rafmacalaba/data-use-mentions-extended huggingface dataset:
|(4)<br>(5)<br>Profts<br>Profts (no subsidy)<br>(Mex$/ha)<br>(Mex$/ha)<br>609.48<br>763.60<br>(529.58)<br>(531.50)<br>223.80<br>36.00<br>(611.89)<br>(612.56)<br>-291.38<br>1539.96**<br>(567.34)<br>(568.21)<br>-57.59<br>-43.55<br>(536.10)<br>(542.22)<br>678<br>678<br>0.21<br>0.23<br>2639.20<br>2718.87<br>4024.33<br>4024.42<br>0.36<br>0.00<br>0.54<br>0.00<br>0.61<br>0.00<br>e run the following regression: _Yit_ = _β_0 +_βEExtensioni_ +<br>riod. We include randomization strata fxed effects and com-<br>group, the omitted category in our regression. We also report<br>zation survey conducted in June 2016. In column 1, we use as<br>ntains the value of farmers’ maize production (per hectare) in<br>mer in the 2015 season by the price the maize could be sold in<br>farmers when computing the value of the maize production.<br>stment in soil preparation activities, fertilizers (chemical and<br>at was the case), as well as the subsidy for fertilizer packages,<br>5, the dependent variable is
35 closed-ended matching arrangement, only local spending up to some maximum amount will be matched through transfer.

The matrix shown in Table 4 provides a convenient way to differentiate among the types of transfer policy choices and evaluate the outcomes of a grant system. 5 The columns of the table represent the three principal ways in which the grant pool is determined; the rows indicate the four primary allocation techniques. Not all of the 12 cells in the table are filled since certain combinations of these policy choices are not feasible; however, within the feasible cells the most likely type of grant scheme-general purpose versus categorical--are noted where one form is the more likely (though not absolutely necessary).

Type A grants are simply shared taxes with the higher level of government collecting and transferring some proportion of locally collected revenues to the local government. While the IMF Government Finance Statistics do not term shared taxes as grants, when the higher level of gov4rnment unilaterally sets the tax rates, defines the tax base, collects the tax and simply distribu;es the proceeds thereof to the local government In which the taxes were collected, the results are no different than if a grant program was established in which the same revenues were distributed on a formula basis to local governments with the only factor in the formula being the location of the collection of the tax. Obviously, there are instances where somewhat greater discretion is allowed to the local 5The matrix is derived from that developed by Roy Bahl and Johannes Linn, Urban Public Finance and Administration in Less Developed Countries (Washington, DC: The World Bank, forthcoming).
Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2010), “Modeling Services Liberalization: The Case of Tanzania,” _Journal of Economic Integration_ , Vol. 25 (4), December, 644-675. http://dx.doi.org/10.11130/jei.2010.25.4.644

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), “The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization,” _Review of Development Economics_ , Vol. 11 (3), August, 482-506.

- Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2006),"Telecommunications Reform within Russia's Accession to the WTO," _Eastern European Economics_ , Vol. 44 (1), January-February, 2558.

- Jensen, Jesper and David G. Tarr (2012), “Deep Trade Policy Options for Armenia: The Importance of Trade Facilitation, Services and Standards Liberalization,” _Economics: The Open Access-Open Assessment E-Journal_ , Vol. 6, 2012-1. http://dx.doi.org/10.5018/economics-ejournal.ja.2012-1.

- Jensen, Jesper and David G. Tarr (2008), “Impact of Local Content Restrictions and Barriers Against Foreign Direct Investment in Services: The Case of Kazakhstan's Accession to the World Trade Organization,” _Eastern European Economics_ , Vol. 46(5), 5-26.

- Jensen, Jesper and David G. Tarr (2003), “Trade, Exchange Rate and Energy Pricing Reform in Iran: Potentially Large Efficiency Effects and Gains to the Poor,” _Review of Development Economics_ , Vol. 7, Number 4, November, 543-562.

- Kee, Hiau Looi, Alessandro Nicita and Marcelo Olarreaga (2009). "Estimating trade restrictiveness indices," _Economic Journal_ , Vol. 119, 172--199.

- Kee, Hiau
sup>_l_)</sup> _∼ N_ (0 _, σu_<sup>2(1</sup><sup>_−γd_))and</sup><sup>_ε_(</sup> _di_<sup>_l_)</sup> _∼ N_ (0 _, σe_<sup>2</sup><sup>_k_</sup> _di_<sup>2),for</sup><sup>_i∈rd_.By</sup> using the vector **y** _dr_<sup>(</sup><sup>_l_)withthesegeneratedout-of-sampleelements</sup><sup>_y_</sup> _di_<sup>(</sup><sup>_l_),</sup> _i ∈ rd_ , in step 3, we construct the census vector **y** _d_<sup>(</sup><sup>_l_)</sup> = ( **y** _ds_<sup>_′,_(</sup><sup>**y**</sup> _dr_<sup>(</sup><sup>_l_))</sup><sup>_′_)</sup><sup>_′_and</sup> calculate the indicator of interest _δd_<sup>(</sup><sup>_l_)</sup> = _hd_ ( **y** _d_<sup>(</sup><sup>_l_)).</sup> For an area _d_ that is not sampled (i.e., with _nd_ = 0), as there is no insample part in this case, we generate the whole census vector **y** _d_<sup>(</sup><sup>_l_)</sup> = **y**
- **[53]** Rama, Martin and Raquel Artecona, 2002.  “A Database of Labor Market Indicators Across Countries, unpublished,” The World Bank, Washington DC.

- **[54]** Shleifer, Andrei and Robert Vishny, 1993.  “Corruption,” _Quarterly Journal of Economics_ v108, No. 3 (August), pp. 599-617.

- **[55]** The World Bank, 2003. _World Development Indicators_ .  Washington DC.

- **[56]** World Bank (2005): _World Development Report._ Washington DC.

24
and the additional transit component is equal to where _dist_ ( _j, k_ ) is the distance between countries _j_ and _k_ , _lang_ ( _j, k_ ) is an indicator variable that is equal to 1 if countries _j_ and _k_ share a common language and _gdp_ ( _j_ ) is the GDP per capita in country _j_ . Total fixed moving cost of moving from _j_ to _k_ for an agent born in _i_ is, thus, given by where _C_ 1<sup>_s,j,j_</sup> = 0, _C_ 2<sup>_s,i,i_</sup> = 0, and the indicator function **1** _i_ = _j_ = 1, if _i_ = _j_ and 0 otherwise. In other words, the fixed moving cost is equal to zero for stayers independent of their birth country; it is equal to _C_ 1<sup>_s,j,k_</sup> for people moving from their birth country to country _k_ and it is equal to _C_ 1<sup>_s,j,k_</sup> + _C_ 2<sup>_s,i,k_</sup> for transit migrants - people born in country _i_ , living in _j_ and moving to _k_ .<sup>7</sup>

# **2 Data**

Among the main constraints faced in estimating dynamic migration models is the availability of detailed and comprehensive data. Most global bilateral migration data sets are based on censuses and population registers of the destination countries where the migrants currently reside. These data sources record only the country of birth or citizenship of migrants. Other important variables, such as the year of arrival or migration status, are not included in most surveys. Detailed migration histories tend to be available only in small and specialized surveys

> 7In reality, both direct and transit cost components might be functions of the time a migrant spends at the current location. Furthermore, the random component might show persistence over time. We abstract
technology may change over time. Since the time sample we use of 2005-2020 is relatively short, this may not be a great concern in our context. For instance, Morris and Zhang (2018) find no technical change in the NO2-GDP relationship for China over a similar length sample. However, the issue may be pertinent for longer horizons.

**Future Work.** There are a number of other possible applications not considered here which may represent fruitful avenues for future work. First, there is the problem of subnational measurement of GDP which we have thus far not considered in a formal sense, and could lead to informative comparisons with previous night lights studies doing the same (Hodler and Raschky, 2014; Henderson et al., 2018). While the high resolution of NO2 is naturally amenable to such an application, this yields other concerns, like discontinuities near borders (Pinkovskiy, 2017).

Second, another application of the data is towards improving the time resolution of low frequency GDP data by interpolation. Especially in developing nations where GDP is only released annually, the potential to reliably estimate at higher frequency is valuable. This may also allow for synergies with previous work; high-frequency estimates of economic activity at the district level in South Asia supported a granular assessment of the impact of several major shocks, including a surge in conflict in Afghanistan, the demonetization experiment in India, and massive earthquakes in Nepal (Beyer et al., 2018; Chodorow-Reich et al., 2020). In such applications, the issue of disentangling seasonality appears to be the key challenge.

Third, with respect to GDP, the complexity of data collection gives rise to a lag between the end of the period to be evaluated and the availability of even the most preliminary of estimates. This “waiting period” may be substantial; one to two months among advanced economies and a quarter to two quarters among emerging markets. Daily NO2 data is made freely, publicly available by NASA online just a few days after measurement. Given
# **_4.2 Limited empirical research uses LSMS-ISA data sets to explore the climate-migration nexus_**

Our literature search on the number of works on migration and climate change which used LSMS-ISA data returned only five papers, four of which published in peer-reviewed journals: Ocello et al. (2015); Kubik and Morel (2016), Mueller at al. (2020), Becerra-Valbuena and Millock (2021), and the recent working paper by Di Falco et al. (2022).<sup>13</sup> Ocello et al. (2015) and Kubik and Morel (2016) both focus on Tanzania, and employ the LSMS-ISA National Panel Surveys. While the first looks at both the 2008-2009 and the 2010-2011 waves (although the latter is used only to identify migrants), Ocello et al. (2015) only employ the first. Both also share similar identification strategies (a two-stage setting with an IV probit model for the former, logit regression the latter) based on a cross-sectional setting which is not invulnerable to identification threats. Despite the similarities, they arrive at somewhat contrasting results: Kubik and Morel (2016) find that a reduction in agricultural income caused by a weather shock _increases_ the probability of internal migration. However, this effect is significant only for middle-income households, whereas it is insignificant for the poorest and the richest households, confirming that the decision to migrate as an adaptation strategy depends on liquidity constraints and initial endowments, with the poorest households that cannot afford migration costs, while the richest ones can afford _in-situ_ adaptation strategies, such as irrigation or drought-resistant crops. Ocello et al. (2015) document that being exposed to droughts or floods or crop diseases is associated with an overall decrease in the likelihood of inter-district mobility, with the exception of low-educated individuals.

Mueller et al. (2020) employ the LSMS-ISA data from four countries, namely Ethiopia, Malawi, Tanzania and Uganda. They combine these household panel data with climatic data from NASA’s MERRA to
protections, vesting provisions and redemption rights are more typical in Common Law countries while milestones are less common. However, while contracts seem to differ across legal regimes, the most experienced VCs implement U.S. style contracts. Cumming and Johan (2011) also find that the legal experience of the VCs is more important than the legal regime of the country of the VC fund in structuring the contracts.

# **_5.5 Small Firm Financing_**

The effect of financial development is not uniform across all firms. In this section, we review the literature that has found a differential impact of financial development and access to finance on small versus large firms. First, studies have shown that small firms are more likely to report access to finance as being a major obstacle than large firms. Ayyagari, Demirguc-Kunt, and Maksimovic (2008) use firm level survey data from the World Business Environment Survey (WBES) and find that small firms report higher financing obstacles than large firms. Not only do small firms report higher obstacles, they are also more severely affected. Beck, Demirguc-Kunt, and Maksimovic (2005) find that the growth of smaller firms is hindered most by financing constraints, especially collateral requirements, bureaucracy, the need for special connections, and interest rate payments. They also find the lack of specific forms of financing such as leasing and long-term finance to be particularly constraining for small firms.

Just as small firms face greater information barriers to raising funds than large firms in economies with underdeveloped financial systems, they also benefit disproportionately as the financial systems develop. Beck, Demirguc-Kunt, and Maksimovic (2005) find that previously constrained small firms benefit the most from financial development. Love (2003) also finds that small firms are disproportionately more disadvantaged in less financially developed countries than are large firms. Laeven (2003) finds that small firms‟ financing constraints decrease following financial liberalization episodes such as interest rate liberalization, elimination of credit controls, privatization and bank entry where as those of large firms actually increases,
As indicated by previous studies, the effect of a woman’s intrahousehold economic status on violence seems to be theoretically ambiguous. Although an increase in household economic resources attributable to women may reduce economic stress and spousal violence, it may also introduce additional tension and conflict. In an effort to maintain the status quo, a woman’s increased economic strength may be countered by an increase in violence. Consistent with this theoretical ambiguity, the existing empirical evidence on the link between women’s involvement in income-generating activities and violence is inconclusive.

In a recent survey of developing countries, Vyas and Watts (2008) find that women’s involvement in income-generating activities is generally associated with a higher lifetime history of physical violence. The National Family Health Survey 1989– 99 for India reveals that women face greater domestic violence, and women who work away from home face even more violence (Eswaran and Malhotra 2009). The authors argue that this increase in violence occurs because these husbands perceive a greater danger for their women to have contact with other men, which triggers spousal jealousy and violence as a response.

In contrast, Panda and Agarwal (2005) find that in Kerala (India), women with regular employment are far less likely than unemployed women to have ever experienced violence. Beyond employment status, Panda and Agarwal’s innovative study (2005) uses women’s ownership of property (land and house) to capture economic status. They find that women’s ownership of property is associated with a sharp reduction in domestic violence. Aizer (2010) uses evidence for the United States for 1990–2003 and finds that decreases in the male–female wage gap reduce violence against women. This evidence suggests that improving the employment and earning opportunities of women relative to men reduces violence and its associated costs. Note that this study is based on administrative data on female hospitalization for assault. Therefore, its conclusions are unaffected by the important degree of nonrandom underreporting that affects other studies based on self-reported measures of domestic violence
shocks related to climate change and commodity price disruptions.

Several empirical studies find that low levels of national incomes are robustly correlated with higher risks of internal conflicts (Hegre and Sambanis, 2006; Blattman and Miguel, 2010). However, the direction of the causal relationship is difficult to establish, especially at the infra-national scale. First, there is an endogeneity issue; the long-term negative consequences of insecurity on the economies and institutions could largely influence the direction of the relationship (Corral et al., 2020). Second, aggregated measures of poverty are coarse proxies for the economic constraints faced by individuals, especially in conflict affected areas (Laville, 2019). Third (and corollary), there is a lack of micro data, since obtaining micro evidence through field surveys is limited by obvious security concerns (Axinn et al., 2012). Fourth, other hard to quantify individual considerations might

> 1Source: Authors’ compilation from ACLED for the period between November 18, 2012 and November 18, 2022.

> 2In essence, the idea that civil war begets more civil war (See Collier, 2003).

2
0.367** 0.361** Aggregate of all activities

**denotes that the figures are statistically significant at less than 5% level. Note: Marginal return is calculated from the findings of Table 11 and is defined as the return (profit) on each additional taka invested in working capital and the value of the enterprise.

Source: HIES 2000, 2005 and 2010

**Table 13: Household distribution by constraints in the operation of microenterprise**

|Constraint type|**2000**|**2005**|**2010**|
|---|---|---|---|
|No constraints|33.0|23.8|49.7|
|Inadequate capital or credit|27.1|25.5|22.1|
|Inadequate knowhow|3.8|3.9|2.2|
|High operating cost|0.1|0|3.6|
|Unreliable/inadequate power supply|0.5|1.1|2.9|
|Problems with equipment/spare parts|1.7|0.6|0.4|
|Government regulations|2.5|4.0|1.1|
|Lack of raw materials|10.5|18.2|2.9|
|Inadequate demand of products|8.3|6.2|7.1|
|Transport problems|2.5|7.1|5.0|
|Other miscellaneous problems|10.0|9.6|3.0|
|N|1424|1418|1904|

Source: HIES 2000, 2005, 2010

**Table 14: Extent of credit and non-credit constraints by microenterprise activity**

|**Enterprise activity**|**Enterprises are**|**Among those ent**|**erprises that are**|
|-
it merely reflects the growing dynamism of the rural non-farm sector.

The aggregate data also fail to offer insights into the operation of land markets. Land transfers come about through subdivision (legal and customary), gifts (usually within families, see Rosenzweig and Wolpin 1985), sales, and redistribution under land reform legislation, but these are difficult to distinguish in aggregate data. To understand land transactions fully, longitudinal village studies are required, tracing who transfers land to whom, why, and on what terms, informed by local histories of changing agrarian conditions (Jayaraman and Lanjouw 1998).

# **SIZE DISTRIBUTION OF RURAL LAND HOLDINGS**

Gross inequities in land ownership in rural India have persisted since independence in spite of the tendency for population pressure to bring about a more even distribution of holdings<sup>14</sup> . But this should not divert attention from the important structural changes that have taken place: the number of small/marginal owners, the total area they owned, and the average size of their holdings have increased, while the importance of large farms has decreased. The number and proportion of households owning no land has declined significantly since independence,

- 13 Indian data, however, are generally believed to be better than those for other South Asian countries (Singh 1990).

> 4 Besley and Burgess (1998) found that the gini coefficient for land distribution declined from 0.686 in 1953/54 to just 0.669 in 1982, even in 'high land reform' states. This places India in the middle range of land inequality by international standards. Latin American countries generally have much higher land inequality (Deininger and Squire 1996).

13
possesses the institutions and mechanisms needed to convert this raw material into useable wealth. This is still lacking in Latvia. The recommendations outlined below are designed to overcome this weakness.

**Table 4 National Innovation Capacity Index and Sub-Indexes**

||_Inno_<br>_Capac_|_vative_<br>_ity Index_|_Prop_<br>_Sc_<br>_E_<br>_S_|_ortion of_<br>_ientists &_<br>_ngineers_<br>_ub-Index_|_Inno_<br>_Poli_|_vation_<br>_cy Sub-_<br>_Index_|_Cl_<br>_In_<br>_E_|_uster_<br>_novation_<br>_nvironme_<br>_nt Sub-_<br>_Index_|_Link_<br>_Sub-_|_ages_<br>_Index_|
|---|---|---|---|---|---|---|---|---|---|---|
||Rank|Index|Rank|Index|Rank|Index|Rank|Index|Rank|Index|
|United States|1|30.3|6|4.3|1|8.1|1|10.9|1|7.1|
|Finland|2|29.1|7|4.2|4|7.3|2|10.9|3|6.7|
|Israel|11|26.5|19|3.9|14|6.8|15|9.1|2|6.7|
|Singapore|13|26.0|17|3.9|2|7.4|17|8.9|15|5.8|
|Ireland|16|25.4|12|4.0|16|6.6|16|9.1|16|5.7|
|Denmark
8 main indicators of readiness for IDA graduation. As noted previously, the approach to graduation has become more circumspect and rounded, to limit the risk of future reverse graduations. This encourages the inclusion of development indicators.

Poverty headcount is potentially an important determinant of graduation, as poverty reduction is a major objective of IDA lending. The data are taken from the World Bank’s PovcalNet database. Coverage is not universal and as such this reduces the sample size, with some IDA-eligible countries having no data at all, while missing country-year observations are accounted for through linear interpolation,<sup>5</sup> but only for at most two consecutive missing years.

We proxy for political and institutional development using the International Country Risk Guide (ICRG) Quality of Government Index produced by the PRS Group. This index combines scores for ‘corruption’, ‘law and order’, and ‘bureaucracy quality’ to give a value from 0 to 1, where higher values indicate higher quality of government.<sup>6</sup> To capture the role of vulnerability to shocks, country size (measured by log of population) is included as, other things being equal, larger countries are likely to have more diversified economies less vulnerable to weather-related disasters or adverse commodity-specific or sector-specific shocks. Larger countries may also benefit from economies of scale in administrative capacity and other aspects of institutional development (regulatory systems, courts, independent audit, other accountability institutions, and so on). In addition, there is a cap on allocations to large blend countries that was established to limit the share of IDA resources allocated to populations with large countries and existing access to IBRD. Historically this has covered China, India, Indonesia, and Pakistan, with only the latter still being IDA eligible. The cap meant that Pakistan’s IDA18 allocation was capped at 7 percent of IDA’s total country-allocable envelope. For some of the countries subject to the cap, the IDA financing as a share of gross domestic product (GDP