &**<br>**Tobago**|**Total**|
|---|---|---|---|---|---|---|---|
|**Barbados**|-|359|21|323|206||908|
|**Dominica**|251|-|723|821|538|1,765|4,097|
|**Grenada**|196|9|-|41|66|3,542|3,853|
|**St Lucia**|874|21|148|-|316|1,722|3,081|
|**St Vincent &**<br>**Grenadines**|8,643|169|211|910|-|24,039|33,971|
|**Trinidad & Tobago **|NA|118|22|743|681|-|1,564|
|**Total**|9,964|675|1,124|2,837|1,807|31,067|**47,475**|

_Source: Based on data from World Bank (2012b)._

> 21 Ro-ro stands for “Roll on/roll-off,” and refers to ships designed to carry wheeled cargo.

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correspond to the transition from low to lower middle and then to upper middle income status by World Bank classification. The results are in Table 9. To show the results compactly, we do not explicitly report those BE elements that do not have significant interactions with either of our two income threshold dummies. But since those income-invariant elements are also relevant in this story of the stage of development, we still discuss those results.

The income-category-invariant BE factors include Basic Protection, human capital, entry and exit barriers (i.e., land access obstacles), labor regulation obstacles, tax rate burden obstacles, and two indicators of agglomeration including Being In Big City and Informal Competition. In contrast, the income-dependence properties of basic and modern infrastructure are quite different. Basic power supply (i.e., Power Outage) is not significantly associated with firm growth in non-poor (i.e. upper middle-income or above) countries. However, in low-income countries, power outage is, counter-intuitively, actually positively and significantly associated with LPgrowth (but not Lgrowth). In sharp contrast, the effect of internet technology prevalence (Web) is income-invariant.

Access to bank finance (i.e. Overdraft Facility) is positively associated with higher Lgrowth for sample countries of all income levels. However, while it is positively associated with LPgrowth for low- and lower-middle countries, it has an effect that is close to zero in uppermiddle-and-above income countries. Access to bank finance thus has the most positive effect in lower-income countries.

The effect of access to trade credit proves to be income-dependent. Trade Credit Access has a positive association with LPgrowth in lower-middle-income countries. By contrast, the effects are close to zero for low- and upper-middle-and-high-income countries. These results make sense: when income is too low, inter-firm linkages may not be important; when it is sufficiently high, reliance on informal financing may be less critical to firms in meeting their financing needs.

Also income-invariant are various forces of economic agglomeration, with one exception. The effect of being in a big city is income invariant, as is the effect of informal competition. The effect of Capacity Agglomeration, however, proves to be income dependent. In middle-income countries, Capacity Agglomeration is positively associated with Lgrowth. However, it is only in low-income countries that we find a positive and significant relationship with LPgrowth with a one-SD increase (0.15) being associated with raising LPgrowth by 2.2 percentage points, a huge effect. Thus (and assuming causality), for poor countries, it is critically important to facilitate clustering of reasonably sized firms for productivity growth to take place.

25
7 Household survey data show the deteriorating (relative and absolute) position of workers' households. In Poland, for example, per capita income of workers' households was a third less than in 1978; per capita Income of farmers', mixed .nd pensioners' households decreased by less than 20 percent, with mixed households doing somewhat better than the other two groups.

In Yugoslavia, in 1987 per capita Income of non-agricultural households was 37 percent less than In 1978. Since these households include pensioners as well, whose relative position has deteriorated less than that of workers, real incomes of the latter group must have been reduced by even more. Real Incomes of farmers' households increased slightly due principally to favorable terms of trade experienced In the early 1980's.

In Hungary real per capita Income of workers' and farmers' households was practically stagnant.

_III. Comparison of Subsistence Hinimum Lines_ Our objective Is primarily to analyze trends In poverty within each country. However, it is certainly helpful If the level of development, type of the social system and macroeconomic performance of the countries studied are relatively similar, both because It enables us to make some Inter-country comparisons, and to draw general lessons about the changes In poverty In conditions of economic crisis. Inter-country comparisons of income inequality or poverty a-e fraught with a number of difficulties; yet It can be argued ta.At a comparison between Poland, Hungary and Yugoslavia makes more sense than a comparison of each of these countries with say, Britain or West Germany. In that sense homogeneity of the sample Is very important.

Comparisons can be valid only If poverty lines that are used in each case are close. We would expect this as similar levels of development , composition of the population (with still significant size of agricultural labor force), and social aystem (e.g. prevalence of state ownership outside agriculture; widespread redistribution and subsidies; egalitarian wage structure in the socialized sector etc.) would tend to Indicate that subsistence minima cannot be too far apart.
13 employment. Thus, a MFG or FIRE revolution causes urbanization and production cities. **Proposition 4 (de-industrialization without de-urbanization and the transformation of existing production cities into consumption cities)** _When Lf is fixed, by definition U_ = 1 _− Lf is also fixed, implying that a productivity shock that decreases (increases) employment in manufacturing would lead to a corresponding increase (decrease) in employment in non-tradables._ Proposition 4 says that shocks to the manufacturing or FIRE sector can cause existing production cities to become consumption cities when de-urbanization is unlikely. In the next section, we show that urbanization rates almost never decrease and discuss why.

**Empirics.** The urban share and the employment composition of urban areas should depend on the resource windfall _R_ , (tradable) agricultural productivity ( _p_<sup>_∗_</sup> _f_<sup>_Af_), and urban</sup> tradable productivity ( _p_<sup>_∗_</sup> _m_<sup>_Am_).In our econometric analysis, we focus on the period 1960-</sup> 2020 and 116 countries that were still “developing” economies in 1960.<sup>15</sup>

We do not have reliable historical measures of _Am_ . It is also not obvious which price levels should be used for _p_<sup>_∗_</sup> _m_<sup>.FIREGDPisonlyreportedforsomecountriesandrecent</sup> years (previous ISIC classifications did not separate FIRE). MFG and FIRE employment is likewise only measured for some countries and years when there is a census or a labor force survey (surveys were rare before 1990). Productivity could then be high because employment is low and/or “selected”, for example if a country has a few MFG/FIRE firms and these belong to high-productivity subsectors or are politically connected. Given such issues, we use the GDP share of manufacturing and services (MFGSERV). Web Appx Fig. D.6 shows that countries with a high GDP share of MFG+FIRE are countries with a high GDP share of MFGSEFV today, thus validating this proxy (correlation of 0.78).

It is also unclear how to distinguish agricultural products belonging to _R_ (those generating high rents) from agricultural products belonging to _p_<sup>_∗_</sup> _f_<sup>_Af_.</sup> In addition, productivity and yields are typically poorly measured for the latter and it is not obvious which price level to use. Since we aim to measure the fact that a country is urbanizing because it is exporting fuels and mining products or agricultural products, and given the difficulty in separating agricultural products in different categories, we aggregate them together and use as a proxy the ratio of natural resource exports (NRX) to GDP.<sup>16</sup>

> 15The sample excludes a few ex-communist countries due to the lack of pre-1989 data.

> 16NRX/GDP differs from the GDP share of natural resources, for which we have no historical data for
Table 6: Medium-term impacts on updating profile picture

|_Dep. Var. (Y):_|_Int_|_ent to upd_|_ate_|_Actua_|_l picture _|_update_|
|---|---|---|---|---|---|---|
||(1)<br>ITT|(2)<br>ITT|(3)<br>ToT|(4)<br>ITT|(5)<br>ITT|(6)<br>ToT|
|Drama|0.056|0.054||0.026|0.029||
||(0.044)|(0.044)||(0.029)|(0.029)||
|Documentary|0.079<sup>_∗_</sup>|0.079<sup>_∗_</sup>||0.068<sup>_∗∗_</sup>|0.075<sup>_∗∗_</sup>||
||(0.045)|(0.047)||(0.032)|(0.034)||
|Play Drama|||0.071<br>(0.057)|||0.039<br>(0.037)|
|Play Documentary|||0.161<sup>_∗_</sup><br>(0.093)|||0.153<sup>_∗∗_</sup><br>(0.067)|
|Controls||✓|✓||✓|✓|
|R-squared|0.002|0.011|0.012|0.004|0.001|0.002|
|P-value equal coef.|0.614|0.588|0.281|0.211|0.192|0.069|
|Observations|619|619|619|619|619|619|
|Wald F-stat|||117.0|||117.0|
|Mean Y (Control)|0.246|0.246|0.246|0.082|0.082|0.082|

Notes: Heteroscedasticity-robust standard errors in parentheses. Controls are described in the notes to Table 2. Independent variables Play Drama and Play Documentary take value 1 if the respondent has played half or more of the assigned video clips, as objectively recorded by the bot. These variables are instrumented using the random assignment indicators to the treatment groups. The first-stage Wald F-statistic is reported. * p < 0.10, ** p < 0.05, *** p < 0.01

# **4.5 Treatment effect heterogeneity**

Annex Tables A17 to A30 present heterogeneous effects across a series of social status indicators, where we hypothesized that effects would be smaller for groups with lower social status (World-Bank 2014). We find no evidence of heterogeneous impacts by individuals’ age, caste, educational achievement or membership in a social group in the short-term. However, both treatment arms generally were less effective for women and for individuals whose household-heads were on average more educated. Heterogeneous effects across gender persisted in the medium term, while those across household-head education vanished. Contrary to our hypothesis, in the medium-term we observe larger effects for less educated individuals in terms of the Global Index as well as the gender norms/roles and VAW indexes. This could be explained by the lower baseline values of the outcomes for these groups of individuals, which therefore had higher margins of improvements.

Annex Tables A22 and A29 present the heterogenous analysis for participants’ perceptions of the conservatism of their Facebook friends prior to the intervention. We study these effects to test our hypothesis that perceptions of public acceptance of regressive social norms affects treatment impacts (Miller and McFarland, 1987). For the short- and medium-term samples, 23
# **Figure 1: Unconditional Multipliers**

<!-- Start of picture text --> 0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>Impact 1 year 2 years Long run<br><!-- End of picture text --> _Note:_ The graph shows the unconditional fiscal multipliers for select horizons. These are based on estimates from the SVAR model of Ilzetzki, Mendoza, and Vegh (2013) that features with no interaction terms. Bars represent the median, and error bands are the 16-84 percent confidence bands.

16
produce an extremely volatile and time-inconsistent poverty profile, resulting in a continuous re-ranking of provinces in terms of poverty rates ( Figure 1). However, the different (and arguably superior) design of the IAF 1996/97 produced higher statistical reliability and, as a consequence, greater coherence between poverty figures and other welfare indicators.<sup>8</sup> The more complete food list in the IAF 1996/97, which included 50 items, most likely led to better consumption measurements, reduced underreporting, and more limited concentration of reported food purchases in the pre-printed items.

**Figure 1: Official Poverty Rates, by Province, by Survey Year**

<!-- Start of picture text --> Official Poverty rates<br>1997 2003 2009<br>Niassa & Cabo Delgado-ruralNiassa & Cabo Delgado-urbanNampula-ruralNampula-urbanSofala & Zambezia-ruralSofala & Zambezia-urbanManica & Tete-ruralManica & Tete-urbanGaza & Inhambane-ruralGaza & Inhambane-urbanMaputo Provincia-ruralMaputo Provincia-urbanMaputo City<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text -->

**Official:** 2008/09 PLs (MPD-DNEAP, 2010); **Simulated:** 1996/97 official PLs deflated by the consumer price index from World Development Indicators (2011)

The authors of the NPA (DNEAP, 2010), after carefully examining the problems in the design and implementation of household surveys, arrive at similar conclusions regarding the magnitude of underreporting and its spatial concentration. They propose, as a preliminary solution, the re-estimation of expenditures for households whose calorie intake falls below a certain threshold. These adjustments produce a substantial downward revision of poverty figures at the regional level in the South and a 3 percent lowering of the poverty rate at the national level. However, since the 2002/03 data also experienced similar problems in estimating poverty in the South, the NPA’s authors argue that even though there is some regional variation, ‘the trend between the two surveys results is very stable’.

> 8 In the IAF 1996/97 provincial poverty rates show a significant negative correlation with all welfare and education indicators.

5
amplification and time delays, feedback and control and stability and damping. Regarding model origins, Meadows (1970) advanced the **ind?Astrial** dynamics approach of Forrester to produce his "dynamic commodity cycle" model, which concentrated on the inventory formation aspect of market adjustments. Two coupled negative feedback loops, consumption and production, each act to adjust inventory coverage to desired levels. Another feature of the approach is that producers and consumers can employ any fuction of current and past prices to form expectations about future prices. In addition, functional relationships are expressed in continuous time with "period" analysis being replaced by "rate of change" analysis. Production and consumption are assumed to adjust continously to price changes rather than discontinuously between equilibrium points.

To apply system dynamics, it is preferable to select those commodities which display some form of cyclical behavior in production and prices. In the original Meadows (1970) study, models were constructed for hog, cattle, and broiLar markets. Among recent applications, Hamilton (1979) has applied it to the coal market, Choucri (1981) to the oil market, and Strongman et al. (1976) to the copper market. A more general application to agricultural commodities was made by Linneman (1976) in the MOIRA model which represented a sequel to the "Limits To Growth" model. Innut-OutDut Models Input-output models represent a conceptualization of economic production systems that reflect a relationship between input. and outputs and intervening technical transformation. This form of model, however, cannot be employed directly to explain primary commodity markets. Rather it provides a disaggregated view as to how the demand and supply patterns for different commodities relate to the interindustry structure and aggregate or 19
children have 0.847 fewer visits overall (Panel B, Column 1), including 0.162 fewer visits at covered providers and 0.292 fewer visits at private facilities (Panel B, Column 5).<sup>16</sup>

# **_IV._** _POSSIBLE CHANNELS_

While the analysis shows that the effects of parental insurance differ by the insurance status of the child, the previous analysis does not explain why these patterns arise. Are parents reallocating time or financial resources between household members in response to the price decrease from enrollment? Or are children healthier, and therefore imparting a positive externality on their siblings, reducing the demand for healthcare?

First, we examine whether the effects of insurance are in response to having enrolled parents, siblings, or both. While all ineligible children in insured families have an insured parent, not all have an insured sibling. Using the sample of ineligible children (N=824), we test whether the effect of parental insurance differs by whether the child has an eligible sibling.<sup>17</sup> The results in Table 4 show that the effects of health insurance do not differ by whether the ineligible child has a covered sibling, suggesting that the results are primarily driven by having insured parents. There are no significant added effects of

> 16 We also conduct falsification checks to determine the sensitivity of the results, where we replace the outcome measures from the follow-up survey with the baseline measures (Appendices S9 and S10). None of the coefficients of interest are significantly different from zero. The exception is that aside from one coefficient on the interaction term for the outcome of whether the child had ever visited an EMP at baseline. We do not believe that this is cause for concern due to the large number of hypotheses tested in this table and because this outcome has a low mean in the sample at baseline (0.028).

> 17 We estimate: ��� = �+ ����������������� �<sup>+</sup>

> +����������������∗���������������� �� + ������������������� + �<sup>�</sup> ��� + ��� where _Parent Enrolled_ is instrumented with random assignment status as in Equation 1, and the interaction is instrumented with random assignment status multiplied by whether the ineligible child has an eligible (covered) sibling.  The primary outcomes we examine are visits; all control variables are the same as in Equation 1.

14