rafmacalaba/datause-encoder-data huggingface dataset:
The marginal temperature results of the rainfed farm regression are shown in Figure 8. The temperature impacts show a marked progression as one moves from the far south to the far north. There are large damages (-166 to -331 USD/ha/<sup>◦</sup> C) in the far south from warming. These turn into smaller damages in most of the rest of the country (up to -165 USD/ha/<sup>◦</sup> C). The far north and a few cold places in the southeast get small gains from warming (up to 127 USD/ha/<sup>◦</sup> C). The results imply that most of China is slightly too warm for rainfed agriculture. Any further warming is therefore harmful except in the far north. The marginal precipitation effects are shown in Figure 9. Figure 9 is almost identical to Figure 5. Increased rain will damage rainfed farms in the wet southeast but benefit rainfed farms in the rest of the country.

# **V. CONCLUSION AND POLICY IMPLICATIONS**

This study conducts a Ricardian analysis on 8405 farm households across 28 provinces in China. Net revenues are regressed on seasonal climate and a number of control variables. Several specifications of the model are estimated. The empirical results are robust. The average impact of higher temperatures is negative and the average impact of more precipitation is positive. However, marginal increases in temperature and rainfall have very different effects on different farm types in different regions. Warming is beneficial to some farmers in China but harmful to others. Rainfed farmers are more vulnerable than irrigated farmers. Warming is likely helpful to rainfed farmers in very cold places but it will likely harm rainfed farmers in most of China and especially the far south. More rain is likely to be harmful to rainfed farmers in the wet southeast but will benefit farmers in the remaining regions. Irrigated farmers are less sensitive to temperature. However, irrigated farmers, like rainfed farmers, will gain from increased rainfall.

These basic results are similar to results from other countries (MNS 1994; Mendelsohn et al 2001; Mendelsohn and Dinar 2003; Kurukulasuriya et al 2006; Seo and Mendelsohn 2007). First, climate has an effect on net revenue in every country. Second, higher temperatures increase the net revenues of irrigated farms. Third, higher temperatures are beneficial to rainfed farms in cooler climates but harmful to rainfed farms in warm or hot climates. Fourth, more precipitation is beneficial unless there is an excessive amount of rain. Fifth, seasonal impacts vary and are offsetting.

Our results, however, are not completely consistent with previous economic work on Chinese agriculture (Liu et al., 2004). Our study finds that warming is harmful to Chinese 15
Feasibility Studies or Assessment of Pipe Borne Water Supply Facilities in DSDs published on the NWSDB website, as at end of December 2015.<sup>10</sup> As the data are available only for brackets of service, a score was assigned to each bracket in the following manner: _Table 3. Hours of supply_ _<u>per day</u>_

|SupplyHours|Score(A)|
|---|---|
|18 ≤ x ≤ 24|1|
|12 ≤ x < 18|2|
|6 ≤ x < 12|3|
|1 ≤ x < 6|4|

If a project covers more than one DSD, then the score for the hours of supply is assigned to each DSD based on their respective hours of supply, and the final score is computed as a weighted average of the individual scores each DSD received and their existing population.

SEI 7. Existing Safe Water Coverage: This criterion measures the percentage of population with access to safe water sources. Projects are given higher priority if they are located in areas where fewer consumers (as a percentage of the population) have access to safe water. Data for this criterion were obtained from pre‐feasibility studies (PFS), 2012 census data, and data from DSD offices (Resource Profile) and Grama Niladhari Divisions (GND).<sup>11</sup> In cases where data could not be directly extracted from PFS Reports, census information and data from DSD and GND were used to calculate the number of households with access to unsafe water sources (i.e., unprotected wells, rivers, tanks, streams etc.).<sup>12</sup> Information for an additional environmental indicator was also considered to measure the sustainability of proposed water extraction (in terms of the maintenance of the ecosystem function). The proposed indicator was the quantity of extraction as a percentage of the surface water flow; however, due to limited information on the total water demand from the source (not just of the particular project) as well as detailed flow / runoff data (to account for floods and low water flows), it was not possible to get accurate estimates for the projects in this analysis. Using the variable without knowing the total water extraction would provide misleading information, as it would not be possible to know to how the additional withdrawal would impact the ecosystem.

4.1.3.b. Input Criteria for Financial and Economic Index This section describes the two input criteria for the FEI, namely the benefit‐cost ratio and the water resource yield.

> 10 <u>http://www.waterboard.lk/web/index.php?option=com_content&view=article&id=78&Itemid=425&lang=en</u>

> 11 Sub‐unit of District Secretarial Divisions (DSDs).

> 12 http://www.statistics.gov.lk/PopHouSat/CPH2011/index.php?fileName=H4&gp=Activities&tpl=3

17
seem to follow log-linear rather than linear processes. We thus follow Mankiw and Campbell (1989,1990), Crucini (1999), and Asdrubali and Kim (2008) and replace all variables by their logs and consider the resulting expression as a log-linear approximation to the permanent income hypothesis equations. (The Appendix shows how to log-linearize the permanent income innovation.) Therefore, (6) is approximated according to where cit = log Cit, yit = log Yit, and In the econometric analysis below we interpret each family as a country and the number of family members as that country’s population. Our objective is then to estimate the degree of risk sharing �i for each country i = 1; 2; :::; N . To that end, we follow the usual procedure in the rational expectations econometric literature (Hansen and Sargent, 1980) and assume that, when performing revisions to their permanent incomes, agents have more information than the econometrician. Let ^"it denote the innovation to the permanent income estimated by the econometrician and rewrite (6) as where uit is a residual that represents the superior information of agents relative to the econometrician, and is given by It is easy to verify that uit is orthogonal to the econometrician’s information set at time 9
<!-- Start of picture text --> Inefficient Efficient<br>Miscoordination<br>coordination coordination<br>LL HH LH<br>1 5 1<br>2 4 2<br>3 3 3<br>4 2 4<br>5 1 5<br>−0.9 0 0.8 −0.9 0 0.8 −0.9 0 0.8<br>Cohort I Cohort II<br>LL HH LH<br>6 5 6<br>7 4 7<br>8 3 8<br>9 2 9<br>10 1 10<br>−0.9 0 0.8 −0.9 0 0.8 −0.9 0 0.8<br>Cohort II Cohort I<br>Period Period<br>Period Period<br><!-- End of picture text --> Figure 3: Period-by-period pair outcomes for the Stag Hunt, by pair type.

a plurality of both pair types were in miscoordination or in the inefficient equilibrium at period 5. This pattern is strikingly different from the finding of Van Huyck, Battalio and Beil (1990) that the frequency of efficient coordination in a Stag Hunt increased over the first five periods and then stayed at that high level for the remaining five periods of a ten-period game.

For periods 6 to 10, _LL_ pairs consistently maintained an efficient convention, with between 67 and 80 percent of pairs playing ( _Stag_ , _Stag_ ). _HH_ pairs initially started without a single pair at the inefficient equilibrium, though a majority started in miscoordination. However, the fraction of pairs playing ( _Hare_ , _Hare_ ) rose from zero in the initial period of the pairing to 33 percent in the last two periods, whereas there was no time trend to the proportion playing ( _Stag_ , _Stag_ ). Evidently, the _HH_ pairs learned to avoid disequilibrium by largely settling on an inefficient convention.<sup>6</sup> Compared to _HH_ pairs, the time trends for _LH_ are more erratic. A majority achieved efficiency in period 10, though this share is not significantly different from 50 percent at the 5 percent level.

To sum up, high caste status is associated with less efficient coordination. Most _LL_ pairs established an efficient convention, while most _HH_ pairs did not. Outcomes for the _LH_ pairs were in between those of _LL_ and _HH_ . The evidence also suggests that social distance was not

> 6We know that the _H_ players who move _to_ ( _Hare_ , _Hare_ ) are generally moving _from_ ( _Stag_ , _Hare_ ) or ( _Hare_ , _Stag_ ), since almost 90 percent of _H_ players in ( _Stag_ , _Stag_ ) in a given period of a fixed pairing continue to play _Stag_ in the next period. Moreover, as indicated in Table 1 below, once the outcome ( _Hare_ , _Hare_ ) is reached, 74 percent of subjects in _HH_ continue to play _Hare_ in the subsequent period.

11
Table A1: Summary statistics on invitees to Screening 0 (S0), by participation status

|_Variable Name_|_Mean_<br>_Did not partici-_<br>_pate in S0_|_Mean_<br>_Participated_<br>_in S0_|_Dif=0 (p-value)_|_Normalized_<br>_Diference_<sup>(</sup><sup>_a_)</sup>|
|---|---|---|---|---|
|Female|0.47|0.47|0.65|0.01|
|Age|20.74|20.60|0.00|-0.04|
|Highest Educ. Level Attained is Primary|0.01|0.01|0.06|-0.03|
|Highest Educ. Level Attained is Junior Sec.|0.01|0.01|0.12|-0.02|
|Highest Educ. Level Attained is Senior Sec.|0.87|0.88|0.12|0.02|
|Highest Educ. Level Attained _>_ Senior Sec.|0.11|0.11|0.55|-0.01|
|Speaks mostly English at home|0.07|0.06|0.28|-0.01|
|First Preferred Language is Yoruba|0.81|0.80|0.11|-0.02|
|First Preferred Language is English|0.17|0.18|0.03|0.02|
|Second Most Preferred Language is Yoruba|0.15|0.17|0.00|0.03|
|Second Most Preferred Language is English|0.78|0.78|0.69|0.00|
|Number of household members aged 18-25|1.03|1.04|0.00|0.04|
|Muslim religion|0.39|0.37|0.00|-0.03|
|No. Obs|10.102|6348|||

Notes: Sample includes individuals invited to attend Screening 0, i.e. a movie unrelated to Shuga.

(a) Normalized difference is the difference in the sample means of treatment and control groups divided by the square root of the sum of sample variances.

viii
Kye, L. 1998. "An Alternative Technical Education System: A Case Study of Mexico." Staff Working Paper No. 554. The World Bank.

Lane, J., and H. Tan. 1996. _Evaluacion del Programa DGETI._ Processed.

Lee, K. W. 1998. "An Alternative Technical Education System: A Case of Study of Mexico." _International Journal of Educational Development._ Oxford.

L6pez-Acevedo, G. 2000. "Teachers' Salaries and Professional Profile." HD Working Paper No.64. The World Bank.

Maloney, W., and G. L6pez-Acevedo. 2000. _A Comprehensive Development Agenda for Mexico: Note on Labor Markets in Mexico._ SEP (Secretaria de Educaci6n Puiblica). 1997. _Informe de Labores._ _---------. 1998. Informe de Labores._

----. 1999a. _Informe de Labores._

_---------._ 1 999b. _Compendio Estadistico por Entidad Federativa._ OECD (Organisation for Economic Co-operation and Development). 1997. _Reviews of National_ _Policies for Education: Mexico Higher Education._ Paris.

---------. 2000. _Education at a Glance._ Paris.

Todd, P. 1999. _A Practical Guide to Implementing Matching Estimators._ Processed.

Power, C. 1999. "Technical and Vocational Education for the Twenty-First Century." _Prospects:_ _Quarterly Review of Comparative Education._ Vol. XXIX. No. 1. pp. 29-36. Paris. UNESCO.

49
Table 4: Summary table of cohort trend for attitudes variables (from World Values Surveys)

|**Variable(percent of men and women who agree)**|**Afghanistan**<br>**Bangladesh**|**India**|**Maldives**|**Nepal**<br>**Pakistan**|
|---|---|---|---|---|
|When jobs are scarce, men have more right to a job (%)|83%|69%||92%|
|Decadal change (percentage points)|5|4||0.5|
|University edu. more important for boy than for girl (%)|45%|38%||61%|
|Decadal change (percentage points)|1|-0.2||-2|
|Both partners/spouses should contribute to hh income (%)|12%|16%||28%|
|Decadal change (percentage points)|-7|-4||-15|
|A woman has to have children to be fulfilled (%)|98%|85%||98%|
|Decadal change (percentage points)|2|-2||2|
|IPV justification (% justifying)|80%<br>20%|8%|22%|29%<br>41%|
|Decadal change(percentagepoints)|-2<br>8|-2|-5|2<br>9|

Source: World Value Survey rounds (see table A2 in the annex). Notes: for each question proxying an attitude or belief, the table reports two rows of numbers. The first row represents the most recent level of the variable, and the second row shows the decadal changes. The levels, in the first rows, are calculated as averages across men and women respondents aged 15 to 50 years, and for the most recent survey year; for Bangladesh is 2018 for “jobs for men” and “university education for boys”, and 2002 for “contributions to household income” and “women and children”; for India is 2012 for the first and second variable, and 2001 for the third and fourth variable; and for Pakistan is 2018 for the first and second variables and 2001 for the third and fourth variables. The decadal changes, in the second rows, are calculated as the difference between two numbers – the average for a recent cohort ( _mostly_ people born in 1990) minus the average calculated for an early cohort ( _mostly_ people born in 1950) – divided by 4 (the number of decades between 1950 and 1990). The exceptions are as follows: for Bangladesh and India 1950 and 1980 year of births are used to calculate the decadal change for “contributions to household income” and for “women and children”. For Pakistan, 1950 and 1980 years of birth are used to calculate the decadal change for “contributions to household income” while 1955 and 1975 years of birth are used in the case of “women and children”. The color of the numbers for the decadal changes, rather than their sign, guides the interpretation of the change. Red means that a specific outcome has worsened in these decades, brown means stagnation, and green signals an improvement.

36
**Table 11.C. Model comparison: Diebold and Mariano (1995) test for copper prices**

|||||**Bivariate**|||**Machine**|
|---|---|---|---|---|---|---|---|
|**Approach**|**Horizon**|**Consensus**|**Futures**|**correlations**|**BVAR**|**OEM**|**learning**|
|Consensus|3|...|||***|||
|Futures|3|***|...||***|*|*|
|Bivariate correlations|3|***||...|***|*|†|
|BVAR<br>|3||||...<br>|||
|OEM|3|†|||**|...||
|Machine learning|3|**|||***|*|...|
|Consensus|6|...|||*|||
|Futures|6|***|...||**|†|*|
|Bivariate correlations|6|||...||||
|BVAR|6|||||||
||||||...|||
|OEM|6|*|||*|...||
|Machine learning|6||||*||...|
|Consensus|9|...|||†|||
|Futures|9|**|...||*|*|**|
|Bivariate correlations|9|||...||||
|BVAR|9||||...|||
|OEM|9|***||†|†|...|**|
|Machine learning|9||||†||...|
|Consensus|12|...||†|*||*|
|Futures|12|**|...|†|*|**|**|
|Bivariate correlations|12|||...||||
|BVAR|12||||...|||
|OEM|12|**||*|*|...|**|
|Machine learning|12||||†||...|
|Consensus|15|...|...|...|†||**|
|Futures|15|...|...|...|...|...|...|
|Bivariate correlations|15||...|...||||
|BVAR|15||...|...|...|||
|OEM|15|***|...|...|*|...|**|
|Machine learning|15||...|...|||...|
|Consensus|18|...|...|...|*||***|
|Futures|18|...|...|...|...|...|...|
|Bivariate correlations|18||...|...||||
|BVAR|18||...|...|...|||
|OEM|18|**|...|...|*|...|***|
|Machine learning|18||...|...|||...|
|Consensus|21|...|...|...|*||***|
|Futures|21|...|...|...|...|...|...|
|Bivariate correlations|21||...|...||||
|BVAR|21||...|...|...|||
|OEM|21|***|...|...|***|...|***|
|Machine learning|21||...|...|||...|
|Consensus|24|...|...|...|*||***|
|Futures|24|...|...|...|...|...|...|
|Bivariate correlations|24||...|...||||
|BVAR|24||...|...|...|||
|OEM|24||...|...||...||
|Machine learning|24||...|...|||...|

_Source_ : World Bank.

_Note_ : Diebold Mariano statistic tests whether the forecasts in the model indicated in the row is more accurate than the forecast of the model indicated in the column. *** indicates statistically significantly more accurate model in the row at the 0.1 percent significance level, ** at the 1 percent level, * at the 5 percent level, and † at the 10 percent level.

54
<!-- Start of picture text --> (1) (2) (3) (4) (5) (6)<br>FinCorp NonFinCorp Households Government DomSavings ForSavings<br>DomSavings<br>Gap -3.81* = -13.1*** 2.95 17.2*** 3.25 -42. B***<br>Constant 0.25 0.86 -0.19 -1.13 -0.21 2.78<br><!-- End of picture text -->

||(1)<br>FinCorpN|(2)<br>onFinCorp|(3)<br> Households|(4)<br>Government|(5)<br>DomSavings|(6)<br>ForSavings|
|---|---|---|---|---|---|---|
|DomSavings|||||||
|Gap|-3.81*|=-13.1***|2.95|17.2***|3.25|-42. B***|
|Constant|0.25|0.86|-0.19|-1.13|-0.21|2.78|
|Observations|263|263|263|263|263|263|
|R-squared|0.043|0.037|0.001|0.097|0.003|0.164|
|ra|0.0394|0.0333|-0.00247|0.0933|-0.000519|0.161|

** 5<0.01, * p<0.05, * p<0.1

Source: UN National Accounts Main Aggregates database.

Notes: The domestic saving gap is that obtained from the benchmarking exercise described in Section 3. The saving shares are the sectorial savings divided by total domestic savings.
# **Table 2: Descriptive** **<u>Statistics at Plot Level</u>**

||Total|Small|Medium||Large||
|---|---|---|---|---|---|---|
|**Area cultivated & output**|||||||
|Yield per hectare (US$)|757.747|1295.723|584.358|***|316.295|***|
|Gross profit (US$/ha)|647.898|1113.093|498.044|***|266.083|***|
|Net shadow profit (US$/ha)<br>|469.367|773.625|385.509|***|221.732|***|
|Net profit (US$/ha)|132.757|107.632|175.483|*|115.207|***|
|Plot size|0.152|0.016|0.061|***|0.410|***|
|Land value US$/ha|13361|19070|11784|***|8387|***|
|**Input use**|||||||
|Labor days/ha|678.171|1301.356|433.852|***|214.708|***|
|Used chem. fertilizer (share)|0.096|0.086|0.089||0.116|***|
|if yes, amount (kg/ha)|121.609|56.176|217.851||92.470||
|Used pesticides (share)|0.050|0.045|0.046||0.060|**|
|if yes, amount (US$/ha)|3.239|5.592|2.696|***|1.070|***|
|Used manure (share)|0.593|0.537|0.603|***|0.650|***|
|**Area shares**|||||||
|Share with grains|0.486|0.478|0.502|*|0.478|**|
|Share with tubers|0.285|0.328|0.267|***|0.255||
|Share with trees|0.217|0.179|0.220|***|0.260|***|
|Share with vegetables|0.012|0.016|0.012||0.007|**|
|**Plot characteristics**|||||||
|No. of years possessed|18.277|15.996|18.439|***|20.780|***|
|Affected by crop shock|0.298|0.229|0.309||0.367||
|Wetland|0.117|0.157|0.112|***|0.076|***|
|Irrigated land|0.058|0.069|0.056|*|0.046||
|Flat land|0.279|0.307|0.271|***|0.253||
|Gently slopped land|0.248|0.230|0.256|**|0.261||
|Plot located in a valley|0.053|0.079|0.052|***|0.025|***|
|Sand soil|0.159|0.171|0.146|**|0.160||
|Loam soil|0.367|0.359|0.382|*|0.359||
|Light clay soil|0.140|0.145|0.137||0.137||
|Heavy clay soil|0.032|0.038|0.030||0.026||
|Gravellysoil|0.133|0.122|0.129||0.150|**|
|Number of observations|7477|2682|2514||2281||

_Source:_ Own computation from 2010/11 LTR baseline survey.

_Note:_ Input use is reported only for those who applied positive amounts. Stars indicated significance levels for t-tests of the equality of means for each of the variables between terciles (* significant at 10%; ** significant at 5%; *** significant at 1%).

17
of firm-level data from 19 Sub-Saharan African countries suggest that a 10 percentage points increase in the share of output sold to the government is associated with a 4 percentage points increase in firm productivity. Public procurement also increases a firm’s capital intensity, ability to pay higher wages, and introduce new products (Hoekman and Sanfilippo, 2018). In Brazil, winning a government contract had a positive impact on firm-level employment growth, which was persistent well beyond the expiration date of the contract. Firm growth mainly occurred because winners were more likely to win more contracts in the future, entered more valuable auctions, expanded in more markets and increased the number of products sold (Ferraz et al., 2015).

# **5. Knowledge gaps in current firm support programs**

# **5.1. Design**

There are four types of knowledge gaps in the available evidence on the design of firm support programs. _Breadth of instruments covered._ Although there are many instruments available to help address both supply and demand-side constraints, most often interventions have experimented with one or two instruments. For example, interventions enabling access to finance have most often focused on grants or loans. There is little evidence on the impact of lines of credit, partial credit guarantees, early-stage financing instruments, fiscal incentives or vouchers, inducement awards etc. This is because either the intervention itself is not frequently used or more critically because the interventions are constrained by weak empirical foundations, poorly articulated logical frameworks, and largely absent monitoring and evaluation systems (including impact evaluations and cost-benefit analyses). Likewise, interventions to upgrade firm capabilities frequently use different types of training programs, however, there is no systematic evidence on the impact of export promotion programs, technology upgrading and so on.

_Evidence on interventions addressing multiple constraints._ Firms face several constraints simultaneously on both supply and demand side, however, most interventions address a single constraint, such as access to finance, or firm capability needs. This could partially explain why many firm support programs, such as entrepreneurial skills trainings, have not been as effective in improving firm outcomes. One of the reasons why researchers shy away from addressing multiple constraints is that it is complicated to single out the impact of one intervention versus the other. Nonetheless, there have been some studies that have made attempts in this direction, which we will discuss briefly in the following section.

_Depth of evidence on instruments experimented with._ Studies have most often found modest effects of support programs on performance of firms. This could be because although these programs are not designed to be beneficial for a particular type of firms, they may have heterogeneous effects by firm’s or 21
generally rate interest rates or access to long-term loans as lesser constraints than enterprises that did not operate outside of their home market (see Table V).<sup>26</sup> _Firm Performance._ As noted previously, the WBES does not provide detailed information on enterprise performance and, therefore, it is difficult to test the link between enterprise performance and financing as an obstacle to enterprise operations and growth using this data set. Although two of the performance-related measures that are included in the analysis, sales growth and export growth, might be endogenous in the equation for the share of investment financed through bank loans (i.e., if access to bank loans allows faster growth), results are similar when these variables are omitted (compare Table VI and Table VII). In practice, neither of these variables appears to strongly affect perceptions about financing (see Table V), although enterprises with faster sales growth did finance more investment through bank loans than similar enterprises that were growing more slowly. Exporters also tended to finance more investment through commercial bank loans. It is possible that this is because exports also serve as a proxy for overall firm performance - several studies have found that exporters are more efficient than similar non-exporters.<sup>27</sup> _Macroeconomic Control Variables._ In addition to the enterprise level controls, several variables are also included in the estimation to account for macroeconomic factors that might affect access to loans or interest rates.<sup>28</sup> In general, the macroeconomic variables have the expected signs and are often, but not always, statistically significant. Table IX provides estimates of the magnitude of the effect of changes in the macroeconomic variables on enterprise performance. Enterprises in countries with higher per capita income report that high interest rates, access to long-term loans, and access to non-bank equity are lesser constraints than sinmilar enterprises in countries with lower per capita income (see Table V). Similarly, enterprises in 17